Former President Donald Trump reiterated many of claims — without evidence — that his criminal trial was rigged, a day after a New York jury found him guilty of 34 counts of falsifying business records.

Trump repeats claims — without evidence — that his trial was rigged : NPR

Idaho murder suspect Bryan Kohberger was back in court for a pretrial hearing on Thursday, in which evidence and cellphone data were discussed.

Attorneys for Idaho murder suspect Bryan Kohberger maintain DA is slow-walking evidence

Manhattan District Attorney Alvin Bragg commended the jurors who found former President Donald Trump guilty on all 34 counts of falsifying business records, saying Monday evening that they reached their verdict on &quot;the evidence and the law alone.&quot;

DA Bragg: Trump Convicted on 'Evidence, Law Alone' | Newsmax.com

Cheryl Bader of Fordham University ... is not to have jurors be so focused on the court’s written legal instructions that it distracts them from the evidence and testimony.”...

Trump and allies step up suggestions of rigged trial — with bad evidence - The Washington Post

Evidence is an open source, code-based alternative to drag-and-drop BI tools. Build polished data products with just SQL and markdown.

Evidence

For modern organizations, there is a need for reliable options for storing and managing their digital media assets, such as videos, audio, images, and documents, and cloud storage options such as Dropbox have become increasingly popular with most of them, including public safety & law enforcement agencies. Having a reliable cloud data storage and backup platform is a requirement for most organizations in the contemporary world. Employees working in small and medium organizations rely on cloud storage services such as Dropbox to store and share their files. Dropbox is even being used in some law enforcement agencies and policing. However, you might need more than just Dropbox-like storage for managing digital evidence. Why Dropbox May Not Be the Best Option for Law Enforcement Agencies? Dropbox has a few features that help securely store digital evidence, such as 256-bit encryption and password protection. Syncing is faster, since the process is done in blocks and the waiting period to access files on another device is minimal, and the solution is easy-to-use which is why it is preferred by over 500 million individuals, including 11.9 million paying users. But when it comes to managing evidence, that is as far as the advantages of Dropbox go. The system isn’t designed for digital evidence management, and there are several gaps and challenges for securely storing and managing evidence using Dropbox. Its privacy policies are somewhat controversial; the application can access your personal information and share it with third-party organizations such as Google and Amazon Web Services. Dropbox lacks metadata search functionality so a file cannot be searched according to the file information and the date it was created. Now, there are a lot of better alternatives that are designed specifically to manage digital evidence and cater to law enforcement. 9 Dropbox competitors for storing digital evidence: 1. VIDIZMO Digital Evidence Management System (DEMS) VIDIZMO Digital Evidence Management System (DEMS) is custom designed specifically to ingest, store, manage, and share evidence files. Apart from its storage options, multiple features of DEMS such as access management, security and chain of custody make it the ideal option for law-enforcement and governmental agencies. The platform does not store your personal information and protects your sensitive data such as evidence files from third-parties, malicious attacks and otherwise unauthorized access. You can use an array of features such as role-based access control, end-to-end encryption and user groups for controlling access to data and evidence. You can share evidence for only a limited time or number of use. The platform also provides a powerful search using AI by automatically indexing meta data for a quick search using keywords. DEMS offers public cloud storage services (e.g.: Azure, AWS etc.), as well as on-premises and hybrid deployments for specific client requirements. The platform also offers a wide range of features and options for meeting compliances necessary for law enforcement and criminal justice systems, such as CJIS, FedRAMP and more. Unlike DEMS, if you use Dropbox for storing and sharing evidence, files need to be deleted if one wishes to restrict the access of a particular person. In addition to that, DEMS provides an exclusive feature which allows you to search for frames in the video. Instead of having to view the entire video or audio evidence, one can quickly search and jump to the relevant part in the evidence. VIDIZMO understands that evidence may be recorded using different sources such as dash cams, aerial footage such as drone cams, smartphones, body cams and CCTVs. Users can rest assured that in addition to password protection and secured cloud, compatibility is also an essential feature of VIDIZMO DEMS. 2. Google Drive Google Drive is a file storage service which allows organizations to have all files in one place, share the video evidence files online and access them from anywhere. Google Drive has the largest market share when it comes to providing cloud storage services. Google Drive offers 15 GB of free storage space which is huge in comparison to the 2 GB provided by Dropbox. Google Drive is also the leader when it comes to paid storage. Google charges $99.99 per year, whereas Dropbox charges $119.88 per year for a 2 TB of storage space with Drive. Google Drive’s syncing also supports multiple devices which is also possible via the Backup and Sync application. In addition to the two-factor authentication, Google Drive’s Cloud Identity Feature provides endpoint management services. Unlike Dropbox, Google Drive clearly states the number of certifications it follows which includes standards such as ISO 27017, FedRAMP, FISC Compliance and PCI DSS. However, Google Drive has file size limitations such as a restriction of 1,024,000 characters, upper limit of 100MB for slideshow presentations and 2 MB for embedded images. 3. Amazon Drive The tech giant Amazon has a fair share of the market when it comes to providing cloud-based services and storage of video, audio and image files. Similar to Dropbox and Google Drive, Amazon uses block-level file copying so faster syncing speed is one of its traits. Amazon Drive’s free plan includes 5GB of storage space. In addition to providing web-based access, Amazon’s Drive’s desktop app allows access to all the files. Amazon Drive’s ease of syncing with Android, macOS, Windows and iOS is another commendable feature. A drawback is that Amazon Drive only has 2 plans which the restricts the options available for users. 4. Office 365 Office 365 is an ideal platform for collaboration as it provides the facility of sharing content, storing files and collaborating on documents with external users and colleagues through its three main products: OneDrive, Microsoft Teams and SharePoint. Small industries with 50-200 employees and revenue in between 10-50 million dollars comprise the major chunk of the users of 5. Microsoft One Drive. OneDrive is a platform which allows easy file sharing, integration with Windows File Explorer and accessing files from mobile devices. There are three different types of OneDrive sharing links: Shareable (which allows anyone with the link to share), Internal (allows only people within the firm to share) and Direct (only specific personnel can share). Although OneDrive works seamlessly with Microsoft Office apps, there are limited options available for third-party integrations. Furthermore, OneDrive’s right to scan saved files raises security concerns amongst the users. 6. pCloud pCloud is an encrypted cloud storage platform that offers up to 10GB of free storage space. It supports a large number of platforms, including Windows, Mac, SaaS, Android, iPhone, and iPad. The target audience of pCloud is digital-savvy consumers who work with large files. pCloud offers a subscription-based payment plan with its two main tiers being Premium and Premium Plus. The majority of the 10 million customers of pCloud belong to the following industries: retail, legal, insurance, education, accounting, real estate, photographers, and PR/advertising agencies.pCloud offers faster sync and share as compared to Dropbox. The Rewind feature allows users to restore the previous version of the file. pCloud’s great flexibility in terms of its pricing, a wide array of features, and a strict privacy policy comprise of its unique selling points. However, pCloud uses cloud storage for saving without utilizing space from local systems because of which it gets tricky to get the whole backup when needed. 7. WeTransfer is a file transfer service that allows users to share files without having to create an account because of which it is used by officers for quick file transfer. WeTransfer allows sending files up to 2GB for free (which is huge compared to 100MB per file transfer on Dropbox). WeTransfer There are two ways of sending files via WeTransfer: sending a download link via email or creating a download link that allows anyone with the link to download the files. Additionally, WeTransfer Pro allows the transfer limit to be increased to 20GB with 1TB of cloud storage space and custom expiration dates. WeTransfer scans the uploaded files only if it feels the users are breaking the laws or rules. Although WeTransfer allows ease of file transfer without having the premium version, files get deleted after a week. 8. Sync is a Canadian company providing the option to store, share and access files from anywhere in the world and is preferred by organizations for whom security is the highest priority. The best feature that offers is having zero-knowledge encryption – a security feature that ensures that only the user can read their data, thus always having a secure file.  Files are synced using a “vault” which operates as a secure file archive. Sync.com Sync.com is affordable as it offers a Personal Pro 2TB annual plan which charges around $8 a month. It also offers a flexible 500 GB personal plan for users. It also has a multi-user team sharing built-in feature which is compatible with both iOS and Android. Some disadvantages of Sync are slow syncing and lack of support especially for Linux. Sync.com 9. Zoho Docs Zoho Corporation provides an efficient file management system for various firms. Zoho Docs is an online document management system that allows storing, uploading, creating, editing, collaborating and sharing over 160 file types such as spreadsheets, images, audio, videos, and presentations. Unlike many other cloud service providers, Zoho Docs offers a free trial in addition to a free version. It also provides extensive training options such as through webinars, live online, and thorough documentation. Zoho also allows syncing offline files using the drag and drop feature. Moreover, the files can be attached directly to which is beneficial for organizations that require customized domain in their email addresses. The ioS application for Zoho needs improvement and downloads are limited to 10GB per day for users not opting for the paid version. Zoho Mail Conclusion It can be said that law enforcement agencies and government organizations that are on the hunt for alternatives to Dropbox have a variety of options at their disposal. Evidence management systems appear to be the optimal platform for law enforcement agencies who specifically want a platform for their evidence video files to be stored, accessed, reviewed and analyzed. Simple cloud storage solutions are suitable for storing and accessing video evidence files, but unlike digital evidence management, they do not offer features such as chain of custody, transcription services, redaction, and sufficient security. Furthermore, most of them do not offer the necessary compliances such as HIPAA and FedRAMP required for law enforcement. In conclusion, it can be said that the choice of cloud storage platform depends largely on the nature of the business and organizations need to decide carefully which platform will yield the best results for them.

