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“Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat” – Sun Tzu, The Art of War
The proper utilization of data is at the center of competitiveness and survival in every industry. However, raw data is of limited value until it can guide the business. As Peter Drucker said, “Information is data endowed with relevance and purpose.” Having a CDO and a data-management function is a start, but neither can be fully effective in the absence of a coherent strategy for finding, understanding, and trusting an organization’s information assets.
Healthcare systems are gathering and analyzing patient data to decrease avoidable infections and improve patient outcomes. Financial institutions realize sharing information across third parties is critical while recognizing the high liability of regulated data means their data sharing agreements need to be transparent and secure. Even traditional machinery manufacturers are collecting billions of facts on weather patterns from sensors in their equipment and selling the analyzed information to improve customer productivity.
Whether its banking, healthcare, retail, or once traditional industries, most organizations understand their high-level objectives and where they want to generate information from the data they collect – BI platforms, visualization diagrams, artificial intelligence, predictive modeling etc. Several others even understand the tactics they need to execute behind the scenes, like the integration tools, master data management centers, data warehouses and big data platforms to ensure they standardize around how their data is sourced, transformed and managed before it’s consumed.
Many organizations, however, still struggle connecting these tactics to their larger objectives. In fact industry analysts report more than half of all analytics projects fail to deliver on what businesses sought out to accomplish. What is typically left behind is a business-driven data strategy.
John Marchese, Sr Director of Global Partners at Collibra, and I were recently discussing how many “data strategists” make the mistake of adopting a data governance platform AFTER implementing a technical data management project (like MDM or data quality). This mistake is repeated too often, causing companies to waste time and money on systems that never deliver their promise to serve the strategic needs of the business.
After getting some bad advice from a prior “data expert”, John introduced one of my Collibra clients to PwC to provide a corrected view on where their data strategy should start. As Richard Inserro, Principal, PwC, mentioned in his presentation at Data Citizens ’17, ”The CDO Mandate and the Path Forward”, data governance must be the starting point of any data strategy. It must have the capability and flexibility to support the productivity aspirations, risk mitigation and operational efficiency needs of the business and appropriately align IT activities. The PwC process outlined below suggests businesses can achieve that only when their programs build a community of common language, laws and responsibilities to begin their Data Strategy journeys. These are the foundations of data governance.
Without prioritizing governance in your data strategy, the desired impact of data and information on your business will never be realized in an efficient and dependable manner over time. As Stephen Covey famously said “Begin with the End in Mind means to begin each day, task, or project with a clear vision of your desired direction and destination, and then continue by flexing your proactive muscles to make things happen.” For you to reach that vision of enterprise data nirvana, you must BEGIN (a data strategy) with the END (data governance)!
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