Representative End User Clients
Representative Automation Clients
Representative Software Clients
CEP, BI, VDD, OI, ESP, OLAP - the analytics world is awash in TFLA's. ( That's three and four letter acronyms by the way...). With so many new technologies being trumpeted - and so much impenetrable jargon being thrown around - it's easy for the basics to slip your mind for a moment. I was reminded of this when I did a strategy session with one of our manufacturing clients recently. The client was in pretty good shape as it turns out.
But, I reviewed the fundamentals of effective business intelligence, operational intelligence and analytics with them anyway. And I'll do that with you too now in a series of three short posts. It's worth noting too, that data generated by the Industrial Internet of Things will not change this. It'll just make it more difficult, because of the volume and complexity of data that will be coming your way.
So, first off, let's not lose sight of why we do any of this business intelligence and analytics stuff anyway. High performance, cost effective analytics (or BI, or OI) boils down to two things at the most basic level:
Basically, it doesn't matter what else you do, if the user population you serve can't find the information they need in time to influence their decisions, you've failed. It's that simple.
In practice, you're not likely to succeed every single time, for every user. But, top performing analytics teams should be aiming for a frequency of 90%+. Or, to put it another way, at least 9 out of 10 management decisions should be supported by accurate, relevant, timely information. There will always be some wiggle room in that metric, depending on the size of the user population, the diversity of user needs etc. And it's not a bad idea to formally survey your users and ask them (amongst other things) “How often are you able to find the information you need in time to impact the decisions you need to make?” and "What is the business impact when you don't have the info you need to guide your decisions...?“ That can provide a great way to help you focus your attention in all the right places…
So, the second part of the puzzle is the lowest possible cost bit. Typically, about 2/3 of the annual cost of BI or analytics is in the people needed to install, deploy, build, maintain, and support it. (Or, try here for a slightly nefarious copy...). So the obvious area to gain cost efficiencies is optimizing how you use your technical team. And that leads us to two parallel strategies.
I'll write more about how and why to execute each of those strategies in the next two posts.
But, I reviewed the fundamentals of effective business intelligence, operational intelligence and analytics with them anyway. And I'll do that with you too now in a series of three short posts. It's worth noting too, that data generated by the Industrial Internet of Things will not change this. It'll just make it more difficult, because of the volume and complexity of data that will be coming your way.
So, first off, let's not lose sight of why we do any of this business intelligence and analytics stuff anyway. High performance, cost effective analytics (or BI, or OI) boils down to two things at the most basic level:
- Get the Right Information, to the Right People, at the Right Time…
- .…at Lowest Possible Cost
Basically, it doesn't matter what else you do, if the user population you serve can't find the information they need in time to influence their decisions, you've failed. It's that simple.
In practice, you're not likely to succeed every single time, for every user. But, top performing analytics teams should be aiming for a frequency of 90%+. Or, to put it another way, at least 9 out of 10 management decisions should be supported by accurate, relevant, timely information. There will always be some wiggle room in that metric, depending on the size of the user population, the diversity of user needs etc. And it's not a bad idea to formally survey your users and ask them (amongst other things) “How often are you able to find the information you need in time to impact the decisions you need to make?” and "What is the business impact when you don't have the info you need to guide your decisions...?“ That can provide a great way to help you focus your attention in all the right places…
So, the second part of the puzzle is the lowest possible cost bit. Typically, about 2/3 of the annual cost of BI or analytics is in the people needed to install, deploy, build, maintain, and support it. (Or, try here for a slightly nefarious copy...). So the obvious area to gain cost efficiencies is optimizing how you use your technical team. And that leads us to two parallel strategies.
- Formalize the approach to analytics projects
- Empower self-service
I'll write more about how and why to execute each of those strategies in the next two posts.