9 Dropbox Alternatives for Evidence Management in Law Enforcement

Table of Links Acknowledgements 1 Introduction to thesis 1.1 History and Evidence 1.2 Facts on dark matter 1.3 Candidates to dark matter 1.4 Dark matter detection 1.5 Outline of the thesis 2 Dark matter through ALP portal and 2.1 Introduction 2.2 Model 2.3 Existing constraints on ALP parameter space 2.4 Dark matter analysis 2.5 Summary 3 A two component dark matter model in a generic 𝑈(1)𝑋 extension of SM and 3.1 Introduction 3.2 Model 3.3 Theoretical and experimental constraints 3.4 Phenomenology of dark matter 3.5 Relic density dependence on 𝑈(1)𝑋 charge 𝑥𝐻 3.6 Summary 4 A pseudo-scalar dark matter case in 𝑈(1)𝑋 extension of SM and 4.1 Introduction 4.2 Model 4.3 Theoretical and experimental constraints 4.4 Dark Matter analysis 4.5 Summary 5 Summary Appendices A Standard model B Friedmann equations C Type I seasaw mechanism D Feynman diagrams in two-component DM model Bibliography 1.1 History and Evidence In 1884, Lord Kelvin estimated the number of dark bodies in the Milky Way from the observed velocity dispersion of the stars orbiting around the center of the galaxy. By using these measurements, he estimated the mass of the galaxy, which he determined to be different from the mass of visible stars. He thus concluded that many of our stars, perhaps a great majority of them, maybe dark bodies [40]. In 1906, Henri Poincare, in "The Milky Way and Theory of Gases," used "dark matter" or “matière obscure” in French in discussing Kelvin’s work [3]. In 1930, J.H. Oort studied the Doppler shift of the stars moving near the galactic plane of the Milky Way, and from that, he calculated the velocities of stars. He found that stars are moving sufficiently fast that they can escape the gravitational pull of the luminous mass of the galaxy, calculated by the mass (M)/luminosity (L) ratio method, which implies that there must be an invisible gravity source that holds stars in the galactic plane [41]. In 1933, F. Zwicky studied the Coma cluster, which is about 99 Mpc away from Earth. He calculated the velocity dispersion of the galaxies using Doppler shift data and applying the Virial theorem, and he found that the total mass of the cluster is just 2% of the one calculated by the M/L ratio method, leading to the conclusion of invisible mass, which he called "Dunkle Materie" or dark matter [42]. In 1960–1970, Vera Rubin, with others [43], studied the rotation curves of sixty isolated galaxies, from which she estimated the rotation velocities of the region of a galaxy using Doppler shift and found that it is independent of distance, as shown in figure 1.1. Assuming Newtonian gravity and circular orbits, the velocity of the star is given by The phenomenon of gravitational lensing, described by General Relativity (GR), states that whenever light from a distant source passes near a massive object, it forms a ring of light called “Einstein ring" around the same object. In 1979, D. Walsh was the first to study two distant objects separated by only 5.6 arcseconds with very similar redshifts. The explanation for his discovery is that there was only one object there, and the other is the image of it formed by an unseen dark matter cloud [44]. In 2004, from Chandra’s observations of the bullet cluster (1E0657–56), Markevitch and Clowe found the most compelling reason for the presence of the invisible matter. A bullet cluster is a system of two colliding galactic clusters. During the collision, the galaxies, which are separated by large distances within the two clusters, pass right through without interacting. However, the majority of the cluster’s baryonic mass exists in the hot gas in between galaxies. Collision dislocates the stellar gasses from their respective galaxies and heats the gases, resulting in a huge amount of X-ray radiation emission. However, the location of baryonic mass (stellar gases) seen by an X-ray telescope is different from the one found by weak gravitational lensing, i.e., the region of space where most masses are located, as shown in figure 1.2. If an unseen mass is electromagnetically neutral, then during a collision, it won’t be dragged and will remain in its separate clusters, which explains this puzzle [2]. In 1964, two American radio astronomers, Arno Penzias, and Robert Wilson, accidentally discovered the earliest radiation (uniform radio waves) or so-called cosmic microwave background radiation (CMBR) in the universe. Later in 1989, the COBE satellite confirmed this extreme uniform relic radiation at 2.73 K, with tiny spatial fluctuations in temperature. In 2001, WMAP and later Planck confirmed the prediction of COBE, measured the temperature fluctuation with greater accuracy, and found that it was of the order of 𝜇K. This whole observation was done via the power spectrum method, which is shown in figure 1.3. It has been found that these temperature fluctuations in the early universe can be explained only if one accounts for the additional non-baryonic matter, which is nearly five times ordinary matter [45]. This paper is available on arxiv under CC BY 4.0 DEED license. Author:
(1) Shivam Gola, The Institute of Mathematical Sciences, Chennai. Table of Links Acknowledgements Acknowledgements 1 Introduction to thesis 1 Introduction to thesis 1.1 History and Evidence 1.1 History and Evidence 1.2 Facts on dark matter 1.2 Facts on dark matter 1.3 Candidates to dark matter 1.3 Candidates to dark matter 1.4 Dark matter detection 1.4 Dark matter detection 1.5 Outline of the thesis 1.5 Outline of the thesis 2 Dark matter through ALP portal and 2.1 Introduction 2 Dark matter through ALP portal and 2.1 Introduction 2.2 Model 2.2 Model 2.3 Existing constraints on ALP parameter space 2.3 Existing constraints on ALP parameter space 2.4 Dark matter analysis 2.4 Dark matter analysis 2.5 Summary 2.5 Summary 3 A two component dark matter model in a generic 𝑈(1)𝑋 extension of SM and 3.1 Introduction 3 A two component dark matter model in a generic 𝑈(1)𝑋 extension of SM and 3.1 Introduction 3.2 Model 3.2 Model 3.3 Theoretical and experimental constraints 3.3 Theoretical and experimental constraints 3.4 Phenomenology of dark matter 3.4 Phenomenology of dark matter 3.5 Relic density dependence on 𝑈(1)𝑋 charge 𝑥𝐻 3.5 Relic density dependence on 𝑈(1)𝑋 charge 𝑥𝐻 3.6 Summary 3.6 Summary 4 A pseudo-scalar dark matter case in 𝑈(1)𝑋 extension of SM and 4.1 Introduction 4 A pseudo-scalar dark matter case in 𝑈(1)𝑋 extension of SM and 4.1 Introduction 4.2 Model 4.2 Model 4.3 Theoretical and experimental constraints 4.3 Theoretical and experimental constraints 4.4 Dark Matter analysis 4.4 Dark Matter analysis 4.5 Summary 4.5 Summary 5 Summary 5 Summary Appendices Appendices A Standard model A Standard model B Friedmann equations B Friedmann equations C Type I seasaw mechanism C Type I seasaw mechanism D Feynman diagrams in two-component DM model D Feynman diagrams in two-component DM model Bibliography Bibliography 1.1 History and Evidence In 1884, Lord Kelvin estimated the number of dark bodies in the Milky Way from the observed velocity dispersion of the stars orbiting around the center of the galaxy. By using these measurements, he estimated the mass of the galaxy, which he determined to be different from the mass of visible stars. He thus concluded that many of our stars, perhaps a great majority of them, maybe dark bodies [40]. In 1906, Henri Poincare, in "The Milky Way and Theory of Gases," used "dark matter" or “matière obscure” in French in discussing Kelvin’s work [3]. In 1930, J.H. Oort studied the Doppler shift of the stars moving near the galactic plane of the Milky Way, and from that, he calculated the velocities of stars. He found that stars are moving sufficiently fast that they can escape the gravitational pull of the luminous mass of the galaxy, calculated by the mass (M)/luminosity (L) ratio method, which implies that there must be an invisible gravity source that holds stars in the galactic plane [41]. In 1933, F. Zwicky studied the Coma cluster, which is about 99 Mpc away from Earth. He calculated the velocity dispersion of the galaxies using Doppler shift data and applying the Virial theorem, and he found that the total mass of the cluster is just 2% of the one calculated by the M/L ratio method, leading to the conclusion of invisible mass, which he called "Dunkle Materie" or dark matter [42]. In 1960–1970, Vera Rubin, with others [43], studied the rotation curves of sixty isolated galaxies, from which she estimated the rotation velocities of the region of a galaxy using Doppler shift and found that it is independent of distance, as shown in figure 1.1. Assuming Newtonian gravity and circular orbits, the velocity of the star is given by The phenomenon of gravitational lensing, described by General Relativity (GR), states that whenever light from a distant source passes near a massive object, it forms a ring of light called “Einstein ring" around the same object. In 1979, D. Walsh was the first to study two distant objects separated by only 5.6 arcseconds with very similar redshifts. The explanation for his discovery is that there was only one object there, and the other is the image of it formed by an unseen dark matter cloud [44]. In 2004, from Chandra’s observations of the bullet cluster (1E0657–56), Markevitch and Clowe found the most compelling reason for the presence of the invisible matter. A bullet cluster is a system of two colliding galactic clusters. During the collision, the galaxies, which are separated by large distances within the two clusters, pass right through without interacting. However, the majority of the cluster’s baryonic mass exists in the hot gas in between galaxies. Collision dislocates the stellar gasses from their respective galaxies and heats the gases, resulting in a huge amount of X-ray radiation emission. However, the location of baryonic mass (stellar gases) seen by an X-ray telescope is different from the one found by weak gravitational lensing, i.e., the region of space where most masses are located, as shown in figure 1.2. If an unseen mass is electromagnetically neutral, then during a collision, it won’t be dragged and will remain in its separate clusters, which explains this puzzle [2]. In 1964, two American radio astronomers, Arno Penzias, and Robert Wilson, accidentally discovered the earliest radiation (uniform radio waves) or so-called cosmic microwave background radiation (CMBR) in the universe. Later in 1989, the COBE satellite confirmed this extreme uniform relic radiation at 2.73 K, with tiny spatial fluctuations in temperature. In 2001, WMAP and later Planck confirmed the prediction of COBE, measured the temperature fluctuation with greater accuracy, and found that it was of the order of 𝜇K. This whole observation was done via the power spectrum method, which is shown in figure 1.3. It has been found that these temperature fluctuations in the early universe can be explained only if one accounts for the additional non-baryonic matter, which is nearly five times ordinary matter [45]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Author: (1) Shivam Gola, The Institute of Mathematical Sciences, Chennai. Author: Author: (1) Shivam Gola, The Institute of Mathematical Sciences, Chennai.

