Organizations today do not suffer from a data shortage. Every hour, every minute, every second, organizations capture and consume massive amounts of data to make strategic and tactical decisions. Yet, as the volume of data grows, the ability to consistently produce useful financial analysis for business partners still eludes a majority of finance departments. Leading finance teams are succeeding at meeting business expectations in analytics by incorporating two critical capabilities — the ability to predict a series of outcomes over time and then use those predictions together with firmwide goals to identify the best course of action.
Predictive versus prescriptive
According to Carlie J. Idoine, research director at Gartner, finance leaders can bring together these two distinct but complementary data science techniques to drive high-impact decisions:
- Predictive analytics addresses the question of “What is likely to happen?” It relies on techniques such as predictive modeling, regression analysis, forecasting, multivariate statistics and pattern matching.
- Prescriptive analytics addresses the questions of “What should be done?” and “What can we do to make “X” happen?” This technique includes graph analysis, simulation, complex-event processing, recommendation engines, heuristics and, increasingly, neural networks and machine lerning.
For some industries — like manufacturing, transportation and financial services — predictive and prescriptive modeling have long been part of their processes. However, even with the known power of these capabilities, they are often isolated and trapped in specific, dedicated departments instead of being used in tandem with others and leveraged across the entire organization.
Many organizations already have people who operate in a data-scientist-type role, even if they don’t have that title
In the age of digital business, flexibility, agility, collaboration and responsiveness are key. In effect, the ability to predict outcomes, quickly assess innumerable alternatives and take action is something that can no longer be reserved for the few.
Read more: Efficient Growth: The New Finance Mandate
To jump into predictive and prescriptive analytics, CFOs will need to expand the individual skills their teams have and can leverage for sounder decision making. Gartner has identified three ways finance leaders can develop and expand their teams’ in-house skills for predictive and prescriptive analytics.
- Change hiring priorities
Organizations that take on data science often start with hiring a data scientist, one of the most in-demand jobs of the 21st century. While not an invalid approach, it’s one that comes with a hefty price tag. Many organizations already have people who operate in a data-scientist-type role, even if they don’t have that title, who are often specialists isolated in individual departments or workgroups. To expand the use of predictive and prescriptive analytics more consistently, finance leaders should find ways to leverage these existing resources throughout their organization before they look to outsource.
- Tap into the business citizen community
A flood of accessible, user-friendly data science products has let employees without data science degrees obtain advanced analytical capabilities. More products now offer detailed analytical guides, or augmented analytics, that can be easily understood by business analysts. These “power users” are typically positioned well enough to take on the role of citizen data scientist. Although this role cannot replace an accredited data scientist, it can complement and extend an organization’s analytics universe.
- Partner with experienced vendors
In some cases, organizations can partner with outside consulting groups or software providers to spearhead an analytics initiative. For organizations that are new to analytics, and data science specifically, or with no in-house skills to build upon, this is a model approach. Firms in this situation should first look to providers with not just predictive and prescriptive proficiency, but that also possess knowledge of their particular industry. Leaders should also ensure the partnership model provides for transfer of skills and lessons, and be cautious of the potential loss of intellectual property with this approach.