Which is not as large of an interval as it may seem. Clarke's Third Law may have come from science fiction, but it is applicable here: Any sufficiently advanced technology is indistinguishable from magic.
Think of the many businesses that suffer from the Establishment Curse: they are so large that their data systems are an amalgamation of different peoples' plans (CIO is a 3 year gig), legacy systems patched together, and business processes that last made sense 10 years ago. Of the businesses that do not suffer from this, many are tiny. These are frequently plagued by The Investment Dilemma. New businesses in a constant state of flux need to come up with a plan for data management (and data use!) while they are still struggling with planning for basic business success. The planning time, the systems, the people: It all costs money. If they spend too much time thinking about the future, they'll never make it there. If they don't spend enough time, they will wind up in the same, costly establishment curse as a large business.
If you can overcome these realities, you will look like a damn magician. Like any other science, business analytics can best be defined by the problems its solves:
Think of the many businesses that suffer from the Establishment Curse: they are so large that their data systems are an amalgamation of different peoples' plans (CIO is a 3 year gig), legacy systems patched together, and business processes that last made sense 10 years ago. Of the businesses that do not suffer from this, many are tiny. These are frequently plagued by The Investment Dilemma. New businesses in a constant state of flux need to come up with a plan for data management (and data use!) while they are still struggling with planning for basic business success. The planning time, the systems, the people: It all costs money. If they spend too much time thinking about the future, they'll never make it there. If they don't spend enough time, they will wind up in the same, costly establishment curse as a large business.
If you can overcome these realities, you will look like a damn magician. Like any other science, business analytics can best be defined by the problems its solves:
First Fundamental Problem Of Business Analytics
Data is difficult to find, complex to aggregate, or non existent.
Commitment-phobic startups and inertia-driven behemoths wind up with the same problem: their data sits in largely unusable pools. ERP systems, Excel files, and even external vendors (think Concur and AMEX) hold companies' data hostage, or, even worse, no one holds it at all.
I know you're thinking that there are about a million companies trying to cover this base. From SAP to TurboTax, there are tons of options for companies to capture all kinds of data. The issue is that every one of those companies wants to be the only solution. Over time, requirements change, acquisitions are made, and you wind up with tons of "solutions", which is a real problem.
I know you're thinking that there are about a million companies trying to cover this base. From SAP to TurboTax, there are tons of options for companies to capture all kinds of data. The issue is that every one of those companies wants to be the only solution. Over time, requirements change, acquisitions are made, and you wind up with tons of "solutions", which is a real problem.
Second FUNDAMENTAL PROBLEM OF BUSINESS ANALYTICS
Businesses are not staffed to quantitatively answer business questions.
Even if most companies could wrangle their data into a usable format, they are unlikely to know how to use it effectively. In the 2010's, there are still a shocking number of people who don't know what kinds of decisions should be driven by data. Few people have the quantitative backgrounds required to formulate problems in a mathematical way and fewer still have the technical ability to engineer enterprise-scale solutions to those problems.
third FUNDAMENTAL PROBLEM OF BUSINESS ANALYTICS
Quantitative results must be implemented, not just communicated.
For argument's sake, let's say you've staffed up your startup with a bunch of PhD's and computer scientists who can formulate the problems and deploy methods of efficiently solving them. How do you actually connect the solution of a quantitative problem to a business decision? Do you want to put another report in front of the same person who has been making a decision the same way for 10 years? Will that actually move the needle?
Depending on the maturity (or state of decay) of a business and the problem at hand, these questions all may have different answers, and I've turned solving these three problems into a rewarding career.
Arming businesses and people with the ability to develop their solutions to the problems above into a cohesive, cogent strategy with an intelligent, quantitative approach that never forgets we deal with humans is the essence of Business Analytics.
Arming businesses and people with the ability to develop their solutions to the problems above into a cohesive, cogent strategy with an intelligent, quantitative approach that never forgets we deal with humans is the essence of Business Analytics.
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