Business Intelligence for Risk Management – Part I

Business intelligence (BI) is a general term for the discipline of gathering, storing, analysing, and providing access to data to help enterprise users make better business decisions.

In recent years all of the tech titans have entered the race to provide smart BI tools to their clients.

“76% of C-level technology executives predict budgets for Microsoft Business Intelligence (BI)/Data Visualization analytics tools to increase the most in 2018.
Microsoft continues to dominate the BI/Data Visualization landscape with the company at the top of the list in each of the enterprise software surveys. It is believed that Microsoft continues to dominate because of the advances they have made in key category products including their traditional BI stack platform (SQL Server), Azure Services and their interactive data visualization product, Power BI. Amazon’s AWS Quick Sight is in 2nd place, with 52% calling it a top investment priority up from 37% in the Cowen December 2017 survey, and 12% in the firm’s May 2017 survey.” Forbes, 31 May 2018

But like most platforms all BI applications, no matter how sophisticated rely on; a solid data model and star schema, a steady stream of accurate data and experienced professionals to be able to get the most from them and provide relevant and beneficial business insights. If a firm is making decisions based on poor business intelligence, then this can be more destructive than not applying BI at all. A successful BI initiative would gather in data from multiple sources and provide a holistic view of specified conditions.

Some people have suggested that ‘data is the new oil’ and most firms invest heavily in people and technology to ensure they are storing and acting on accurate and timely data (not mentioning security). Despite increased automation and technology advances the reality is that in most cases there are small armies of data professionals and a number of applications tirelessly working to generate a few simple BI reports. And let’s say that report is wrong – then they’re back to square one!

  • To understand how we solve this problem we first need to understand the typical problems in an organisation.
  • Multiple disparate systems performing different parts of the supply chain – typically multiple front office trade capture systems, separate market data systems, possibly multiple risk, and accounting systems, as well as a potentially massive wealth of public data with relevance.
  • Geo-location differences and numerous sites – Valuations could be computed in different locations at difference times of the day.
  • Different needs and requirements – Traders need access to live positions and pricing. Risk analysts need views of the data across complete portfolios, trading books, products or counterparties. Back office users require different views again.

The spread of this data and the ever-changing requirements make timely and accurate data availability hugely problematic.

So how can effective BI help to overcome these problems?

Most trading firms want answers to the following types of questions…

  • What were the total sales of a particular product last year?
  • How does our profitability for the first quarter of this year compare to the same time period during the past five years?
  • How much trading was done in a particular sector, and how has that changed over time?

And it’s important to consider, when answering the above questions, how a BI application would differ from a traditional database. What is the added benefit? What does BI really mean and how does it differentiate itself in providing better solutions to organisations for their reporting and data management needs?

  • One single version of the truth — enabling confident decisions by integrating all of the data; bringing together a simple, accurate version of business performance.
  • Turning data into action — giving users access to the relevant stores of data throughout the organisation, and turning it into insights they can act upon.
  • Integrated data giving greater insight — allowing users to identify trends, key risks or trading opportunities that generate business by creating personalized reports for better data analysis.
  • Invaluable insights — helping drive business forward into the future with proactive views of exactly what’s going on at any one time.
  • Integration of big data sources – gathering data from a variety of public sources, in a controlled, structure manner to complement and enhance market insights
  • What If and stress testing – helping to perform various potential scenarios quickly and efficiently to provide stress testing, regulatory compliance and proactive risk managed future-proofed trading.

BI is extremely effective because it brings all the data together from different sources, and then allows for extremely flexible analysis. Most operational databases are designed to store your data, not to help you analyse it. Also, performance is a key concern when aggregating huge amounts of data. In some cases the analysis might require large scale simulations or very granular slicing. In all cases an advanced data aggregation engine is needed to enable this to take place on the fly. Cube technology and column store databases offer a solution for the core calculations.

Stay tuned for part II which will look at how we can achieve advanced analysis and visualisation.