How chief financial officers optimize KPIs with data, automation

How chief financial officers optimize KPIs with data, automation

Tax performance management has always been a numbers game; now, big data and automation enable financial leaders to take their key performance indicators to a new level.

While the deluge of data has created ample opportunities to improve financial KPIs, managing that data and turning it into actionable information is proving to be a challenge, an issue explored in a panel discussion at 19th Annual MIT Sloan CFO Summit.

The challenges are particularly acute in companies with data assets spread across multiple systems and punctuated by silos, data gaps, and inconsistencies in the type and quality of data stored, CFOs agree.

To take full advantage of data-driven performance, financial organizations must start over with the first step: obtaining the business data to ensure that the right data is captured and that data initiatives are aligned with the underlying fiscal strategy and business goals. The key to that effort is a renewed focus on data governance and processing the question of who owns the data model.

“Before we could even think about getting information and value from data, we [need] a good way to get that data into our systems and a good way to govern it so it can be usable for us, “said panel moderator Peter Irwin, a partner at KPMG Lighthouse who specializes in data analytics, automation and intelligence artificial.

Here are the key points of the discussion:

Finance needs an ownership stake in the data model

The data explosion is a double-edged sword. There is so much potential in leveraging data and advanced analytics to increase tax performance, but without a single source of truth, organizations can be locked in a cycle of never-ending reconciliations and questionable data integrity that reduces the data value for the business.

Historically, the information technology department has had sole responsibility for the data model, but without a complete understanding of what is required for fiscal KPIs, this can lead to a lot of redundant and unproductive work. Aligning goals and responsibilities is critical for data governance; as part of that process, finance should have an ownership stake in the data model, along with IT.

“Finance has a deep understanding of the calculations, sources, definitions and mapping” of financial data, said Kae Arima, vice president of finance at Workday, who has a data model stake at the cloud-based service provider. software for finance and human resources.

“We are able to reduce the time spent on our reconciliation … and there is a lot more quality and integrity to the data.” The ability to leverage financial data effectively has allowed Workday to scale as an organization during a period of explosive growth with relatively low administrative and overhead costs, she added.

Correct data, internal and external, make the difference

To generate actionable insights and perform data-driven metrics, organizations need to identify the correct data from internal and external sources. In ChaosSearch, a cloud-native and venture-capital-backed data lake platform, the emphasis is on top-of-the-funnel KPIs for evaluating the speed of go-to-market plans, marketing programs, and leads. sales qualified, explained Melinda Smith, the startup’s CFO.

“From a KPI perspective, it’s about generating as much recurring monthly revenue as possible,” he explained. “Therefore, it is important to identify the point in the sales funnel where you go from selling one more deal to one where you are sure it will close.”

Related articles

As a startup building IT infrastructure from scratch, ChaosSearch has no legacy platforms or data silos to consider – all of the company’s relevant data resides in a variety of cloud systems, making integration between them critical, Smith said.

Workday is also combining data from different functions to deliver new information and KPIs. For example, by combining human resources and finance data, the company has created a new metric called “performance to plan”, which is used to help project managers optimize execution plans for various initiatives.

The company is also drawing on 15 external data sources on the financial side – databases like Bloomberg and Dun & Bradstreet – to help automate and scale processes, Arima said.

For example, automated workflows draw on transactional data from E * Trade, the company’s equity administration platform, to generate accounting records and create a virtual subledger with high assurance of data integrity. “We are confident that we know the source of the data and it has saved us a lot of hours,” explained Smith.

According to Michael Mozzer, CFO of TraceLink, TraceLink, a software-as-a-service provider that helps pharmaceutical companies track and trace products along their supply chains, combines its internal data with data from external partners to offer a new service to its customer base.

Data generated by a network of 200,000 entities that track pharmaceutical product serial numbers has value to customers in a variety of use cases, from automating the recall process to optimizing production cycles, he said. .

“We can help these pharmaceutical companies understand if it is time to start another production cycle or if they already have enough products in the supply chain,” Mozzer explained. “We have anonymous rights to all of this data so that we can monetize it and resell it to our customer base.”

Automation and artificial intelligence help optimize financial processes

While most businesses are still in the experimental stage, automation is starting to take over in financial processes such as leveraging OCR technology to scan invoices and automating other Accounts Payable processes. Workday combines data from a weekly employee engagement survey with attrition data, then uses artificial intelligence and predictive analytics to facilitate adaptive planning of financial forecasts.

For its part, ChaosSearch leveraged various cloud applications to automate billing-related approval flows, reducing the number of internal employees, which is critical for a startup with a limited budget. “We used to have a payroll person, but now I have someone who does both accounts payable and payroll, for example,” Smith said. “This is because of the efficiency we are gaining from these tools.”

Progress is incremental, and that’s okay

The process is a journey, not a sprint, and financial organizations shouldn’t let perfection interfere with good as they move towards achieving some KPI automation, the speakers agree.

Understanding how to define a requirement for a minimum workable metric can still offer some benefits of automation. This however requires that the data acquisition processes are well tuned, because ensuring that the data is of the highest quality leads to accurate KPIs.

“If there’s garbage inside, it’s garbage outside. So we really spend a lot of time making sure we have the right validations in place early in the process to make sure everything downstream is good quality data, ”said Workday’s Arima.

The priority of the projects is fundamental. “People in finance have such a huge appetite for measuring everything,” Arima said. “We are working hard to promote the automation and accuracy of forecasting that goes into sales data and people data. It is a matter of understanding what you have to measure first ”.

Read next: Digital business needs new KPIs. This is why they are important

Leave a Comment

Your email address will not be published.