Read the article and learn about:
- The importance of knowing what insights you want your data to provide and how you achieve and use these insights
- The value of applying a business intelligence (BI), analytics and data management strategy
- The potential of an extended approach to business intelligence and data analytics
- Related benefits of BI and analytics on data quality
Director Financial Services Industry Solutions, Qlik
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Director, Product Manager, Performance Measurement and BI, SimCorp
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Director, Product Manager, Data Warehouse, SimCorp
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This article describes the significant value that good data analysis can provide to the investment management sector and explains how to overcome the challenges in achieving true business insights.
The investment management industry today is facing many challenges including political, regulatory, and competition-based concerns. Firms are also increasing their focus on customer centricity and embracing digital transformation whilst trying to effectively “keep the lights on” for their day-to-day activities.
Of course, one of the consequences of all this change is a significant increase in the volume of data that investment management companies are holding and handling. Now, having a lot of data could be considered a good thing, but as stated by the UK mathematician, Clive Humby, “Data is the new oil. It’s valuable, but if unrefined it cannot really be used.” In other words, if investment managers want to drive real value from data, they need to know what they want from the data and how to work with it.
The importance of strategy
In order to do great things with data, you need to know what you want to use it for. What do you want to achieve, how do you want to achieve it, and how will you know when you’ve accomplished it? These questions are best articulated by defining and documenting a business intelligence (BI) and analytics strategy. Your BI and analytics strategy will help you focus on the important matters for your company and, ideally, your strategy will align with the wider company strategy, goals, and objectives.
Executing the strategy
For years, business intelligence was a synonym for modernized reporting tools, where business users received visualizations in a nice interactive front-end. This has changed a lot in recent years. Today, a mature BI implementation requires many things to be best-in-class, including:
- an agile ETL layer to collect and model the disparate data into multi-dimensional data structures
- a powerful analytics engine to support the ever-increasing demands of the data literate business users on the front-end applications
- a fast and flexible charting layer with the ability to easily slice, dice, and drill into the key dimensions across the data.
Another key element being demanded by BI users and developers today is a powerful suite of open API’s that allow for 3rd party chart libraries and in-house web applications to bring visualizations and interactivity to the business user. The most important criteria in financial services is the speed of results delivery and the capability of handling complex data.
The most important criteria in financial services is the speed of results delivery and the capability of handling complex data.
The challenge of achieving and leveraging the value of new business insights
Whilst many companies succeed in the technical implementation of software tools, a greater challenge arises when integrating a new way of handling information and decision making in the organization. Often, the technical implementation just continues to replicate existing traditional reporting processes and does not leverage the real strength of new business insights.
Today, we are witnessing a growing trend whereby the market and commercial success of investment management companies is highly dependent on the BI, analytics, and data strategy that the company has implemented. Therefore, we believe it is highly important that firms identify and are able to realize the significant value of the data available within their systems, also when being combined with external data. Further, it’s important that technology vendors provide their clients with best-in-class data management and analytics solutions that will enable them to maximize the value of their data.
… we believe it is highly important that firms identify and are able to realize the significant value of the data available within their systems, also when being combined with external data.
Adoption of the self-service mindset – from reporting to insights
Whilst traditional reporting tools have focused on making pixel-perfect layouts that could be printed, archived, and stored for different reporting purposes, a BI and analytics strategy goes beyond this thinking and asks the question, “What do you need this report for?”. If the answer is simply that the user wants this report due to existing (potentially outdated) habits or if this report is shipped to another colleague, the new BI and analytics strategy should challenge this and transform the traditional reporting strategy to a self-service mindset including the options to discover data and provide business insights.
Self-service business intelligence is defined by Gartner as “… end users designing and deploying their own reports and analyses within an approved and supported architecture and tools portfolio.” Where traditional reporting tries to answer the questions put forward by the business, BI aims at putting the data in the hands of the users, providing tools that not only give answers to known questions, but which inspire curiosity and where every action may give thought for new questions. An example could be a portfolio manager or data scientist that explores correlations between events and social media in conjunction with changes in investment market data.
Whilst traditional reporting tools have focused on making pixel-perfect layouts that could be printed, archived, and stored for different reporting purposes, a BI and analytics strategy goes beyond this thinking and asks the question, “What do you need this report for?
Improving investment decision-making with machine learning
Some of the more modern BI platforms even use machine learning to derive automated insights from large data sets that exist in the financial industry. Precedent-based machine learning allows the BI platform’s cognitive engine to get smarter over time, continually learning from user interaction and feedback as well as other sources. So, the next time the portfolio manager explores data in relation to their asset holding, the tools may make some suggestions based on previous interactions. This kind of artificial intelligence and machine learning capability together with human intuition will enable the investor in a way that truly augments his/her power to discover insights and improve decision making.
Strengthen operational efficiency
Apart from strengthening the decision-making process, business intelligence also plays an important role in helping financial institutions improving their operational efficiency. Instead of manually reading through printed reports or randomly looking for optimization opportunities, modern BI tools can help improve operational workflows by exposing key performance indicators on trading and settlement processes in order to measure the effect of process changes or dive into the root cause of inefficient workflows.
Business intelligence also has the potential to automate routine tasks, release efficiency bottlenecks, and improve existing business processes. The efficiency and productivity gains can be considerable including better use of portfolio managers time to analyze investments and gain insight on the financial markets – rather than struggling to get a report ready in the right format for senior management.
… modern BI tools can help improve operational workflows by exposing key performance indicators on trading and settlement processes in order to measure the effect of process changes or dive into the root cause of inefficient workflows.
Improved data quality
Another valuable application of BI is related to data quality. Today’s computational power combined with end-user-oriented analytics tools enable data management teams to quickly identify outliers in huge result sets. Take, for example, those related to risk and performance measurement. The consequence of incorrect data in these parts of the business can be severe and the additional operational cost of correcting and re-calculating historic information can also be substantial.
The figures themselves are naturally not getting more correct than the source data allows but enabling strong BI capabilities to look for patterns and breaches will make it easier to correct the discrepancies and to continuously deliver higher data quality long term.
Leveraging the value of data
Going forward, the amount of data available will increase and so will the need to find ways to create and sustain competitive advantage. Implementing a holistic BI, analytics, and data management strategy supported by best-in-class technology can be what enables firms to turn data into a competitive differentiator.