This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
This article was co-authored by Duke Dyksterhouse , an Associate at Metis Strategy. Data & Analytics is delivering on its promise. Some are our clients—and more of them are asking our help with their datastrategy. Building a datastrategy is like spinning a flywheel. We discourage that thinking.
DataStrategy creation is one of the main pieces of work that I have been engaged in over the last decade [1]. In my last article, Measuring Maturity , I wrote about Data Maturity and how this relates to both DataStrategy and a Data Capability Review. Larger PDF version (opens in a new tab).
When I occasionally re-read articles I penned back in 2009 or 210, I’m often struck that – no matter how many things have undeniably changed over the intervening years in the data arena – there are some seemingly eternal verities. These articles have a certain timeless quality to them. True then, true now.
Apart from these internal costs, there’s the greater problem of reputational damage among customers, regulators, and suppliers from organizations acting improperly based on bad or misleading data. While the CEO lost his job, the parent company, Arena Group, lost 20% of its market value.
As someone deeply involved in shaping datastrategy, governance and analytics for organizations, Im constantly working on everything from defining data vision to building high-performing data teams. My work centers around enabling businesses to leverage data for better decision-making and driving impactful change.
It takes a new mindset and an understanding that all the data and ML components in a real-time data ecosystem have to work together for success. Special thanks to Eric Hare at DataStax, Robert Chong at Employers Group, and Steven Jones of VMWare for their contributions to this article. Artificial Intelligence, IT Leadership
Once upon a time, the data that most businesses had to work with was mostly structured and small in size. This meant that it was relatively easy for it to be analyzed using simple businessintelligence (BI) tools. All this adds up to a significant upfront investment that can be cost-prohibitive for many businesses.
Similarly, Deloittes 2024 CxO Survey highlights that while CDOs prioritize AI and business efficiency, sustainability remains a secondary focus. However, embedding ESG into an enterprise datastrategy doesnt have to start as a C-suite directive.
Gathering businessintelligence is a process that starts from within. Collating internal intelligence is of vital importance before searching the market. Oftentimes, the internal departments of your business will offer better suggestions and methods than any others you can find.
Data is the lifeblood of modern organizations, and as such, it must be carefully managed and protected. Whether it’s financial data, personal health information, or customer data, organizations that generate and manage data must implement a comprehensive data governance strategy.
Strong metadata management enhances businessintelligence which leads to more informed strategy and better performance. Donna Burbank is a Data Management Consultant and acts as the Managing Director at Global DataStrategy, Ltd. He is the Director of TDWI Research for businessintelligence.
In the past few years, the term “data science” has been widely used, and people seem to see it in every field. Big Data”, “BusinessIntelligence”, “ Data Analysis ” and “ Artificial Intelligence ” came into being. For a while, everyone seems to have begun to learn data analysis.
ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and businessintelligence. In this article, we will explore the significance of ETL and how it plays a vital role in enabling effective decision making within businesses.
The content on A-Team Insight covers financial markets and the way in which technology and data management play a part. This site offers expert knowledge and articles geared towards decision-makers in investment management firms and investment banks. Techcopedia follows the latest trends in data and provides comprehensive tutorials.
Regardless of how accurate a data system is, it yields poor results if the quality of data is bad. As part of their datastrategy, a number of companies have begun to deploy machine learning solutions. In a recent study, AI and machine learning were named as the top data priorities for 2021, by 61% […].
1 In this article, I will apply it to the topic of data quality. I will do so by comparing two butterflies, each that represent a common use of data quality: firstly and most commonly in situ for existing systems, and secondly for use […]. We know the phrase, “Beauty is in the eye of the beholder.”1
This article explores why you should consider adopting the Disciplined Agile (DA) toolkit from several points of view: Why Should Agile Practitioners Adopt Disciplined Agile?
Real-Time BusinessIntelligence (RTBI) lets users access the information that happened recently via a dashboard. The latency of this data depends on the analytical purpose. The recent information can be an event that occurred a few milliseconds ago or before an hour. For marketing, the latency must be of least value.
Many CIOs are now working with an IT environment that can deliver a modern datastrategy but are struggling to unlock the full potential. The first step for CIOs is to break down internal barriers and address the problems caused by data siloed in legacy environments. 1) Match the tech strategy to the businessstrategy.
Everyone’s talking about data. Data is the key to unlocking insight— the secret sauce that will help you get predictive, the fuel for businessintelligence. It relies on data. The good news is that data has never […]. The transformative potential in AI?
Finding the right data sets and knowing how to use them is key to any data implementation strategy. In this article, we take a look at three different sets of goals your business might have—branding, marketing, expansion. Read on to learn more about how you can revolutionize your data implementation strategy.
