Competency framework competencies are a bit of a munging of knowledge and skills. They're actually typically knowledge skills and also some kind of an attitudinal dimension or kind of behavioral traits that people have but basically common sees try to find. What does somebody need to know. And where the skills they need to have to perform the the certain specific tasks associated with a job so to build out a compensate framework. You have to kind of you have to unpack all that you have to really get into dissecting of the jobs figure out what those are figure out what are the actual tasks that tend to make up the job and even that takes some doing and then figure out within that what are the skills and the knowledge that drive that and break that all down into content that actually becomes meaningful that somebody couldn't can read and figure out gum can assess themselves against. I can figure out like ways in which they want to get they want to improve or get better or look for those qualities in others. So if you're a manager or you're interviewing how do you find those qualities when you're looking at other people come a ton of work went into building this competency framework. It is one of the things. I think the DAA should be most proud of having gotten done the last year. Because this is a very robust framework it was built in a very professional way. It was a significant investment. Not just of money but even more importantly of time by the board and of a lot of DA members to really help make this thing good so there's a core kind of group of eleven people that really shepherded this and sort of owned it but they built on literally. The input of hundreds of people hundreds of analytics professionals had input into this as well as like a set of people that we were working with outside consultants that have expertise in building competency frameworks and then doing that to a very high standard. So compensate frameworks that actually can be statistically analyzed to show that they really do differentiate between what one task is or another task the.

UM the Scopus was primarily us but the content was reviewed by numbers in Asia um in India and in Europe so it does have a somewhat you know global dimension and relevance to it gone and really now it's time to be bringing it out to you and so the framework was first released in February and this is something as DEA members you can go to this you can go to the DEA website digital analytics Association org you can download it for free it's a valuable piece of IP it's um it's kind of interesting reading I mean it particularly if you manage analytics professionals it's interesting but most competency frameworks and I speak from some experience here remember and then that the marketing community marketing professional development in Microsoft is part of my organization so I've got some experience and competency frameworks this is the underpinning of a lot of other things that you want to create the content itself can be can be a little bit dry this framework that was actually it's it's it's light enough that it really is it is pretty consumable so that's something that people have had available since since February but what's only interesting now is we're bringing out tools that enable people to get into it and so my first kind of call to action or thing I'd really like you to sit up and take notice of and think about like taking action on for today is there an the digital analyst competency framework so when you have a set of competencies one of the first things you want to do is enable people to self assess against those competencies. It is far more interesting to learn about a competency framework by going through a self-assessment than to actually try to read the thing and figure out where it relates to you. What you'd see in the competency framework if you actually look at it what you experience and you go through. The assessment is the competency framework identified two broad kinds of of types of roles in analytics.

There's a set of the roles that are really what we call two technical roles and those are roles that revolve around and getting that like acquiring the data organizing the data bringing it in the the tools and the and the systems that do that. And there's another set of rules that are - called the analytical track and those are much more about the what kind of analyses do you do and how do you actually work with the data to get some kind of a business impact from it. And then we found was that was that defining knowledge and skills really seem to gravitate to roughly three levels an entry level kind of a credit category of jobs. You know mid-level where somebody's got a few years of experience in their career they might be starting to manage other analysts they're definitely acting as a mentor they're helping to train other and other analysts and then and then senior where that person is actually leading a team of analytics professionals and when you look at the competency framework what you'd see is there's for each level there's around seven competencies that tend to be common to both tracks and then it branches out into here's the additional competency is usually another six to eight that are specific to analytical or technical so in total for any one level you're dealing with like a dozen to 14 seize its really pretty digestible and it means that you're able to get through this this comp this self-assessment pretty quickly. I mean I have gone through it. Numerous times as part of kind of beta testing and shaking it down. It's like a 15 minute exercise and it's really like it's not a big chunk of time for the return that you would get on it. You know this is a view of what the analytics of what an actual part of the competencies looks like the things that are in the light. Gray there those are were the actual specific competencies. And the. It's this is where it starts to get interesting when you look at what's in these competencies and how they're different by job level so for example the competency around attribution through all of these interviews and building this what it turned out is that compensate.

Attribution lis tends to gravitate to entry level and that was. I thought pretty interesting that that's a skill set that in general like you really want entry level an analyst early in their careers to be very solid odd conversely. When you get to that mid-level benchmarking is what starts to distinguish the analysts who really perform well there and at a senior level the something like this loved was of this this word prognostication which is I get kind of a foretelling. The future comes up numerous times and I mean it comes up in connection with people who are leading analytics groups are actually expected by you know by their managers and even their teams to have a sense of where the puck is going so what technologies are gonna matter what are gonna be the bait trends. What's gonna matter for their business. Yeah and just even though that small example is meant to show you how there are real differences that this thing calls out between how analytics is practiced at different levels. And so when you're thinking about hiring. I mean you want to obviously be tapped into that when you're thinking about your own career though it's really useful to be able to look kind of side to side and also sort of look up a level and see what are the things that maybe. I want to start building in my skill set to advance my career.