Deep statistical analytics is fun. It's also pretty far down the road as a need for most businesses.
My experience is that when people say analytics in the corporate world, what they really mean are simple reports. The only reason it is remotely high paying is that you get locked into a vendor (Microstrategy, Cognos, etc) and have to use consultants with those specific skills.
It's a hard field to break into because people are very hung up on specific tools, eg "oh, you haven't used Informatics PowerCenter 29.4.3 for 6.4 years along with Cognos Magic Report Web Engine 79.63.3 for 9 years? sorry . . . "
As an example of how simple what is mostly needed is: I spent some time working at one of the biggest financial companies in the US. Specifically on a project that was pulling data together from server monitoring (eg resource load), web app use ( hits on each web app deployed by the company), and cost info from the accounting teams.
There were two big goals - use some simple predictive models to understand resource (server) capacity needs in the future and to tie cost per user per app back to revenues generated per user. Amazingly - to me at least - this wasn't being done at any but the grossest levels.
Nothing deep there from an analytics perspective at all. I'm not ignoring that a few companies are doing crazy stuff with huge data warehouses, but most (even very big) companies aren't anywhere close to needing petabyte data analysis.
What is more challenging for most companies is building the capability to do the simple reports. This often involves delicate negotiations with groups reluctant to let you even have the data to build the report - it is a turf thing, eg "No, all financial cost reports come from us" or "oh, that's highly sensitive accounting info and can't be shared." You ignore this political negotiation challenge of analytics at great peril to your project. It is an order of magnitude bigger issue than actual analytics on the data.
You ignore this political negotiation challenge of analytics at great peril to your project. It is an order of magnitude bigger issue than actual analytics on the data.
This is absolutely true. For many projects, more time is spent negotiating with various data providers and waiting for them to fulfill their end of the bargain than actually doing something productive. And you are correct, it is mostly due to the fiefdoms that have developed over a long period of time which have then been codified in layers upon layers of politics.
Of course when you can break this down by feature and integrate multivariate tests, ad campaigns (inbound and outbound if that is your thing,) and so on.. the numbers can get complex, quickly.
Opinion & gut-based decisions are a terrible way to run a business. Numbers! You should be analyzing your numbers from day 0. I don't think that analytics should be an "eventually" thing, it should be deeply ingrained in your corporate culture from the start.
My experience is that when people say analytics in the corporate world, what they really mean are simple reports. The only reason it is remotely high paying is that you get locked into a vendor (Microstrategy, Cognos, etc) and have to use consultants with those specific skills.
It's a hard field to break into because people are very hung up on specific tools, eg "oh, you haven't used Informatics PowerCenter 29.4.3 for 6.4 years along with Cognos Magic Report Web Engine 79.63.3 for 9 years? sorry . . . "
As an example of how simple what is mostly needed is: I spent some time working at one of the biggest financial companies in the US. Specifically on a project that was pulling data together from server monitoring (eg resource load), web app use ( hits on each web app deployed by the company), and cost info from the accounting teams.
There were two big goals - use some simple predictive models to understand resource (server) capacity needs in the future and to tie cost per user per app back to revenues generated per user. Amazingly - to me at least - this wasn't being done at any but the grossest levels.
Nothing deep there from an analytics perspective at all. I'm not ignoring that a few companies are doing crazy stuff with huge data warehouses, but most (even very big) companies aren't anywhere close to needing petabyte data analysis.
What is more challenging for most companies is building the capability to do the simple reports. This often involves delicate negotiations with groups reluctant to let you even have the data to build the report - it is a turf thing, eg "No, all financial cost reports come from us" or "oh, that's highly sensitive accounting info and can't be shared." You ignore this political negotiation challenge of analytics at great peril to your project. It is an order of magnitude bigger issue than actual analytics on the data.