In Finance, not all analytics are equal

The IBM Center for Applied Insights asked over 1,000 organizations across five industries about their approach to data and analytics, cloud and engagement.

Which of the three primary types of analytics—descriptive, predictive and prescriptive—did they use and to what extent?

Which data sources did they use as input to their company’s analytic processes?

Which analytic techniques were used in their organization?

How are digital channels being used?

Which solutions have they deployed in a cloud environment?

In their answers, these organizations shared, among other insights, how their Finance departments use analytics. Analysis is a fundamental part of the profession, and it makes sense that today’s Finance teams would adopt analytic technologies to aid in decision making.

However, not all analytics are equal.

The analytically advanced

Breaking the data barrier: Lessons from analytically advanced Finance organizations

Of the three primary types of analytics—descriptive, predictive and prescriptive—only 25 percent of organizations polled said that their Finance teams use the most sophisticated: prescriptive analytics. They are not just forecasting what will happen, but recommending a course of action based on sophisticated analytical models.

Enterprises with these analytically advanced Finance organizations also exhibited greater maturity using analytics in general. For example, 70 percent use predictive modeling as compared to only 58 percent of other enterprises. In addition, they’re more inclined to do next best action modeling (53 percent versus 43 percent) and automate process decisions based on analytics (62 percent versus 49 percent).

There were other differentiators as well.

By integrating more data sources  into your analysis, you’re able to generate new or better insights, such as more accurate forecasts. These enterprises with analytically advanced Finance organizations tap into a broader range of data sources—including advanced data, such as location, mobile app and social media data.

They are also more likely to use other transformational technologies, such as cloud, mobile and social.

Cloud reduces IT overhead, shifts costs from capital to operating expenses and eliminates up front capital expenditures as compared to on-premise deployments of transactional systems as well as web apps and solutions.

Mobile increases access to information for faster decision making. And social provides an important channel for collaboration and engagement.

The payoff

In Finance, the conversation always circles back to the bottom line. What is the return on investment?  And, here was the most significant news from the research.

Enterprises with analytically advanced Finance organizations reported better outcomes when it came to decision making, growth and agility.

Among the key findings:

  • Companies with analytically advanced Finance organizations led the others by 14 percentage points when it came to reacting to new and disruptive market entrants.
  • They were 18 percentage points ahead in terms of making better, faster decisions.
  • Their share of wallet was 6 percentage points higher.

In today’s marketplace, advanced analytics clearly gives these organizations an edge.

Where do you fit in?

While the potential business outcomes are substantial, the reality is that 75 percent of the companies surveyed said their Finance organizations fell into the “other” category.

If you’re among the 75 percent, what steps can you take to move beyond the status quo and join the ranks of the analytically advanced?

Here are a few recommendations based on the data:

  • Establish an integrated Finance data strategy that takes into account multiple data sources. For instance, Finance could implement planning around the sales and order/refresh process. As part of this work, order data could be incorporated into the forecast process to increase accuracy, provide greater visibility and reduce supply chain issues.
  • Find opportunities to use analytics to prescribe action. For example, an analytics algorithm could assist with sales and operations planning. The algorithm could link financial planning with demand planning and a constraint-based master plan (supply planning), and then recommend a best plan based on the constraints.
  • Use mobile and social technologies to improve information transparency and accelerate decision making. Collaboration platforms can help decision makers share ideas and comment on different budget scenarios and opportunities more effectively than email.
  • Leverage web apps and solutions in the cloud. Expense management, reporting and performance management solutions are all prime candidates for cloud-based deployments.

Given the returns the 25 percent experience, the investment is well worth it.

To learn more, read Breaking the data barrier—Lessons from analytically advanced Finance  

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