Join Alan Simon for an in-depth discussion in this video Selling prescriptive analytics to your organization, part of Business Analytics: Prescriptive Analytics.
- Hopefully at this point you have a very good idea by now of how prescriptive analytics can be one of the most important initiatives that your organization can ever undertake, particularly if your organization is already investing large amounts of money in big data, predictive analytics and other core technologies. Now though your job is to convince others in your organization, executives, stakeholders, budget holders, of how important prescriptive analytics actually are. Beware though, this will not be an easy sell for you.
In fact, here are some of the many objections that you're likely to hear the first time you bring up the idea of prescriptive analytics but don't worry, we'll look at some powerful counter arguments for you in a moment. You may hear that prescriptive analytics are just another trend, that it's way too early in the lifecycle for your organization to spend any time or effort in this discipline. You may hear something along the lines of "We tried all this data-driven stuff in the past "and we've never really gotten any "business value out of it." Some people have a natural skepticism towards new concepts such as prescriptive analytics and assume that they've only been invented by consultants to sell customers expensive systems that aren't really needed.
And especially if your company has already invested a large amount of money in big data, predictive analytics and other core technologies, the argument you might get on that point is, "What's so great about prescriptive analytics, "we're already doing a lot of this stuff anyway." Here's a list of counter arguments for you, point by point. If someone tells you that prescriptive analytics are just another trend you can counter with the argument that actually prescriptive analytics are the convergence and culmination of a number of disciplines, many of which have been around for a while, all coming together with incredible synergies with one another, and that's what we've put together as part of prescriptive analytics.
If someone tells you it's too early in the lifecyle to try prescriptive analytics or to put any efforts into it, your natural counter argument would be that if you're making any investments at all in analytics, this is where you need to be anyway, that if you aren't taking actions as a result of those analytics, then why is the money being spent in the first place? Over the years, as we've seen, many organizations have had less than wonderful results with their data warehousing and their business intelligence and there's a natural resistance to any efforts that involve driving insight-sided data.
One of the points you should make though is that the past shortcomings have primarily been business-process-related and human-factors-related, far more so than technology and in fact that's one of the key advantages of prescriptive analytics, and by following a prescribed workflow you overcome a lot of those shortcomings and allow the technology to do the job that it's able to do. A good counter argument to someone telling you that prescriptive analytics are only invented by consultants to sell expensive systems is to try a small, high-value pilot project first to prove out the concept.
Take one of your most critical business processes that you're already doing analytical work in and then follow it all the way through our prescriptive analytics workflow and see how that works out over three months or six months, and then prove out the value that way. And finally, if you hear that all of the investments in big data and predictive analytics are already there anyway, counter with the point that unless you get to the point of prescriptive actions you're only producing insights, which may or may not have any business value and may or may not ever lead to any action.
You can also sum up all of your individual counter arguments this way. If your organization is not confident that you can always take definitive action based on what's really happening and what you're seeing in the data, you will be at a major competitive disadvantage.
- Exploring the analytics taxonomy
- Understanding prescriptive analytics fundamentals and workflow
- Looking at data warehousing and business intelligence
- Exploring big data
- Collecting and processing data
- Exploring triggering events
- Formulating business hypotheses
- Refining and enriching business hypotheses
- Reaching definitive conclusions
- Putting the finishing touches on prescriptive analytics