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With increasingly complexity and challenges of Big Data systems, governance is a critical foundation for realizing the value from big data and accelerating the time to find critical data for decision making. Our Big Data Governance is part of our broader information governance program which leveraged our current operational governance and scaled to add a BIG DATA working council accountable to develop an operating model for big data. Our 4 pillars of success are focused on delivering the capabilities for the technology platform, Operating Model, Business Value and Governance. We are transforming the culture to shift from searching for data and context to using the data to harness the power of analytics to drive business value.
With the volume, velocity and variety of data in the lake, you need to focus on the key business problem you’re trying to solve as a first step. The capstone to our operating model is the business value assessment which is captured in a central analytics enablement center and reviewed by our Executive Governance committee. This ensures functional alignment and optimization of data science resources to focus on meaningful business information and problems. This is also a valuable asset for other business analysts and data scientist to understand the current projects to see if they can leverage existing community models and apply them to their specific lines of business.
A major challenge in governing data is ownership, business context and categorization of data in the lake. It is estimated that 70-80% of d data scientist time is searching, preparing and prepping data. Our operating model has a goal of reducing that time spent to less than 30% by the adoption of governance practices. Metadata management and business glossary will be a key enablers, helping the business to find, understand and consume data in a timely and consistent format.
Data quality is no longer optional, it is getting the attention deserves. Our data lake will require a standard quality criteria and validated it is fit for use and the data is similar to the description. As one of our data scientist articulated “if don’t have some sort of validation moving the data “as is” should be seen as taking on a “Technical Debt”. It’s important to stress that these debts compound and are going to cost the organization a lot of money in future investments.
Big data is shining a big spotlight on data governance and business ownership at a new level. It requires a different paradigm of how to governance data focused on business value, on-boarding new data sets, shared entity models, map-reduce, access management and subscribers. Our team is excited driving Governance2.0 helping to accelerate data discovery and insight to deliver value for our customers.
Barbara Latulippe, EMC, Senior Director, Enterprise Information Management, has spent her career in high-tech and healthcare leading SAP, Data Quality , MDM and Governance solutions. Her diverse experience gives her a broad and balanced perspective supporting both business and IT objectives leveraging data as an asset. You can follow her at #EMCDataDiva.
Her team resides in the Total Customer Experience organization responsible for developing and executing the Big Data Governance and MDM programs in support of a Customer 360 view and advanced analytics. This included leading Data Scientist as a Service; utilizing predictive analytics to solve key business issues.
Barbara is currently focused on the building the framework to govern and deploy Big Data for the most business impact resulting in actionable insights about EMC customers, products and operations that can drive revenue, profit growth, customer satisfaction and innovation.
In 2010 she was named one of Information Management’s Top 25 IT Managers and Laureate of the coveted 2010 Computer World Honors Program for initiatives which bring quantifiable business value through management of data as a corporate asset.