Corporates and their treasury teams understand that data is a critical element for any operation. In today’s economically perilous times, more than ever companies must leverage it efficiently in making informed decisions to achieve transformational growth or to effectively track cash. One industry that has re-awakened to the fact that data is a strategic asset and a vital component of core company strategy is financial services, where it is better used to predict customer sentiment and to aid efficiency.
Financial service organisations depend on client and transaction data – as well as unstructured data, such as emails, customer service calls and social media interactions – to understand customer behaviour, prevent and minimise customer attrition and maximise retention and loyalty. They are also increasingly using this data to assess internal and external risk, develop marketing programmes and prevent fraud. The risk data could also be beneficial to treasurers, if shared.
With such a sizeable amount of data to collate and analyse, doing so efficiently is a tall order for even the leanest of companies, let alone universal financial institutions (FIs) that not only span services such as retail, investment and corporate banking, but also various regional and local markets.
Whatever strategic obstacles a FI needs to overcome, the fundamental challenge remains the same; these institutions are exposed to the ‘3V’s of data: volume, variety and velocity. To combat these and implement effective data management strategy, financial services companies need to introduce a fourth ‘V’: value.
Quality Rather than Quantity
Not all data is necessarily business-critical; indeed some of it will be of limited value. Identifying and channeling the targeted valuable data from the mass of available data is the key challenge for treasurers and others. In order for big data analytics to truly work, companies need to focus their analysis on high quality data. However, to make it even more challenging, the value of data can go awry if it is not captured accurately or not continuously updated or monitored. Getting this right is imperative for any organisation, but is especially important for financial professionals as it can have a huge bearing on companies’ ability to comply with regulatory requirements. For instance, wrong data can lead to the incorrect calculation of minimum capital requirements for a bank, which can have disastrous consequences and lead to a funding crunch that adversely impacts treasuries. Treasurers could get their risk data all wrong if the incorrect parameters are used.
Garbage in, garbage out (GIGO) is the age-old truism that sums up data quality succinctly. The common reasons for poor data quality include duplicate records, incompatible data sources and change of data sources. These can impact every business function, from customer relationship management to risk management. To unlock the valuable data, FIs require a data quality strategy that revolves around profiling, auditing and cleansing the data. This data quality process framework must also ensure that data is easily available, correct, consistent, comprehensible, dependable and auditable.
Data Governance is Key
It’s equally important that the data management strategies and frameworks focus on governance. Unless the corporate has dedicated data governance roles which span across multiple functions, including business, operations and IT, it will always face challenges such as increased time to market, revenue loss or inefficient operations. To combat these risks, many companies are now driving shared data services by creating data utilities to cater to all areas of their organisation.
Among the potential challenges in data governance is deciding who should have the ownership or stewardship of data in an enterprise and creating a targeted valuable data strategy. With the focus on data at its peak and valuable data becoming increasingly critical, the data governance role should be given to a C-level executive, such as a chief data officer (CDO) or chief information officer (CIO), who can create and maintain a ‘data focus’ within the organisation, which aligns data to business goals. Obviously only large firms can afford to do this.
A Common Approach
The true success of any organisation is in its ability to link business opportunities with its core data assets, supported by underlying technology solutions that liberate hidden and stored data elements, enabling true value creation. Data-savvy firms are looking at strategies that utilise valuable data to create significant revenue streams or business value; be it cost reductions, revenue multiplication, sweating cash or regulatory adherence.
For any business opportunity, the CDO should define a data strategy and the CIO should create a technology strategy that is in sync with this data strategy. As depicted in the figure below, the CDO creates a data strategy, prioritising customer experience, agile execution, analysis and data transparency. It is imperative for the CIO to create a technology strategy to match the data strategy. If these two strategies work together, then it’s easier to achieve the business goal.
Figure 1: Matching Technology Strategy and Data Strategy.
For instance, to achieve the business goal of enhanced customer experience, the CDO needs to create a data strategy to analyse valuable customer feedback data in real time. To ensure the success of this business goal, the CIO should ensure the technology is in place to support this.
The types and volumes of data that firms must contend with on a daily basis will continue to increase exponentially. Just having access to a mass of data alone is no longer enough. Without understanding the quality and value of its available data, finance professionals are highly exposed to risks. It’s therefore in any firm’s interest to establish a robust, well thought-out and long-term data strategy, with value, quality and governance at its core.
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