What Is Dark Matter? Shining a Light on Its History and Evidence   

New Evidence Changes How We Understand Inflation and Monetary Policy

Nye beviser ændrer, hvordan vi forstår inflation og pengepolitik

Νέα στοιχεία αλλάζουν τον τρόπο με τον οποίο κατανοούμε τον πληθωρισμό και τη νομισματική πολιτική

Nuevas evidencias cambian nuestra forma de entender la inflación y la política monetaria

Nuove prove cambiano il modo in cui comprendiamo l’inflazione e la politica monetaria

新たな証拠がインフレと金融政策の理解を変える

ភស្តុតាងថ្មីផ្លាស់ប្តូររបៀបដែលយើងយល់ពីអតិផរណា និងគោលនយោបាយរូបិយវត្ថុ

Nowe dowody zmieniają sposób, w jaki rozumiemy inflację i politykę pieniężną

උද්ධමනය සහ මුදල් ප්‍රතිපත්තිය අප තේරුම් ගන්නා ආකාරය නව සාක්ෂි වෙනස් කරයි

Humbowo Hutsva Hunochinja Manzwisisiro Atinoita Inflation uye Monetary Policy

Ushahidi Mpya Unabadilisha Jinsi Tunavyoelewa Mfumuko wa Bei na Sera ya Fedha

பணவீக்கம் மற்றும் பணவியல் கொள்கையை நாம் எவ்வாறு புரிந்துகொள்கிறோம் என்பதை புதிய சான்றுகள் மாற்றுகின்றன

新证据改变了我们对通货膨胀和货币政策的理解

What does an organization have to do when it is in complete uncertainty? For example, the company doesn’t know where to go next, the next step of its journey, and where to get the mission. Of course, we can say to go and try to find a mission, but this is easy only on paper. It is hard to make a first step regarding actual experiments. So, let’s try to understand what it means to find the right path in a complex world. First Steps Whether we are looking at a well-established company or a group of friends looking to start their first startup - the first step is probably similar - understand the environment. What does the environment look like? In the idea you are trying to get - who are the competitors? Is there much competition? These are just some questions our mentioned team has to ask to understand their future steps. It is essential to realize that while we can observe what’s going on next, we should not be limited by what we see but also try to look beyond it. One of the examples of why we should look not only at the facts we can observe is the so-called survivorship bias. It is the logical error of concentrating on entities that passed a selection process while overlooking those that did not. During the Second World War, planes were reinforced at the places where the most hits occurred, but the problem was that this conclusion was based on the examination of planes that returned safely. Instead, all aircraft hit in other places didn’t return, so perhaps reinforcement could have been used elsewhere. So, try to get the best ideas from competitors, but look around if there is anything you are missing, but stay aware. Think About Metrics While we have discussed four key value areas, plenty of metrics can be implemented to track those areas, and the beauty of the approach is that it is customizable. Now that you know what the future product or feature might look like - think about measuring value - metrics that are easy or cheap to track and how to implement them. There is no value in proceeding further without setting the measuring guardrails first. The best that can happen is that you will not understand what you have just implemented. The worst can drag you straight to the bottom. Experiment While having to work and not-so-working ideas from the competitors is a great idea, most likely, this will not be enough for you to outcompete those who stand firm on the ground already. Start experimenting as fast as possible. Experiments are probably the best way of trying things.Use hypotheses, i.e., the idea that you believe is true or functionality that you think customers would love, and devise a way of implementing it. You do not need to build something - instead, you can gather people who you think might use the project and ask them what they believe - either about the mockup, a brief description, or if you have enough capacity - a prototype that can do some basic operations. It didn’t work. No worries - you spent just a few days and, most importantly, got the data as you had set the metrics before. Do metrics provide valuable insights - great, go with the future iteration of your product. If metrics are almost useless, your next move might be to adaptation of these metrics to what you need to build your future steps. Remember, we are not looking for perfection here. Our main goal is to understand the future path and create a mission and goals for the product. Thus, even the craziest ideas might help if we try them and get valuable insights. Acting Based on the Experiment Results Once you have gathered insights from the experiments and proved or disproved your hypotheses, you might be ready to set the mission and goals to start building the product. And don’t get me wrong, if you want to go without these steps - you are more than welcome, there might be situations when you want to dump all the possible resources on the problem and win. However, in a world where it is not only you who tries to win - that is a big gamble, especially if you are limited with resources. Setting the Mission We have discussed how to set up a mission and goals on different levels, so it is worth looking at one more time. While we know what goals and levels to set up, we must correctly process all the evidence gathered. Get the buy-in from the team, establish accountable people, and ensure you have guardrails and check-ins in place to routinely inspect what you have implemented and how it is performing. While understanding the performance - you guessed it right- look at the critical value areas and identify the most crucial for a specific part of your business. This will allow you to get insights without much trouble and help you not only to follow the mission but also to make sure that you are following the shortest possible path toward it. Conclusion We have discussed how to use Evidence-based management to find or return to the path toward the end state of your product if you are lost. While this is not a step-by-step guide, hopefully, some insights we have provided can be implemented on the scale. If you are looking for a new challenge or already have one you are trying to solve - Evidence-based management might be the right tool to look at it. What is your step to navigate in the world of uncertainty, especially when you have lost the path? Share your thoughts in the comments. What does an organization have to do when it is in complete uncertainty? For example, the company doesn’t know where to go next, the next step of its journey, and where to get the mission. Of course, we can say to go and try to find a mission, but this is easy only on paper. It is hard to make a first step regarding actual experiments. So, let’s try to understand what it means to find the right path in a complex world. First Steps Whether we are looking at a well-established company or a group of friends looking to start their first startup - the first step is probably similar - understand the environment. What does the environment look like? In the idea you are trying to get - who are the competitors? Is there much competition? These are just some questions our mentioned team has to ask to understand their future steps. It is essential to realize that while we can observe what’s going on next, we should not be limited by what we see but also try to look beyond it. One of the examples of why we should look not only at the facts we can observe is the so-called survivorship bias. It is the logical error of concentrating on entities that passed a selection process while overlooking those that did not. During the Second World War, planes were reinforced at the places where the most hits occurred, but the problem was that this conclusion was based on the examination of planes that returned safely. Instead, all aircraft hit in other places didn’t return, so perhaps reinforcement could have been used elsewhere. So, try to get the best ideas from competitors, but look around if there is anything you are missing, but stay aware. Think About Metrics While we have discussed four key value areas, plenty of metrics can be implemented to track those areas, and the beauty of the approach is that it is customizable. Now that you know what the future product or feature might look like - think about measuring value - metrics that are easy or cheap to track and how to implement them. There is no value in proceeding further without setting the measuring guardrails first. The best that can happen is that you will not understand what you have just implemented. The worst can drag you straight to the bottom. Experiment While having to work and not-so-working ideas from the competitors is a great idea, most likely, this will not be enough for you to outcompete those who stand firm on the ground already. Start experimenting as fast as possible. Experiments are probably the best way of trying things. Use hypotheses, i.e., the idea that you believe is true or functionality that you think customers would love, and devise a way of implementing it. You do not need to build something - instead, you can gather people who you think might use the project and ask them what they believe - either about the mockup, a brief description, or if you have enough capacity - a prototype that can do some basic operations. It didn’t work. No worries - you spent just a few days and, most importantly, got the data as you had set the metrics before. Do metrics provide valuable insights - great, go with the future iteration of your product. If metrics are almost useless, your next move might be to adaptation of these metrics to what you need to build your future steps. Remember, we are not looking for perfection here. Our main goal is to understand the future path and create a mission and goals for the product. Thus, even the craziest ideas might help if we try them and get valuable insights. Acting Based on the Experiment Results Once you have gathered insights from the experiments and proved or disproved your hypotheses, you might be ready to set the mission and goals to start building the product. And don’t get me wrong, if you want to go without these steps - you are more than welcome, there might be situations when you want to dump all the possible resources on the problem and win. However, in a world where it is not only you who tries to win - that is a big gamble, especially if you are limited with resources. Setting the Mission We have discussed how to set up a mission and goals on different levels, so it is worth looking at one more time. While we know what goals and levels to set up, we must correctly process all the evidence gathered. Get the buy-in from the team, establish accountable people, and ensure you have guardrails and check-ins in place to routinely inspect what you have implemented and how it is performing. While understanding the performance - you guessed it right- look at the critical value areas and identify the most crucial for a specific part of your business. This will allow you to get insights without much trouble and help you not only to follow the mission but also to make sure that you are following the shortest possible path toward it. Conclusion We have discussed how to use Evidence-based management to find or return to the path toward the end state of your product if you are lost. While this is not a step-by-step guide, hopefully, some insights we have provided can be implemented on the scale. If you are looking for a new challenge or already have one you are trying to solve - Evidence-based management might be the right tool to look at it. What is your step to navigate in the world of uncertainty, especially when you have lost the path? Share your thoughts in the comments. What is your step to navigate in the world of uncertainty, especially when you have lost the path? Share your thoughts in the comments.

