This is my attempt to lay out a framework for lenders to take a step back and look at the impact of an ever increasing stockpile of industry knowledge and the role that data plays in the marketplace.
An important first step is to make clear the distinction between Data and Information. Think of data as the raw components of information: individual facts and statistics. Think of information as data that has been analyzed and interpreted so that a business can use it to make informed decisions.
There are a multitude of challenges and issues facing lenders today. Certainly pending and future regulatory changes, increased focus on consumer interaction, and determining how business strategies may make the best use of voluminous volumes of data in different formats—and from multiple sources—are among those challenges.
So let’s look at this from three perspectives. Opportunities are creating big changes and more focused needs in the technology sector. It is easy to think of this as a technology issue, but in reality it is a business issue. The business has to determine which overall goals and objectives may be served by technology solutions. Only then can the IT side of the business shop consider the technology issues.
Business Strategy: Changing business strategies typically involves analyzing your current business practices and determining where and when adjustments are required. Adapting your business model involves risk, so plan ahead to examine the alternatives before taking any action to modify your company’s strategic plan. Introduce changes slowly and be prepared to return to your previous strategies if your new strategies don’t pan out for your business.
You need to consider how to best support the ebb and flow of data to enable users to access data when needed, as well as how data can be analyzed within the business’s time constraints. You need to be committed to improving the customer experience and have a better understanding of your customer preferences and behavior. Access to the right data, along with sophisticated analytics and an evolving business strategy, can help organizations balance risk, growth, and efficiency to realize a competitive advantage.
You need to identify your business requirements, processes, and capabilities to achieve that organizational optimization in order to build your technology roadmap.
Data Governance Strategy: In recent years, the concept and discipline of data governance has grown in importance as organizations are forced to comply with industry or governmental regulations while simultaneously cutting costs to improve margins and using new data-driven initiatives to increase revenue. The goal of data governance is to provide better visibility into a corporation’s data assets to drive better and quicker business decisions, comply with regulatory requirements, or simply to improve the efficiency and operations of data management at an enterprise level.
Traditionally, each of these challenges might have been managed by a particular line of business or by a specific application used in the company. Although the goal of data governance initiatives is often easy to define and understand, organizations struggle to implement the appropriate implementation of programs that manage those initiatives. The inability to collaborate, the lack of controls, and ultimately, the difficulty in measuring the success of these programs often results in a lack of ongoing support and commitment at an organizational level.
Data Strategy: A recent white paper, ‘The 5 Essential Components of a Data Strategy’ by SAS frames it this way, “Despite heavy, long-term investments in data management, data problems at many organizations continue to grow. One reason is that data has traditionally been perceived as just one aspect of a technology project; it has not been treated as a corporate asset. There’s no shortage of blue-sky thinking when it comes to organizations’ strategic plans and road maps. To many, such efforts are just a novelty. Indeed, organizations’ strategic plans often generate very few tangible results for organizations – only lots of meetings and documentation. A successful plan, on the other hand, will identify realistic goals along with a road map that provides clear guidance on how to best get the job done.”
The article continues, “Once upon a time, data was perceived as a byproduct of a business activity or process. It had little value after the process was completed. Today, business is very different. The value of data is accepted; the results of reporting and analytics have made data the secret sauce of many new business initiatives. It’s common for application data to be shared with as many as 10 other systems. While the value of data has evolved tremendously over the past 20 years – and business users recognize it – few companies have adjusted their approaches to capturing, sharing and managing corporate data assets. Their behavior reflects an outdated, underlying belief that data is simply an application byproduct. Organizations need to create data strategies that match today’s realities. To build such a comprehensive data strategy, they need to account for current business and technology commitments while also addressing new goals and objectives.”
A variety of problems are evident in the absence of a data strategy. First, the quality of data cannot be accurately ascertained, and in fact it becomes difficult to determine what data is actually available. Access to data can be surprisingly difficult; when data collection and storage are not well-planned, retrieval—even from sophisticated databases—is fraught with inconsistencies and dead-ends. And, without the adoption of data management standards, all points of the process, from collection through retrieval and analysis, are subject to instability and variability. What is needed is a plan designed to improve all of the ways you acquire, store, manage, share, and use data to find patterns in its inherent complexity.
Summary: A data strategy will not naturally appear as a consequence of other business activities. Teams and departments may tactically determine micro-standards that serve transient annual objectives, but the width and breadth of a cross-functional data strategy is beyond the scope of any single organizational division. It requires the visualization of a flat organizational structure, even within the most hierarchical companies, so that a transversal view of data management activities can be determined. Only then can a strategic plan be put in place not only to make smarter use of available data, but to collect missing data that may be limiting corporate growth.