Many banks and credit unions feel pretty confident that they are effectively using analytics to drive their business. But the truth is, they are usually thinking about old school concepts and basic models, not big data. Take this self-test to see how you really stack up.
Various methodologies have been created to help companies review their analytic strengths and weaknesses. The jargon in the industry for this type of evaluation is often called a “maturity model.” These examinations are essentially a report card — comprised of questions that assign various values for varying levels of analytics competency.
One such model is based on the work of both IBM and MIT. In this model, there are six categories addressing the various functional areas that affect the impact of data analytics inside your organization. The model not only covers both the technology aspect of data processing and the business factors that help create a successful analytics platform.
Data/Information. Using big data to support decision making is the core concept of this section. Simply put, data is raw material. Once it has been analyzed and interpreted, it becomes information; taking on definition and context. Information, along with the corresponding wisdom, lays the foundation to make data-driven decisions and improve results.
Core Analytics. This is a prerequisite to understanding the rationale of current customer behaviors, and the ability to predict future behaviors. At the base level of this category, financial institutions can carry out basic reporting for regulatory requirements and to share performance values. Banks and credit unions that score higher in this section already use analytics to optimize their marketing strategy and tactical implementation. This functionality is relevant to all customer facing departments — improving sales, marketing and operational efficiency.
Business Focus. It is important that your big data analytics platform be designed to support the bank’s overall business strategy. The analysis of the data must be designed so that meaningful insights can be used to drive improvements and efficiencies throughout the bank. Having a business focus is critical because any of the insights derived from analytics must be relevant to the business challenges the organization is facing.
Content Architecture. This measures the institution’s analytics platform to see to what degree it can grow and expand. It is important that internal stakeholders have accurately ascertained the level and scope of their future needs. To score high in this category, they must be able to manage and scale each of the four characteristics of big data: velocity, veracity, volume and variety.
Administrative/Governance. The governance of big data is critical — as much if not more so than a conventional analytics platform. All data and organizational insights gathered must be used ethically. Policies that govern the use, security, insights and applicability of data must be covered, and continually updated and matched against any potential threats (legal, technological or regulatory).
Company Culture. The ability to translate big data into insights with value hinges on the organization’s culture. A culture where senior management accepts calculated risk along with a positive working environment and a high degree of trust with respect to data skillsets will score higher in this category.