“Our mission is to be an indispensable partner to business leaders,” says the CFO of one technology company. “Accordingly, we are investing in capabilities like data science as well as business analytics platforms that will help us deliver better information and insights that guide business decisions.”
Many finance leaders are doing the same, but they also need to take deliberate steps to mature their financial analytics to fully capture the value of these investments.
Quantify the potential for increased ROI and understand how analytics will improve current analysis
Analytics spending has increased 50% from 2015 to 2017, and now makes up more than 7% of corporate finance team budgets, according to a 2018 Gartner Finance Leader Analytics poll. Gartner research also shows that the number of finance departments deploying advanced analytics will double within the next three years.
“Few finance organizations are even half-way on the scale of analytical maturity, meaning they aren’t beyond the basics of supporting business partner requests and have yet to test higher-level hypotheses or provide support in a centralized manner,” says Randeep Rathindran, Vice President at Gartner. “To achieve this maturity and capture the associated return on investment, most finance departments need to establish new strategies, capabilities and delivery methods for their analytics programs.”
Gartner has identified three actions to take to achieve higher levels of analytics maturity in finance organizations and strategies to clear the way to financial analytics maturity.
Set out an analytics strategy
Few finance departments have clearly defined the opportunities, so they need to quantify the potential for increased ROI and understand how analytics will improve current analysis.
Companies that have successfully deployed financial analytics have used them in a variety of use cases and seen meaningful results, from drastically reducing customer churn rate through predictive analytics on client preferences to optimizing supply chain data to reduce inventory costs from weather-related spoilage.
Once the target benefits are understood, it’s time to pilot business test cases — tying data to concrete business problems. Begin with finance-owned challenges to limit organizational resistance. Once the finance team can demonstrate the benefits within its own projects, rolling out analytics across the enterprise becomes easier to sell to business partners.
Redefine financial analyst roles
First, redefine the role of the financial analyst from the typically overtaxed generalist to a more focused decision-support expert. Doing so ensures that analysts are more accountable for measurable operational decision outcomes in their area of focus, eventually leading to the types of anticipatory analysis that is the hallmark of more advanced analytical maturity.
Incentivizing scalable solutions within their area of decision support means that embedding analytical solutions to tools and reporting becomes more apparent than it otherwise would be to a generalist with many more responsibilities.
Define what it means to be a ‘finance data scientist’
Second, clearly define the essential role of “finance data scientist” and be precise about who to attract to the role.
“In a supply-constrained market for data scientists, finance departments have to work extra hard to differentiate the career track for this role and define what it means to be a ‘finance data scientist’ in an attractive manner,” says Rathindran. “Focus on the more unique applications of the role, such as using data to teach and persuade business partners and having direct input into uncovering future customer and product trends.”
Make analytics delivery seamless
Companies that already use analytics capabilities often find that the way in which analysis is delivered can undermine its overall usefulness. For example, a siloed approach leads to work duplication and politically driven competition and can also overwhelm business decision makers who are grappling with too many analyses from different functions.
For more seamless analytics delivery, use the decision-support model and take two more steps:
- Emphasize cross-functional collaboration. Formalizing enterprise-wide analytic domains enables each function of the business to provide analytical support where it is strongest. Creating the expectation of collaboration and unifying efforts around one final report to the business decision maker helps to ensure ongoing cross-functional communication while presenting all trade-offs related to a decision in a single place.
- Establish centers of excellence. Not all analytics support should be embedded within the business unit. Some analytic activities are inherently cross-functional and benefit from establishing a dedicated center of excellence (COE) model. Before setting up a COE, establish principled criteria for deciding which analytical activities would benefit from such an approach. Doing so will provide clear guidelines on when and how to involve the COE, while also preserving its focus on best-fit activities.