Evidence-Based Management: When the Path Is Missing

Now that you have implemented Evidence-Based Management in your organisation and are starting to see what has gone well and what has not, where should you head next? Before we start, it is worth saying that there is no silver bullet, and most likely, some actions and ideas can’t be implemented using a blanket approach and would require some adjustments. If you believe it would be helpful for you to chat about your case, don’t hesitate to drop me a message on LinkedIn. I would gladly discuss this and see if we can solve that problem together. Start with Retrospective To be honest, this is a common approach that I use and would prefer everyone to use for any situation that has happened and didn’t go as planned, go as planned, or, frankly, any situation. As retrospective is a crucial part of empiricism, collecting evidence that will be considered for future decision-making can’t be very helpful. Look back at the data you have collected and decisions made, analyse it, and ensure you are getting the most out of it. If you still need to, adjust what you collect and try again. Simple as it is, people and organisations are known for wasting time with an initial idea that might not be valid anymore or provide little to no value in a specific setting. Once you are done with Retrospective, you will have actions that are specific to your case and context, and we propose to reinforce them with four additional topics that are worth looking at: Integration of Multiple Sources of Evidence
Critical Thinking and Decision-Making
Systematic Approach
Organisational Culture and Skills Development Integration of Multiple Sources of Evidence Way before an organisation starts its journey towards Evidence-Based management, they have some data collected. Whether it is customer research, financial data, professional expertise or stakeholder involvement of any kind - it is present. The added value introduced by four key value areas (if you are unfamiliar with this concept - we recommend reading the guide to the management technique before reading further) usually provides additional input that creates the challenge itself. To ensure all data is consumed and used to the full extent, ensure you integrate all sources of your evidence. Integration will differ depending on the organisational context. However, we can definitely say that prioritisation techniques should be used. It is worth remembering that there is such a thing as “too much data,” so make sure you assess if a specific piece of evidence is valuable for a specific situation to prevent looking in the wrong direction. Critical Thinking and Decision-Making As a leader, you are responsible for thinking critically and seeing the helicopter-view picture. You cannot be involved in all organisation or team activities, but you are absolutely accountable for being aware of what’s going on. When the data collected thanks to Evidence-Based management lands in your inbox, this is an opportunity to look at it and ask questions about its quality and relevance to the problem you are trying to solve, whether it is the launch of a new product or a routine operation process. As you get more information, you should start categorising the data and be able to pull specific evidence required for decision-making in certain situations.While both of these skills are common sense and are expected from leadership on any level, it is hard to implement them in real life and ensure that you live by these values. Always question assumptions, identify biases, and use decision aids to enhance your judgement to achieve the best result. Systematic Approach While data can provide valuable insights, it is worth nothing without the actions you will take after consuming it. Therefore, it is a good idea to implement a systematic approach to the decision-making process. If any monitored parameter requires a repeatable task to be adjusted, it has to be standardised. There is no reason to have knowledge hidden in the heads of anyone in your team. If you can fix the actions on paper and create a stable way of repeating them, do it. The same goes for an unpredictable event—a good motto is to be prepared for worth. If you believe there might be a bug, create a backup, a plan for building a backup, a plan for reverting to a backup, a plan for creating, and a standardised way of capturing the reasons for a failure. However, while creating a system, it is necessary to avoid making a local optimisation. If you need to become more familiar with the concept - check our previous article that covers this topic - The Rabbit Hole: Local Optimizations That Harm. Organisational Culture and Skills Development Organisational Culture and Skills Development are somehow similar to retrospective - you better consider them at any point in your job (there are cases when you don’t need any of these, but those are not a complex software development process). Evidence-Based management is going to evolve, and it is going to sustain only if, through the times, you and the tour team can adapt to get the best result out of it. So, a culture of curiosity, feedback, and powerful questions is required. As the system and the world evolve, new skills will be needed. AI is a hot topic right now, and most organisations have to implement it to some extent - the same is true for any new technology or concept that must be adapted. We don’t know what is coming next, but we must build a proper learning muscle to help people develop their skills when needed. What’s next? It is always worth sticking to the pillars of empiricism of Transparency, Inspection, and Adaptation to adapt almost any existing framework or process. So, whether you take your actions or try to look at some ideas in this article, make sure you act based on what’s true and revisit your decision frequently to check if they still offer value. What is your experience with Evidence-Based management? Share in the comments. Now that you have implemented Evidence-Based Management in your organisation and are starting to see what has gone well and what has not, where should you head next? Evidence-Based Management Before we start, it is worth saying that there is no silver bullet, and most likely, some actions and ideas can’t be implemented using a blanket approach and would require some adjustments. If you believe it would be helpful for you to chat about your case, don’t hesitate to drop me a message on LinkedIn. I would gladly discuss this and see if we can solve that problem together. Start with Retrospective To be honest, this is a common approach that I use and would prefer everyone to use for any situation that has happened and didn’t go as planned, go as planned, or, frankly, any situation. As retrospective is a crucial part of empiricism, collecting evidence that will be considered for future decision-making can’t be very helpful. Look back at the data you have collected and decisions made, analyse it, and ensure you are getting the most out of it. If you still need to, adjust what you collect and try again. Simple as it is, people and organisations are known for wasting time with an initial idea that might not be valid anymore or provide little to no value in a specific setting. Once you are done with Retrospective, you will have actions that are specific to your case and context, and we propose to reinforce them with four additional topics that are worth looking at: Integration of Multiple Sources of Evidence Critical Thinking and Decision-Making Systematic Approach Organisational Culture and Skills Development Integration of Multiple Sources of Evidence Critical Thinking and Decision-Making Systematic Approach Organisational Culture and Skills Development Integration of Multiple Sources of Evidence Way before an organisation starts its journey towards Evidence-Based management, they have some data collected. Whether it is customer research, financial data, professional expertise or stakeholder involvement of any kind - it is present. The added value introduced by four key value areas (if you are unfamiliar with this concept - we recommend reading the guide to the management technique before reading further) usually provides additional input that creates the challenge itself. To ensure all data is consumed and used to the full extent, ensure you integrate all sources of your evidence. Integration will differ depending on the organisational context. However, we can definitely say that prioritisation techniques should be used. It is worth remembering that there is such a thing as “too much data,” so make sure you assess if a specific piece of evidence is valuable for a specific situation to prevent looking in the wrong direction. Critical Thinking and Decision-Making As a leader, you are responsible for thinking critically and seeing the helicopter-view picture. You cannot be involved in all organisation or team activities, but you are absolutely accountable for being aware of what’s going on. When the data collected thanks to Evidence-Based management lands in your inbox, this is an opportunity to look at it and ask questions about its quality and relevance to the problem you are trying to solve, whether it is the launch of a new product or a routine operation process. As you get more information, you should start categorising the data and be able to pull specific evidence required for decision-making in certain situations. While both of these skills are common sense and are expected from leadership on any level, it is hard to implement them in real life and ensure that you live by these values. Always question assumptions, identify biases, and use decision aids to enhance your judgement to achieve the best result. Systematic Approach While data can provide valuable insights, it is worth nothing without the actions you will take after consuming it. Therefore, it is a good idea to implement a systematic approach to the decision-making process. If any monitored parameter requires a repeatable task to be adjusted, it has to be standardised. There is no reason to have knowledge hidden in the heads of anyone in your team. If you can fix the actions on paper and create a stable way of repeating them, do it. The same goes for an unpredictable event—a good motto is to be prepared for worth. If you believe there might be a bug, create a backup, a plan for building a backup, a plan for reverting to a backup, a plan for creating, and a standardised way of capturing the reasons for a failure. However, while creating a system, it is necessary to avoid making a local optimisation. If you need to become more familiar with the concept - check our previous article that covers this topic - The Rabbit Hole: Local Optimizations That Harm . The Rabbit Hole: Local Optimizations That Harm The Rabbit Hole: Local Optimizations That Harm Organisational Culture and Skills Development Organisational Culture and Skills Development are somehow similar to retrospective - you better consider them at any point in your job (there are cases when you don’t need any of these, but those are not a complex software development process). Evidence-Based management is going to evolve, and it is going to sustain only if, through the times, you and the tour team can adapt to get the best result out of it. So, a culture of curiosity, feedback, and powerful questions is required. As the system and the world evolve, new skills will be needed. AI is a hot topic right now, and most organisations have to implement it to some extent - the same is true for any new technology or concept that must be adapted. We don’t know what is coming next, but we must build a proper learning muscle to help people develop their skills when needed. What’s next? It is always worth sticking to the pillars of empiricism of Transparency, Inspection, and Adaptation to adapt almost any existing framework or process. So, whether you take your actions or try to look at some ideas in this article, make sure you act based on what’s true and revisit your decision frequently to check if they still offer value. What is your experience with Evidence-Based management? Share in the comments. What is your experience with Evidence-Based management? Share in the comments.

