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VMblog's Expert Interviews: Io-Tahoe Talks Smart Data Discovery Leads to Faster and Better Business Decisions

interview-iotahoe-datadiscovery 

Today, we are speaking with Io-Tahoe (www.io-tahoe.com) CEO, Oksana Sokolovsky, on the topic of data discovery, data tracking and data cataloging, and why these capabilities have become so critical in today's dynamic, information rich enterprise. 

VMblog:  Traditional methodologies for data discovery and cataloging have been deemed by many as unsuccessful and poorly aligned with business objectives.  Do you agree, and can you explain why?

Oksana Sokolovsky:  Perhaps traditional methods for data discovery and cataloging were considered to be unsuccessful because of the sheer difficulty in managing a growing number of platforms, databases and data lakes; and an inability to monitor or track how data moves through the enterprise. The inability to manage these data volumes, means governance processes may be inadequate, and the value of enterprise data may not be fully realized or monetized. Such a combination of scenarios could lead to situations whereby different stakeholders across the enterprise may be dissatisfied with these traditional methods because they have not been able to obtain the business intelligence required or anticipated. Expectations of the business simply were not met. Organizations aren't able to cope with such volumes of data and have instead exhausted their resources on time consuming and potentially error-prone manual or traditional methodologies. Therefore, organizations need to move to more advanced ways of discovering and cataloging the data, which is driven by automation and machine learning and reduces the manual effort thus enabling the organization to focus on output of discovery not the process of discovery.

There has been a momentous leap towards data-driven decision making, so I'm pleased to say that empowering the business is at the forefront of the new data discovery technologies we are seeing today. There are a number of market drivers which are helping to fuel the importance of data discovery, whether in relational database management systems (RDBMSes), data lakes or other modern repositories. For example, regulatory and audit pressures require firms have the ability to discover and understand their data; more businesses are starting to view data as an asset with measurable return; and limited resources and availability of in-house experts have reduced the dependence on manual options in favor of automated data discovery platforms.

VMblog:  What is meant by "tracking data relationships" and why is it important?

Sokolovsky:  At Io-Tahoe we look at the data and its relationships within and across data instances and discover hidden, implied or undocumented relationships across the enterprise. Our platform helps organizations discover data across a heterogeneous technological landscape. Irrespective of whether data is stored in RDBMSes or data lake platforms, we enable companies to auto-discover the location, movement and flow of data throughout their organization. Being able to trace elements through the heterogeneous enterprise and provide the information on each and every instance of the particular element is at the core of Io-Tahoe's functionality.

Discovery of such relationships is of particular significance for organizations because data discovery is the fundamental requirement for all other data disciplines. Organizations can then take the necessary next steps, such as data monitoring; once the data and how it flows through the enterprise has been identified. Ultimately, starting with data discovery better positions organizations to analyse the data and glean the insights required for business intelligence. Additionally, discovery also provides a foundation for regulatory compliance. Knowing where the data is and how it flows through the enterprise; and understanding each and every instance is a critical step towards addressing regulatory requirements. Furthermore, enabling organizations with the ability to govern this data on an ongoing basis is the next important step - it is one that Io-Tahoe can help organizations with.   

VMblog:  What is meant by a "modern information catalog" and why is it important?

Sokolovsky:  At Io-Tahoe, we would refer to this as a smart data catalog - one which utilizes machine learning and empowers data owners and data stewards. The machine learning component allows enterprises to enhance information about data automatically, regardless of the underlying technology and build a data catalog.

VMblog:  How does the use of smart data discovery solutions enable better tracking of data relationships and the creation of an information catalog? 

Sokolovsky:  Smart data discovery, that uses machine learning algorithms, makes it easier to auto discover data, extract insights, patterns and relationships that can be used to make decisions. By using machine learning algorithms and heuristics enterprises can quickly and automatically discover and untangle the complex maze of data relationships, making for a smart data discovery process.

From the discovered data, enterprises can create, maintain and search business rules; define policies and provide governance workflow functionality. Io-Tahoe's data discovery capability provides complete business rule management and enrichment. It enables a business user to govern the rules and define policies for critical data elements.

VMblog:  Which areas in an organization can appreciate the greatest IT and/or business benefit from these capabilities?

Sokolovsky:  Most roles in IT as well as business functions or specific roles, such as data owners and data stewards will benefit from improved visibility and understanding of the data landscape. From Business Analyst to System Architects to Data Architects, automated discovery will enable the organization to better understand the data, have visibility into how data moves through the enterprise or changes over time. Machine learning-driven data discovery introduces a significant level of empowerment to the organization. The automation of tasks that were previously manual and/or dependent on Subject Matter Experts (SMEs) can relieve demands and pressures on human and financial resources. From this perspective, there are enterprise-wide benefits.

VMblog:  Can you talk about Io-Tahoe's recent product announcement -- and how it addresses the above?

Sokolovsky:  Io-Tahoe has announced the general availability of its machine learning-driven smart data discovery platform. This includes the addition of Data Catalog, a new feature that allows data owners and data stewards to utilize a machine learning-based smart catalog to create, maintain and search business rules; define policies and provide governance workflow functionality.

Io-Tahoe is unique as it allows the organization to conduct data discovery across heterogeneous enterprise landscapes, ranging from databases, data warehouses and data lakes, bringing disparate data worlds together into a common view which will lead to a universal metadata store. This enables organizations to have full insight into their data, in order to better achieve their business goals, drive data analytics, enhance data governance and meet regulatory demands.

Anything other than automated data discovery is not practical, so we encourage organizations to seek out automated platforms. The technology-agnostic platform spans silos of data and creates a centralized repository of discovered data upon which users can enable Io-Tahoe's Data Catalog to search and govern. Through convenient self-service features, users can bolster team engagement through the simplified and accurate sharing of data knowledge, business rules and reports. Here users have a greater ability to analyze, visualize and leverage business intelligence and other tools, all of which have become the foundation to power data processes.

VMblog:  Is there anything else you would like to add?

Sokolovsky:  To learn more, we would encourage your readers to visit: https://io-tahoe.com.

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About Oksana Sokolovsky

Oksana Sokolovsky is an ex Wall Street executive turned entrepreneur; an experienced CEO who has achieved early stage acquisition. Sokolovsky is passionate about developing disruptive technology. Her technology expertise combined with business acumen, allows her to bring a unique perspective to developing innovative products, commercializing them, and taking them to market. She is a technologist with experience running large IT departments within leading global Financial Services firms, establishing and transforming technology functions, and leading global high performing teams. In her 20+ years technology career, Sokolovsky has held a number of senior roles at JPMorgan Chase, Morgan Stanley, and Deutsche Bank, as well as United Health Care, Instinet, and BarnesandNoble.com. Most recently, Sokolovsky built disruptive data discovery technology, which was acquired by Centrica's Io-Tahoe.

Published Wednesday, April 04, 2018 7:29 AM by David Marshall
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