The financial services industry is highly dependent on quality data for day to day operations as well as driving growth. This industry plays a pivotal role in fulfilling financial needs of individuals, organisations and governments. Financial institutions hold a wide range of valuable data about their customers which is also shared indirectly (through regulators) as Data-as-a-Service to Reserve Banks and government agencies such as the Treasury and Taxation for critical data-driven decisions on the countries’ economy and finance. The industry has seen a significant increase in investment on data collection, data quality improvement, reporting and analytics, to meet regulatory expectations and adapt to changing customer and business needs.
Yet, compared to other data-rich industries such as Streaming Services, Social Media Platforms or eCommerce (e.g. NetFlix, Facebook or Amazon); financial institutions have been lagging in digitisation and maximising the value of its data for developing customer-centric offerings and driving enterprise growth. Broadly, there are 3 key factors that is slowing the pace of change in the financial services industry:
Demand chaos – Problem or Opportunity?
Financial institutions are challenged from multiple directions to introduce change, driven by either regulations, competition, changing customer expectations, need for innovation using new technology.
Below are some of the major drivers for change.
Change driven by technology innovation
Rapid increase in digital disruption, in the past decade, with wearables, virtual assistants, sensor devices, etc., has fundamentally changed customer interactions and expectations. Organisations are competing to provide similar experience to retain their customer base.
Use of Big Data
There is also a growing need to leverage Big data (both external and internal / structured and unstructured) and incorporate them in the decision making process as well as improving the customer experience.
In recent years, financial institutions have also seen a significant increase in regulatory requirements for reporting data. There has been a lot more focus off late by the regulators on data quality and risk management, due to increasing economic volatility, security threats, financial crimes and customer privacy breaches. Regulators are wanting more data within much shorter delivery window. New regulations such as Open Banking require different dynamics to deliver data on customer demand.
The below picture provides a high-level overview of the various regulatory requirements on financial institutions and how data risk management is tied to Board and Senior leaders’ accountabilities. It also illustrates commonality in data requirements (directly or indirectly) across the various regulations.
*GDPR – General Data protection Regulation for Personal Data
** CDR – Consumer Data Right (links to Open Banking)
+ Privacy of Customer data is covered under Notifiable Data Breach governed by Office of the Australian Information Commissioner (OAIC)
++ COVID-19 reports also require Risk / Capital Adequacy data
In the midst of chaos, there is also opportunity – Sun Tzu
It is important to recognise the data needed for these regulations are also the ones critical for customer experience, revenue generation, product development, operational efficiency uplift and overall enterprise growth.
The need for fundamental transformation is inevitable
Financial institutions are starting to understand a transformational change is inevitable as the current legacy technology platforms, tools and data capabilities are inadequate, and will not scale to meet the various customer, business and regulatory demands. Many organisations have already launched cloud migration initiatives of their technology stack. However, the biggest challenge faced by these organisations is the need to take a more holistic approach to delivery across multiple initiatives that are in flight currently.
In addition, it is important for organisations to understand migrating to Cloud is not a matter of “lift and shift” from legacy. The real complexity and challenges pertain to Data Architecture and Data Management aspects that involve
integrating data from legacy platforms with the broader Big Data that needs to be captured
transforming and enriching the data consistently into meaningful information with right business and time horizon to distribute “in the moment”
extracting valuable and timely insights for consumption
ensuring the data risks and quality are appropriately controlled, managed and governed across the end to end data lifecycle.
Embedding the right Data architecture, Data Management and governance capabilities is critical to avoid facing the challenge of data quality issues growing at the ratio of the Big Data volume, at real-time speed, which is a lot more expensive and riskier than current state!
A Paradigm shift in approach is a must for fast tracking transformation
Siloed projects with duplicated efforts and inconsistent delivery in the past decades, are some of the root causes for organisations’ current data challenges, increasing cost and risks, and what is significantly hindering their transformation agenda, despite ongoing investments.
Continuing with existing divisional business model for project investments and delivery, despite the commonality in Data, Insights and Technology needs across the business and regulatory initiatives, will lead to a number of long-term issues such as
More point to point redundant solutions (often tactical due to cost constraints) adding further complexity to the organisation’s data and technology landscape
Significantly increase overall cost & impact return on investment due to duplication of effort across projects, staff productivity and throughput (due to continuous context switching between initiatives) also leading to delay in realising the transformational benefits.
We cannot solve our problems with the same thinking we used when we created them – Albert Einstein
To increase the speed of Data and Technology transformation and generate material and sustainable business value, organisations need to have laser sharp focus on top agendas and enterprise wide discipline in investment and delivery execution.
As there are several commonalities in data and technology capability needs across customer, business and regulatory initiatives they can be converged and consolidated for delivery. Taking a holistic enterprise perspective with stronger business-technology partnership, opens the opportunity to optimise investment, transform the overall Technology and Data landscape, and delivering consistent data, insights and technology capabilities across the organisation for much greater value.
Senior executives need to set the tone from the top, to break the divisional silos and the individual KPI-driven culture of the organisation, to get to a “one company” and “winning together” mindset. Project investment and delivery model should be fundamentally changed to get maximum value from the limited funds and resource capacity, and fast track transformation.
Here are some of the key considerations for decision makers while reshaping their transformation delivery approach:
Data and technology transformation initiatives need to be positioned as a vehicle to deliver the prioritised business outcomes – not an independent project, driven primarily by the Technology division.
Top enterprise priorities should be set to a narrowed set of focus areas, and divisional KPIs need to be inter-linked to wider enterprise priorities to motivate cross team collaboration.
Synergies in Data and Technology capability needs should be identified upfront across the prioritised objectives during project portfolio optimisation.
Delivery sequence should be defined factoring the urgency of the initiatives in line with the enterprise strategy but also consider the readiness of new Technology and Data capabilities – unless absolutely critical, business leaders should commit to pausing the initiative to give time to build out the foundational capabilities of the Technology/Data landscape in line with the strategic target state.
Have the discipline to execute the delivery as per agreed sequence, with executive level support to manage priority conflicts caused by any unplanned or new demand.
Progressively measure success of the transformation delivery against the success of the planned business and regulatory outcomes. .