# Use Case centric data management – Why it is the future of data management for financial institutions
Author:  Pal Sinha, Barnali 
Author URL: https://financedigest.com/author/pal-sinha-barnali
Published: 2020-10-30
Category: FINANCE
Category URL: https://financedigest.com/category/finance
Meta Title: Revolutionising Data Management in Financial Institutions
Meta Description: Learn about the struggles financial institutions face with data management and how trends in the industry are changing to enable easier access and better use
URL: https://financedigest.com/use-case-centric-data-management-why-it-is-the-future-of-data-management-for-financial-institutionshtml

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_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to the classic data management challenge they face, namely that they have data stored in many different locations using outdated technology.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](https://www.financedigest.com/9-issues-in-corporate-finance-and-strategies-to-overcome-them.html "9 Issues in Corporate Finance and Strategies to Overcome Them").

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](https://www.financedigest.com/how-the-global-insurance-market-will-keep-pace-with-evolving-risks-through-data-analytics-and-technology.html "How the global insurance market will keep pace with evolving risks, through data, analytics and technology") 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](https://www.financedigest.com/data-centres-and-the-changing-financial-trading-landscape.html "Data centres and the changing financial trading landscape"), 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](https://www.financedigest.com/four-ways-data-and-ai-can-transform-financial-services.html "Four ways data and AI can transform Financial Services"), 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](https://www.financedigest.com/global-share-markets-rise-bonds-fall-on-u-s-jobs-data.html "Global share markets rise, bonds fall on U.S. jobs 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](https://www.financedigest.com/airline-body-iata-sees-industry-recovery-now-in-2023.html "Airline body IATA sees industry recovery now in 2023"), 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](https://www.financedigest.com/esg-investing-what-is-the-regulatory-position-in-2022.html "ESG investing – what is the regulatory position in 2022?") reporting information.

**The convergence of data and analytics**

Historically, [data management and analytics have been separate within financial](https://www.financedigest.com/big-data-analytics-fraud-prevention-in-the-financial-sector.html "BIG DATA, ANALYTICS & FRAUD PREVENTION IN THE FINANCIAL SECTOR") firms. The [data](https://www.financedigest.com/optable-announces-new-data-clean-room-capabilities.html "Optable Announces New Data Clean Room Capabilities") management process typically involves activities such as data sourcing, cross-referencing and ironing out any discrepancies via reconciliations and data cleansing processes. [Data analytics](https://www.financedigest.com/top-5-data-analytics-trends-for-2023.html "Top 5 Data Analytics Trends for 2023") 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](https://www.financedigest.com/gary-mcgaghey-explains-how-cfos-can-strengthen-decision-making-processes-and-reimagine-the-finance-model.html "Gary McGaghey Explains How CFOs Can Strengthen Decision-Making Processes and Reimagine the Finance Model") 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](https://www.financedigest.com/sodium-tetraborate-market-key-players-end-user-demand-and-consumption-by-2030.html "Sodium Tetraborate Market Key Players, End User, Demand and Consumption by 2030") data.

As the cycle of managing and processing data extends to take in analytics, users within financial services organisations increasingly want to be [empowered](https://www.financedigest.com/the-digital-finance-revolution-empowered-consumers-turn-digital-to-protect-financial-security.html "The digital finance revolution: empowered consumers turn digital to protect financial security") 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](https://www.financedigest.com/outside-door-handle-market-analysis-trends-top-manufacturers-share-growth-statistics-opportunities-and-forecast-to-2027.html "Outside Door Handle Market Analysis, Trends, Top Manufacturers, Share, Growth, Statistics, Opportunities and Forecast to 2027") and investment processes.

![Martijn Groot](https://prod.superblogcdn.com/site_cuid_cm5qst7v3003gwirgwqtxn8i8/images/martijngroot-450x488-1736838755073-compressed.jpg)

Martijn Groot

By adopting this a use case centric approach, users gain access to [multiple](https://www.financedigest.com/how-to-navigate-multiple-data-privacy-regulatory-frameworks.html "How to navigate multiple data privacy regulatory frameworks") 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](https://www.financedigest.com/exclusive-tiktok-nears-deal-with-oracle-to-store-its-data-sources.html "Exclusive-TikTok nears deal with Oracle to store its data-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](https://www.financedigest.com/early-detection-of-mismatched-trades-is-key-to-managing-risk-and-maximizing-profits-on-the-pl-desk.html "EARLY DETECTION OF MISMATCHED TRADES IS KEY TO MANAGING RISK AND MAXIMIZING PROFITS ON THE P&L DESK") 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](https://www.financedigest.com/sterling-little-changed-after-mixed-labour-market-data.html "Sterling little changed after mixed labour market data"). 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](https://www.financedigest.com/true-operational-resilience-must-include-investing-in-esg.html "True operational resilience must include investing in ESG") decision-making process.

The new [approach to user enablement and merging analytics and data](https://www.financedigest.com/how-finance-firms-can-unify-two-data-approaches-to-improve-both-compliance-and-security.html "How finance firms can unify two data approaches to improve both compliance and security") 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](https://www.financedigest.com/cbd-gummies-market-current-scenario-and-industry-growth-forecast-with-major-key-players-data-2030.html "CBD Gummies Market| Current Scenario and Industry Growth Forecast with Major Key Players data 2030"), can use to drive business decisions.

This in itself [drives a more agile operation but the combination of data](https://www.financedigest.com/how-alternative-data-drives-e-commerce-success.html "How Alternative Data Drives E-commerce Success") 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](https://www.financedigest.com/covid-19-is-impacting-the-variable-speed-drive-market-size-business-revenue-forecast-leading-competitors-and-growth-trends-2028.html "COVID-19 Is Impacting The Variable Speed Drive Market | Size, Business Revenue Forecast, Leading Competitors And Growth Trends 2028") decisions.

This in itself drives business agility but the combination of data and analytics can also help [businesses optimise costs](https://www.financedigest.com/esg-prepared-why-ignoring-esg-is-a-costly-business.html "ESG Prepared: Why Ignoring ESG is a Costly Business"). It does this by supporting greater agility with the data, selecting only those elements strictly [needed to help drive the business](https://www.financedigest.com/3-reasons-your-business-needs-customer-reviews.html "3 reasons your business needs customer reviews") 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](https://www.financedigest.com/how-application-management-can-help-tackle-the-finance-industrys-carbon-emissions.html "How application management can help tackle the finance industry’s carbon emissions"). Today, technology, process, macro-economic [factors and business](https://www.financedigest.com/lane-change-assist-systems-market-overview-business-growth-development-factors-application-and-future-prospects.html "Lane Change Assist Systems Market Overview – Business Growth, Development Factors, Application And Future Prospects") awareness are all joining forces to bring analytics and data together. The result for [financial institutions](https://www.financedigest.com/how-can-financial-institutions-make-the-most-of-data-for-their-business.html "HOW CAN FINANCIAL INSTITUTIONS MAKE THE MOST OF DATA FOR THEIR BUSINESS?  ") 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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