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Data is the oil of the digital era. This is not only true for companies like Facebook or Netflix: there is a strategic imperative for financial institutions to be data-centric, especially in the context of Big Data and the Internet of Things. The volume, velocity, and variety of data processed in a contemporary organization is exploding, and the new wave of technology—which includes complex pieces such as data analytics systems that use artificial intelligence powered by machine learning—is growing rapidly. 

For banks, the name of the game is gathering data and creating wisdom out of it as a foundation for automated, operational, and strategic decision-making—while simultaneously creating a valuable customer experience (especially key considering the competition coming from specialized fintechs). Paramount to these goals is the quality of the underlying data, since bad data results in bad decisions and diminished functionality. But banks are still struggling to fully leverage on the opportunities that sophisticated data management generates, even though everyone is talking about it. Ultimately, when it comes to structural changes in the way data is managed, senior management still hesitates to prioritize this topic—for many, the budget currently allocated for tackling those challenges is nothing but a drop in the ocean. 

Why are banks still failing to properly adapt to these trends? Why can’t they fuel their organizations with the new oil called data? From experience, I can see several reasons:

Lacking a long-term holistic vision

First off, data management is rarely embedded as a key component of the digital strategy. But it should be! When these topics are tackled separately from each other, each one is crippled. Data and digitalization need to be part of a holistic business strategy at the organizational level, focusing on translating data into various internal business initiatives with clear, simple, common objectives.  Rather than this, however, you normally see data and digital initiatives launched in a fragmented way, from different business functions with different sponsors. Consequently, no common responses emerge to key questions like which approach to follow, infrastructure and technologies should be used to manage data. Plus, different sponsors usually have different priorities, meaning that these initiatives don't realize the organizational impact they intend and remain highly dependent on individuals. 

Under current norms, banks are usually held back when their priorities change (e.g. due to new regulatory requirements or decreasing margins), because data management projects are usually among the first to put on hold --as they are not embedded in the strategy properly and their added value is underestimated.

This approach also hugely reduces the risks that occur following inconsistent decisions around technology, scope, and methodology. 

Why is it so hard to create a holistic data management strategy? It's not the complexity of the topic that poses a challenge, but its execution. Organizations often end up maintaining legacy systems, as they have done over decades, continuing with their landscape of fragmented IT applications instead of an integrated IT infrastructure due to the complexity and costs associated with change. To replace all of these with new technologies is usually an expensive project lasting many months or even years, a task that looks especially daunting when you realize that the whole underlying infrastructure needs rethinking. Carrying it out is a big job but, from a long-term perspective, it's the only way to ensure operational, technological, and financial efficiencies.

Not thinking enough about transparency and traceability

A holistic data management scheme needs to enable great customer experience, digital service offering, business processes, and regulatory compliance. And it cannot do this without its data being transparent and traceable.

Most firms already know that there is no escape from having to manage data in a whole new way, if they want to survive the digital age. Therefore, sticking to the old methods of working is, at this point, just procrastination. A deep change of mindset is crucial.

Sonia Dribek Pfleger
CFO Financial Services Leader

Failing to find (all of the) new opportunities

New data management systems and approaches unleash new opportunities across every part of the organization --many of which are missed. Look for quick wins that emerge as the holistic strategy gets divided into smaller initiatives. Pay attention to any aspect that saves time and effort. See what can be easily implemented with the new capabilities. 

An important way to catch opportunities is by drawing on the knowledge of the entire firm. Create a culture centered on innovation and agility by accepting more proofs of concepts for new ideas and agreeing to pause and restart (rather than abandon) the implementation of new ideas. Employees will pick up on the willingness of the firm to take them seriously and be encouraged.

Wrongly estimating the ultimate prize

Investments in data management, especially for large banks or finance conglomerates, are complex and huge projects that consume money and time. When creating business cases, the return on investment (ROI) is often hard to quantify as the output is sometimes not concrete enough in the short-term and efficiency of operations as well as growth are well leveraged in the mid-long-term. This phenomenon increases especially if stakeholders are looking into short-term periods (one -two years) and if the cost -margin pressure continues to increase. Organizations tend to do the work as they are used to doing it, accepting a risk-taking strategy brought by operational inefficiencies and bad data. As long as that approach still works, management focuses on costs associated with investments and overlooks certain opportunities that data transformation can offer: massive cost reductions in the mid- and long-term, growth options resulting from enhanced customer experience and analytics, improvement in management's decision-making process.

Data and digitalization need to be part of a holistic business strategy at the organizational level, focusing on translating data into various internal business initiatives with clear, simple, common objectives.

Sonia Dribek Pfleger
Data and Digitalization Lead
Associate Partner

Mismanaging the skillset transition

If a bank is motivated to transform its data capabilities mainly by a reduction in employee costs, that is a bad sign. The potential of the project will surely not be met, because attention is on reducing rather than evolving.

Indeed, the foundation of every successful initiative is personnel. While automation does (and will increasingly) serve humans and facilitate their work, it won't replace them. It still takes skilled and motivated people to run the IT systems, breathe life into the processes, and understand the business.  Transforming data management will, however, have a massive and uncertain impact on employees, especially when it comes to the automation of data processing. Some employees' jobs will become obsolete, while others may face changes in their daily work. For those focusing on data cleaning, consolidation, and correction, it will most probably result in a shift towards more analytical activities. Organizations will have to prepare their employees for changes. It is important to motivate them to rise to the challenge, rather than despair over the risks: their experience is likely invaluable, and thus great benefits are usually to be had when this experience is brought to new or evolved roles. 

One of the best motivations is to involve your employees in the transformation process. In contributing to the success of project, they contribute also to their own success. Help them by investing in their skills so as to prepare them for their, and the company's, future needs.

Underestimating the scale of change

Most firms already know that there is no escape from having to manage data in a whole new way, if they want to survive the digital age. Therefore, sticking to the old methods of working is, at this point, just procrastination. A deep change of mindset is crucial. 

In the process of investing in your digital and data transformation, margins will narrow before value is created. Nevertheless, with a sound business model, this is where growth can ultimately be ensured. The Financial Stability Review (2018) showed that more profitable banks were those focusing on growth and spending more on operating costs. Banks focusing on cost-cutting did not perform as well.     

Ultimately, these pitfalls and challenges show that successfully implementing data analytics and data management requires a vision, scrupulous planning, and attention to detail. Human resources, business processes, and the IT landscape all need adapting in a harmonized way. They must work together in small initiatives towards a common objective. High-quality data will become the foundation for creative problem-solving and adequate decision-making, and in an unpredictable and dynamic future this will only become more important to ensure agility and remain relevant.