Predictive analytics can help recognise the characteristics and behaviour of individuals likely to commit fraud, identify customers with the highest risk of attrition, and better engage audiences through personalisation.

However, these scenarios are a distant dream for most organisations. The more commonplace scenario is businesses faced with hefty challenges to reaching a stage where predictive analytics has a sizeable impact on their bottom line.

IDC predicts that 30% of CIOs will have introduced an organisation-wide data and analytics strategy by 2018. Analytics is clearly on the agenda, and will continue to inch its way up the priority list.

So what’s the block with predictive analytics? Perhaps unsurprisingly, the most pervasive challenge is that well-known resource constraint. With IT budgets continuing to feel the pinch and skilled data specialists in short supply or stretched to capacity, it’s hard to get the job done.

Individuals within organisations are likely spending a lot of time chasing colleagues to get the information they need. This dependency wreaks inefficiency, and before you know it business opportunity after business opportunity falls through the net.

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That’s frustrating for any good businessperson or dedicated staff members, and it’s clear to see it doesn’t make business sense. Even those equipped with an engaged and responsive IT team can find it challenging.

Preparing the correct dataset is no small feat for even the crème de la crème of data scientists out there. It is simply an extremely time-consuming process.

Businesses have access to solutions to make data preparation much faster, easier and automated. These alleviate the pressure from the data experts, enabling wider teams and line-of-business users to work with the data themselves.

However, they don’t address the associated challenge: needing to know and understand the specific types of data, and the volume of it, that is required to answer the business issue faced ahead.

What’s more, it takes time to learn when predictive analytics should be used for maximum impact, and that’s before we’ve even thought about understanding and navigating the intricacies of specific analytic techniques.

That’s no fault of line-of-business users. The responsibility lies with the developers of predictive tools – they’re too complex.

Lots of line-of-business users know some standard techniques and can recognise the situations they should be applied to, such as looking at which customers are most likely to engage with a campaign. But the knowledge gap soon becomes clear when more complex techniques come on the scene.

What about Monte Carlo simulations or optimisation? These are the techniques data scientists and expert statisticians use, so why shouldn’t everyone else?

And that’s only the first part of the job. Once you’ve applied the techniques, it’s about interpreting the results as well, so you need to make sure you’re working with the right tools.

Simple is best

So what can organisations do to overcome these challenges and give business users the power to run their own predictive analytics?

It starts with a commitment to wanting to give business users this autonomy, and having the confidence to move away from the trusted IT department and its supporting resource.

Businesses need to make data preparation a priority. There’s no excuse not to. The age-old saying ‘you get what you give’ is true here – a predictive analytic model is only as good as the data that goes into it. If the data’s not right, building a predictive model will soon become a long and frustrating process.

Last but not least, organisations must make sure they choose the technology that best suits and supports the business users’ skillsets.

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On the outset, it might sound great that the product utilises R, or can support SAS code, but if the people working in the business don’t know these languages, they’re not going to use them.

To put the power of predictive analytics in the hands of business users, analytics software vendors must make it easier to not only build predictive models but also to understand and consume the outcomes.

By eliminating the need for coding, simplifying modeling techniques based on users’ various skillsets, implementing automated modeling, and even creating wizard-based systems or applications that walk users through a process of data preparation and predictive modeling, vendors will provide the tools needed to positively impact an organisation’s bottom line.

It’s crucial to find the solution that’s best for the business. Once that’s achieved, businesses will enjoy greater productivity and the opportunity to get much more done than ever before.

Sourced from Matt Madden, director of product marketing, Alteryx Inc.