In the world of baseball, there is a saying that hitters can’t hit what they can’t see. In the enterprise world, organizations are increasingly realizing that being able to see as much data as possible is crucial for making successful business decisions.
A new forecast from International Data Corp. shows that an estimated 41 billion connected internet of things devices will generate 79.4 zettabytes of data within the next five years. To put that in perspective, the entire annual run rate of global internet traffic reached only one zettabyte of data in 2016.
The exponential increase in data volume has put pressure on organizations to find tools for data discovery. One of the key providers in this space is Io-Tahoe LLC, which enables enterprises to discover and search data across a wide range of technology platforms using artificial intelligence and machine learning.
“There’s a lot of pressure on data, a lot of demand on data to deliver more value to the business,” said Ajay Vohora, chief executive officer of Io-Tahoe. “To be able to put data in context and search across the entire enterprise estate, then you can start to do some exciting things and piece together the fabric across different systems. We look to build on top of that with data automation.”
Vohora spoke with Dave Vellante, host of theCUBE, SiliconANGLE Media’s livestreaming studio, as part of a CrowdChat panel discussion on enterprise data automation with Io-Tahoe and Webster Bank. Vohora was joined by Lester Waters, chief technology officer of Io-Tahoe; Yusef Khan, head of partnerships and alliances at Io-Tahoe; and Paula D’Amico, senior vice president of enterprise data architecture at Webster Bank. They discussed how Io-Tahoe provides context and meaning to information, ways that one financial institution leverages the technology to improve customer service while reducing cost, consolidating data before migration, and the value of industry partnerships. (* Disclosure below.)
Automation delivers value
Because time is money, enterprises are interested in finding ways to search and analyze data without grinding up hours of effort. Automated tools represent a key factor in solving this problem. A research report from Enterprise Management Associates found that AI-enabled analytics and data management solutions were generating cost savings for 83% of companies surveyed.
Io-Tahoe’s automated tools employ machine-learning algorithms to deliver accuracy and speed of identification for complex data elements. The platform also determines data relationships in the enterprise environment.
“We can put context and meaning around data,” Vohora said. “It’s the nuts and bolts of the algorithms, the models behind machine learning, the functions, that’s where we invest our research and development. We inject that automation into the business processes that are going to drive a business to serve its customer.”
Bank breaks down silos
Institutions that are highly regulated, such as banks, are prime candidates for this kind of automated approach. Vast stores of detailed, highly sensitive financial data must be constantly protected, yet also made available to deliver precise customer service at a moment’s notice.
Webster Bank, with 3 million customers and over $30 billion in assets, offers an example of how Io-Tahoe’s solution can make an impact.
“I wanted to give the lines of business an ability to do search within the data,” D’Amico said. “I wanted the bankers to be able to walk around with an iPad in their hands and be able to access data for a customer really fast and be able to get them the best deal.”
D’Amico’s challenge also extended to processes behind the scenes. Seven of the bank’s different lines of business would each seek answers to questions that triggered analytically demanding projects from the information-technology department.
“Each project used to be siloed,” D’Amico explained. “It used to be 100 hours to do analytical work, and another analyst would do another 100 hours on the other project. Now I can do that all at once. I’m using Io-Tahoe’s data automation right now to bring in the data and start analyzing data flows to make sure I’m not missing anything.”
Consolidate before migration
Discovering where data is located and analyzing it to provide key insight is important, yet redundant data remains a thorny problem as well. Snapshot tools are now common in the enterprise toolkit, but they can rapidly lead to multiple stored copies of the same data set.
“It’s not uncommon for an organization to have 20 ‘master instances’ of a customer,” Waters said. “You can see where that will go. You’ve got to understand what’s being used and what’s not.”
At the heart of this effort is “dark data,” defined by Gartner Inc. as information collected routinely by organizations that is seldom leveraged for other uses. To realize maximum cost savings through the use of platforms such as Io-Tahoe, enterprises need to get a handle on all of the stored data, especially when it’s time to move it.
“As part of your cloud-migration journey, you really want to plan where there’s an opportunity to consolidate your data,” Waters noted. “You don’t want to bring your legacy issues with you. Find those, identify and remediate them.”
The problem remains that data migration can be risky, time consuming, and expensive. One notable example of what can go wrong occurred in 2018 when 2 million customers of a bank in the United Kingdom could not access their accounts after the institution failed to test a new data center following a significant migration.
“You start from a position where you have pretty high risk,” Khan said. “We’re able to automate a lot of this process from the very beginning. You very quickly have an automated view of the data, a data map, and the data flow. It’s much less time and effort and much less cost.”
Io-Tahoe has not been shy about partnering with other technology players. In addition to major companies, such as Google LLC, the company has formed alliances with a wide range of firms, including data discovery provider OneTrust LLC in September.
“One of the trends that I wanted us to be part of was being open, having an open architecture,” Vohora said. “I wanted to ensure we could openly plug in using APIs that were available. It’s part of the reason we’ve been successful with partners like Google, AWS, and increasingly a number of technology players such as Red Hat, MongoDB and Snowflake.”
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s CUBE Conversations. (* Disclosure: TheCUBE is a paid media partner for Io-Tahoe LLC. Neither Io-Tahoe, the sponsor for theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
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