Gartner predicts that a whopping 80% of traditional banks will go out of business by 2030. The financial services industry is at the cusp of digital transformation.

It faces rising customer expectations, fierce competition from disruptive fintech start-ups, increasing regulations, and relevancy challenges in a world that is getting fully multi channel, cloud native, and analytically smart. 

Read: By 2022, Fintech will account for 8% of ME financial services revenue

Regulators want to ensure the safety of customers wealth resulting in stringent regulations which force banks to focus data governance. Banks stand in dire need to leverage vast volumes of readily available data to improve business performance and efficiency. To do so, banking institutions need to rapidly evolve how they access, analyze, and manage their most valuable asset, their data.

What stops banks in leveraging this seeming unlimited supply of data?

To successfully transform and create value for the customer, banking institutions need to leverage data as their lifeblood, flowing across the enterprise, enriching each touch-points with customers and decision points within the enterprise.

A third of financial services CIOs identified going digital as their top priority for 2019, up by 8% from last year. Leaders face tremendous pressure in this competitive age to harness data at the right time, in right quantity, and make right insights to facilitate successful decision-making.

While they don’t hesitate in making adequate investments, something hinders them. Only 12% of financial services organizations are mature in their digital transformations and fall into the transformation cluster, according to the same report.

Here are a few data challenges banking organizations struggle with:

• Achieving true data-centricity – When customer-centricity is making rounds in nearly all industries, it is essentially data-centricity we are talking about. Banks have been operating traditionally for so long that the systems are used more for recording the transactions and not as providing the rights insights for best value for the customer. 
• Extracting insights – Data exists in silos. Banks have been applying technologies on top of legacy systems, which has led to a complex mesh of silos that hold critical data from various standpoints. To boost decision-making with insight, banks now need to create a single source of truth.
• Data security – Sure, concepts such as the Internet of Things sound all too exciting. But, they give security experts, and data managers chills down their spine. Banks hold sensitive customer information and run the risk of compromising this data along with their trust and reputation. Security is one of the reasons why banks have been holding back data and analytics initiatives.
• Unstructured data – Storing new and valuable data is no more a challenge for banking organizations. However, making use of this data to its full potential remains uncharted territory. The data is unstructured or not captured within the firm, which makes leveraging it a hassle.
• Tools: In most banks the tools for data driven decisions are very complex with steep learning curves
• Democratization of Data: In the name of security, the internal controls have focussed on giving minimum data access to decision makers. Most often, effort overcoming internal friction of data access negates the benefits.

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Accelerate Business Growth Through Data Fabric

Your tech-savvy customers are creating digital footprints across channels. Banking institutions can tap into this data to learn more about their customers, market trends, and use insight to predict outcomes and strategize action steps.

A data fabric is necessary because, before big data, data was stored across different locations, but a majority of it was on-premise. Now, it has evolved more to the cloud and is also spread across platforms such as Hadoop. As data continues to get bifurcated, each of these sources adds their typical challenges to analyzing and harnessing this data.

Attempt to define the data fabric is not succeeded. A data fabric ensures that timely availability of accurate data at the appropriate decision points along with right tools

Here’s what you stand to gain with a reliable data fabric:

• Risk Management – Before taking up data initiatives, learn how and where they could disrupt your business. Gauge the effectiveness of data analytics campaigns by generating blueprints, pivoting, and rebooting. Prioritize areas where data and analytics can lead to quick ROI and create buy-in from across organizational hierarchy.
• Fail-proof scaling – Use data validation experience of a trusted partner to minimize the disruption risk of Big Data adoption and management. Leverage data assets through integration of banking applications and create a single source of truth. Gain complete ownership of your data transformation program with KPIs and metrics.
• Efficient processes – Data integration across banking operations can help you automate certain parts of the business that hog up productive employee hours. Freed up hours can be used to ensure your customers are served better
• Business Intelligence – For banking institutions, business intelligence can lead to enhanced customer experience and a seamless path to purchase. Enable daily, real-time metrics, forecast performance, and gain more control over your results and efforts across marketing, sales, market intelligence, and finance.
• Visibility and monetization – With improved visibility into organizational performance, banks can enable data-driven decision-making with custom, granular reports, dashboards, visual metrics, and regulatory and governance insights. To enable monetization from data, banks need to streamline the flow of data across touchpoints and its effective harnessing by the right people within a constrained and secure environment.

Does a data transformation strategy guarantee results for your bank?