If your company hasn’t embraced predictive analytics yet – but you’re thinking about it – your first step should be to map out a strategy. Even if you may already be dabbling in it, don’t go much further until you have thought through a solid plan.
It’s one of the more complex technologies out there right now, and without a comprehensive strategy and roadmap for what you want to accomplish, you could find yourself knee-deep in a predictive analytics quagmire.
As John Crupi of Greenwave Systems has said here previously, interest in predictive analytics is skyrocketing thanks to the availability of open-source tools and the ease of using the cloud to process an immense amount of data. The analytics systems now have the advantages of machine learning and artificial intelligence to sift through virtual oceans of data and determine the patterns that can help you predict the future.
What’s your goal?
So what is it, exactly, that your organization wants to accomplish? Is the primary objective to increase sales? Better understand customer wants and needs to guide future product development? Detect fraud? Determine optimal product maintenance schedules? Whatever that primary objective is, it should guide your strategy.
From there, you need to assess your company’s overall readiness for predictive analytics, through a close examination of the volume of your data, your data history, formats in use, and how the data overlaps across various systems and processes.
And as is recommended with so many technologies, before you implement predictive analytics on a large scale, experiment a bit. Do some informal testing to better understand how it can help you address real-world business situations. Perhaps start with areas that are already data-heavy, such as your customer service or marketing departments.
If you aren’t the originator of your data – perhaps you are obtaining it from an outside provider and the quality of it is beyond your control – reviewing it for quality should be your first step. If you don’t, you could find yourself in the midst of the analytics process facing a lot of re-work.
You don’t have to be obsessive about it, because the analytics tools have advanced to a point where they won’t necessarily choke on not-so-quality data. The artificial intelligence and machine learning capabilities of today’s tools can help deal with data that may be less than top tier. Still, there’s no doubt that cleaner and higher quality data will yield the best results.
But when it comes to quantity, you don’t want to be overwhelmed. So stay on top of the volume. Typically, if you’re drowning in data it’s more than likely because there’s too much irrelevant data mixed in with the good stuff. And when you jam too much of the wrong data into predictive analytics tools, you end up slowing down the process, gumming up the workflow, and missing opportunities to leverage the quality data as quickly as you would like.
The key to managing your data volume is in clearly understanding exactly which data sets may have value and which you can ignore. That is where data scientists can prove their value, because they have the skills to make better decisions about which data is relevant and what can be dismissed.
Predictive analytics is more successful when the systems – as complex as they are – are designed to deliver results that are easy to understand yet thorough and actionable. Too many organizations that have gotten into analytics end up mired in results that are understandable only to the most skilled data scientists.
The designs, particularly the user interfaces, need to be crafted with end users in mind. Keep the sophistication and complexity behind the screen, with the interface as simple as possible.
As you launch a predictive analytics model, learn from it in terms of usability, and tweak it for later iterations. The flexibility of the technology and the tools allows that, so take full advantage of it.
Especially in these confidentiality-conscious times – and with the regulatory constraints of GDPR and other data privacy regimes – another priority should be the utmost security of the data that you are working with.
For example, restrict file access and use to that data specifically needed for analysis. You can also mask data fields that identify individuals to reduce risk.
Don’t break the bank
Predictive analytics costs can run up quickly, from the investment in the data to the salaries of the data analysts to the cost of storage to keep up with your data stockpile. In order to keep costs from getting out of control, it’s important to make sure you are spending your money wisely in terms of the data you are obtaining and that you are also managing your data efficiently, perhaps through a combination of a centralized repository and good governance.
Because of the growth in the predictive analytics space, there are many platforms and tools available. This makes it tough for enterprises to make the right choices, since few have the in-house talent to guide those decisions. Your best bet – unless you are already blessed with data talent or are willing to be patient and make the investment of time and money to build a crack staff – is to seek external guidance for your predictive analytics planning and implementation.
Just be sure that any solutions recommended will be a good fit for your overall strategy. You don’t want solutions that lock you into specific analytics algorithms or learning stacks. You want to be able to dynamically upgrade as time goes on.