Next Practices For The Intelligent Enterprise Bank

Karen McDermott

Huge amounts of customer data.

That’s one big advantage that banks have over their new-age competitors. Today’s savviest banks are using it, along with other data sets from inside and outside their organizations, to compete in a world of dynamic fintechs, alternative currencies, and radically changing customer expectations.

You already know the benefits of data analytics: growing revenue, mitigating risk, reducing costs, enhancing the customer experience. But tapping data from across and outside of the enterprise for analytics on a grand scale is easier said than done.

Next practices

From our global experience working with the leading innovative banks, here are some SAP “next practices,” capabilities, and outcomes to help banks align and prioritize their data analytics journeys.

Integrate diverse data sources. Data is scattered. It’s in multiple applications, files, data warehouses, data lakes, and public and private clouds. Each silo walls off the data with proprietary rules and complexity. You need visibility into that data. Without it, your bank lacks an overview of the business. With it, you can use process automation to recognize patterns and opportunities. You can redeploy staff to model and test decisions, invent new products and business models, and enhance the experience for your customers.

Next practice #1: Integrate your data by combining data sets — including Big Data, transactional data, and analytical data — as needed, into a single data universe for much greater visibility.

Make data more useful. Your data comes to you structured, semi-structured, and unstructured. It may be spatial, chart, numeric, geographic, time-series, relational, JavaScript object Notation (JSON), et cetera. Integrating all of these different types of data is extremely complex. But without it, your bank is at a competitive disadvantage, squandering available resources.

Next practice #2: Integrate your data sources using orchestration and governance solutions. Go from raw feed to intelligence with real-time analysis of vast data sets. How? With solutions to understand, integrate, cleanse, manage, associate, and archive data to optimize business processes and analytical insights.

Simplify your data landscape. Centralized. Easy to use. Automated. That’s what you want from your data analytics environment. But that’s been a challenge with all of the different databases, apps, and clouds in your IT environment. But now a centralized data management solution is available that manages all facets of an enterprise bank’s data universe. Represented visually, the architecture is easy to share and understand. Stakeholders assigned to an architecture team within your bank can collaborate through a user-friendly Web application in the planning, design, and governance of the architecture.

Next practice #3: Create and maintain a complete landscape architecture that is easy to share and understand. Open up this landscape to an array of banking employees and managers to jointly manage your data environment as an agile, strategic tool.

A growing number of data analytics use cases for banks

Today, data analytics fuels the decisions of the most successful and innovative banks. It is touted as a vital tool for innovating faster than the competition, creating new markets and products, and attracting and retaining customers.

SAP customers in banking are using data analytics to measure and take proactive actions related to customer sentiment, retention, and personalization. They are using process mining to analyze vast amounts of transactional data in real time and then represent it visually for a clear view of as-is business processes to support compliance and improvements.

Banks are managing integrated data landscapes via a user-friendly Web interface that is accessible to an array of internal business and IT stakeholders. Liquidity risk is being managed in real time. Multiple sub-ledgers for financial accounting are being consolidated into one to support real-time planning and financial consolidation.

These are just some of the many quickly evolving, creative ways that financial, customer, operational, and related data is being accessed, integrated, processed, analyzed, and applied to guide strategy by banks today. Some use cases are relevant to every bank. Others are more suited to different geographies, markets, and other unique characteristics.

For more on how banks around the world are transforming into intelligent enterprises, read the new SAP white paper: “The Data-Driven BankData Management for the Intelligent Enterprise.”

And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.

Karen McDermott

About Karen McDermott

Karen McDermott is Global Head of Financial Services Industries Marketing and Communications at SAP, responsible for driving the growth of SAP's value proposition as a technology provider, trusted business partner, and thought leader for the financial services industry.