The organizational view of data has evolved from often being an afterthought to that of a fundamental currency that drives decisions.
A key element in this data evolution has been the exponential growth in technology that has enabled organizations to better aggregate, sift through, understand and reason with data. These abilities have transformed organizations from being “data aggregators” to “data-based decision makers.” Indeed, more than 75 percent of organizations plan to move to data-driven decisions by 2020.
To realize the potential benefits of data-driven decisions, many organizations need to transform itself from the inside out. To become a true data-driven Organization (DDO) it is not sufficient to just invest, purchase and implement tools. Organizations need to embark on a journey from being a “data collector” to “data aware” to “data-driven” in a timely and strategic fashion.
To become data aware, organizations should focus on the right tools and technologies to refine their data architecture. This enables the understanding, extraction and integration of actionable data relevant to the business. Once data capabilities are enabled in this “data aware” stage, organizations should focus on data governance and data quality. In parallel, data architecture is refined and scaled to include analytics, predictive modelling, big data and artificial Intelligence.
On this journey of becoming data-driven, we see many organizations fail or stagger. As a result, 58 percent of today’s businesses still make at least half of their business decisions based on gut feel or experience instead of by leveraging data. Primary reasons for this stem from a lack of quality data, poor data governance, siloed data management, inflexible data architecture or lack of proper orchestration of multiple source systems.
A lack of alignment or communication on critical data systems and projects among senior leadership also hastens failure. Ill-equipped organizations often undermine and de-incentivize data capabilities. Eventually they fail to achieve the much-needed culture change of being data-driven and resort to old habits that fail to deliver desired results.
Data analytics has moved from being a support capability to a core competency with key analytics resources managed in an integrated model. Our engagement observations have shown that data-driven retailers are optimizing operations and leading their markets in various areas.
For example, data-driven retailers are forecasting inventory and managing supply variation based on environmental factors, maximizing profits based on price experimentation, reducing churn, enhancing personalization, tracking customer and product together, optimizing customer and product affinity optimization, optimizing trade through omni-channels, and more.
An Organization can transform into a DDO by adopting different models. In evaluating their fit for an appropriate model, organizations should select the model that works culturally and structurally for their business. In recent years, a few core flavors of DDO have emerged including (but not limited to) the following:
Integrated Model: A standalone function within existing IT organization reporting to the CIO.
Parallel Model: A parallel structure outside of the current IT setup supporting the entire organization reporting to CXO/CEO.
