by Martijn Groot, VP Marketing and Strategy, Alveo
Financial services firms are collecting ever-greater volumes and an ever greater diversity of data. Concurrently, they generate more data too as a by-product of business activities, not least driven by regulatory demands for increased pre- and post-trade transparency. Safe to say that most if not all processes in the financial services industry have gotten increasingly data intensive. But to capitalise on the insights this data brings, they must home in on the most relevant information, filter it, integrate it, curate it and embed it into their decision making to use it to deliver competitive edge.
There are multiple ways and techniques that can be used in setting up the right process to do this. Firms can use methods such as Natural Language Processing to directly extract content from text-based data. Working with shopping lists of instrument or entity identifiers or keywords to analyse textual data can help them focus on extracting the required content. The curation or quality-control of data then requires the integration of multiple data sets from different sources to attain a composite picture.
In this context, a conservative approach to data acquisition is no longer viable. Historically, drawn-out data preparation processes were typically driven by monthly or quarterly reporting cycles, leading to insights that were inaccurate, dated or both. Processing data over a long period and relying on poor-quality data to drive business decisions will be insufficient to enable firms to keep pace with nimbler fintechs and challenger banks.
To properly steer data management requirements, firms must first decide its objectives. This can focus on regular supply of data sets to streamline BAU operations, improve data quality, setting SLAs for turnaround time on data deliveries or onboarding new financial instruments or entire datasets or, more generically, on enabling data scientists to deliver self-service data collection and analysis. But there must be a defined business goal to work towards.
Firms need to leverage data scientists to gather the right data and ‘ask the right questions’. What constitutes the right data will depend on the clients, markets and geographies the firm works with and can lead to lists of interest specifying what needs collecting. Linked to that are metadata requirements, e.g. SLAs that specify service windows, turnaround times and quality metrics. The cycle time required for data preparation and curation is continually shrinking thanks to the advanced technologies now in place to harvest data, combine data sets and derive live insights. The questions that need answering and the use cases in scope will steer data collection and curation processes.
Today, a skilled data analyst can do all this and translate data into the big picture view the C-Suite needs to base decisions on. Here we look at what’s making this possible and the benefits it brings.
Catering to changing business needs
Recent years have seen significant changes in the data management and analytics processes employed by financial services firms and together these changes are helping empower analysts, quants and data scientists.
Historically the two disciplines have been separate. The data management process involves activities such as data sourcing, cross-referencing and ironing out discrepancies. Data analytics is typically carried out afterwards, close to the users and on separately-stored subsets of data. This divide has created problems for financial institutions, with the separation impacting time to insight and holding back decision-making.
Today that’s changing. The availability of vastly more data, the benefit from using more data analysis to distil insights and the emergence of stronger data management tooling is helping firms transition to a more integrated approach to data management and analytics.
Any data used to drive decision making also needs to be of the highest quality. Otherwise, the analytics may not work and the intelligence derived may not be accurate.
All the above explains how analytics has been empowered within financial services organisations. But how do organisations get that analytics quickly to decision-makers and ensure they can use it to drive business strategy?
From on-prem, to managed services, to DaaS
As the data management function expands and extends into analytics, it is positioned to empower staff working in different functions through providing them self-service capabilities and easy access to data to drive better informed decision-making. On the BAU data operations side, the availability of managed services has caused a shift from implementing solutions on-prem to sourcing services. This allows firms to source new services based on SLAs and metrics such as uptime, turnaround time and performance rather than implementing bespoke requirements.
Suppliers of data management solutions have shifted their service model from software to managed services. Increasingly this is now evolving further into a Data-as-a-Service (“DaaS”) model where suppliers not only host and run the data management infrastructure, but also verify data and perform root-cause analysis to fix data quality issues. A client can view complete data sets; have dashboards into the data preparation processes but can also get different selections of data formatted in different ways for last-mile integration with business applications.
Onboarding DaaS models for pricing data, reference data or corporate actions allows firms to hit the ground running, frees up operations staff and can lead to an uptick in productivity. DaaS can cover any data set but includes processing a range of third-party data sources in pricing and reference data, to curves and benchmark data, ESG and alternative data and corporate actions. Offering a firm cleansed, fully-prepared data will facilitate any consuming business process including risk management and compliance.
Quants and data analysts can then take these prepared data sets and use them to attain the key metrics that then play into senior decision-making processes. Data scientists are looking at historical data across asset classes looking to distil information down into factors including ESG criteria to operationalise it into their investment decision-making process. Increasingly too, they are incorporating innovative data science solutions, including AI and machine learning, into market analysis and investment processes.
The new methodology enables the faster creation of proprietary analytics to support activities including investment decisions, valuations, stress-tests, performance analysis and risk management. By disseminating such information to C-Suite decision-makers and providing them with the necessary context and detail, data scientists can help drive business strategy. Self-service capabilities to request new sources or review the lists of interest make for a much shorter change cycle in data supply.
For many firms though, it will be Data-as-a-Service that will act as the ultimate catalyst for success. It can deliver that will act as the foundation for both operations and analytics across the business. Combined with quality metrics on the different data sets and sources, it can lead to ongoing improvement in data operation effectiveness. Perhaps, most important of all, it will shorten the change cycle and increase the quality of data provisioning to all business functions.
