The smartest way to make a big world seem small

Business Intelligence

Page One
Part 1 of 4

Business intelligence and analytics developed with infrastructure availability and the demanding nature of industry sectors that have adopted digital transformation. Moving ahead, IAAS and SAAS made the software and hardware available for even average consumers to try BI & analytics tools as if they had fully-stacked teams of analytics professionals at their fingertips.

There is a cost associated with this power. The mystery that comes into play is that well-trained teams of business analysts and data scientists manning Fortune 500 analytics teams are only as good as the tools they use. On the surface, these tools can range from the command line interface for DevOps to software engineering tools for data scientists. Something that analytics professionals and BI software end-users have in common is that they have a knack for retaining mission critical business information. In spite of sifting through piles of data and bouncing from one software package to another the tools of the trade stay the same.

A large clothing maker has been given the word from upstairs “make their new clothing line more attractive to adult women who are in a relationship”. They have a few other pieces of information, getting clear pictures of the ideal group of shoppers and market research from a vendor. The company doesn’t handle design, production or distribution in-house. In fact, every team in the company boils down to an extension of a vendor. Ensuring that the various managers instructions are carried out in order to meet the companies quarterly financial numbers. These high-level tasks involve keeping the vendors inline while keeping close eye on all sorts of metrics that aren’t published anywhere publicly. Typically, everything works flawlessly. The new instructions from upstairs, aren’t anything new for the different teams to do.

Is there anything wrong with this picture?

From the perspective of anyone in the organization at any particular moment there likely aren’t any issues. Fortunately, customers that are being targeted as buyers of the new line of clothes don’t actually exist except on paper. When the company launches the line of clothes, performance will never be measured or reviewed by the different teams. The team operates in a silo because data stays where it began and is shared minimally through a manager.

Everyone has to have a customer, right?

A glaring problem is that “you don’t know, what you don’t know”. Teams that don’t have ways of being able to passively filter decisions; also use the status quo as a counterbalance for a lack of reliable and accurate insight. Keeping it simple, digital transformation takes any common business intelligence property, classifies it, and employ’s proprietary methods to create a competitive advantage.

Business intelligence sounds unapproachable to even software power users. The 20 years of BI software development has produced more user-friendly software. No-code apps and incredible flexibility to manipulate data at the click of a button are examples of modern features. Now for the kicker, all the usability gains and capability increases made everything more approachable to address the learning-curve. Simple functions like adding new patient’s insurance information, is still hidden behind mountains of implementation details that can make even the most experienced person on the team scratch their head. There is nothing the end-user can do but move on to the next priority. With data-driven insight, an approachable method makes even the unknown become part of a well-oiled machine.

“Clean data is important when doing analytics. Taking into account non-trivial human factors and trivial human errors. Just don’t confuse the two.”

Depending on the software package available, an end-user could do machine learning on variance, training a model to identify human error all with a no-code visual algorithm editor. Of course, that is not a trivial task. To avoid flying in the face of reason – the business analyst – who has years of experience in the same market or industry can’t do the machine learning task without a business intelligence tool. All the ultra usable software platforms have hidden that fact from the consumer. This white lie, giving end-users the power to do anything the situation prescribes works best with the added constraints of a managed team environment. In the end, there is no substitute for hard-earned knowledge and experience. Listening to your gut on an empty stomach at the office could reveal unexpected surprises. Unpacking that further makes small evil’s look like big trouble without a proper data governance policy and decision-making process.

In the previous clothing company story, there is a sign of what is wrong no one is paying attention to. That is when someone says something about what is wrong the whole ship might sink. So everyone does their best to keep their head down and keep moving. Nobody typically jumps to think of a solution for an employee theory that is not their own.

In a sub-standard organizational environment. If everything is coming along fine, why rock the boat?

Well, let’s consider the holes in the ozone layer. The thing that is breaking is far removed from the source of the troubles. Pardon the green analogy. Staying neutral in the matter means harping on bits of information that produce the most sensible follow-up. On the other hand, introducing digital transformation in this series without personal biases won’t yield enough continuity to tie the articles together.

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