What is Data Intelligence

A company achieving data intelligence

We are asking the question “What is Data Intelligence?” because we want all Data Citizens to understand and appreciate its value as much as we do.

What is Data Intelligence?

Data Intelligence is not a cute slogan or an abstract concept: It’s optimal, achievable and hugely beneficial. In this operating environment — awash in massive data volumes, buffeted by a constant flow of new technologies that will deliver more data, under pressure from evolving compliance mandates, always in pursuit of digital transformation — Data Intelligence is critical. 

However, this ideal state can only be realized when it’s fully understood. There isn’t a one-off tactic or resource allocation that will get it done. It certainly requires particular technologies to support enterprise-wide data collation and inter-departmental collaboration, but there’s more to it. It means creating policies and processes to allow authorized access to the right data, always with full transparency and context. It’s also important to develop metrics to ensure that the system is working, and changes can be made to adjust and enhance each step.  

This won’t happen overnight — even now, within too many enterprises, the data is everywhere yet nowhere, locked away in disparate silos, draining resources, clogging processes, bordering on non-compliance. Data Intelligence means changing that dynamic. It unlocks the value of in-house and incoming data and transforms it into a strategic and competitive asset.

How to achieve Data Intelligence?

First, consider the technology — and most organizations are definitely doing that, even if it started in reactive mode. However, a few factors need a sharper focus. 

  1. What’s often missing in these infrastructures is a foundation that offers full visibility across the entire data landscape. Many organizations have a heterogeneous mix of data management technologies that grew over time, and the fragmentation leads to a siloed network. A comprehensive, cloud-based platform can ensure enterprise security and scaled up to meet specific standards for reliability, privacy and compliance. 
  2.  It’s all about the purpose — the data should be secure and compliant, but it must also serve business needs. There should be a full complement of solutions that automate governance and privacy; extract the right data regardless of location; apply a single definition to ensure that users are building on the same foundation; prioritize consent, usage and retention policies; and rely on valid data transparency and lineage to deliver meaningful business intelligence. That’s the path to digital transformation. 
  3.  It must be built into the corporate DNA that technology is dynamic — there are always innovations coming down the pike, and it’s important to make judgments on each as they emerge. Artificial intelligence and machine learning alone will likely be integral to 75% of all enterprise applications by 2021, and all infrastructures must have the flexibility to integrate the best of these.  

But there’s an even more important variable here. Data Intelligence is a people and process issue: Intelligence is a fundamentally human characteristic, and so we need policies and processes that drive knowledge sharing and collaboration. That takes a revolution.

Where we’re coming from

There was a time when all enterprise technologies belonged to the IT team, a skilled group of practitioners who alone had the knowledge to decipher coded language and software. Today that’s laughable — IT consumerization has sparked greater innovation than the glasshouse ever did. We need a similar transformation with data, and embrace the notion that data belongs not only to data architects and administrators but a whole generation of knowledge workers. 

The data can be structured or unstructured, formal or ad-hoc, automated or manual, crowdsourced or not, but it must feature transparency and context. In the absence of appropriate background, we attach our own judgments, which can be off the mark. The siloed nature of data management must be abandoned and replaced with policies that attach the right context and transparency to all appropriate data as it is accessed by each knowledge worker, then passed on with added context and clarity. 

So now what? 

There’s no golden rule here — every institution must evaluate its own philosophy and network settings to create a full-fledged data culture. It must suit the often-random ways in which we connect, or get inspiration sparked by full-blown research or individual data nuggets. The core mission is to make it easier for knowledge workers to find the data they need, learn from it, add to it and collaborate with it. 

Think of it as a companywide knowledge graph that connects data elements to business terms to data quality scores to systems to business processes to policies to datasets to reports to algorithms and more. This is full visibility, and it shows the answer to the question “What is Data Intelligence?”

It doesn’t benefit only a few executives or particular disciplines; it’s all-encompassing and helps reimagine every function across the enterprise. It gives everyone the power to use data to solve problems, implement ideas and grow businesses. It fosters collaboration to drive business value. It enhances operational efficiency and identifies new revenue opportunities. It lets data do what it’s supposed to do: offer strategic value. 

Here’s a possible target:

Can data enable you to reinvent, digitize or eliminate 80% of all business processes and even products from just a decade ago? (That was an actual 2020 prediction from research firm, Gartner several years ago.) The answer is likely, yes. On a related note, some analysts believe that in-house data should be listed on the balance sheet as a corporate priority. This happens when data is seen not as an end in itself but a powerful weapon to deliver new insights and drive better decisions. Data Intelligence gets us there. 

Finally, let’s accept that none of this is static. As new technologies and data streams emerge, as new revenue opportunities appear on the horizon, and as new professionals get recruited with different interests and skills, the policies in place must be adapted accordingly. Data Intelligence doesn’t stand still — it keeps us moving forward. 

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