Finance Analytics: Using The Right Data to Generate Actionable Insights

We are living in the information age. Data is everywhere and affecting every aspect of our lives. As the use of data and analytics become pervasive, in future it will be uncommon to find an industry not capitalizing on the benefits they can provide.

CFOs are increasingly leveraging data and analytics tools to optimize operational and financial processes, and drive better decision-making within their organizations. Finance is no longer simply a steward of historical reporting and compliance.

Today, the expectation on finance is to engage with new technologies, collaborate with various business stakeholders to create value and act as a steward of enterprise performance.

That is, deliver the right decision support to the right people at the right time.

Delivering impactful data-driven insights does not happen on its own. You need to have the right tools, processes, talent and culture working well together.

Here are some of the questions you can start asking to kickstart you on this journey:

• Does your organization have the necessary tools and capabilities to collect and analyze the data (structured and unstructured) and make sense of it all?
• How robust are your data governance and management processes?
• How do you define the structure of your finance analytics team? Are you focused heavily on traditional and technical skills or diversity?
• How are people armed to make better decisions using the data, processes, and analytical methods available?
• As a finance leader, are you promoting a culture that bases key decisions on gut feel versus data-driven insights?

Both intuitive and analytical decisions are important and can create significant value for the business.

However, I’d recommend against promoting a culture that embraces the randomness of intuitive thinking and avoids analytical thinking completely.

What are you doing with your data?

In a world where data is ubiquitous and plentiful, it can be overwhelming to separate the noise from the important.

According to IBM, 80 percent of today’s data is originating from previously untapped, unstructured information from the web such as imagery, social media channels, news feeds, emails, journals, blogs, images, sounds and videos.

And this unstructured data holds the important insights required for faster, more informed decisions. Take news feeds as an example. Breaking news on economic policy can help you reevaluate production and supply chain decisions, and adjust forecasts accordingly.

But, for fear of missing out, many businesses end up hoarding data with little to zero analysis performed on this data. You don’t need to collect and store each and every single piece of data type out there.

Instead, only data which matters most and is key to unlocking answers to your critical business performance questions. The rest is all noise.

That’s why it’s critical to regularly ask the question, “What are we doing with all the data we have?” This way you would also be able to identify any information gaps that require filling via new data sources.

For analytics to work, people need the foresight to ask the right questions

People play a very critical role in an organizational analytics efforts. Without insightful guidance on the right questions to answer and the right hypotheses to test, analytics efforts are foiled. They cannot direct themselves.

Asking the right questions is key to drawing the right conclusions. Let’s look at the steps involved in making better decisions using data and analytics for one of your product lines. After a strong start, you’re now starting to witness a decline in online revenue but are not sure why.

Before jumping into complex analytics:

• First, identify the problem that the data need to answer. You already know What happened. However, this is not enough. To address the issue, it’s important that you have a better understanding of the impacted segment and potential reasons for the revenue decline. This is where you start asking questions such as why is revenue declining? When did the revenue decline start? Where did it happen (country, region, city)? What might have caused this (potential drivers)? What actions can we take to address the decline?
• Then come up with hypotheses of the issue to be addressed. A hypothesis describes a possible solution, such as a driver or a reason behind your business question. Many people make the mistake of simply jumping into data collection and analysis before forming a hypothesis to answer the critical business question. Going back to our revenue decline example, supposedly you have identified several hypotheses and then prioritized two to explain the revenue decline – A slowing down economy and impeding recession causing customers to tighten their purse strings quite a bit, and a new website design with enhanced security features but also additional online check out steps.
• Based on these two hypotheses you can then identify the exact data that should be collected and analyzed to prove or disprove each hypothesis.

Irrespective of how advanced your analytical tools are, asking the wrong business performance questions results in flawed data being collected, and ultimately poor insights and recommendations. Hence the importance of diversity within the team.

Remember, no data or analytics model is perfect. Finding good data and making sure it’s cleaned and validated is fundamental but the organization also shouldn’t wait for perfection.

It’s easy to get fixated on perfection and fail to see the forest for the trees.