A New Take on Data Discovery

A New Take on Data Discovery, Data Management, and its Relationships

A New Take on Data Discovery, Data Management, and its Relationships

Having herself held senior roles in IT at Wall Street companies including Deutsche Bank and Morgan Stanley Smith Barney, Oksana Sokolovsky is quite familiar with the challenge of Data Management and data discovery. As co-founder and CEO of ROKITT, her goal was “to build a product that solves that challenge,” she says.

The challenge exists across large enterprises in multiple industries, but is often especially acute in those dealing with regulatory pressures and compliance requirements – healthcare, for instance, and of course, the financial sector. Basel Committee on Banking Supervision (BCBS) 239 compliance for effective risk data aggregation and reporting, for example, is a big driver of improved Data Management for global systemically important banks.

In fact, a McKinsey & Company and Institute of International Finance survey showed that more than half of the world’s biggest banks faced significant challenges meeting the January 1, 2016 deadline for compliance, with the Global Association of Risk Professionals commenting that “many institutions continue to struggle to fully implement the requirements across the business under the most demanding interpretation of those requirements.”

Read Also:
BI and data management moving rapidly to the cloud

ROKITT’s Astra solution, Sokolovsky believes, can help banks support adherence to both internal and external regulations and policies, like BCBS 239, across complex data landscapes, as well as support other use cases for a variety of enterprises: Data asset governance to better utilize data to enhance business value, for example.

Astra debuted in March after a year of development. What sets the technology apart, according to Sokolovsky, is its ability “to let customers discover data and information about data and its relationships, in order to manage data better or meet regulations more efficiently, using our custom-built machine learning concepts and other advanced algorithms.” Astra’s algorithms automatically discover and self-learn data relationships with up to 90% accuracy, the company says.

It can recognize, for example, connections or dependencies between values within a database that may not be obvious – perhaps that a column in one table contains data that refers to a column in another table, and why that relationship exists. It will learn that two columns in different tables that both carry customer information, but are called “Customer” in one instance and “Company” in another, for example are the same. It will then establish the relationship that ‘customer’ is the primary key and ‘company’ is the secondary key, she says, and apply that knowledge from that point on across databases, XML documents, and flat files. Columns don’t even need to have reasonable names like this for the solution to figure out the relationship – if one table was called ‘xyz’ and another ‘qwerty17,’ it can still figure out that both hold customer information.

Read Also:
Who Leads in the Race for Better Master Data Management?

The system reads data in repositories and learns its true Metadata. In fact, applying its Machine Learning algorithms to the data itself, rather than just to Metadata, is critical: As companies and systems grow, Metadata only holds so much information about data, Sokolovsky says, limiting possibilities. Applying Machine Learning algorithms to the data itself expands the ability to discover data relationships beyond the 10 to 20 percent possible when such algorithms are applied to Metadata only. The data discovery process, she says, is fast, too, using its next-generation async processing architecture. “It’s measured in hours and minutes,” says Sokolovsky.

 



Chief Analytics Officer Europe

25
Apr
2017
Chief Analytics Officer Europe

15% off with code 7WDCAO17

Read Also:
Most Industries Are Nowhere Close to Realizing the Potential of Analytics

Chief Analytics Officer Spring 2017

2
May
2017
Chief Analytics Officer Spring 2017

15% off with code MP15

Read Also:
12 Habits Of Genuine People
Read Also:
12 Habits Of Genuine People

Big Data and Analytics for Healthcare Philadelphia

17
May
2017
Big Data and Analytics for Healthcare Philadelphia

$200 off with code DATA200

Read Also:
Collaborative Data Governance: Getting Value from your MDM Program

SMX London

23
May
2017
SMX London

10% off with code 7WDATASMX

Read Also:
Collaborative Data Governance: Getting Value from your MDM Program

Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
What is the Future of Data Governance for the Financial Services Sector?

Leave a Reply

Your email address will not be published. Required fields are marked *