How a data-driven business supports analytics goals


Coworkers examine data for their data-driven businessThe goal of all types of analytics is to provide business insight. Consider that:

  • Descriptive analytics provides the business with insight on what happened in the past and what is happening now.
  • Predictive analytics provides the business with insight on the probability of what will happen in the future.
  • Prescriptive analytics provides the business with insight on the best course of action for predicted future scenarios.

While these analytics goals are valuable to a wide variety of business models, the degree of value an organization can get from analytics is often determined by how much of a data-driven business model the organization has. David Loshin recently blogged about what differentiates data-driven, data-infused and data-informed business models.

Data governance as the steering wheel

Data can be a powerful driver, but organizations evolving to become more data-driven will need data governance as the steering wheel. The biggest data-driven roadblocks are often neither process nor technology, but people. Even self-driving cars will have humans that occasionally want, and need, to be more than just passengers. Staff will be slow to adopt. Leaders will be slow to buy-in. Departmental silos and data fiefdoms will be potholes on the roads where data is trying to drive the business.

Becoming a data-driven business requires people at all levels of the organization to work together effectively. This is why the keys to igniting a data-driven business model are measurement and learning. The organization has to quantify and learn from its analytical activities and connect the dots between analytics and business outcomes.

Data drives the business

Since driving is more about the destination than the journey, becoming data-driven requires you to continuously evaluate where data is driving your business. Many organizations implement data quality thresholds to close the feedback loop evaluating the effectiveness of their data management and data governance. But few implement decision quality thresholds to close the feedback loop evaluating the effectiveness of their data-driven decision making. Closing the feedback loops that make data-driven decisions more transparent (through better monitoring) is central to measurement and learning. This approach allows the organization to learn from its decision-making mistakes and adjust when necessary.

Becoming data-driven is about giving business decision makers the power of analytics to make smarter, fact-based decisions. Driven by data, analytics will empower the enterprise to more accurately define corporate strategy and more rapidly innovate. In turn, the business can perform better, become more profitable and achieve competitive advantage. A data-driven business supports these analytics goals by making business decisions faster, using better data – including more varied sources and types of data – and, most importantly, by having more transparency about decision making and the business results those decisions produce.

Download – SAS: A Comprehensive Framework for Big Data Governance, Data Management and Analytics


About Author

Jim Harris

Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ)

Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.

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