Technology advancements and the proliferation of data are dramatically reshaping the role of the finance function. Today, advanced analytics is the cornerstone of business strategy.

Big data adoption reached 53 percent in 2017, up from 17 percent in 2015, with financial services leading as early adopters. Companies can no longer afford to rely on information generated from their ERP systems to drive business decisions.

While there is an increase in this awareness, the real question to ask is if organizations are actually ready to truly adopt this change. The reality speaks otherwise. Many CFOs don’t feel their finance teams are ready for the future and have major talent gaps. In “The DNA of the CFO 2016” study, 47 percent of CFOs say their current finance function does not have the right mix of capabilities to meet the demands of future strategic priorities.

We have collated a list of elements that can help you prepare better to adopt advanced analytics into your culture. Take a look.

1. Data strategy

Data lies at the heart of advanced analytics. But collecting, strong and interpreting data can prove to be a very costly affair if not done right. Which is why the initiative needs proper planning in order to avoid wastage of resources and efforts.

Establish the need for the data, and then identify the right type and sources of data to use. The objectives of data collection need to be clearly established from the very beginning.

Along with stating the objectives, the correct data also needs to be analyzed. There are so many analytical tools, be it descriptive, diagnostic, predictive or prescriptive. The purpose of each of these tools are different and teams need to have a clear understanding of what fits their requirements best.

Finally, data analytics initiatives need to be aligned with business goals in order to be able to interpret data in the right manner.

2. Data accessibility and consolidation

Major organizations face the challenge of accessing data from third party APIs or connect to existing transaction systems with no coherency. If you want to be analytics ready, overcome these problems on priority.

Just having data is not enough. You need tools to model and extract it to establish as a single source of truth. Finance teams must be able to combine multiple and disparate data sets, irrespective of location or format. This process should be automated with data cleaning and integrity checks put into place.

This allows for applying just enough governance to manage and secure your data. At the same time, it should allow self-service business intelligence and advance analytics to the rest of the organization.

You also need to ask how easy it is to pull the data on demand. Access to data should be easy, and not a cumbersome process.

3. Expertise

While data and technology can create greater value, there will be no real benefit to the business without the right people with the right skills to use it effectively. Taking a plunge into big data requires a new breed of talent.

CFOs and finance leaders should begin to prepare to restructure their teams, or risk falling behind. Required skills would range from data science skills to an understanding of industry trends, cost and revenue drivers, and the ability to chart out action plans and understand financial impacts.

To achieve this, companies either need to have the skill sets in-house or least a partner that can provide data architects, data engineers and data scientists. This would depend on what stage of the advance analytics journey the company is on.   

The CFOs can act as a catalyst here by offering their financial expertise and helping the leadership make strategic decisions. With their help, the need for hiring full time architects and data engineers can be met, thus leading to reduced costs.

4. Business partnership

According to “The DNA of the CFO 2016” study, 67 percent of CFOs worldwide believe that improving business partnering between finance and the business is a major priority.

A potential partnership between the finance business partner, data scientist and a leadership team member can help achieve more clarity and reduce threats. Such a collaboration can help analyze data, identify reasons for issues and trends and also help model different scenarios for solutions.

The ability to communicate findings among the stakeholders will remain the foundation to make such business partnerships successful.