Evidence-Based Management Evolution

If you haven’t read my article about evidence-based management's history and core principles, I recommend you do so by following the link: https://hackernoon.com/evidence-based-management-in-business-will-it-make-a-differenceIf you have decided to start implementing evidence-based management in your organization, I would like to talk about the first steps. The whole idea is to use the objective evidence of our work to steer towards a better/faster/more efficient way to achieve the goal. While we can aim to uncover the best path, it will be extremely hard or almost impossible if we do not know where we are going. So, let's start by understanding the organization's vision and mission. If you understand your organization's vision and mission, skip to the next section. If you are unsure - let’s take it simple and try to understand both for a well-known company - SpaceX. Vision and Mission The Vision, which can be derived from their statement, would be something similar to “make multi-planetary life possible,” while the Mission would be “revolutionizing space technology that will enable multi-planetary life for all people.” In other words, Vision is the ultimate direction of travel, while mission is a close objective that is the company's current focus.Whether you are building a multi-planetary civilization, optimizing a search engine, or trying to establish world domination in the production of raw cashew snacks, it is super important for you and your company to make sure everyone in the organization understands both. This way, when critical decisions have to be made, they can be checked for compliance with the vision and mission, and if they are, that’s probably a good decision to make. Goals The next step is to introduce a set of goals on different timelines: long-term (strategic, 1+ years), mid-term (or intermediate, 3-6 months), and short-term (or tactical, up to 3 months). The goals are the ultimate tools for your organization to move toward its mission and vision. Long-Term Goals Strategic goals are those that could be more ambitious. Ideally, they are nearly impossible and are purposefully created to guide your organization toward its mission. When we speak about mission, we still need to figure out what our product or general solution might look like. It is good to start framing an authentic look for a long-term goal. Whether it is a certain market penetration or a specific level of revenue per customer, make sure the direction of an organization toward the long-term goal is a step toward your mission and vision. Medium-Term Goals Intermediate goals should be refined and understood before even starting. Whether this is a new proposition for the customer, a redesign of your current infrastructure, or an entirely new marketing approach - this is an essential step to set boundaries for a couple of months that will help you to try a hypothesis, idea, or experiment with the information you already have. You still have to remember about the principles of Agile and be ready to give up the goal if it makes sense that you are not able to fulfill the goal, but if you ever decide to make a change - think what else can be done to get closer to the strategic goal and take appropriate decisions. Short-Term Goals Ideally, an iterative process can be achieved by a team or set of teams in a really quick period of time and, if failed, will not cause much harm to the whole organization and its mission. Think about improving customer experience, creating new opportunities for them, and using these propositions as a testing ground to get feedback and help from those higher-level goals that will lead you to success in the broader picture. Measures It is important to be able to track progress with measures that can be counted and visualized in order to understand how all goals contribute to the mission and vision. In Evidence-based management, these measures are called Key Value Areas: Current Value (CV)
Ability to Innovate (A2I)
Time two market (T2M)
Unrealized Value (UV) Current Value Quantifies the value that the product delivers today. It can be represented by factors that are low on Wall Street, such as Revenue per Employee, Product Cost ratio, and other measures that are more specific for your business, such as Employee Satisfaction, Customer Satisfaction, Clicks to order, etc. Unrealized Value These are usually the metrics directed towards potential growth, i.e., potential market share that the product might achieve if it better meets customer needs or the difference between the customer's ultimate experience and the current level of experience. Time to Market These are metrics that are widely adopted by the industry as DORA metrics, as well as other metrics that show how fast the value is delivered to the customer, i.e., Lead Time, Time to Learn, Time to pivot, etc. Ability to Innovate This is an extremely important metric that is usually shadowed by other, more catchy metrics and is also the hardest to measure. The amount of technical debt, average number of defects per release, the time needed for the release process, amount of known vulnerabilities, and skills distribution in the team/organization are good examples of this key area.Most organizations need to improve their understanding of why they are taking too long to introduce change or make decisions, and the ability to innovate can help. What Next Once everyone understands the vision and mission, has a clear set of goals, and knows how to measure their progress with four key areas, there is a big room for improvement. As it says in Scrum Guide - “Easy to learn but hard to master.” You will probably see lots of potential improvement areas and make many mistakes. But as soon as your progress is transparent (Transparency), you know what went wrong (Inspection), and you can change (Adaptation) - you are on the right path to success. Have you tried Evidence-based management? How did it go for you? Share your thoughts in the comments. If you haven’t read my article about evidence-based management's history and core principles, I recommend you do so by following the link: https://hackernoon.com/evidence-based-management-in-business-will-it-make-a-difference If you have decided to start implementing evidence-based management in your organization, I would like to talk about the first steps. https://hackernoon.com/evidence-based-management-in-business-will-it-make-a-difference The whole idea is to use the objective evidence of our work to steer towards a better/faster/more efficient way to achieve the goal. While we can aim to uncover the best path, it will be extremely hard or almost impossible if we do not know where we are going. So, let's start by understanding the organization's vision and mission. If you understand your organization's vision and mission, skip to the next section. If you are unsure - let’s take it simple and try to understand both for a well-known company - SpaceX. Vision and Mission The Vision, which can be derived from their statement, would be something similar to “make multi-planetary life possible,” while the Mission would be “revolutionizing space technology that will enable multi-planetary life for all people.” In other words, Vision is the ultimate direction of travel, while mission is a close objective that is the company's current focus. Whether you are building a multi-planetary civilization, optimizing a search engine, or trying to establish world domination in the production of raw cashew snacks, it is super important for you and your company to make sure everyone in the organization understands both. This way, when critical decisions have to be made, they can be checked for compliance with the vision and mission, and if they are, that’s probably a good decision to make. Goals The next step is to introduce a set of goals on different timelines: long-term (strategic, 1+ years), mid-term (or intermediate, 3-6 months), and short-term (or tactical, up to 3 months). The goals are the ultimate tools for your organization to move toward its mission and vision. Long-Term Goals Strategic goals are those that could be more ambitious. Ideally, they are nearly impossible and are purposefully created to guide your organization toward its mission. When we speak about mission, we still need to figure out what our product or general solution might look like. It is good to start framing an authentic look for a long-term goal. Whether it is a certain market penetration or a specific level of revenue per customer, make sure the direction of an organization toward the long-term goal is a step toward your mission and vision. Medium-Term Goals Intermediate goals should be refined and understood before even starting. Whether this is a new proposition for the customer, a redesign of your current infrastructure, or an entirely new marketing approach - this is an essential step to set boundaries for a couple of months that will help you to try a hypothesis, idea, or experiment with the information you already have. You still have to remember about the principles of Agile and be ready to give up the goal if it makes sense that you are not able to fulfill the goal, but if you ever decide to make a change - think what else can be done to get closer to the strategic goal and take appropriate decisions. Short-Term Goals Ideally, an iterative process can be achieved by a team or set of teams in a really quick period of time and, if failed, will not cause much harm to the whole organization and its mission. Think about improving customer experience, creating new opportunities for them, and using these propositions as a testing ground to get feedback and help from those higher-level goals that will lead you to success in the broader picture. Measures It is important to be able to track progress with measures that can be counted and visualized in order to understand how all goals contribute to the mission and vision. In Evidence-based management, these measures are called Key Value Areas: Current Value (CV) Ability to Innovate (A2I) Time two market (T2M) Unrealized Value (UV) Current Value (CV) Ability to Innovate (A2I) Time two market (T2M) Unrealized Value (UV) Current Value Quantifies the value that the product delivers today. It can be represented by factors that are low on Wall Street, such as Revenue per Employee, Product Cost ratio, and other measures that are more specific for your business, such as Employee Satisfaction, Customer Satisfaction, Clicks to order, etc. Unrealized Value These are usually the metrics directed towards potential growth, i.e., potential market share that the product might achieve if it better meets customer needs or the difference between the customer's ultimate experience and the current level of experience. Time to Market These are metrics that are widely adopted by the industry as DORA metrics, as well as other metrics that show how fast the value is delivered to the customer, i.e., Lead Time, Time to Learn, Time to pivot, etc. Ability to Innovate This is an extremely important metric that is usually shadowed by other, more catchy metrics and is also the hardest to measure. The amount of technical debt, average number of defects per release, the time needed for the release process, amount of known vulnerabilities, and skills distribution in the team/organization are good examples of this key area. Most organizations need to improve their understanding of why they are taking too long to introduce change or make decisions, and the ability to innovate can help. What Next Once everyone understands the vision and mission, has a clear set of goals, and knows how to measure their progress with four key areas, there is a big room for improvement. As it says in Scrum Guide - “Easy to learn but hard to master.” You will probably see lots of potential improvement areas and make many mistakes. But as soon as your progress is transparent (Transparency), you know what went wrong (Inspection), and you can change (Adaptation) - you are on the right path to success. Have you tried Evidence-based management? How did it go for you? Share your thoughts in the comments. Have you tried Evidence-based management? How did it go for you? Share your thoughts in the comments.

