Welcome to Data Management Insight 2022 and some of the hot topics we will be exploring as capital markets participants continue to mainstream data as a resource to improve business value, extend digitalisation, drive up efficiencies, foster innovation, and last but not least, improve regulatory compliance processes.
It will not all be plain sailing, however, with ongoing problems of legacy systems, poor quality data, and fractured company culture littering the path to potentially game-changing data sourcing and management processes that can not only help firms extract value from data, but also gain ever greater business insight.
While compliance necessarily hovers near the top of the agenda at most financial institutions, let’s set it aside for a moment, and discuss the data management outlook for 2022, which looks bright, with some cloud, and scattered showers.
The past two years of uncertainty caused by the coronavirus pandemic have been difficult, but also enlightening for data management teams that have put vast amounts of effort into operational resilience across constantly changing hybrid work environments, and while doing so uncovered the shortcomings of existing systems, new approaches to data management, the need for data standards, and the potential of emerging technologies. As a by-product, they have also accelerated data management development.
These factors indicate a bright outlook for data management in 2022, with firms using their cache of new-found knowledge and experience to turn theory into practice and answer questions such as who owns data, who is responsible for data quality, and how data management solutions can provide easy access to data that is of required quality, accuracy and timeliness.
Data strategy
As a catalyst for change, the pandemic highlighted the need to enable everyone in the business to access data, although this can be difficult to police, along with data created and used outside the data strategy. It also reinforced the need to embed data as a strategic asset – while ensuring it is not a strategic liability, and use the data as part of an offensive approach to support business growth and move towards monetisation.
While the theories are sound, the reality is challenging, with financial institutions still beset by legacy systems, fractured operations, cultural reluctance to change, and a lack of senior management buy-in to data management projects beyond the regulatory imperative.
As we move into a new year, these challenges can best be addressed by business-wide education, building trust in data, ensuring data ownership, rethinking organisational design, making best use of data scientists, and encouraging cultural commitment to data strategy. For many firms, these issues will continue to be the hardest to resolve. They will also be the subject of discussion at A-Team Group’s 2022 Data Management Summits in London and New York City.
Data management modernisation
The modernisation of data management platforms is underway at some firms, but will speed up through 2022 as data maturity increases and techniques and tools such as data lineage, data governance, knowledge graphs, abstraction layers, and metadata management make it easier to integrate legacy and new data environments on the route to more complete modernisation.
Public, private and hybrid cloud solutions also play well into modernisation, with the caveat that wherever data resides it still needs ownership, and the advantages of self-service data acquisition and analytics, and the potential for more data sharing. Bringing data operations together into unified DataOps is also key to data-driven transformation and modernisation.
The old adage garbage in, garbage out (GIGO) remains a problem, but more will be done through 2022 as firms adopt proactive approaches to data quality with a view to getting to the root cause of data problems faster, and even solve quality issues at source.
Replacing ‘yes/no’ measures of data quality, solutions vendors and financial firms will continue to develop business glossaries and data dictionaries, data matching techniques, scorecards for data attributes, and peer-to-peer data quality reviews.
Emerging approaches
With data warehouses, hubs and lakes already a feature of the data management environment across capital markets, newcomers include data fabric and data mesh. Their potential has yet to be proven and the jury is out on whether they will be useful technologies, but again, the theory has potential.
The terms are often used interchangeably to describe data access architecture in a hyper-connected data management world. More precisely, data fabric is more of an architectural approach to data access, and data mesh attempts to connect data processes and users. It is based on modern, distributed architecture and is designed to make data more accessible, available, discoverable, secure, and interoperable, the argument being that faster access to query data translates into faster time to value without needing to move data – have we not been in this movie before?
Whatever the answer, these and other emerging and maturing technologies being implemented to drive insight and business value out of data, such as AI, machine learning, and natural language processing; and tools designed to manage unstructured, alternative, ESG, and corporate actions data, are all on A-Team Group’s 2022 Data Management Insight agenda – we look forward to discussing them with you during our forthcoming webinars and summit events.
Subscribe to our newsletter
