Digital transformation is driving new business models – stickier value propositions that drive top line growth, and large, streamlining opportunities that take out operating costs. In the emerging competition between the disruptors and disrupted in this new economy, unlocking the value of the data that sits across an existing book of business is becoming the new value driver for large enterprises. As a result, in 2018, we will see more businesses looking to cash in on this data currency.
Those that succeed are the companies that know how to prepare data for consumption, and use deep statistical inferencing and machine learning techniques to uncover smarter insights. And then, know how to transform these insights into action through active nudging – providing the right guidance at the right time, at the right stage in the process. Let’s explore how companies can make the most of their data, apply analytics and artificial intelligence (AI) to generate real, impactful insights, and then drive meaningful change in their organizations.
Start with a clear vision and goal
It is important to have the right start. An effective analytics strategy addresses more than data and insights – it includes people, process, and change management. Too often, we see fantastic insights end up underutilized by businesses that did not invest in a broader program to drive analytics into the core of their business. Industry evidence clearly shows a business-driven center of excellence around analytics always delivers more than a technology-driven analytics group.
Moving to the core of the analytics work, it starts with data strategy – design thinking through what data is needed, and making thoughtful choices that ensure processes and technology systematically capture the data. For instance, we see elevator companies now capturing load and stops at different times of the day so they can deliver better user experiences – data that we never captured before. Or, manufacturers capturing data from sensors on aircraft engines so they can optimize asset maintenance. Combining strategically important new data with other existing data from both internal and external sources creates a universe of rich, big data that businesses can draw upon.
Extracting, structuring and engineering data
Data engineering is the foundation of the analytics practice and involves data architecture (discovering, understanding, sourcing, and housing the data); data orchestration (ingestion, cleansing, transforming, and unification of data); and data governance (master data management, security, and provenance), making it consumption-ready for running modeling techniques to deliver business insight. […]