Making meaningful MI: Technology is not a silver bullet
Too much information to manage
Organisations, individuals and computer systems are generating data at an unprecedented rate. IBM estimate that 2.5 quintillion bytes of data is created daily — so much that 90% of existing data has been created in the last two years alone. Mountains of data create an issue for banks: how can data be effectively managed, processed and presented in a way that positively influences the business and informs strategic decisions? This isn’t a new problem, but it is one that still presents a significant challenge to banks today.
A common response from banks has been to invest heavily in Big Data and analytics technologies. Whilst the ability to collect and store large amounts of data is greatly enabled by these technologies, many banks will still struggle to quickly present meaningful information to boards, employees, clients, investors, and regulators in a way that is quick and easy to interpret. Clear, concise, up-to-date data that can be easily digested is the Holy Grail for executives, and explains why many are also turning to sophisticated visualisation tools to provide insight, analysis, and a better understanding of key business drivers. The recent introduction of the Senior Manager Regime into Banking and Insurance has only increased the need for clarity and confidence in Management Information (MI) when discharging responsibilities.
Laying the foundations for successful MI
However, implementing new tools and technologies is only one step of the journey to better MI. New tools do not function in isolation; they are part of a larger operational picture. When embarking on a journey to create better MI, banks first need to plan for and prepare for the organisational change, always being mindful of what the outcomes and decisions they ultimately want their MI to support. Applying a few simple principles can help:
- Understand the business case for change: Work with relevant stakeholders to define a compelling vision and case for change which will mobilise appropriate sponsorship. Continually articulating a clear reason for change will help build long term buy-in, whether it is supporting the blueprint for independent MI project teams within a specific function, or a preference for a central MI change programme that cuts across functions to draw upon efficiencies and best practice. If a wide-scale change programme is required, it would be prudent to group target MI into logical categories (e.g. Products, Business Units, Regions, etc.), and deliver each category in phases to ensure that lessons learned during each phase can improve the delivery of subsequent phases.
- Define core metrics and key indicators aligned to strategy and targets: Regardless of the area of the business that is being impacted by the change, all metrics and indicators should align to the bank’s agreed design principles and broader organisational needs. A new MI requirement within the Finance function to support a regulatory requirement should be defined and developed in a way that allows it to be compared against other functional metrics and easily reconciled when rolled up to a higher level report.
- Develop an approach to data sourcing: Behind the charts and graphs sits a vast bulk of data that needs to be shaped and manipulated, often using complex IT programming across a number of business lines and data channels. Firstly, the change programme must identify all data sources needed to fulfil the MI requirements. Depending on the nature of the data and business priorities, these can be combined into a single Big Data “lake” or drawn upon from different databases. However, in all cases data quality rules and governance frameworks must exist to standardise reporting structures, align data capturing procedures, and streamline systems. This will ensure data can be rolled up into a singular format that can be processed by a front-end visualisation tool.
- Capture & analyse data, highlighting key trends, risks and opportunities: Innovative systems and tools will enable visualisation of key indicators. These should be incorporated into graphs, slides and reports that are structured to focus on the key elements of information that drive improved business performance and proactively highlight any early surfacing opportunities or threats. For example, an effective risk management framework will report on potential and emerging risks, in addition to crystallised risks and known losses. This is only possible when the business priorities are understood, the underlying data has been standardised and the reports have been structured effectively.
Don’t blame the tools
A myriad of tools can help banks harness data and present it in a meaningful way, but generating meaningful MI is not an outcome enabled by technology alone.
Equally, if not more, important is the careful management of change within the organisation, and ensuring that the right tools are deployed on top of reliable and relevant data, that is sourced organised and presented in a manner that aligns to business priorities.
It’s not easy, but getting this right is an extremely powerful mechanism for facing the challenges of modern day banking.