Evidence-Based Management: Where to Start?

In the early 2000s, the concept of evidence-based medicine emerged. Dr. David Sackett defined it as using current best evidence in patient care decisions, and it has gained traction. Despite abundant research, only about 15% of medical decisions are based on evidence, with many relying on outdated knowledge, traditions, experience, and vendor information. Similarly, managers often need to pay more attention to reliable evidence, leading to less effective decision-making. An evidence-based approach in management could provide a competitive edge, though it is challenging due to the variability among organizations and the weaker evidence base. Adopting better logic and evidence in management practices can enhance organizational performance. What is wisdom? When doctors or managers make decisions based on something other than the best evidence, it is often due to reliance on personal experience over research. Practitioners may find firsthand information more convincing than journal data despite acknowledging the limited sample size of their observations. Managers similarly rely on past practices without considering differences between organizations. Specialists often default to familiar treatments, and marketing can skew the information practitioners receive, promoting products with exaggerated benefits. Ideology also affects decisions. For example, stock options are defended as fostering an ownership culture, though evidence shows no consistent performance benefits. The first-mover-advantage myth persists despite mixed evidence, and cultural beliefs further reinforce this notion. Uncritical emulation, or casual benchmarking, is another issue. While benchmarking can be helpful, copying top performers without understanding the underlying logic can be ineffective. Failed attempts by dozens of ride-sharing apps across the globe to imitate Uber’s success are notable examples. Six common substitutes for evidence: outdated knowledge
personal experience
specialist skills
marketing hype
ideology
casual benchmarking Illustrate why evidence-based decision-making is rare. Relying on these substitutes is not optimal for evaluating practices. Evidence-based management can improve outcomes, as demonstrated by companies successfully implementing it. Is Forced Ranking good? At Oxford’s Centre for Evidence-Based Medicine, decision-making starts with framing an answerable question, like whether to adopt forced employee ranking. Popularised by General Electric, this system ranks employees on a bell curve, rewarding the top 20%, developing the middle 70%, and dismissing the bottom 10%. Many companies consider this system due to GE’s success and its promotion in business literature. However, evidence suggests that forced ranking may not be beneficial. Evidence-based management would question if their company is similar enough to GE to expect similar outcomes. They would scrutinize the supporting data, noting flaws such as incorrect causality assumptions in studies like The War for Talent. Negative evidence includes findings that performance improves with team continuity and that wide pay disparities harm productivity and morale. Studies show that forced ranking can decrease collaboration and trust, and pay dispersion correlates with poorer performance, even in sports like baseball. Additionally, research in the auto industry indicates that systems emphasizing teamwork and minimizing status differences outperform those focusing solely on individual talent. This evidence suggests that adopting forced ranking may yield different results than the desired ones. How to reinforce your company with evidence-based managers Implementing evidence-based management is challenging but essential. Managers should prioritize training, technologies, and practices that support informed decision-making. Encouraging a culture of inquiry, where evidence of efficacy is demanded, logic is scrutinized, and experimentation is rewarded, fosters better decisions. Cultivating an "attitude of wisdom" ensures continuous learning and applying the best available knowledge. Here are some tips to create such an environment within a company. Demand evidence Kent Thiry, CEO of DaVita, emphasizes evidence-based management to turn around the struggling company. Thiry and his team implemented systems to track performance, ensuring data-driven decisions. At DaVita Academy, Thiry supports claims of industry-leading treatment quality with specific, quantitative comparisons. Reports start with patient care effectiveness data; monthly reports include a DaVita Quality Index. Missing but important metrics are noted as "not available" to motivate data collection. This focus on evidence contributed to a 50% decrease in voluntary turnover, superior patient care, and strong financial results. Emphasizing evidence-based decision-making in an environment that requires complete transparency is crucial. Scrutinise Logic More than merely requesting research to support proposals are required for accurate, evidence-based management. Managers must examine gaps in logic, exposition, and inference, especially since most management studies are non-experimental. Understanding research limitations and thinking critically are essential. Senior executives should unpack assumptions behind policies or practices, fostering a culture of critical evaluation. They should explore whether assumptions make sense, asking if they align with organizational realities. Be wary of consultant claims; for example, Bain & Company claims that clients outperform the market but lack proof that their advice is the cause. Adopt the Attitude of Wisdom True wisdom in evidence-based management comes from recognizing how much remains unknown. Effective managers act on the best available knowledge while continually questioning their assumptions. This balance of humility and decisiveness can be achieved by supporting ongoing professional education and encouraging inquiry and observation even without rigorous evidence. For example, executives at a struggling computer company discovered product issues firsthand by visiting stores and gathering qualitative data. Such practices demonstrate that real-world observations are crucial for informed decision-making, fostering a culture that values evidence over guesswork and belief. What will be the impact? Like evidence-based medicine, evidence-based management relies on sound data for decision-making. Initial studies show improved medical outcomes, but business settings need broader evidence. The theoretical argument remains strong: decisions grounded in solid evidence foster better outcomes. Practical studies, like those showing stand-up meetings saving time without sacrificing decision quality, exemplify its benefits. However, adopting this approach may challenge traditional power dynamics, as data-driven decisions level hierarchies. Leaders must choose between preserving their status and improving organizational performance. Embracing evidence-based management transforms managerial thinking, leveraging logic and facts for effective leadership and better organizational results. Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments! In the early 2000s, the concept of evidence-based medicine emerged. Dr. David Sackett defined it as using current best evidence in patient care decisions, and it has gained traction. Despite abundant research, only about 15% of medical decisions are based on evidence, with many relying on outdated knowledge, traditions, experience, and vendor information. Similarly, managers often need to pay more attention to reliable evidence, leading to less effective decision-making. An evidence-based approach in management could provide a competitive edge, though it is challenging due to the variability among organizations and the weaker evidence base. Adopting better logic and evidence in management practices can enhance organizational performance. What is wisdom? When doctors or managers make decisions based on something other than the best evidence, it is often due to reliance on personal experience over research. Practitioners may find firsthand information more convincing than journal data despite acknowledging the limited sample size of their observations. Managers similarly rely on past practices without considering differences between organizations. Specialists often default to familiar treatments, and marketing can skew the information practitioners receive, promoting products with exaggerated benefits. Ideology also affects decisions. For example, stock options are defended as fostering an ownership culture, though evidence shows no consistent performance benefits. The first-mover-advantage myth persists despite mixed evidence, and cultural beliefs further reinforce this notion. Uncritical emulation, or casual benchmarking, is another issue. While benchmarking can be helpful, copying top performers without understanding the underlying logic can be ineffective. Failed attempts by dozens of ride-sharing apps across the globe to imitate Uber’s success are notable examples. Six common substitutes for evidence: outdated knowledge personal experience specialist skills marketing hype ideology casual benchmarking outdated knowledge personal experience specialist skills marketing hype ideology casual benchmarking Illustrate why evidence-based decision-making is rare. Relying on these substitutes is not optimal for evaluating practices. Evidence-based management can improve outcomes, as demonstrated by companies successfully implementing it. Is Forced Ranking good? At Oxford’s Centre for Evidence-Based Medicine, decision-making starts with framing an answerable question, like whether to adopt forced employee ranking. Popularised by General Electric, this system ranks employees on a bell curve, rewarding the top 20%, developing the middle 70%, and dismissing the bottom 10%. Many companies consider this system due to GE’s success and its promotion in business literature. However, evidence suggests that forced ranking may not be beneficial. Evidence-based management would question if their company is similar enough to GE to expect similar outcomes. They would scrutinize the supporting data, noting flaws such as incorrect causality assumptions in studies like The War for Talent. Negative evidence includes findings that performance improves with team continuity and that wide pay disparities harm productivity and morale. Studies show that forced ranking can decrease collaboration and trust, and pay dispersion correlates with poorer performance, even in sports like baseball. Additionally, research in the auto industry indicates that systems emphasizing teamwork and minimizing status differences outperform those focusing solely on individual talent. This evidence suggests that adopting forced ranking may yield different results than the desired ones. How to reinforce your company with evidence-based managers Implementing evidence-based management is challenging but essential. Managers should prioritize training, technologies, and practices that support informed decision-making. Encouraging a culture of inquiry, where evidence of efficacy is demanded, logic is scrutinized, and experimentation is rewarded, fosters better decisions. Cultivating an "attitude of wisdom" ensures continuous learning and applying the best available knowledge. Here are some tips to create such an environment within a company. Demand evidence Kent Thiry, CEO of DaVita, emphasizes evidence-based management to turn around the struggling company. Thiry and his team implemented systems to track performance, ensuring data-driven decisions. At DaVita Academy, Thiry supports claims of industry-leading treatment quality with specific, quantitative comparisons. Reports start with patient care effectiveness data; monthly reports include a DaVita Quality Index. Missing but important metrics are noted as "not available" to motivate data collection. This focus on evidence contributed to a 50% decrease in voluntary turnover, superior patient care, and strong financial results. Emphasizing evidence-based decision-making in an environment that requires complete transparency is crucial. Scrutinise Logic More than merely requesting research to support proposals are required for accurate, evidence-based management. Managers must examine gaps in logic, exposition, and inference, especially since most management studies are non-experimental. Understanding research limitations and thinking critically are essential. Senior executives should unpack assumptions behind policies or practices, fostering a culture of critical evaluation. They should explore whether assumptions make sense, asking if they align with organizational realities. Be wary of consultant claims; for example, Bain & Company claims that clients outperform the market but lack proof that their advice is the cause. Adopt the Attitude of Wisdom True wisdom in evidence-based management comes from recognizing how much remains unknown. Effective managers act on the best available knowledge while continually questioning their assumptions. This balance of humility and decisiveness can be achieved by supporting ongoing professional education and encouraging inquiry and observation even without rigorous evidence. For example, executives at a struggling computer company discovered product issues firsthand by visiting stores and gathering qualitative data. Such practices demonstrate that real-world observations are crucial for informed decision-making, fostering a culture that values evidence over guesswork and belief. What will be the impact? Like evidence-based medicine, evidence-based management relies on sound data for decision-making. Initial studies show improved medical outcomes, but business settings need broader evidence. The theoretical argument remains strong: decisions grounded in solid evidence foster better outcomes. Practical studies, like those showing stand-up meetings saving time without sacrificing decision quality, exemplify its benefits. However, adopting this approach may challenge traditional power dynamics, as data-driven decisions level hierarchies. Leaders must choose between preserving their status and improving organizational performance. Embracing evidence-based management transforms managerial thinking, leveraging logic and facts for effective leadership and better organizational results. Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments! Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments!

