As economies and financial markets work to bounce back after almost two years of turbulence, many business leaders are considering how to position themselves for growth. This is where I see significant potential for DataOps – optimised data management from collection to making data ready for business uses – to drive value. For 2022, Gartner predicts that data fabric will emerge as a top strategic technology trend, able to reduce data management efforts by up to 70%. I would add that, as DataOps forms the basis for an effective data fabric, this area will experience accelerated growth over 2022.
Important lessons were learned during the pandemic, not least that banking and other financial institutions’ business models, created pre-COVID, were not suitable for weathering a major crisis. As noted by McKinsey, this could be due to the fact that most business models rely on historical data, “without access to high-frequency data that would enable recalibration”, as well as infrastructure that lacks the agility for effective risk management. In the current landscape of economic recovery, plus the need to navigate the effects of the UK leaving the EU, evolving regulations, intensified competition and more, it’s crucial for financial organisations to rethink their models and data strategies now to strengthen future resilience. Therefore, attention will turn to implementing DataOps practices to make themselves nimble enough to identify and react to sudden micro and macro issues, integrate robust risk assessment and mitigation, and capitalise on newly emerging market opportunities.
Further, the digital economy in which we live necessitates an elevated approach to engaging with consumers, who have become accustomed to instantaneous, always-on digital and omnichannel communication and personalisation of products and services on convenient platforms (as evidenced by the ongoing success of the Netflix and Amazon models). None of this will be possible without firms adopting innovation to harness the data they hold in real time for deeper analytics and predictions. Also, vitally, to tap into prescriptive analytics, which offer tangible suggestions based on the situation at any given moment. If visibility is hindered by incomplete, outdated or poor-quality data, the accuracy and power of this valuable tool is limited.
Financial companies are flooded with an overwhelming amount of internal and external data but data on its own doesn’t generate value. The key to long-term value creation will be through unlocking actionable data insights to inspire more informed strategic decisions and finally monetise the vast pools of information they generate.
How financial services companies are gaining value from cloud adoption
Establish the right architecture to benefit from DataOps
Despite the increasing importance of data insights, 55% of company leaders say they do not trust the accuracy of their data assets, with bad quality data affecting the reliability of their analytics (36%) and negatively impacting customer experience (32%). There are still inefficiencies in many traditional organisations, where much of data analysts’ time is spent on data wrangling – cleaning raw data and getting it ready for use – when their knowledge are better directed to extracting valuable insights to support businesses’ growth. Such manual efforts will start to be automated as part of an efficient DataOps framework. DataOps methodology is also central to being able to apply the power of AI and ML when processing data but the reality is that most traditional banking infrastructure is not prepared for dealing with big data in real time.
Cloud-native environments go hand-in hand with DataOps, affording traditional firms the same scalability and flexibility with their operations as the neobanks that are consistently winning customer share. Cloud technologies also help enterprises to overcome other typical roadblocks such as legacy systems and data siloes. This can form the start of a modernisation programme that is not monolithic but carried out iteratively to introduce innovation while lowering technical debt, without the hefty costs often associated with digital transformation projects. For instance, businesses of all sizes can choose from a host of ready-to-use ETL tools and cloud-based data management platforms available, to kick-start effective, automated data management organisation-wide almost immediately, and deliver insights where they are most needed.
This makes it easier to instil a culture of experimentation, rendering new products simpler to test, and resulting in cost and productivity savings that can be re-directed towards the next phase of modernisation. By focusing on improving and streamlining formerly unwieldly data architecture, companies can recover and re-use up to 35% of their current data spend. The cloud facilitates continuous integration and innovation – growth drivers in the digital age. Look at Fintech Clearbank, for example, rated the UK’s fastest growing tech company (recording over 21,500% growth in just a few years) through its scalable, reliable platform that adheres to cross-border data protection regulation, designed for immediate recovery in case of outages. This model relies on total agility, with real-time monitoring, interaction, and delivery.
Another challenge is that competition for financial companies is now coming not just from Fintechs, but technology, e-commerce, and more. So it’s no surprise that, across Europe, the pandemic gave rise to greater use by banks of digital platforms to market their products and attract customers. This will continue, as accelerating advances in data analytics will open the door for these institutions to capture the same level of innovation and create new digital user journeys to bolster customer satisfaction, leading to better overall financial performance.
Though still at a relatively nascent phase, I predict that the field of DataOps will continue its considerable upwards momentum over the next year and beyond. Awareness is growing, and research shows that the top two priorities for finance leaders across business sectors are advanced data analytics tools and automation technologies as a way to drive cost efficiencies, speed and market competitiveness. In the financial services industry, this has additional urgency due to the credit losses and squeezed profits brought about by the pandemic, as well as low interest rate environment. DataOps will play a transformative role due to its increasing applicability to solving business problems. It can leave financial firms well-positioned to take advantage of continuous insights – critical to be able to grow in today’s ever-moving, changeable market.
