Predictive Analytics: Improving Financial Forecasting by implementing Good Governance

By Hugh Owen, senior vice president of product, MicroStrategy

The financial sector is in the midst of a digital transformation, which is forcing directors to reconsider traditional business models. The key to responding to digitally-savvy disruptors in the marketplace is agility. Hugh Owen, Senior Vice President of Product Marketing at MicroStrategy, argues that predictive analytics, supported by good governance, is crucial for financial services organisations to remain competitive in today’s marketplace.

The rapid advancement of technology in the big data space has created unprecedented opportunities for financial services organisations, as firms can now access vast amounts of information about customers and target markets. However, a large volume of data is worthless without the correct tools to extract meaningful insights. This is where predictive analytics can have a massive impact.

Why use Predictive Analytics?

Predictive analytics combines artificial intelligence (AI), machine learning, data mining, and modeling to create useful forecasts. While it cannot tell the future with certainty, it can help financial services organisations visualise likely outcomes. This, in turn has huge applications for audience targeting, risk anticipation, marketing, and more.

Interactive mobile dashboards provide one of the best ways to deliver predictive analytics, as these mobile apps put real-time information in decision makers’ hands at any location. This is just one style of self-service BI—a capability that allows individuals across the enterprise to easily connect to and analyse data, whilst simultaneously freeing up IT team to focus on other critical issues. Such tools empower employees to import and model data, build interactive dashboards, and create reports on their own.

Industry watcher Aberdeen Group found that financial services organisations that used predictive analytics enjoyed a 10 per cent increase in new customer opportunities, an 11 per cent rise in customer growth, and an eight per cent rise in upsell/cross-sell possibilities. It is clear that predictive analytics can have demonstrable benefits for financial services organisations, but there’s a catch: it must be implemented within a governed environment.

The Key Role of Governance

Data is often stored in departmental silos across an organisation. For example, marketing will take web and social media data and use it in a certain way to help achieve its objectives; sales will use its data to drive increased revenues; and IT will look at data with a view to improving the performance of the infrastructure. Big data is typically categorised as structured (sales and website performance data) or unstructured (customer feedback data).

Problems arise when all departments across an organisation fail to communicate with each other and use data for the greater collective good of the company. With a sound strategy for governance, data can be controlled, and shared with business users – allowing them to extract their own insights without the risk of corrupting the data for other departments. Governance puts a process in place to ensure that teams throughout the organisation – from sales, to marketing, to finance, to IT – adhere to strict data hygiene and avoid unintentional data manipulation, which can produce aberrant results.

With the growing popularity of self-service analytics tools, the need for proper governance is more urgent than ever before. The industry research group Gartner recognised this by highlighting the rise of the Chief Data Officer (CDO) in recent years. The CDO’s chief concern is breaking down internal silos and creating a single view of data that can be accessed across departments.

While not all organisations can hire a CDO, companies of any size can leverage the power of predictive analytics. Sophisticated analytics tools were once confined to the IT domain, but today, modern iterations are intuitive enough for the everyday business user. Now business users can use the device they are most comfortable with to easily access data tools and learn how different factors could potentially impact their organisations’ business performance. Those with access to rich datasets have the most to gain – they simply need the right team structure, culture, and technology to enable users to access the information.

The Limitations of Forecasting

Once an organisation has the cultural and technological structure in place to use predictive analytics to good effect, it is then essential to understand the challenges of interpreting that data. What can predictive analytics actually tell us?

First, it’s important to understand that predictive analytics helps companies gain clarity on what will likely occur, not what will definitely happen. For example, if a financial services organisation knows that 60 per cent of residents in a certain area will likely be interested in a discount on its products or services, the finance department can make revenue predictions based on the projected ratio of residents who are interested versus those who are not. The finance department should realise the limitations and not, however, take those predictions as a guarantee of revenue for the quarter.

Problems around predictive analytics typically stem from data preparation, governance, and an overreliance on the validity of the results rather than the analytics alone. One area where this can happen is financial forecasting, where a shortage of data or the quality of that data can impact results. For example, if data has been prepped or blended incorrectly, it can result in misleading or wrong conclusions.

Incorporating Predictive Analytics into a Business Intelligence Strategy

Financial services organisations first need to consider their goals, before selecting a predictive analytics solution. Once they understand their specific needs, and the insights they want to gain, they can find a vendor that matches their expectations and provides the right technology solution. To ensure long-term success, it’s critical to partner with a vendor that provides both the technology and the strategic support necessary for an effective predictive analytics programme.

Financial services organisations that aim to become more agile and data-driven need to take a forward-thinking approach to decision making. While these institutions may be used to looking at historical data, they now have a chance to anticipate future outcomes. With the right strategy, technology, and governance, predictive analytics can provide a significant competitive edge.