Predictive analytics: good governance holds key to accurate forecasting

Financial organisations need to be more agile and respond to digitally-savvy disruptors in the marketplace. 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 oceans of information about customers and target markets.

However, all the data in the world is worthless without the tools to use it in a meaningful way. That’s where predictive analytics can have a massive impact.

The benefits of predictive analytics

Predictive analytics combines artificial intelligence (AI), machine learning, data mining, and modelling to create incredibly useful forecasts.

While predictive analytics cannot tell the future with certainty, it can help financial services organisations visualise likely outcomes. This has huge applications for audience targeting, risk anticipation, marketing, and more.

>See also: More analytics – speed and agility through prediction

One of the best ways to deliver predictive analytics to users is through interactive mobile dashboards. 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.

Such self-service tools empower employees to import and model data, build interactive dashboards, and create reports on their own—freeing up IT team to focus on other critical issues.

Industry watcher Aberdeen Group found that financial services organisations that used predictive analytics enjoyed a 10% increase in new customer opportunities, an 11% rise in customer growth, and an 8% 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 role of governance in predictive analytics

Big data is typically categorised as structured (sales and website performance data) or unstructured (customer feedback data). Either way, 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.

The problem arises when all three departments fail to communicate with each other and use data for the greater collective good of the company. But with a sound strategy for governance, data can be organised, controlled, and shared with business users – allowing them to extract their own insights without the risk of corrupting the data for other departments.

>See also: What’s the block with predictive analytics?

Governance puts a process in place to ensure that teams throughout the organisation – from sales, to marketing, to finance, to IT – do not contaminate data.

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 tools are intuitive enough for the everyday business user.

Now business users can easily access data tools from the device they are most comfortable with 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 challenge of predictive analytics

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% 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 not, however, take those predictions as a guarantee of revenue for the quarter.

>See also: Heterogeneous predictive analytics: solving data management challenges

Problems around predictive analytics typically stem from data preparation, governance, and an over-reliance 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.

Getting started with predictive analytics

Before selecting a predictive analytics solution, financial services organisations first need to consider their goals. Once they understand their specific needs, and the insights they want, 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 financial institutions may be used to looking at historical data, they now have a chance to anticipate future outcomes by deploying predictive analytics. With the right strategy, technology, and governance, predictive analytics can provide a significant competitive edge.


Sourced by Hugh Owen, senior vice president of product, MicroStrategy

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Nick Ismail

Nick Ismail is a former editor for Information Age (from 2018 to 2022) before moving on to become Global Head of Brand Journalism at HCLTech. He has a particular interest in smart technologies, AI and...