In a recent survey, respondents indicated that they spend more than 70 percent of their time finding data, validating it or defending it, rather than focusing on what they find most important: analyzing the data.
Other findings in this new paper indicate that while big data projects in production are expected to triple over the next 18 months, confidence in big data itself is low as compared with confidence in structured data from internal systems.
Even as the number of big data initiatives is set to rise, data uncertainty (or lack of confidence in data) will hinder the adoption of big data and analytics. If we spend too much time looking for data and defending it, and too little time focusing on data analysis, organizations will derive sub-optimal returns from their big data initiatives.
Big data is about bringing together data from diverse sources and gleaning insights from it. According to Unisphere Research, users tend to expect the same level trust in data whether it originates outside of enterprise boundary or from within. No wonder that respondents were not satisfied with progress in data governance programs, finding them limited in scope and lacking in business sponsorship, as well as strategic planning.
Why invest in information integration and governance (IIG)?
All data brought into the organization needs some level of governance, but the levels vary based on the data type and the intended use of the data. This means that data governance is an ongoing process where you mix and match capabilities from a comprehensive information integration and governance (IIG) solution, as per the business needs.
Research shows that there is a clear link between IIG maturity and business growth. IIG enables organizations to focus on business outcomes and maximize the value from big data. Therefore, a comprehensive and mature IIG solution is critical for adoption of big data and analytics.
Where to start the data governance journey?
If you are starting with your data governance journey, then here is the list of my favorite blogs that will help you orient yourself to the many requirements you need to consider for a big data governance program:
In this blog James Kobielus explains why trustworthiness is not intrinsic in data and why we can't trust data unless we have visibility into the entire process under which it was created, handled, interpreted and applied.
David Corrigan explains the need for having data confidence given the big data context. He gives an overview of the factors that contribute to data confidence and introduces the concept of a data confidence score for every piece of data.
The term “governance” conjures up an image of rigidity: rules and regulations, measurements and assessments. But yesterday’s data governance practices are not ideally suited to the new, more fluid environment. Read this blog from Paula Wiles Sigmon to understand the need to have an agile data governance process that adapts easily to change and supports a continuous cycle of problem definition, solution implementation and assessment that can lead to new problem definitions and new projects.
Forrester Research published The Forrester Wave: Data Governance Tools report recently and, in this report, Forrester evaluates the top 10 software vendors on various data governance criteria. This report will help you select the right partner for your data governance journey.