Skip to content

Information Governance Innovations in 2019

If 2018 showed us anything, it’s that information governance has captured the attention of organizations of all sizes. Maybe they don’t all refer to the work they do on ensuring their information is well governed as “information governance,” but they are thinking about what’s needed and doing the work to make it happen.

While companies are developing strategies and defining projects technology is evolving to help organizations take control of their information. From what we see, there are some very interesting innovations happening in 2019, including work in our own Innovation Lab. Here’s a look at some of the things we believe is going to help organizations get better at information governance. They may not all happen this year, but they are coming.

The intersection of Structured and Unstructured Data

Today there is a clear separation on how you manage structured data (database, transactional data) and unstructured data (documents, text, videos, images, email, social media, etc..). From a governance perspective, most organizations have different teams and solutions that manage each. But it doesn’t have to be this way.

The lines between structured and unstructured data are blurring which means data governance and information governance can no longer be looked at as separate strategies. Hybrid solutions are rising that support the management of both types of data. Why is this important? To be competitive in today’s digital landscape, you need to have a clear understanding of your business activities, and that means you need to analyze all your information for insights. Two things are key to point out here:

  • An analytics engine that can connect all your data enables you to get a deeper understanding of what’s happening and support improved decision-making. Without it, you are forced to analyze both types of information separately and then manually connect the dots, leaving room for errors and incorrect analysis.
  • All information, regardless of type, requires governance. Compliance and other business requirements, records retention, and more does not necessarily apply to information based on its structure, so why would you manage your policies for each type separately?

We think this is the year organizations will stop looking at managing their information based on its structure and start finding ways to combine the two to improve how they do business. The technology to support this new way of looking at information will take center stage this year.

Information Governance as a Service

This year we will start to see organizations switch from governing in a silo to deploying information governance as a global Information layer. They will move from managing a separate records management system, an enterprise archive, an enterprise CMS and so on to an approach and strategy that will manage data and content wherever it resides (information governance in-place).

This new way of governing information requires a new set of solutions that include content federation services, and content and data connectors.

The Creation of a Complete Information Register

To support the integration of structured and unstructured data for analysis and governance requires new technology. One of these is the Information Register. Most organizations today have a solution that tracks and manages records retention policies.

But they don’t necessarily have a solution that tracks non-record information. And with privacy laws like GDPR, CCPA, and other regulations combined with growth in data quality programs (e.g., ISO27001), organizations need to improve how they manage all their information assets. To do this, we see the traditional records retention system evolving to become a full-fledged information register.

An information register is not necessarily a new idea, it’s part of the requirements for GDPR, and is used in industries such as library services (in these cases it’s referred to as an Information Asset Register). But we see the Information Register as more than tracking information types across the organization; it also needs to be extended to become an accountability framework, a library of laws and internal directives or to a list of key activities and processes generating sensitive information. We also see it linking to defined policies, including retention policies or data minimization policies, and to the systems that store information to ensure those policies are properly enforced.

Everteam.policy is a full-fledged Information Register, and we are continuing to develop it to provide more capabilities to support the full governance of all information across the organization.

Auto/Assisted Classification

The idea of manually classifying all your information is a noble one. After all, it’s the business users who understand the information best, so why shouldn’t they be responsible for classification? In the real world though, organizations create, capture and manage too much information for manual classification to be possible.

A recent report on File Analytics from Forrester stated that most organization store 100 terabytes or more of unstructured information in their data centers alone. Gartner file analytics research notes that more than 80% of an organization’s information is unstructured data composed of the following manner:

  • Regulatory or compliance data = <1%
  • Data on legal hold = <1%
  • Record-worthy data = <2%
  • Data that is work in process, reference data or of analytical value = ~15%
  • ROT (redundant, obsolete and trivial) = ~80%

Organizations are still mainly focused on trying to govern the first 4% when proper information hygiene should apply to all data and content. There is an urgent need to classify data quickly and in a more automated manner to define the type of information and by association the policies to apply to them.

In the eDiscovery world, assisted classification is known as TAR – Technical Assisted Review, or Predictive Coding. In simplest terms, auto-classification leverages pre-defined rules or AI-powered machine learning algorithms to classify documents and assist humans to accept, modify or reject them. Through a continuous learning virtuous circle, the machine gets better and better at classification over time.

Most file analytics solutions use rules-based classification and some machine learning. This is an area where there is a lot of room for growth and improvement in how auto and assisted classification work. The more regularly you train the model using the most recent information, the better it will be.

The Evolution of Content Intelligence

The companies that leverage their information for effective decision making are the ones with a competitive advantage. We all understand that. Unfortunately, this is easier said than done. Finding all the information across your company that you need to make the best decisions is challenging on the best of days.

Enterprise search solutions need to evolve into more than big federated index engines to improve findability. They need to incorporate content intelligence capabilities that help business users better understand the context of the information they are searching for, help them find the best information and include new analysis perspectives.

Consider the ability to take advantage of manual (human curation) or automated content enrichment to do things like assign content types, classify content, identify PII/PHI and apply custom corporate entity detection, improving search efficiency. Or provide quality and governance context on the information a business user accesses, such as data lineage and history, governance controls applied, and so on. Predictive insights can also help business users find problems before they occur or opportunities that others might not see.

We wrap these concepts all under the idea of cognitive search, and it’s an area we are focused on building out more in our solution, so watch us talk more about that this year.

Just Scratching the Surface

A quick note here on Infonomics. Coined by Gartner Analyst Doug Laney, Infonomics is the practice of really treating your information as an asset as opposed to just calling it an asset. It’s about how you monetize, manage and measure it in a way that demonstrates its value as a tangible asset. I mention here because at Everteam we believe every organization’s information has value in ways it may not yet understand and we’re looking at ways to help bring this value to light and help manage and measure that value accordingly.

I’ve only mentioned a few things we’re watching and working on at Everteam. There’s more in our toolkit and roadmap, and we’ll be sharing more insights on topics such as blockchain, AI, analytics, intelligent information management and more. We’re excited for 2019; we hope you join us on our journey.