At the recent Arrow Technology Summit in Denver, CO, David Fearne, Arrow global data intelligence practice leader, led a discussion with senior executives from Hitachi Vantara and IBM as they reviewed the importance of having an enterprise data strategy.
“Gaining value and insight from data to drive business change is very difficult,” emphasized Fearne. To help make reseller conversations with their end-customers easier, Arrow has broken down data intelligence into four strategic stages:
- Ingest: The manner of getting data into a system
- Transform: What you do with data to make it into useable information
- Store: Where you keep your data so that it is available yet safe
- Explore: The process of extracting value to deliver results
Ingest the Data
“In the ingest stage, most of the data growth is coming from unstructured data, photos and videos along with streaming data from social media or machines,” said Shawn Rosemarin, Hitachi Vantara SVP and CTO. Rosemarin thinks most of unstructured data comes with no metadata, making it difficult to search for when you need access. End-customers aren’t starting out with an analytic strategy up front, so they aren’t tagging it and then moving it through the data intelligence stages as they should be doing.
“End-customers need to tag the data and make sure they are applying governance and legislation as it comes in,” commented Paul Zikopoulos, VP Cognitive Big Data Systems for IBM. “It’s important to think about governance and having an audit trail right from the start,” said Zikopoulos. The panelists also felt there was a big opportunity to challenge end-customers to start thinking about data governance and how it can accelerate their path to artificial intelligence.
Transform the Data
With more and more organizations implementing some form of artificial intelligence, the transformation stage takes on new meaning. Zikopoulos thinks the first step is to just get started using artificial intelligence with the data you have available. “If a human doesn’t have the data to solve a problem, the computer won’t be able to solve the problem either,” said Zikopoulos.
Panelists thought it was important that the end-customers understand the operational technology and how the business is run to be able to connect the data. They felt the quality of the data and subject matter expert working with the data was more important than the data scientist in getting accurate results.
Store the Data
The store stage is also critical to data intelligence. Fearne thinks people overlook the importance of storage and are just glad that they were able to get the data into the system at all. Companies have a tendency to transform the data and put it in a database and then forget about it. Rosemarin thinks a good opportunity for resellers is what he called “copy data management.” People will make copies of data but they also need to remember where the copies are located, which can avoid problems in the future. This will allow your end-customers to protect the data, as well as automate it.
Explore the Data
Finally, in the explore stage, the data is put in another place and used as a virtualization tool or an API. Organizations need to figure out the best tool for them to get the most value from all the hard work they have put into the process. “It’s all about end-customer flexibility, so that the data is accessible, and you can run compliance and discovery across the data set with less complexity,” said Rosemarin.
End-Customers Need a Strategy
Data intelligence is all about having a strategy to derive value from data, create information, steer processes and help end-customers make informed decisions. To view this entire presentation and gain additional insights about data intelligence and other related topics, go here. You can also view all of the Arrow Tech Summit presentations and discussions here.