Evidence-Based Management in Business: Will it make a difference?

COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 34 of 42. 32. “BitCoin: SEIR-C Propagation models of block and transaction dissemination” (Reliance Document) {ID_000550} / {L3/237/1} 622. The document purports to be precursor work to the Bitcoin White Paper, dated 12 December 2008. (a) COPA’s Reasons for Alleging Forgery 623. The document has been backdated. It is an altered version of a document actually published by Dr Wright in 2017 (the “2017 SSRN Paper”). [PM1 [25-38] and [55]]. 624. The document contains hidden, embedded Grammarly timestamps indicating its true date to be 15 April 2017 or later. [PM1 [48-53]]. 625. The Grammarly software did not exist in 2008 [Madden1 [62c]] but is contemporaneous for 2017. [Madden1 [70-72]]. 626. {ID_000550} contains hidden, embedded traces of the 2017 SSRN paper, indicating that both are derived from a common source document. [PM1 [25-33]]. 627. The content referred to in the hidden, embedded traces of the 2017 SSRN paper includes news articles and government publications which did not yet exist in 2008 but which are contemporaneous for 2017. [PM1 [19-21] and [55]]. 628. The document contains equations and formulae which have been corrupted in a manner consistent with conversion from a more modern format to an older format [PM1 [34-45]. The document includes tampered content that apparently represents an attempt to explain away this problem as the result of using older equation-editing MathType software. However, analysis indicates that equations in the document were in fact authored with much later version of that software, which did not yet exist in 2008 but which are contemporaneous to 2017. [PM40 [22-42]]. 629. The document contains references to Microsoft schemas which did not yet exist in 2008 but are contemporaneous to 2010 onwards (including 2017). [PM1 [46-47]]. 630. The document includes anachronistic references to events that had not occurred by 2008 but which had occurred by 2017 [PM1 [21-22]]. 631. The internal metadata records an anomalous edit time in excess of 70 days and the editing period of this document overlaps with several other documents in Dr Wright’s disclosure, consistent with being created using clock manipulation techniques. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 632. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto (suggesting academic work done by Dr Wright in his supposed development of Bitcoin), contrary to fact. 633. Dr Wright has positively asserted that {ID_000550} is a document on which he primarily relies as supporting his claim to be Satoshi Nakamoto. 634. Dr Wright is recorded as an author of the metadata. The doctored content is phrased by Dr Wright in the first person: “Note- I have released online as an anonymous programmer using the pseudonym "Satoshi Nakamoto". 635. Dr Wright is a user of Grammarly software. 636. {ID_000550} contains hidden text embedded within the document which contradicts the information presented on the face of the document, a characteristic of manipulation of a series of MS Word .DOC files found throughout Dr Wright’s disclosure in these proceedings. 637. The 2017 SSRN Paper is a version of the same document that was published on the SSRN website by Dr Wright himself, bearing a “date written” of 28 March 2017. The “date written” is contemporaneous with the hidden Grammarly timestamp in {ID_000550} and not to the purported date of the document. 638. The metadata of the 2017 SSRN Paper lists “craig” as the creator of the document. 639. {ID_000550} contains hidden, embedded traces of the 2017 SSRN paper, indicating that both are derived from a common source document. 640. Dr Wright has not disclosed the 2017 SSRN paper or the common source document. 641. In his first witness statement in these proceedings, Dr Wright lists this document as a document to which he has been referred when preparing his evidence. (c) Dr Wright’s Explanations and COPA’s Rebuttal 642. In Appendix B to Wright11, Dr Wright attempted to explain the presence of embedded text showing a remnant version which referred to Bitcoin as an existing system and included various references to events after 2008 (including for instance to a report of May 2013). He claimed that these references were added “as a result of the corruption of the document, probably while it was kept at nChain or one of the other corporations”: {CSW/2/54}. In cross-examination, he claimed that two documents became merged into one another as a result of both being open at the same time on a Citrix environment and an edit not happening correctly on one of them: {Day2/135:1} to {Day2/138:19}. 643. In Appendix B to Wright11, Dr Wright sought to explain away the presence of the anachronistic Grammarly timestamps, the references to Mathtype software dating from 2017 and references to Microsoft schemas dating from 2010 and later by saying that they resulted from “someone in nChain opening the document in 2017 or later” and these elements becoming embedded: {CSW/2/53}; {Day2/143:1} to {Day2/143:23}. It is inherent in that explanation that these elements could become embedded without the person saving or otherwise interacting with the document (since such interaction would cause the metadata timestamps to update). 644. When pressed in cross-examination with the point that the remnant text in the document matches text in a 2017 version of the document uploaded by Dr Wright to the SSRN website, Dr Wright could give no coherent answer. When further pressed that this reliance document contains clear signs of corrupted equations (which appear properly readable in the SSRN version), thus suggesting errors resulting from conversion, he likewise failed to give any intelligible answer. See {Day2/140:7} to {Day2/142:25}. 645. Dr Wright also claimed that the references to CheckBlockHeader, UTXO and Bitcoin Core in this document were there because he used them. {Day2/134:14} to {Day2/140:5} 646. COPA submitted that Dr Wright’s explanation should be rejected as dishonest for the following reasons: 646.1. It presupposes that the features of a shared computing environment would cause two documents to become merged, with remnant text added in the way seen here, but in a manner which resulted in the document not being corrupted and appearing normal on its face. This possibility is rejected by the independent expert evidence. See Madden4, [159]; Madden / Placks joint statement 2 at [8]. 646.2. Dr Wright’s explanation for the presence of anachronistic artefacts in the metadata (including the anachronistic Grammarly timestamps, the references to Mathtype software dating from 2017 and references to Microsoft schemas dating from 2010 and later) is contradicted by the clear expert evidence of Mr Madden, which is that such references could not be inserted without the document being interacted with by a user, which would in turn result in updating of the metadata timestamps of the document (notably the “last saved” timestamp). The fact that the metadata timestamps for this document were dated to 2008 but the document contains the elements set out above shows that the document has been forged by backdating. See Madden4, [158]; Madden / Placks joint statement2 at [8]; {Day16/35:19} to {Day16/38:11}; {Day16/125:7} to {Day16/125:18}. 646.3. The correspondence between parts of the hidden remnant text of this document and the 2017 SSRN version of the document can only rationally be explained by this document being a copy forged and backdated in recent years. 646.4. This conclusion is further reinforced by the corrupted equations, which are clearly indicative of a document having been converted from a later version of MS Word into an older format which does not support the correct font or automatically applies an incorrect text editing format: see PM1 [44] {H/1/19}. 646.5. The anachronistic terms Dr Wright says he used are not found in other genuine documents before their first use. These first uses were as follows: 646.5.1. CheckBlockHeader was first introduced into the code in March 2014, Wuille1 [25] {C1/1/6}; 646.5.2. Bitcoin Core was first introduced in version 0.9 of the software in 2014 Wuille1 [50] {C1/1/12}; and 646.5.3. UTXO first introduced in version 0.8 of the software by Mr Wuille, with him first seeing the term on 21 June 2012 Wuille1 [29-32] {C1/1/7}. (d) Conclusion 647. In addition to COPA’s submissions in this section, I also refer to [737]-[759] in the main Judgment, all of which demonstrate that the points gathered in Appendix 1 to Dr Wright’s Closing in relation to this document do not begin to explain all the indications of forgery set out above. Dr Wright plainly forged this document from the 2017 SSRN paper. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.

