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Use Case centric data management – Why it is the future of data management for financial institutions

Use Case centric data management – Why it is the future of data management for financial institutions 35

By Martijn Groot, VP Strategy, Asset Control

Financial institutions today still continue to struggle to make effective use of the data they have at their disposal and use it to power business decision-making.  Part of this relates to the classic data management challenge they face, namely that they have data stored in many different locations using outdated technology.

As a result, quants and data scientists are facing logistical issues in accessing the data they need for their decision-making.  Often, these analysts find they have to contact the IT department to write them a query, set up a report, or they might confront a corporate licensing restriction or a permission issue.

Even when quants access the data they need, there are often additional issues to address. Invariably they find that the metadata that should give them an indication of freshness, where it came from, what the license permissions are and who has approved its use, has not been tracked. As a result, they may conclude that they do not have enough context to decide whether data is fit for purpose. Furthermore, because data and analytics typically remain decoupled within many organisations, quants will need to run two separate processes to get hold of usable data. Apart from that, the scope, breadth and depth of data often changes, leading to repeat request to keep the data up to date and as comprehensive as possible. Different selections of data may need to be presented in different ways, depending on the use case.

If quants want to run a financial model, they will typically look to access the data relevant for their use case, store it in their own database and then run analytics on it.  They will not be able to push their own model to a central processing framework that runs as a shared store of market data. These are limiting factors on user enablement within financial organisations and stimulate redundant copies of the data – with all the overhead and operational risks that stem from that.

Fortunately, we are now seeing trends in the industry, which are changing this dynamic and enabling users to access data more easily and to get more from it when they do. The business domain data models too need to keep up and reflect the latest coverage in investment decision criteria such as ESG aspects as well as regulatory reporting information.

The convergence of data and analytics

Historically, data management and analytics have been separate within financial firms. The data management process typically involves activities such as data sourcing, cross-referencing and ironing out any discrepancies via reconciliations and data cleansing processes. Data analytics procedures are typically carried out afterwards in a variety of desk-level tools and libraries, close to the users and typically on separately-stored subsets of data.

That separation has created problems for many firms, acting as a brake on the decision-making processes that drive business success.  Today, many firms understand they need a better way to provision their data scientists and other key users with clean price and market data.

As the cycle of managing and processing data extends to take in analytics, users within financial services organisations increasingly want to be empowered by the process and use these new capabilities to drive better informed decision-making. This move to data-as-a-service (“DaaS”), when combined with the latest analytics capabilities, is making this happen for financial organisations today.

Beyond the data scientist

Using the proper tools, data scientists and quants can incorporate innovative data science solutions into market analysis and investment processes.

Martijn Groot

Martijn Groot

By adopting this a use case centric approach, users gain access to multiple data sources and data types, from pricing and reference data to curves and ESG data. They can visualise, format and cross-compare data across these disparate sources. With the help of open source database technology like Apache Cassandara, processing platforms like Apache Spark and languages like R and Python, users can more easily share these analytics across their entire data supply chain and  develop a common approach to risk management; performance management and compliance.

We are seeing many data analysts today that are looking to dig into the data to find signals that help them discover sustainable returns in the market. All these data scientists are looking at historical data across asset classes looking to distill information down into factors including ESG criteria to operationalise it into their investment decision-making process.

The new approach to user enablement and merging analytics and data management is also helping to democratise analytics, bringing it into the orbit of those who are not data or quantitative experts. Today, thanks to the contextualisation provided alongside analytics, it is not just the preserve of the quant or the data scientist, but a key tool that those less expert in data, can use to drive business decisions.

This in itself drives a more agile operation but the combination of data and analytics can also help businesses reduce costs. It does this by preventing redundant buying of the data and more closely tracking data usage, bringing clarity to what data is used and what data isn’t. This is, however, also about centralising data more efficiently and removing data duplication into the bargain.

Broader benefits

Quants and data scientists benefit from all of this. But this approach to user enablement is also helping to democratise analytics, bringing it into the orbit of those who are not data experts. Today, thanks to the contextualisation provided alongside analytics, it is not just the preserve of the quant or the data scientist, but a key tool that those less expert in data, can use to drive business decisions.

This in itself drives business agility but the combination of data and analytics can also help businesses optimise costs. It does this by supporting greater agility with the data, selecting only those elements strictly needed to help drive the business forward. This is, however, also about centralising data more efficiently and removing data duplication into the bargain.

Looking ahead, we are on the cusp of a new age in financial data management. Today, technology, process, macro-economic factors and business awareness are all joining forces to bring analytics and data together. The result for financial institutions is a new world of opportunity where they optimise costs, drive user enablement and maximise the value they get from data.

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