Data analytics – pragmatism is key to commerciality and adoption by stakeholders

In today's world of abundant data, the necessity for clarity and sophisticated data analysis to structure this wealth of information into actionable insights has never been more imperative.

The imperative of pragmatic data analysis

Data analytics, big data, data mining and data science all essentially refer to the same thing i.e. taking large, often unstructured datasets and making sense of it in a way that gives insight and allows for better decision making. 

For example:

• what channels of acquisition provide customers that are most loyal and give the greatest lifetime value;
• which customers, when a more appropriate cost allocation model is applied, are loss making and a drag on overall performance;
• what may be the impact on a portfolio of assets if inflation rates were to spike up again given the nature of contracts and type of debt in place; and
• how may earnings to shareholders be diluted on adding new assets to a portfolio given the initial funding and potential capital expenditure requirements.

It seems an obvious point to make, but stakeholders are interested in getting at these answers in the quickest way and often in a way that is replicable time after time. They are also willing to accept an 80:20 answer if that is given at pace and with appropriate understanding of the limitations.

Navigating pitfalls: pragmatic solutions for stakeholder adoption

At Amberside, a Steer company, as financial and commercial advisors, our job is to provide the pragmatic solutions that meet these needs. We can sometimes be seduced by the software and technology that is available and offer solutions, which at first glance appear great, but are undermined by:

1. Requiring investment in new, sometimes expensive software;
2. Being perpetually reliant on the advisor or their internal IT department; and
3. Providing what appears to be a black box solution that lacks transparency.

These are reasons why despite the almost universal complaints about Excel, as a tool for modelling and analytics, it remains the go to software i.e. it is available to all, can be self-sufficient and relatively easy to follow the coding.

Of course, Excel has drawbacks, and so sometimes it might be advisable to use an alternative platform but often incorporating Excel as part of the final analytics solution works best. For example, a well-structured process can employ Excel as the calculation engine, use Power BI as the data modelling and visualisation, with coding in VBA and Python that turbo charges the analysis in the background.

Being innovative and going to market with a broader and deeper proposition is important for a competitive edge, but some advisors continue to make the mistake of investing or innovating at a speed that clients are not ready for – commercial self-harm in the world of advisory services.

A lot of these mistakes stem from not being focussed enough on these basic principles of analytics solution development – have it accessible, make it self-sufficient and build in sufficient transparency.

Data analytics at Amberside Advisors

At Amberside, a Steer company, we understand the importance of providing quick, reliable, and insightful answers to stakeholders' most pressing questions. Our approach ensures that our clients can leverage powerful insights from their data without the need for expensive software investments or perpetual reliance on external advisors. We empower them to make informed decisions efficiently, giving them a competitive edge in today's dynamic market environment.

If you wish to discuss how data analytics can support your current business operations and decision-making processes, please get in touch. I’d be happy to explain how we utilise data analytics to enhance our financial advisory and modelling services; and look at how we can support your business objectives.