This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 34 of 42. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 34 of 42. HackerNoon’s Legal PDF Series HackerNoon’s Legal PDF Series here here 32. “BitCoin: SEIR-C Propagation models of block and transaction dissemination” (Reliance Document) {ID_000550} / {L3/237/1} 622. The document purports to be precursor work to the Bitcoin White Paper, dated 12 December 2008. (a) COPA’s Reasons for Alleging Forgery (a) COPA’s Reasons for Alleging Forgery 623. The document has been backdated. It is an altered version of a document actually published by Dr Wright in 2017 (the “2017 SSRN Paper”). [PM1 [25-38] and [55]]. 624. The document contains hidden, embedded Grammarly timestamps indicating its true date to be 15 April 2017 or later. [PM1 [48-53]]. 625. The Grammarly software did not exist in 2008 [Madden1 [62c]] but is contemporaneous for 2017. [Madden1 [70-72]]. 626. {ID_000550} contains hidden, embedded traces of the 2017 SSRN paper, indicating that both are derived from a common source document. [PM1 [25-33]]. 627. The content referred to in the hidden, embedded traces of the 2017 SSRN paper includes news articles and government publications which did not yet exist in 2008 but which are contemporaneous for 2017. [PM1 [19-21] and [55]]. 628. The document contains equations and formulae which have been corrupted in a manner consistent with conversion from a more modern format to an older format [PM1 [34-45]. The document includes tampered content that apparently represents an attempt to explain away this problem as the result of using older equation-editing MathType software. However, analysis indicates that equations in the document were in fact authored with much later version of that software, which did not yet exist in 2008 but which are contemporaneous to 2017. [PM40 [22-42]]. 629. The document contains references to Microsoft schemas which did not yet exist in 2008 but are contemporaneous to 2010 onwards (including 2017). [PM1 [46-47]]. 630. The document includes anachronistic references to events that had not occurred by 2008 but which had occurred by 2017 [PM1 [21-22]]. 631. The internal metadata records an anomalous edit time in excess of 70 days and the editing period of this document overlaps with several other documents in Dr Wright’s disclosure, consistent with being created using clock manipulation techniques. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 632. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto (suggesting academic work done by Dr Wright in his supposed development of Bitcoin), contrary to fact. 633. Dr Wright has positively asserted that {ID_000550} is a document on which he primarily relies as supporting his claim to be Satoshi Nakamoto. 634. Dr Wright is recorded as an author of the metadata. The doctored content is phrased by Dr Wright in the first person: “Note- I have released online as an anonymous programmer using the pseudonym "Satoshi Nakamoto". 635. Dr Wright is a user of Grammarly software. 636. {ID_000550} contains hidden text embedded within the document which contradicts the information presented on the face of the document, a characteristic of manipulation of a series of MS Word .DOC files found throughout Dr Wright’s disclosure in these proceedings. 637. The 2017 SSRN Paper is a version of the same document that was published on the SSRN website by Dr Wright himself, bearing a “date written” of 28 March 2017. The “date written” is contemporaneous with the hidden Grammarly timestamp in {ID_000550} and not to the purported date of the document. 638. The metadata of the 2017 SSRN Paper lists “craig” as the creator of the document. 639. {ID_000550} contains hidden, embedded traces of the 2017 SSRN paper, indicating that both are derived from a common source document. 640. Dr Wright has not disclosed the 2017 SSRN paper or the common source document. 641. In his first witness statement in these proceedings, Dr Wright lists this document as a document to which he has been referred when preparing his evidence. (c) Dr Wright’s Explanations and COPA’s Rebuttal (c) Dr Wright’s Explanations and COPA’s Rebuttal 642. In Appendix B to Wright11, Dr Wright attempted to explain the presence of embedded text showing a remnant version which referred to Bitcoin as an existing system and included various references to events after 2008 (including for instance to a report of May 2013). He claimed that these references were added “as a result of the corruption of the document, probably while it was kept at nChain or one of the other corporations”: {CSW/2/54}. In cross-examination, he claimed that two documents became merged into one another as a result of both being open at the same time on a Citrix environment and an edit not happening correctly on one of them: {Day2/135:1} to {Day2/138:19}. 643. In Appendix B to Wright11, Dr Wright sought to explain away the presence of the anachronistic Grammarly timestamps, the references to Mathtype software dating from 2017 and references to Microsoft schemas dating from 2010 and later by saying that they resulted from “someone in nChain opening the document in 2017 or later” and these elements becoming embedded: {CSW/2/53}; {Day2/143:1} to {Day2/143:23}. It is inherent in that explanation that these elements could become embedded without the person saving or otherwise interacting with the document (since such interaction would cause the metadata timestamps to update). 644. When pressed in cross-examination with the point that the remnant text in the document matches text in a 2017 version of the document uploaded by Dr Wright to the SSRN website, Dr Wright could give no coherent answer. When further pressed that this reliance document contains clear signs of corrupted equations (which appear properly readable in the SSRN version), thus suggesting errors resulting from conversion, he likewise failed to give any intelligible answer. See {Day2/140:7} to {Day2/142:25}. 645. Dr Wright also claimed that the references to CheckBlockHeader, UTXO and Bitcoin Core in this document were there because he used them. {Day2/134:14} to {Day2/140:5} 646. COPA submitted that Dr Wright’s explanation should be rejected as dishonest for the following reasons: 646.1. It presupposes that the features of a shared computing environment would cause two documents to become merged, with remnant text added in the way seen here, but in a manner which resulted in the document not being corrupted and appearing normal on its face. This possibility is rejected by the independent expert evidence. See Madden4, [159]; Madden / Placks joint statement 2 at [8]. 646.2. Dr Wright’s explanation for the presence of anachronistic artefacts in the metadata (including the anachronistic Grammarly timestamps, the references to Mathtype software dating from 2017 and references to Microsoft schemas dating from 2010 and later) is contradicted by the clear expert evidence of Mr Madden, which is that such references could not be inserted without the document being interacted with by a user, which would in turn result in updating of the metadata timestamps of the document (notably the “last saved” timestamp). The fact that the metadata timestamps for this document were dated to 2008 but the document contains the elements set out above shows that the document has been forged by backdating. See Madden4, [158]; Madden / Placks joint statement2 at [8]; {Day16/35:19} to {Day16/38:11}; {Day16/125:7} to {Day16/125:18}. 646.3. The correspondence between parts of the hidden remnant text of this document and the 2017 SSRN version of the document can only rationally be explained by this document being a copy forged and backdated in recent years. 646.4. This conclusion is further reinforced by the corrupted equations, which are clearly indicative of a document having been converted from a later version of MS Word into an older format which does not support the correct font or automatically applies an incorrect text editing format: see PM1 [44] {H/1/19}. 646.5. The anachronistic terms Dr Wright says he used are not found in other genuine documents before their first use. These first uses were as follows: 646.5.1. CheckBlockHeader was first introduced into the code in March 2014, Wuille1 [25] {C1/1/6}; CheckBlockHeader 646.5.2. Bitcoin Core was first introduced in version 0.9 of the software in 2014 Wuille1 [50] {C1/1/12}; and Bitcoin Core 646.5.3. UTXO first introduced in version 0.8 of the software by Mr Wuille, with him first seeing the term on 21 June 2012 Wuille1 [29-32] {C1/1/7}. UTXO (d) Conclusion (d) Conclusion 647. In addition to COPA’s submissions in this section, I also refer to [737]-[759] in the main Judgment, all of which demonstrate that the points gathered in Appendix 1 to Dr Wright’s Closing in relation to this document do not begin to explain all the indications of forgery set out above. Dr Wright plainly forged this document from the 2017 SSRN paper. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. judiciary.uk judiciary.uk

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