Data governance is now maybe more than ever a charged topic.
Business-driven data discovery is becoming fundamental for organizations to explore, iterate, and extract meaningful and actionable insights from vast amounts of new and diverse internal and external data. To achieve the maximum benefits of discovery, analysts require the ability to move quickly and iteratively through the discovery process with as little friction - and as much IT-independence as possible and with more self-service capabilities than ever before. However, with more data and more capabilities, the role of governance becomes increasingly critical to protect and secure both the data being used and the discoveries being generated.
As constructs, data governance and data discovery seem inherently at odds. IT is held accountable for ensuring that data is accurate, complete, and secure per data owner policies, while business users want to freely explore all the data and earn insights without IT handcuffs. This is the familiar “freedom versus control” paradox that seemingly puts the goals of IT and business analysts at a crossroads and brings governance to the forefront of the discovery conversation.
Data governance is a framework that focuses on risk management, efficiency, and consistency. It is the accountability metric by which organizations make sound decisions and drive business value from their data and it is a carefully cultivated combination of people, processes, and technology policies that ensures data is leveraged as an enterprise asset. The process of data discovery, instead, is an extension of business intelligence with the goal of exploring unfiltered and blended data to discover new insights. Discovery requires that analysts pair unique business knowledge with intuitive, self-service tools to power a “fail fast” and frictionless process that enables them to move through all available enterprise data agilely and iteratively driven by business needs and goals.
To make it even more complicated, governance is not one-size-fits-all: it’s a framework, and every organization (or even groups within organizations) requires their own rules and definitions in the discovery culture. This is an additional challenge for today’s CIOs and CDOs who are charged with balancing opportunity, efficiency and risk. With more access to data and self-service analytical and visualization capabilities, ensuring data and are trustworthy and protected becomes more complex and imperative. However, the power of discovery is in numbers: the more analysts empowered to do data discovery and the quicker they can do it—, the more insight opportunity, and thus the more potential business value.
Rather than approaching governed data discovery as a rivals, discovery-oriented organizations should instead approach it as a collaborative and proactive process between business data owners and users to define discovery-oriented processes (guidelines) that sets clear data requirements, responsibilities and expectations, and opens the lines of communications to better understand the needs and priorities of both sides. With this context, discovery is really intrinsically collaborative: a discovery layer fills the gap between business need to explore data and IT need to enforce governance: subject experts are empowered in their business domain, and IT actively supports and takes a data management role in facilitating the needs of the business.
This collaboration achieves better-vetted data context and use, and helps organizations become data-driven organizations. When approached collaboratively, analysts are more likely to be satisfied with the speed and timeliness of information delivery when they understand IT controls and policies that have been established by the business. Likewise, when data owners understand the needs of analysts across the organization, they can better understand the needs and data priorities of users and ready policies that reduce friction and allow governed freedom of exploration in an optimized discovery experience. Collaborative governance, then, makes business and IT partners in data discovery. It is the catalyst to managing the barriers and risks of self-service while enabling agile data discovery across the organization by extending existing data governance frameworks for the data-driven and discovery-oriented business.
Have Your Voice Heard
A session on this topic presented by Radiant Advisors has been proposed to share highlights of our research at the Hadoop Summit. Summit session voting ends on February 27th and I invite you to cast your vote for Enabling Governed Data Discovery!