FinTechBig DataFinance, Big Data and analytics

Finance, Big Data and analytics

How can the finance department harvest the full value contained within Big Data and analytics? The authors analyse the findings of The Hackett Group’s most recent Key Issues Study and focus on four dominant business strategies that emerged.

Published annually, the 2015 edition of The Hackett Group’s Key Issues Study contained some interesting implications for finance teams – among them companies’ main business strategies, as outlined below in Figure 1. As they become more integrated into the wider business, organisations require more comprehensive profitability analysis.

If you’re a financial professional working in a finance organisation, you should be able to allocate costs in detail – for example stock keeping unit (SKU) or store location for retail; patient diagnosis, physician, or operating room for healthcare – and align them to desirable outcomes. Because you need to know actual profitability, not average profitability, it should also be possible to see data across multiple dimensions: geography, channel-to-market, products, and more (such as profit by city, customer type, etc.).

The emergence of Big Data opens up other possibilities. It hasn’t always been easy to reconcile the hype with its usefulness to finance departments, but this is changing. The potential is now there to change the way that customers are categorised based on factors such as social media sentiment analysis and smart metres.

Figure 1: Companies’ primary and secondary business strategies
Figure 1
Despite the availability of these analytics, many finance organisations lack the ability to effect the right cost reduction measures, which leads to significant capability gaps. Tellingly, none of the critical development areas outlined in Figure 2 below are aligned with a “high” ability to meet the objective.

Figure 2: Companies’ critical development areas
Figure 2

We have identified four areas that require particular improvement when it comes to decision-making.

Transparency and believability:

Often, business users will express their doubts – or even outright disbelief – about the accuracy of profitability information. This reaction has more to do with a misunderstanding of cost allocation than the actual results. If the system designer leaves, or important documentation is misplaced, it can be hard to understand why a particular rule exists.

Should this be the case, it’s necessary to remove any “black box” processes and ensure that the business and mathemeatical reasoning behind the allocation process is clear to all.

Accurate cost consumption figures:

The best costing methods reflect “cause and effect”. Generic volume-based allocations are insufficient; they’re too broad to accurately indicate how costs are truly consumed. While it hasn’t always been easy to trace cause/effect relationships across millions of key dimensions, contemporary technology makes recording precise cost consumption a simple task – for those who are willing to do so.

Generate cost pools to tackle these issues on an atomic, multi-dimensional level: these figures are not genericised or obfuscated and it will be easier to make the cost assignment actionable and increase accountability.


It’s seldom easy for organisations to adapt to business changes, but it is necessary and finance is no exception. Profitability analytics are often custom-built solutions, which require regular updates and substantial support from IT teams; change is a regular occurrence and must be dealt with regularly.

It’s necessary to select systems and processes that can be calibrated to adapt to these changes, as and when they occur. All things being equal, it’s usually preferable to model them in advance where possible.


When it comes to world-class decision-making, time is always of the essence. Detailed, multi-dimensional profitability analysis is a complicated process, and getting deep, useful insights where they’re supposed to go – and in a comprehensible form – can be tricky.

Performance represents a major obstacle in the way of these profitability solutions. It is therefore vital to design cost pools, reporting, data integration and everything else needed for a technical solution appropriately. If information cannot be provided in the right timeframe, it’s often because the solution isn’t built to accommodate multi-dimensional profitability.

What finance teams need to remember

When it comes to detailed, multi-dimensional profitability, there are a number of steps that finance teams can take to ensure robust analysis.

Naturally, the cause-effect relationship is paramount, and every allocation method should be designed with it in mind. To effect behavioural change, it’s necessary to accurately report the impact of altered business consumptions or conditions. That said, it’s better to start as soon as possible and adapt your allocations where appropriate than it is to delay the project.

There’s no “perfect” method of allocation; the goal should be directional accuracy instead of precise inaccuracy. The best available method is better than nothing, and should tide you over until you can replace it with a more optimal cost driver.

Finally, whatever method you use should be replicable, comprehensible, justifiable, and transparent.

Key leassons

When using detailed, multi-dimensional, multi-market profitability solutions, there are several lessons worth keeping in mind.

Firstly, set your measurement goals. Too often, projects begin without a full and proper notion of how the results will be used. All the information that you accumulate should support key decisions: begin with the end firmly in your sights.

Next, it’s important that any data yielded is shared and disseminated: it needs to be part of a broader business strategy, and should thus be shared with the wider business. You’ll need costs from multiple departments, so make sure your findings benefit these departments.

Finally, consistency and clarity should be paramount. Technology makes it possible to undertake rigorous capacity analysis and causal variance. Whoever owns the budget, they should be able to easily compare it against actuals.

When it comes to analytics, the aim is to seek hidden value on the margins and apply your findings to the rest of the business. For example, where healthcare is concerned, if one patient procedure is most cost-effective in one location – with one specific doctor – find out what’s being done differently and see if it can be replicated on a wider level.

With the right analytical processes in place, it’s possible to distil complex patterns into actionable – and profitable – insights. If you take advantage, finance’s role could well be more critical than it’s ever been before.

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