![]() Data defense is about minimizing downside risk. ![]() Few if any data-management frameworks are as business-focused as ours: It not only promotes the efficient use of data and allocation of resources but also helps companies design their data-management activities to support their overall strategy.ĭata defense and offense are differentiated by distinct business objectives and the activities designed to address them. Although information on enterprise data management is abundant, much of it is technical and focused on governance, best practices, tools, and the like. Unlike other approaches we’ve seen, ours requires companies to make considered trade-offs between “defensive” and “offensive” uses of data and between control and flexibility in its use, as we describe below. Our framework addresses two key issues: It helps companies clarify the primary purpose of their data, and it guides them in strategic data management. As such, they’re not just the concern of the CIO and the CDO ensuring smart data management is the responsibility of all C-suite executives, starting with the CEO. The “plumbing” aspects of data management may not be as sexy as the predictive models and colorful dashboards they produce, but they’re vital to high performance. The strategy enables superior data management and analytics-essential capabilities that support managerial decision making and ultimately enhance financial performance. The framework draws on our implementation experience at the global insurer AIG (where DalleMule is the CDO) and our study of half a dozen other large companies where its elements have been applied. In this article we describe a new framework for building a robust data strategy that can be applied across industries and levels of data maturity. Indeed, without such strategic management many companies struggle to protect and leverage their data-and CDOs’ tenures are often difficult and short (just 2.4 years on average, according to Gartner). 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 organizing, governing, analyzing, and deploying an organization’s information assets. Data breaches are common, rogue data sets propagate in silos, and companies’ data technology often isn’t up to the demands put on it. More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data. Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions-and less than 1% of its unstructured data is analyzed or used at all. But even with the emergence of data-management functions and chief data officers (CDOs), most companies remain badly behind the curve. More than ever, the ability to manage torrents of data is critical to a company’s success. Regardless of its industry, a company’s data strategy is rarely static typically, a chief data officer is in charge of ensuring that it dynamically adjusts as competitive pressures and overall corporate strategy shift. The SolutionĬompanies need a coherent strategy that strikes the proper balance between two types of data management: defensive, such as security and governance, and offensive, such as predictive analytics. But data theft is common, flawed or duplicate data sets exist within organizations, and IT is often behind the curve. To remain competitive, companies must wisely manage quantities of data. Using this approach, managers can design their data-management activities to support their company’s overall strategy. Data offense focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction. The framework will help managers clarify the primary purpose of their data, whether “defensive” or “offensive.” Data defense is about minimizing downside risk: ensuring compliance with regulations, using analytics to detect and limit fraud, and building systems to prevent theft. In this article, the authors describe a framework for building a robust data strategy that can be applied across industries and levels of data maturity. More than 70% of employees have access to data they should not. Although the ability to manage torrents of data has become crucial to companies’ success, most organizations remain badly behind the curve.
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