Benefits of insight-driven data management swell

By David Beach | 1 February 2019

On January 28 bobsguide hosted a webinar on mapping the opportunities of insight-driven data management with participation from Adox Research and Asset Control, the data management technology firm.

A new generation of data management tools is moving beyond operational and IT efficiency to data exploration for the business user. In this webinar recent research findings and use cases on how firms are adopting insight-driven data management are covered.

Firms that adopt insight-driven data management are able to move beyond data collection and data mastering to servicing business users’ needs for data exploration and improving the speed and quality of decision-making.

It also enables them, perhaps more crucially than ever, to keep pace with increasingly data-intensive business processes.

Further, it lets them keep an eye on the potential for emerging technologies. The adoption of cloud and NoSQL technologies help organisations realize material efficiency gains as well as opening up their data for distribution and discovery to improve investment performance, shorten change cycles as well as manage risk and meet compliance goals.

But that is easier said than done, and requires a fair degree of explanation from the industry’s frontline. Here, Martijn Groot, VP of strategy at Asset Control, answers a number of audience-submitted  questions from the webinar.

The webinar is now available on demand.

Q&A with Martijn Groot, VP of strategy, Asset Control.

How does the self-service model and the democratisation of data approach work alongside this vision?

They complement the insight-driven data management approach. Self-service effectively means providing users with direct access to a data mall. The reason for that is not only to speed up change cycles but also that in some cases users may not know in advance what they are looking for and need the tools to navigate and in some cases discover new relationships in the data.

 

How are these additional tools and capabilities change the way we deal with data regulations in different regions?

I don’t think these tools change the way we deal with these, they may make it easier to track permissions and make sure they are abided by. Different regions have different demands on data, primarily on customer and personal data. This may limit where data is stored and accessed. On top of that, access to certain data may be restricted due to stipulations in content license agreements. These can be observed through proper permissioning and entitlements in the solution.

 

What sort of artificial intelligence (AI) and machine learning (ML) are you seeing being planned or deployed?

I think there is a lot of experimentation and labbing going on at the moment so this is a bit open-ended. Certainly we see applications in improving the efficiency of data aggregation and cleansing by feeding back the human interventions as feedback into the rules. This is linked to RPA. Also on the process of valuation and proxying we see improvements. The real interest is in discovering actionable patterns for trading and investment decisions.

 

What controls would the panel recommend to ensure consistent use of master reference data across different lines of business - to ensure, for example that "Legal entities" are identified consistently? 

There are many controls and business rules that can be linked to data sets, including checking against missing data, missing mappings in a cross-reference schedule, checking for duplicates and twilighting process to prevent stale information. In some cases, multiple sources can help build a fuller aggregate picture, but most important is to have clear cross-reference and accurate master reference data to draw from (industry sectors, industry taxonomies). Other than that, an effective feedback loop whereby the input from any user finding errors or gaps is channelled back into the data service is critical.

 

What regulatory challenges do you see coming up in the year ahead that could unsettle data management?

I think regulatory challenges have been both a driver to further develop data management solution as well as a strain on many existing data management infrastructures. I think in the coming years the implementation of FRTB is going to put a lot of demands both in terms of volumes as well as in terms of sourcing and preparation processes on data management solutions. Longer term I see the integration of alternative data sources, both for compliance purposes as well as for business enablement functions.

 

What segments of the market would you say struggle the most with data management capabilities?

I see several areas that can benefit from using more data management capabilities. One is that part of post-trade that looks after asset servicing, eg handling corporate actions where there is enormous variety in the data, as well as tax information. Another one is the mid office and valuation where many sources need to be integrated for eg Prudent Valuation and more historical data combined with traded prices needs to be provided for FRTB.

 

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