Charles Reeves: BI Strategies for IoT and Big Data
In this Episode, Wayne Eckerson asks Charles Reeves about his organization’s Internet of Things and Big Data strategy. Reeves is senior manager of BI and analytics at Graphics Packaging International, a leader in the packaging industry with hundreds of worldwide customers. He has 25 years of professional experience in IT management including nine years in reporting, analytics, and data governance.
Charles has served as chairman of the American SAP Users Group and he is CEO of a non-profit organization, called Teach Kids Technology, which provides free technology hardware, software and services to disadvantaged kids in Atlanta and Wilmington, Delaware area.
- Reeves dumps streaming data into a SQL database with a permanent segment and staging area
- Excel is powerful but time-consuming
- Many different vendors offer complete, end-to-end IoT solutions, so customers don’t have to send data from one place to another
- SAP Leonardo lets you consume IoT data as a live connection or store it in a solution
- IoT will help predictive maintenance, parts inventory, and quality control
- Big Data is external data they can use to add value to their internal data
- Ideally, Reeves would not bring big data into the internal data warehouse
Below is one question and answer from the podcast
Wayne Eckerson: Can you give one example of what kind of big data you’d acquire and what the company would do with it and how it would change things?
Charles Reeves: One of the things we’re kind of looking at and researching is the impact of weather data. For example, if we can use weather data and some of our facilities don’t have air conditioning, so the temperature could impact the machinery and parts that may cause predictable maintenance or not last as long as it should. So we’re going to see if we can look at weather data and see if weather has an impact on our machines. Instead of having four machines shut down, maybe we can only have two because we see the true impact of weather and where it may be hurting certain plants and facilities. It also may help with performance. We may be able to increase performance in some locations where weather is not a factor. So, weather big data could help us, and predictive maintenance is a huge area where there could be significant cost savings as well as increased machinery performance.