Optimizing Data Governance in the Age of Self-Service Data Analytics By Donna Burbank Managing Director, Global Data Strategy, Ltd Sponsored By: Contents Introduction ................................................................................................................................................. 3 The Risk of Organizational Silos Balanced by the Reward of Collaboration ................................................. 3 Data Governance: Finding the Right Balance ...............................................................................................4 Know Which Data to Govern Closely, and Which to Leave Alone ................................................................ 5 The Process is Important .............................................................................................................................. 6 The Right Technology for the Right Job ........................................................................................................ 7 The Business Value of Collaborative Data Discovery.................................................................................... 8 About the Author ......................................................................................................................................... 9 About Alteryx ...............................................................................................................................................9 www.globaldatastrategy.com 2 Introduction Today, business is driven by data. And the data-driven business transformation has given rise to new business models and strategies that simply weren’t available with the technology of the past. The rise of big data, streaming technologies, IoT, cloud, machine learning, and other relatively new technologies provide an unprecedented opportunity for growth and innovation. Think of a leading company in today’s marketplace, and there is a high probability that that company is harnessing data to strategic advantage. Look at Amazon, with its data-driven recommendation engine and streamlined distribution chain, or Lyft and Uber, who capture big data and IoT sources in near real- time to revolutionize the transportation industry. The list goes on and continues to grow as budding entrepreneurs envision new ways to make strategic use of the wealth of available data. To capitalize on this trend, according to one recent survey1, over 96% of media and marketing executives have stated they are deeply committed to using data to transform their businesses into data-centric companies. At the same time, however, there is a significant lack of technical skills to support this growing need. In the same survey1, only 5% of executives are extremely confident that they have the skills within their organization to support their data-driven initiatives. With the growing business demand for data-centric skills and an increasing gap in IT skills to meet this need, more business-centric staff are taking an active role in the management and strategic analysis of data. This active involvement by business users has led to significant growth in the demand for self-service analytics. In fact, the analyst firm Gartner predicts that by 2020, self-service data preparation will be used in over half of new data integration efforts2. This doesn’t even account for self-service analytics platforms. From recent experience in my consulting practice, I expect the number to be even higher from that. We see organizations, from Fortune 100 companies to small nonprofit organizations, looking to leverage self-service data preparation and analysis since more stakeholders are eager to take an active role using data to strategic advantage. The Risk of Organizational Silos Balanced by the Reward of Collaboration While it is a positive trend that more roles across the organization are looking to take an active part in data analysis and curation, without proper coordination and collaboration across these roles, organizations run the risk of creating silos that hamper productivity. With more departments looking to “own” their data for strategic advantage, ownership must carefully be defined to mean curation and maintenance of this data, not obstructionist control. When data is a strategic asset, like any asset, there is a risk that groups will compete for control of this valuable resource. Marketing, Sales, and Finance, for example, may all feel that they “own” customer data and may wish to limit other groups’ access to this information. In addition, there is often an organizational and cultural divide between business users and technical IT staff, and questions of role delineation often arise as business users take a more active role in the management and analysis of their data. While IT staff often do not have the time or inclination to 1 The Data-Centric Organization: Transforming for the Next Generation of Audience Marketing, a Winterberry Group white paper, September 2016 2 Gartner Market Guide for Self-Service Data Preparation, August 2016, by Rita L. Sallam, Paddy Forry, Ehtisham Zaidi, and Shubhangi Vashisth ID: G00304870 www.globaldatastrategy.com 3 support every query or data discovery effort required by the business, at the same time, they are often cautious to relinquish control over organizational data sets. Finding the right balance between roles and responsibilities is key to success in building a data-driven organization. With this balance in place, business stakeholders can discover new insights in the data that is important to them, based on curated data sets that have been vetted by all relevant parties across IT and the business. Getting this balance right is the purview of data governance and, once proper governance is in place, organizations can work together to become a truly data-driven organization. Data Governance: Finding the Right Balance Data Governance is defined as, “The exercise of authority, control, and shared decision-making (planning, monitoring, and enforcement) over the management of data assets,” according to DAMA International’s Data Management Body of Knowledge (DAMA DMBOK2) 3. This definition highlights the inherent tension between control and collaboration when building a data governance program, i.e. authority and control vs. shared decision-making. On the one hand, business-critical data sets need to have strict controls in place to ensure data quality and consistency. A recent Harvard Business Review article estimates that the US economy loses $3.1 trillion per year due to poor quality data4. With data as the cornerstone of the data-driven business, closely managing this business-critical asset is paramount to success. On the other hand, controls that are too strict can limit collaboration, innovation, and data-driven discovery. Getting this balance right and creating an environment where proper controls are in place, but still allow for self-service discovery by a range of users, can have significant benefits. According to Gartner estimates, by 2019, data and analytics organizations that provide agile, curated internal and external data sets for a range of content authors will realize twice the business benefits of those who do not². A large benefit of the self-service and collaborative approach to data governance and data discovery is the ability to harness an organization’s “tribal knowledge” that is often lost when using a more formal, top- down approach. When business users are actively involved in data management activities, decisions are made closer to the source of domain knowledge. Not only does this approach typically yield better results, but these results can be achieved more quickly. Rather than IT staff having to spend time tracking down business definitions and rules through extensive interviews and questions, these rules can be documented, in real-time, since business staff are directly involved in data management and governance. The self-service approach to data governance requires a new platform and methods to allow effective collaboration between business and IT staff. If traditional approaches to data governance are the “encyclopedia approach,” where a small set of individuals publish a body of definitions to be consumed by the masses in a top-down manner, data governance in the world of self-service analytics is the “Wikipedia approach.” With Wikipedia, definitions are created collaboratively. While there may be the occasional error, the “wisdom of the crowd” can quickly spot and resolve these errors, ensuring 3 Data Management Association (DAMA) Data Management Body of Knowledge nd2 Edition, Technics Publications, 2017 4 “Bad Data Costs the US $3.1 Trillion Per Year”, Harvard Business Review, by Thomas C. Redman, September 2016 www.globaldatastrategy.com 4 eventual consistency. While there may be short-term inconsistencies, longer-term quality is ensured by a constant, active review that keeps information from becoming stale or outdated. Each of these approaches has its place, even beyond the world of data. For example, the release of a new cancer drug has strict controls put in place by a small set of qualified individuals. The release of this information is controlled by formal rules and processes to ensure the community’s safety. Movie reviews, on the other hand, which in the past were published only by a small set of critics, have been replaced by crowdsourced review sites, where many viewers can post their opinions, providing a wider range of views and, arguably, a more accurate representation of the community’s preferences. Each