Is Data-Management Dead? The Data-Empowered Organizations And How To Build A Modern Data Estate

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Data management is dead!

That might sound like a somewhat controversial comment, but it’s straight from the mouth of Heine Krog Iversen, who recently joined me for a webinar on moving beyond digital transformation – and what comes next.

The CEO of low-code drag ‘n’ drop data management software, TimeXtender, believes that we are moving into the age of the “digitally-empowered organization." This argument centers on three remarkable statistics: Firstly that 70 percent of GDP growth over the next decade will be driven by artificial intelligence (AI) and advanced automation. Secondly, AI will contribute more than $15 trillion to the global economy in the same period. And thirdly, that 62 percent of business leaders are already preparing for this future by putting plans in place to profit from the innovative technology revolution.

Discussing his statement that data management is dead, Heine tells me, “Yes … it is a little provocative – but to cut through the noise, you sometimes need to say the obvious … the industry now has 500 different definitions of what data management is – so that’s my first conclusion, because if we don’t have a single definition [for something], then it’s dead.”

His belief – and I indeed share – is that truly data-empowered organizations need to evolve beyond simply looking at data they have access to as a data management problem.

Instead, it’s about understanding how data flows through every part of the organization and how we leverage tools like machine learning, computer vision, or natural language processing to ensure the insights we derive from it are helping us make better decisions and hit strategic goals right across the business.

Heine then continues, “It’s not about data management, it’s about data empowering … yes, we still need to figure out what data we have, we need to design a model – that’s not going away – we need to change the mindset about why we are doing it. We’re not doing it to manage the data. We’re doing it to empower the business – to take significant action based on that data.”

One of the problems with taking the principle of being “data-driven” too literally is that it limits our ability to look – and therefore make predictions – that fall outside of the context in which our data is collected or generated.

As an example of how this can limit our ability to predict and react to real-world events, Heine points to the impact that will be felt by thousands of contracting and subcontracting businesses thanks to Apple’s recent decision to move the early production of the latest iPhone from China to India.

He says, “I guarantee that there’s no data-driven decision-making system that could have guessed or suggested this … because it’s a political decision … and that’s why we need to flip it around and talk about being data-empowered so that every person, every single business unit, every decision-maker has access to as much relevant data as possible.”

Moving towards models that can predict truly transformative events and opportunities involves understanding that data is the “lifeblood” of the smart machine age. Understanding how it moves through the internet of things (IoT) sensor networks to train machine learning models, which then empower automated applications and augmented working tools, is central to leveraging today's opportunities available to organizations.

That’s not to say that there isn’t a place for what we might now think of as “traditional” data management tools – business intelligence platforms, PowerBI, and Tableau, for example. Instead, the focus should be on building the underlying structures and pipelines so that organizations can become application-agnostic. Because no matter how up-to-date and cutting-edge your stack may be, there will always be newer and more powerful tools just around the corner.

According to Heine, this means adopting both data lake and warehouse methodologies. On top of that, there also needs to be the semantic layer, where data is prepared and labeled and ready to be made available to whatever makes up the front end of the stack.

"I need all my raw data," he tells me, "you could argue that you can leave it in the source system, but the reality is … with cloud apps, if we change from one source system to another – from Dynamics to Salesforce - then the moment I stop paying Microsoft for the Dynamics license, my data is gone. So we need to start thinking about taking ownership of our historical data in a raw form, and the best and fastest way to do that is to dump it in a data lake.”

In this form alone, though, very few of a company’s employees will be able to access the data and get any real value from it. So the next part of the stack needs to be what Heine refers to as a “modern data warehouse.”

This means that “you haven’t made any decision … on what is a dimension, what is a fact, and how you are going to use it.”

Finally comes the semantic layer, where you can define a schema in ways that make data accessible to whatever tools and dashboards it will end up in. These three elements make up what Heine calls the “data estate.”

Heine says, “I think when you have that example of just a data lake with some tools on top, that’s when companies sometimes have a checkbox – ‘ok, that’s checked off – without really thinking about what being data empowered means.

It’s different from company to company and business unit to business unit … self-service BI is not always a good idea because the majority of our employees in a business are not hired to be data analysts. They are hired to do what they do best.

That’s why we see it as an integrator… you need the data warehouse; you can’t just have a data lake because it turns into a data swamp … but also your semantic models tailored to your needs.”

As companies move from merely collecting and storing large amounts of data, which they then mine for insights to solve isolated problems, towards being truly intelligent organizations, able to apply data across the board, it’s clear that new strategies are required.

Businesses that want to make sure they are leading the pack in profiting from technology need to keep this firmly in mind as they work to graduate from simply being “data-driven” to genuinely being “data-empowered”.

You can click here to watch my webinar with Heine Krog Iversen, CEO of TimeXtender in full, where we go into more depth about the challenges that companies face when moving towards data empowerment, as well as discuss the ongoing skills crisis and cover more advice for CIOs in the smart machine age!

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About Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the field of business and technology. He is the author of?21 best-selling books?(and winner of the?2022 Business Book of the Year?award), writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, over 1.2 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.