Isn’t my Core System my data warehouse?

data warehouseIt’s a common misconception in the financial services industry that a database and a data warehouse are the same thing. Put simply, they are two different aspects of data storage, and each has its own role to play. Both are critically important, however, to enable financial institutions to hold their own in an increasingly competitive environment, and having either system without the other is like having a car with only three wheels.

Understanding the Big Difference

The core database system in a financial institution is designed to handle transactions. It’s usually organized for storage, access to and retrieval of records. Most often, a database is in the form of an online transaction processing (OLTP) system, and are restricted to being used with a single application. This could be Excel, CSV, XML or text, all of which are great for storing data but limited in their ability to analyze and make sense out of it.

A data warehouse, however, is an online analytics processing (OLAP) system that incorporates data from multiple different sources. It’s capable of identifying a single source of truth for all your data and can perform much more complex queries than a transactional database can, and has vastly superior reporting abilities than an OLTP system.

The Importance of Analytics in Finance

In the financial environment, analytics are vital to enable banks and other institutions to withstand the enormous economic pressure they are under. According to McKinsey, 54% of the world’s top 500 institutions are priced below book value, and just 18% hold all the value in the industry. That leaves a large number of players pressed to make improvements, and with the advances in technology and the push to digitization being exploited by the top institutions, it’s essential the rest follow suit. Without the ability to produce and utilize advanced analytics, maintaining any sort of position in the industry is likely to be impossible.

Where Analytics is Going

So, just what can analytics do for the financial services industry (FSI), which could argue in favor of acquiring a data warehouse? We’ve identified three trends worth noting because of their implications:

  • Automation is burgeoning, with the increase in robotic processing automation (RPA) and cognitive automation. Financial institutions are rapidly turning to automation for investment reporting, fraud detection, interpretation of regulatory requirements and non-human customer service. All these use complex repositories of business rules and logic gathered from data sources to deliver their services.
  • In spite of huge investments by the FSI in technology, mobile apps, social mining and data lakes, the customer experience hasn’t changed significantly enough for the struggling players to retain their clients. The solution to this is enhanced personalization, which can only be done based on the thorough analysis of data collected from multiple sources.
  • The FSI might be well-positioned to leverage large repositories of client data, if they can reach a balance between the loss of privacy and the value gained in its place. So far, the jury is still out on this one.
  • Cyber-security is an ongoing problem, and as institutions move their data and analytics to the cloud they acquire a whole new security challenge. Frequently, a data warehouse hosted with a professional ELT provider (extract, transform and load) is not only more secure than an in-house OLTP system that’s subject to any number of social engineering opportunities, but it also has the benefit of regular backups and disaster recovery protocols.

The Take-Away

The value of your analytics depends on the value of the data input as well as the platform you use for analysis. A database alone is not enough to generate the insights you need for survival. A data warehousing application will give you that.

Setting Up Your Analytics Goals for Success

analytics goalsIn these days of the big data revolution, multiple ways exist in which financial institutions can use analytics to support strategy and drive its execution. Statistics show approximately 90% of senior bank executives believe in the power of big data, and its potential impact on growth and profitability over the next five years. To achieve this, however, institutions need to develop a clear understanding of how to use advanced analytics tools to align with client interactions, anticipate needs, and make decision processes simpler.

Getting the Most Out of Your Analytics

Retail financial institutions typically have narrow margins, which forces them to run lean, efficient operations. Analytics can help them to understand issues such as:

  • Most profitable customer segments, with identifying characteristics
  • Their geographic locations
  • Percentage of market share occupied by them
  • Most frequent needs and wants
  • Communication channels most often accessed
  • Most effective marketing actions
  • Average lifetime value

The availability of rich, real-time data in the form of numbers, text, voice and images now exists for practically every customer interaction, every product and service financial institutions offer, and the processes they use to deliver them, according to McKinsey & Company.

Making Your Analytics Work for You

Set your financial institution up for success by using advanced analytics to identify the real challenges facing the company. Perhaps you have a large market share, but lag behind competitors in products per customer? Find out who your clients are, what products they hold, examine their credit card statements, transactions and point-of-sale data. Review their online and mobile transfers and payments, and map this against their credit scores. By doing so, it’s possible to determine key characteristics that define microsegments in your customer base.

Ensure the data you collect is high-quality information, and the input is done correctly. Use a specialized data model and reporting solution like JOHO OneSource™ to analyze your intel and “connect the dots.” This will enable you to identify insights from different data sets and build a complete picture.

Use the data to inform your product development. For example, knowing what the next product a microsegment is likely to buy enables you to produce a targeted product with a higher chance of success.

Putting Theory into Practice

Some ways financial institutions are making analytics work for them could give you inspiration for using them in your own operations. For example:

A bank in Europe was experiencing significant customer churn, and implemented various retention techniques aimed at making inactive customers active again. By using machine learning algorithms based on big data to predict which active clients were more likely to reduce their interactions with the bank, it became possible to target them with a campaign at an earlier stage before they became inactive. The level of churn was reduced by 15% as a result.

A U.S.-based institution reviewed the discounts private bankers were giving clients and found patterns of unnecessary concessions. By implementing a number of policy changes the bank’s revenue increased by 8% in a few months.

These are completely opposite examples, with one being a cost saving and the other a revenue-generating action, but both would be impossible without insights delivered by analytics data.

Don’t let your institution lose out on the benefits offered by advanced analytics. Contact JOHO today to schedule a consultation and discover what we can do for you.