For many people, the new year means a fresh start. A time to cleanse themselves of bad habits and resolve to make new, better ones for the year ahead. In that spirit, I’d like to share a data prediction – and a New Year’s Resolution for data – in 2017.
Let’s start with a prediction. In 2017, data governance will extend its scope from a system of record to a system of engagement. Geoffrey Moore, in his AAIMS paper, coined the term referring to the transition from current enterprise systems designed around discrete pieces of information (“records”) to systems which are more decentralized, incorporate technologies which encourage peer interactions, and which often leverage cloud technologies to provide the capabilities to enable those interactions.
Now, for the New Year’s Resolution: to bring data governance closer to the actual workplaces that are using the data by tapping into the tools that shape these systems of engagement ranging from physical board rooms and design workshops to virtual team collaboration tools such as Confluence and Slack. In doing so, we will capture more of the data usage context from the bottom-up as well as empower individual contributors to promote their own data sets resulting from engagements and ultimately build trust around them.
But how did we get here? And what can we learn from the past?
2008: A Data Governance Odyssey
Eight years ago, when we founded Collibra, many enterprises did not believe us when we said that data is a valuable asset. Even less said they would appreciate the fact that you would need leadership and a system of record around data assets, and that Collibra could help with all that. Today, hundreds of companies in a variety of industries, across the world do exactly what nobody believed would be relevant. And we approached the problem from a completely different angle: the Collibra platform was designed as a ‘system of record’, i.e. to contain (or ‘record’) the authoritative source of information for any given data asset. We tailored the platform’s interface to the business users, providing collaborative functions such as roles and workflows. These were the early signs of engagement systems.
One could fairly say that 2008 marked a new decade where Collibra has managed to rekindle the faith in (the often failed) data governance initiative. Despite powerful incumbents in the enterprise technology market, we established trust in the data management organizations of the world’s biggest companies, as the de facto automated data governance platform that data leaders need to drive their transformation towards a data-driven business.
As we continue on our journey, in 2016, a number of compelling events shaped the data governance practice, demanding a further extension of data governance concerns from within the business ecosystem to society as whole.
First, in May of 2016, after a decade of year-on-year records in data breaches, the European Commission published the General Data Protection Regulation (GDPR). The GDPR provides for a harmonization of the data protection regulations or all 508 million citizens throughout the EU, thereby making it easier for non-European companies to comply with these regulations. However, this comes at the cost of a strict data protection compliance regime with severe penalties of up to 4% of worldwide turnover.
Second, companies such as Google and Nest have come to the market with consumer-oriented IoT (e.g., Google Home) but received a backlash of criticism for its silence on data governance. Indeed, increasingly ‘data-aware’ consumers – while in paper assured by the company about the protection of their data – lack transparency from the company on how their data protection is effectively managed.
Finally, the recent reports concerning the circulation of false information on social media amplify the need for a debate about data governance in our data-driven society involving both private and public parties and build scalable and reliable platforms for it.
A Fool with a Tool is …. Just A Smarter Fool
Hundreds of customers (many among them in the Fortune 500) and industry analysts such as Gartner and Forrester have acknowledged the leadership of Collibra in the data governance and stewardship as well as metadata management space. Various Data Management and Business Administration curricula of leading academic institutes invite us to their classrooms to lend their students’ eyes and ears to our vision on data literacy and the future of the data-driven workplace. However, a much larger opportunity lies ahead of us: the realization of our aspiration to make every [fill in any tool here] user a Collibra user. But as the old saying goes, a fool with a tool is … just a (smarter?) fool.
Collibra Data Governance Center is more than a software tool. It is SaaS and even more. Contemporary tech businesses thrive on a larger innovation ecosystem that is populated by not just data leaders, stewards, and developers, but anybody who is concerned with data in the workplace. Collibra innovates from the inside out by providing a platform. Its ecosystem complements from the outside in with new applications to engage frictionless with the platform as well as opinions on best practice around it.
The Collibra Community and Collibra University are signs of this extended ecosystem. The latter’s growing activity demonstrates the need to nurture data literacy and connect its peers. This further strengthens the community’s sense of importance and credibility and, in turn, attracts even more members. Students as part of a class or through internships are being exposed to the Collibra platform and can experiment with its API. These positive network effects in our ecosystem have the potential to ultimately lead to product innovation and adoption across the board.
Systems of Engagement
Collibra was focused on securing the traditional data management space during the last eight years with a powerful set of methods based on state-of-the art technologies such as big data, secure cloud, and AI. Yet the technology landscape surrounding the Collibra ecosystem has evolved and brought new ways of data-driven interaction with it that challenge our state of affairs. Geared with a range of mobile apps and the new Collibra Catalog product, Collibra has taken a major step in linking these new technologies to support new interactions ‘natural’ to data scientist teams and knowledge workers in general, for all kinds of data, all within the established Collibra platform that ties all governance concerns of those interactions together.
Our conservative telescope on the data universes inhibits us from seeing the underlying fabric that glues all the data records together. In the end, the bulk of operational and analytic data are mere records summarizing the transaction history, not including the more complex (often physical) person interactions. This data is scattered across engagement platforms and often largely unstructured, usually expressed by humans in context heavy natural language. Hence, hard to record and analyze. However, when observed as a whole, these core interactions generate a network effect digitized as social capital. More and more systems of engagement such as Slack digitalize social capital. Collibra could tap into these systems to record a richer context for any given data asset that was used or produced.
Our universe consists of an insofar undefined dark energy that also could explain its expansion. Similarly, the Big Data bang consists of social capital that we know exists but is hard to understand because it’s scattered and unstructured. Understanding it will help us understand how the data universe will further expand. This will bring us greater insights into how people, workplaces, and perhaps entire societies interact rather than just a snapshot of the mere transactional data.
The world of data is changing, that’s for certain. I’m excited to see what the future holds for data – and for Collibra. And as for predictions, see what else Collibra predicts for data in the year ahead.
Pieter leads the company’s Research & Education group, including Collibra University, an online learning platform for data governance and data science education.