In this article, we elaborate on the benefit, effective operations, and value of information governance in your organization. Organizations are compelled to organize their information. With that said, organizations can operate in an organized and holistic manner with the application of information governance. Even though […].
Detecting and mitigating API abuse is critical to protect businesses and customers from data breaches, service disruptions, and compromised systems. This article explores effective strategies that empower organizations to safeguard their systems and valuable data.
With that much data flowing into analytics systems, the right data model is vital to helping your users derive actionable intelligence from them. Building the right data model is an important part of your datastrategy. What is data modeling? Discover why. Dig into AI.
Most current data architectures were designed for batch processing with analytics and machine learning models running on data warehouses and data lakes. In this article, I’ll share insights on aligning vision and leadership, as well as reducing complexity to make data actionable for delivering real-time AI solutions.
You know your company has a lot of data, but are you using it to make smarter decisions? Analytics and businessintelligence (BI) are no longer the province of a handful of specialized experts. The rise of cloud data storage has completely changed the business world. It’s all going to one place today: the cloud.
That means you need to put all that data somewhere. Chances are it’s in a data warehouse, and even better money says it’s an AWS data warehouse. This article gives you a handful of great reasons to get to know this powerful data warehouse. Closer Than You Think: DataStrategies Across Your Company.
As a techno BI geek, I marvel at the sheer “what could be” in businessintelligence and decision support arenas. I shouldn’t go to BI tool demonstrations anymore. Don’t get me wrong. Advancements in the BI/analytics tool space are nothing short of remarkable.
The flip side is that making the necessary investments to provide even basic information has been at the heart of the successful business turnarounds that I have been involved in. The bulk of BusinessIntelligence efforts would also fall into this area, but there is some overlap with the area I next describe as well.
In a prior blog , we pointed out that warehouses, known for high-performance data processing for businessintelligence, can quickly become expensive for new data and evolving workloads. Returning to the analogy, there have been significant changes to how we power cars.
Delivering business value should be the foundation of a real-time data cloud platform; the ability to demonstrate to business leaders exactly how a data ecosystem will drive business value is critical. Data Management
In today’s world, access to data is no longer a problem. There are such huge volumes of data generated in real-time that several businesses don’t know what to do with all of it. Unless big data is converted to actionable insights, there is nothing much an enterprise can do.
Top-quality data currently represents one of the most important resources for any company. This is especially true for young businesses that don’t have much experience in their market and that still don’t know enough about their customers.
In my journey as a data management professional, Ive come to believe that the road to becoming a truly data-centric organization is paved with more than just tools and policies its about creating a culture where data literacy and business literacy thrive.
However, for the first time in history, we have tools like artificial intelligence (AI) to help us fight a global pandemic. Even the word sounds repulsive. This horrendous disease has killed over 100,000 people in the past 6 months and devastated billions in its wake. AI may prove to be one of the best […].
Some of the Answers May Surprise You, an article about […] Although impressive, it isn’t perfect and as a result, we run the risk of underestimating the potential impact of this swiftly improving environment. Recently, CBC published, We Asked an AI Questions about New Brunswick.
This article attempts to analyze and make sense of a harmonization between Information Architecture and SAFe, and will address how their cooperation will contribute to the development of an Agile Business. SAFe is a very modern Agile Framework and has replaced TOGAF in many organizations.
We hope that this article gives you some insight into the current state of advanced analytics and sheds light on its future development to inform your business decisions. Notably there is a large gap in the importance that best-in-class companies and laggards attribute to investment in training and a holistic datastrategy.
Accessibility is one part of the “Intelligence Elevated” equation. By now, we’re all familiar with how self-service data can drive businessintelligence that helps the bottom line. This blog focuses on how that access is achieved. Bottom-line BI benchmarks.
Clearly, data is becoming more important to organizations. In this article, we explore the role and responsibilities of the chief data officer and the challenges they are facing. The role of the chief data officer. Not all organizations are at the same point in their data journey.
Top Data Management Problems The modern world functions on information. A primary aspect of data management is digitizing large amounts of documents, books, and reports that have been collected for hundreds of years. The Great Volume of Data The more that data is digitized and […].
We just finished a conversation with a client who was justifiably proud of having centralized what had previously been a very decentralized business function (in this case, it was HR, but it could have been any of a number of functions).
Our reactions to the data we receive can cause a great deal of suffering. They used data to alert people about various medical actions that they recommended. For example, there was a hospital with an outstanding reputation for being trusted at a deep level by many families. One such alert was that when a person […].
Is it possible to listen without opinion, judgement or stories [i]? In this coronavirus pandemic, many people have strong opinions, judgement, and stories. For example: — “This is ridiculous, and we are overacting.”— — “We have not been careful enough and are not being careful enough. This is serious!”—
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content