BI and Tech
Predictive analytics: An essential tool or an unaffordable luxury?
We’re increasingly being asked questions like: “what can Analytics do for me?” and “how is it different from business intelligence?”
It’s no wonder we all struggle with the meanings of all the different buzzwords surrounding the industry. Stories around AI, robotics, machine learning and analytics are often jargon-filled and not always easy to understand. So, here’s the 101 on what we mean when we talk about analytics.
BI and analytics tools help users understand what is happening within their business
It’s worthwhile starting with what we mean by Business Intelligence tools. BI tools use existing historical data to deliver business insights. They are great for improving access to data, visualising data and helping users to make sense of data.
This helps businesses make better decisions. Business Intelligence tools rely on looking at the trends, patterns, highlights and exceptions in data which then gives meaning that information. So how does analytics differ from business intelligence?
Predictive analytics goes a step further
In a nutshell, analytics also uses historical data, but it then provides users with access to previously undiscovered insights by predicting future outcomes.
It’s this predictive capability that sets Analytics tools apart, as it enables users to build a model which then exposes the most likely indicators which will produce a certain outcome. This enables users to take decisions to change or influence those outcomes.
Much of what we mean when we talk about AI, machine learning and analytics has been around for a very long time. In Finance and Insurance, it’s ‘business as usual’ for lenders and insurers to use data to identify customers who are high risk versus low risk to manage liabilities. This is achieved by identifying those factors that are the most important indicators for an individual’s calculated risk.
Predictive analytics is commonplace in the Finance industry
Financial organisations use their vast databases of historical data to look at all these factors affecting risk, so they can then apply those learnings to new data and predict how much of a risk a customer represents. They can then set a lending rate or premium at a suitable level.
Most of us have experienced the pain of applying for insurance or loans and the seemingly endless list of questions before we get our quote. Put simply, this is just a process of the insurer collecting all the risk factors associated with you as an individual.
Once they have this information, they can predict a risk factor for you with the data you’ve supplied, using a Predictive Analytics model to calculate a risk.
Analytics use cases in Insurance
Let’s consider another example. If you’ve got a young person in your house, who has recently passed their driving test, you’ll be aware of the painfully high insurance costs which recently qualified drivers are charged because they have been identified as ‘high risk’ drivers.
It’s not hard to work out that a combination of age and length of driving experience are the key indicators which insurers are using to assess the risk factors in this instance.
How do they know that these newly qualified drivers represent a high risk? It’s all based on their analysis of historical data which is then used within a model, and learnings are then applied to the ‘new data’ gleaned from that seemingly endless list of questions you’re asked when applying.
Affordable analytics for SMEs
So, is analytics just something for big financial institutions or enterprise-level organisations with big budgets? Definitely not! There are now simple self-service Analytics tools which are affordable and accessible to any size of business.
This is important because it’s the experts in your business that understand the context of your data and can apply the learnings to real business problems.
There are few small businesses that can afford their own data scientists, so it’s much better to provide the experts in your business with easy-to-use Analytics tools and develop these capabilities in-house.
What can analytics do for you?
So why should you care about analytics? The data in your business represents a treasure trove of insights which can help improve performance. Businesses that want to respond to competitive pressures and grow their businesses need to get on board with using affordable self-service analytics.
This will allow them to increase efficiency, improve performance and deliver competitive advantage through the better use of data. If you start by asking ‘what if I knew…’ questions within your business or organisation, you’ll soon see how powerful analytics can be.
Pi Analytics could tell you the following:
- ‘What if I knew which customers were most likely to be late paying their invoices? Once identified, I could arrange for credit control to focus on those customers and improve cash-flow.’
- ‘What if I knew which product lines are likely to go out of stock? If I could predict those product lines at risk, I could order in earlier and ensure sales still go out of the door on time.’
- ‘What if I knew which students were most likely to drop out of their course? I could work out what interventions to put in place for this group of students to keep them studying and stop them from dropping out.’
- ‘What if the NHS could predict which patients were most likely to miss their appointments and what the most important indicators were for those patients? Let’s say age and distance from the hospital are the most important characteristics influencing this. What measures could I put in place to prevent this to save costly resources from being wasted in the NHS?’
In each of these scenarios, there’s a deep insight from data that those closest to the problem might have some insight around, but otherwise those learnings would not be available.
Analytics allows the organisation to use data to predict and validate an outcome which allows them to consider what action to take. In each of these cases there’s a real business benefit which has the potential to deliver cost-savings or increased revenues which affect the bottom-line.
And that’s the power of analytics. Predictive Analytics can sharpen decision-making in ways that leave traditional approaches far behind.