Analytics is the new source of competitive advantage. Thomas H. Davenport, author of Competing on Analytics: The New Science of Winning, has been writing about analytics for over 10 years. Unfortunately, even when I attended Cisco Live Barcelona a few weeks ago, many customers from IT are still confused about why their line of business data scientists focusing on analytics are making seemingly strange requests of the IT department. Ranging from high data ingestion rates to GPUs for deep learning, IT leaders are not fully comprehending the rapidly changing requests coming from their line of business counterparts. Furthermore, IT requirements made in one week are often changed depending on what data scientists have uncovered during the following week.
At the end of the day, data scientists are embarking an adventure of data discovery, no different than Christopher Columbus trying to uncover a shorter route to India. They are literally diving into the data and uncovering gems as well as garbage. During this adventure of discovery, the tools that they need change as well depending on what new facts, data sources, and insights have made along the way. As a result, IT requirements in supporting the data scientists change as well. A Harvard Business Review article pointed out that big data analytics projects are driven by experiment and discovery. Hence, the IT requirements are fluid and constantly changing.
At the SAP BI + Analytics Conference last week where I was fortunate to give the introductory keynote, there were numerous successful examples of IT working closely with the business analysts. Use of analytics to optimize UPS brown trucks have been well documented. UPS Jack Levis was able to share some of the challenges developing multiple versions of the On-Road Integrated Optimization and Navigation (ORION) used by all the UPS drivers. Clearly, the IT team worked very closely with the deployment of ORION enabling $300-400M savings annually.
At the same SAP BI + Analytics conference, there was a presentation describing the use of analytics at Organic Valley. Sue Klingaman and Diane Callaway discussed in detail how they worked so closely with the Organic Valley Information Resource (also known as Information Technology) team that they wanted to adopt Diane as part of the IT team. As an example, the better analytics ensures that Organic Valley can anticipate and predict more than 72 scenarios of milk supply. Moral of the story: Working closely between the IT and the line of business is essential to a successful analytics project.
Here at Cisco, we have been blessed to be working with so many of our customers on big data and analytics. We have authored books, such as Transforming Industry through Data Analytics, and Cisco Validated Designs to help our customers to be more successful with analytics. Is your IT team working closely with line of business ensuring a successful analytics? I hope that you, too, can use analytics to be a source of competitive success.