<< back Page 2 of 2

Enabling Data Intelligence: Q&A with Zaloni's Ben Sharma

BDQ: Does that tie into the rebranding last year of the Zaloni data platform as Arena?
Sharma: Absolutely. If you think about Arena and its significance, our software platform provides a common gathering place, if you will, so that data, and the data consumers, the data governance folks, and other partners are aligned with a unified view. We are allowing our customers to create these experiences where data's potential is realized through collaboration and controlled access across the organization. From our perspective, when we talk about Arena, Arena is that space where data and information is not only organized, accessed, and shared, but it is also transformed into insights, into something that is meaningful for your business use cases. With Arena, we think about having a unified view of data across all your different environments and we call it the three C's. It’s where we catalog the data, then allow you to control the data no matter where it exists so that you have the right governance model for the data.

BDQ: And the third C?

Sharma: And then the third piece which is critically important is: How do we allow you to consume that data so that your data consumers can come in and have easy access to it?

BDQ: Can you provide some examples of how it's being used?
Sharma:  Sure—I'll give you an example of a large asset management firm in the financial services space. It needed to create a new set of mutual funds that are not only evaluating companies based on their financial performance data but also on how good they are as corporate citizens—how good they are for the society, how good they are for the environment, and how good they are from a governance standpoint with not much pay disparity among the ranks and gender equality among their employee base. You can imagine how much more data has to be brought in to be able to evaluate these companies based on those metrics.

BDQ: How is this done?
Sharma: These datasets are not coming from the traditional sources like a Bloomberg or S&P Global. This is coming from many mom-and-pop data companies that are mining data, looking at press articles and other information, and creating new streams of data about environmental incidents and so on. Now you have to bring in this multitude of datasets, in addition to the traditional datasets you have, and take it through a governance process, with data quality and various measures before you can make it available for your downstream users. In this case, portfolio managers can now see new scores being created on top of this data based on ESG measures—which are environmental, social, and governance measures—that they can use to evaluate these companies. That's a very specific example of how our platform has been used in an asset management scenario to enable new business outcomes.

BDQ: Looking ahead, is there a direction that you're going in that you think is possibly different than other companies or in the way that you see the market?
Sharma: There are two key things that we're focused on. One is, now that we have a base foundation where we have all these different capabilities along the data supply chain for enabling a DataOps approach in these organizations, we're adding more and more machine learning capabilities in our platform so that we can make data management and data governance much more intelligent. The idea is that as you bring in the data, and our platform can automatically detect that data and not take bad records forward in the process so that you can automatically enable trust in that data. Our platform also automatically detects sensitive data so that, as you need to comply with various regulations, we can flag datasets that have PII so that you're not making them generally available or you are automatically applying our masking and tokenization functions to anonymize that data. Things like that—that are more about augmenting that data management approach with system-generated intelligence—are what we call broadly “data intelligence.” Enabling data intelligence from a DataOps perspective, and from a data supply chain perspective, is one of the key things we are focused on.

BDQ: And the second?
Sharma: The second thing that we are all focused on is becoming that single pane of glass—that single cockpit—for customers across all of the different cloud providers. We are not just providing a shim layer on top of these cloud service providers. We're actually doing deep integration with these cloud service providers: talking to their APIs, leveraging the innovation and the new services that they're bringing to the market, but at the same time, providing that layer of abstraction so that our customers do not have to deal with the internal details and they have much more portability in terms of moving the data from one environment to another environment. Those are the two things that are front and center in our focus as we go forward.

Interview conducted, edited, and condensed by Joyce Wells

<< back Page 2 of 2


Subscribe to Big Data Quarterly E-Edition