There are many statistics that illustrate what everyone already knows to be true: businesses who have Data Analysis as a core competency outperform those who do not. That is a large part of the reason why the Digital Continuum includes data at the foundational mindset level.
“Data-Driven decisions tend to be better decisions. Companies in the top third of their industry in the use of Data-Driven decision-making were, on average, 5% more productive and 6% more profitable than their competitors. This performance difference remained robust after accounting for the contributions of labor, capital, purchased services, and traditional IT investment.”1
Identifying problems and opportunities using real-time data can drive change to meet customer and business goals. Predictive analytics utilize data, algorithms, and machine learning to assess probabilities against future outcomes. Business can surface products and information based on probability models to enhance and personalize experiences.
Data can move beyond identifying problems to offering immediate solutions as well. Prescriptive analytics inform an experience, using a variety of variables, mathematical models, business rules, constraints, and machine-learning. Common examples include self-driving cars, traffic rerouting, and airline pricing models that all rely on a complex set of variables and factors to optimize results.
In today’s landscape, organizations are using Data Analysis as a disruptive force. From organizations that are using predictive analytics to more intelligently route calls in a call center2 to using sensors and Data Analysis to detect potential failure in oil refineries3, Data Analysis is more than a tool, it is a competitive advantage.
With data providing a competitive advantage in today’s landscape, organizations have a clear mandate to adopt it as a core competency. It is expected that the amount of data ingested by an organization could increase exponentially as organizations adopt new IoT devices at a record page, integrate new commercial data sources, and adopt new data-producing technologies.
Successful implementation of Data Analysis requires both the resources to effectively analyze it and a culture bound to enforce its use.