Business Analytics: Getting Value From Data

Amazing view from Haozhi's Beijing apartment (PRISMA)

Business Analytics exists to...

On my current business trip to our Beijing office I was asked to give an overview of what Business Analytics does. It's a very good ask; Business Analytics is a relatively new function and means different things at different companies.

At FreeWheel, business analytics exists to...

Manage and analyze Enterprise Data to help decision makers make better decisions.¹ 

To most of you that probably sounds like white noise: a wave of words with very little information. The goal of my Beijing presentation is to make that statement meaningful; this blog post is an overview of part of that presentation.

I'll start by reviewing a framework that describes the phases of data refinement (how data gains value). Then I'll introduce an approach of applying knowledge to drive business results.

¹ Philosophically, we strive for a hybrid analytics culture where Bus. Analytics may do the analysis or may empower other departments to do their own by accessing our managed, central EDW (enterprise data warehouse). More on this in a future blog post.  

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Data, Information, Knowledge Framework

Data, Information, and Knowledge are words that are colloquially used interchangeably, but have very specific meanings in Business Analytics.  They each describe a different phase in the transformation of facts into insight.  Yes, this sounds painfully pedantic.  Stay with me for a minute because it's quite useful for understanding how to refine raw measurements. 

Full disclosure: DIK is actually 3/4 of the DIKW pyramid (Data, Information Knowledge, Wisdom), but I prefer just the first three for this purpose.

Here's a short story that describes the terms above (stolen from this video):

I think this is what a factory looks like.  Don't ask me, I make software.

Imagine at 8AM you are walking down a factory floor.  You are walking along a pipe.  On that pipe you come across a pressure gauge; itt reads 15 PSI.  This is a fact.  That is data.

• data:  Data is raw, unorganized facts[2].
  At 8AM, on that specific pipe, the pressure is 15PSI.

You continue walking for a while.  Then you go up some stairs and into a room.  This room is the control center and it has a monitor in it.  The monitor shows a graph of pressure over time, and it shows the pressure in that pipe is rising very rapidly.  That is structured data.  That is information.

• information:  Aggregated, organized, structured data presented with context[2].
  Between 7AM and 8AM, the pressure in the pipe you walked past has raised evenly from 1PSI to 15PSO

Interesting, the pipe pressure is increasing.  What do you do?  Well, it's hard to say because you don't have any expectations or understanding of this pipe.  Does this pipe do this every morning?  Maybe this isn't normal, and the pipe is in danger of critical failure!  Maybe that's what you want because you're testing what happens under critical failure.  The point is, that information is only valuable if you have a conceptual model for the situation.  That is knowledge.

• knowledge: Knowledge is a collection of information, beliefs, and expectations that form understanding.  It is the most reformed and useful of the three.  With knowledge, we know what actions will have the best outcome.  [4]
  The pipe shouldn't be doing this!  The pipe is in danger!  Emergency release!

Data Refinement Flow

The take-away is: 

1. We want knowledge because knowledge is the only thing that can influence decisions.  In other words, only knowledge has any value.
2. Knowledge can be gained through analysis of data and the subsequent interpretation of information.  

To Find Answers, Start with a Question

The DIK framework outlines the technical approach of generating knowledge.  Before I wrap up this post, I wanted to quickly introduce how to practically generate business value from that knowledge.

The above DIK framework makes it sounds like you can put a bunch of measurements into one end of a machine and shoot out decisions from the other (at least that's what it sounded like to me!).  However, the only way for those answers to deliver business value is if they inform some decision that drives some action with business value. A good way to ensure that happens is to start from the result we want to achieve and work backwards.

This image is borrowed from Peter Murray

1. Start by identifying the desired results.  This may be a specific Operating Plan or goals from an initiative.  You may begin with a general idea like: do X better, but it's critical to be specific.  Being specific enables result tracking and ensures different departments are aligned on what success looks like.
2. Next, consider the actions you believe are required to achieve those results.  These can be broad like: make people happier, or specific like: change our pricing to increase revenue.
3. Then, consider questions that inform those actions.  Those are the questions you want to answer with knowledge.  These must be testable and quantifiable.  

There's a lot more to this approach.  Stay tuned for more in future blog posts.

Business Analytics in a Nutshell

I found this handsome guy in a Beijing 7-11 today

That's what Business Analytics does at a very high level.  On one side, we will work with decision makers to build understanding around decisions they need to make.  On the other side, we build and manage the enterprise's Information that helps inform those decisions.  By being data-informed, FreeWheel makes better decisions.  

Sorry for such an abrupt stop to a meaty topic.  Expect a future post on how you tactically do this.