Machine Intelligence

We’ve all heard the facts and figures associated with the Industrial Internet. There will be 50 billion devices connected to the Internet by 2020. The Industrial Internet of Things could add $14.2 trillion to the global economy by 2030. The productivity and economic potential of the Industrial Internet is clear. What is less clear is how we get to this future. The classic iceberg example comes to mind – data visualization and KPI tracking are only the tip of the iceberg and according to Gartner about 20% of an analytics project budget. The rest, the sleeping giant beneath the surface, is a mammoth problem that encompasses everything from integrating data, moving data, meta data management, data quality challenges and more. By the same Gartner estimates, this accounts for nearly 80% of a data analytics project budget and comes at a significant resource cost of a large team, consultants, architects and engineers.

The heaviest lift for an industrial enterprise is data integration, the Achilles’ heel of the Industrial Internet of Things (IIoT). This is blocking progress on the transformations and ROI that companies originally envisioned. Developments have been stymied by challenges in handling the complexity, diversity, volume, and velocity of data as well as in the disparity of data characteristics such as quality, completeness and timeliness. Companies are now recognizing the heavy-lift involved in supporting Big Data strategies that can handle the data that is generated by information systems, operational systems and the extensive networks of old and new sensors. To compound these issues, business leaders are expecting data to be captured, analyzed and used in a near real-time to optimize business processes, drive efficiency and improve profitability. However, integrating this vast amount of dissimilar data into a unified data strategy can be overwhelming for even the largest organizations.