Banks and other financial institutions are now investing a lot of money in data or artificial intelligence-based solutions. But to harness the full potential of data, they must create a data-driven culture.
Data should not just play a role in the bank, but form the basis of decisions and options for action. In a world where data dominates, data culture is becoming a crucial success factor for companies of all types.
Data democratisation
Data culture can be defined as the way a company uses data to make decisions and achieve business outcomes. It’s not just about collecting and analysing data, it’s also about creating a fully data-driven culture. This means that data plays a role in every aspect of the business, from strategy development to capital investment and sales management.
A successful data culture requires not only strong data analysis skills, but also a change in business culture. In a data-driven company, all employees are aware of this and base all of their decisions on data. However, many companies still maintain a ‘gut feeling’ culture, where decisions are made based on experience and intuition. From this starting point, transformation can be challenging.
many companies still maintain a ‘gut feeling’ culture
When banks look to become more data-centric through the use of data and analytics technology, they face certain challenges. These aren’t primarily a lack of data or the necessary software, but issues with data-centric behaviours, beliefs about the use of data, and cross-functional collaboration.
An open data culture starts at the top
Banks need to create a culture of leadership at the management level that guides their data decisions. To do this, leaders must first understand the importance of data themselves and integrate data analysis into their decision-making.
Incentives for leaders who are successful at using data to solve problems and identify opportunities can help. The optimal incentives must be chosen very carefully to have the best effect.
In addition to management characteristics and incentive compatibility, a structural and process gap analysis should also be performed in relation to data products and their degree of maturity. Is there already a central organisation of data? The structure of this department should be based on the specific requirements of the business.
Within this framework, a data culture multiplier can be appointed, who is responsible for taking the lead on and implementing the data culture. The multiplier must be well connected in the company, have sufficient knowledge and experience in dealing with data, and be able to motivate and inspire others.
It is important to define visions and goals at each level of management. Without an explicitly formulated and communicated data strategy that is embedded in the company’s business strategy and which influences the company’s IT strategy, a data culture will not be able to make a long-term strategic contribution to the company’s success.
Invest in the necessary tools and resources
Employees must be trained for the different roles that come with the advent of a data culture. Investing in the tools and resources needed to promote data analytics skills across the enterprise is a key part of this.
Investment must also be made in the employees themselves, so that they understand the fundamentals of data analysis, and the tools and technologies necessary for the operational implementation of data products in all departments.
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While data and product owners are responsible for the data in their departments as decision-makers, the data strategy officer is responsible for the detailed design of the data organisation and implements the data strategy.
Data managers, who take care of the consistency of and connection between the different source systems, work a little more operatively. In turn, data stewards are responsible for individual data domains and act as a link across specialist departments and the central data organisation.
Business processes are the lifeblood of the data factory
Data is created from business processes. Clear sequences for collecting, analysing and using data are even more important. These processes must be centrally documented, accessible and understandable to all employees.
Another important element of a functioning data culture relates to the data itself. In the highly regulated financial services industry, companies must ensure they have high-quality, clean and reliable data relevant to decision-making. By forming a data quality strategy as part of data governance, companies can ensure that data quality is high and data security is assured.
The cornerstone of data culture
The elements described above are all necessary conditions of a data culture and form important pillars that really bring the culture to life. The culture can be reinforced through workshops, user profiling in the form of data organisation, as a kind of social data feed on the intranet, or on social media.
Interactive presentations, newsletters or virtual meetings can form the framework. These pillars should be combined with proven methods, use case catalogues, reviews, and internal knowledge management/transfer. These all flow into a detailed plan for introducing a data culture, which outlines the various implementation and deployment phases.
A data-driven culture is an important success factor for banks in transition. They must change their organisational culture and ensure that data plays a central role in their business decisions and processes.
This requires, first and foremost, an integrated data strategy, continued investment in training and education for all stakeholders, fostering an open and transparent data culture through corporate networks with a focus on business objectives, and a willingness to experiment and iterate.
Adopting the right strategy can help specify the resources and timelines needed to move towards a real data culture and to create a roadmap. Only if banks can form a data culture can they make data-driven decisions, gain a competitive advantage, and assert themselves against their peers.
Lenildo Morais has a Master’s in computer science, and is a teacher, researcher and project manager.
