Intelligent Metrix

Data to Metrics to Insight to Intelligent Decisions

Social Media: Back to Spreadsheets

It’s a dirty word right now – spreadsheets.  IT departments want to remove our dependence on spreadsheets and convert us over to a secure, controlled, shared, and robust analytic environment.  I would love that!  But, I have a problem, social media.

I’m managing more properties and content that is outside the realm of my corporate environment but I still have to report back and show how it is doing.  The only way I can do this is by using several analytic tools across multiple properties.  I grab the stats I need and punch that into a spreadsheet.  Then, I go to my web analytics reports, grab those stats, and consolidate them with my social media data on my spreadsheet. After that, I consolidate my lead metrics with my internet metrics for a 360° view of my marketing efforts.

It is all very time consuming and open to data entry error.

Business Intelligence is great to track internal process, but it is doing nothing to help track activities outside the corporate environment.  So, I’m stuck with spreadsheets.  Can you help?

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Business Intelligence: Decisions, Decisions

Business Intelligence is all about supporting business decision.”

How many times have you heard that?  It’s become the standard mantra.  It is so ubiquitous that I don’t think anyone questions anymore the validity of the statement.  It just is.  However, this is probably the hardest part to facilitate when building out you business intelligence practice.  Facilitating decisions is what makes BI stragetic.

Just what is the business decision? What does a business decision look like?

Elements of a Business Decision:

  • Purpose:  drive a business outcome – ex: revenue, shareholder value, profitability, market share
  • Position:  leads a company, division, department
  • Point in Time:  transition along a process or environment

A typical approach during the business analysis phase for BI is to at business decisions across a business process and where questions are asked to change behavior in that process.  Although, the difficulty with this level of granularity is that it is too deep.  These transition points are tactical.  Intelligence across this process and at these decision points is important, but you don’t get the strategic value of BI at this level.  You need to look at the outcome of the process and provide a platform that supports the decision of what to do next.  This is the unstated question.

Let’s take an example.  Sales management will always want a perspective on the pipeline and forecast.  This shows them how they are meeting their numbers quarter to quarter.  However, outside of conversion and volume, there are business decisions that sales managers need to make.  Should they adjust their territories to capture new opportunity or shore up existing business?  Are there changes needed in commissions to incent sales people along certain products and services to improve profitability or revenue?   BI can lead sales management with insights that will guide them to optimize their processes and management rather than just data.

Purpose:  market share, revenue, profit
Position:  sales
Point in Time:  aligned to quarterly pipeline and forecast

To align BI to the business decision it is important to include executives in the discussion.  Get beyond the reports they want to see and ask the question about how they manage their business.  Walk through scenarios of what they ask as changes in the market or the business arise and how information can help them make a decision.  The better able you are to see how they manage their business, the more valuable the BI practice will be to supporting the business.

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Stuck in First Gear

porscheBig investments were made in recent years in IT.  IBM, Oracle/Siebel and SAP lead the market and were successful not only with the multi-national enterprise companies but, also with mid-sized companies.  There are a lot of companies out there that have purchased application and data management/data warehouse solutions only to find themselves using a portion of what it could do.  It’s like driving a Porcshe in first gear.

There are some fundamental reasons for this, outside of the fact that companies may feel it is the fault of their sales execute selling them the wrong bill of goods.  IT will blame the business for not knowing what it wants.  The business will blame IT for not getting it.  Doesn’t really matter, there is plenty of blame to go around.  What matters is that now you have a solution that isn’t giving you the benefits that it really could and should be.

Maybe I’m a bit biased since I’m the data chick.  Well, more than a bit.  Regardless, I think that from a data management perspective, companies are failing.  The maniacal focus on process efficiency has drowned out the fact that process runs on data and feeds data.  This focus has put data in the back seat too long and now when we need it to better understand our customers, our business, and make decisions, it is sorely lacking.  Our data lacks unity, structure, definition, and most of all purpose.  Companies simply cannot leverage their information except at very basic levels.  When things are good, this may be okay.  When things are bad, this is a real problem.

What makes this even more sad, is that companies are looking to spend more money on applications and data infrastructure to ‘fix’ the problem.  The promise of the new model and more sophisticated bells and whistles that will solve anything you throw at it is just marketing.  Until you can understand and control what you already have under your hood, getting something bigger, better, and shinier isn’t going to help anymore than it does now.  So, there was no ROI on existing purchases and there won’t be any ROI on new purchases.

There are two things companies need to do to make the investments in enterprise solutions worthwhile:

  • Clean-up the back-end data management practice so that it is fluid with business process and application usage.
  • Have a clear data management strategy for new applications that is fluid and scalable outside of application databases.

Your company may already be embarking on SOA or MDM projects.  But, have you looked at how these new practices will support applications outside of changing the oil?  Can the data drive process?

Today, applications are bogged down because data is treated as something to put in the trunk and horde.  Until data is thought of as fuel, you’re IT investments will stay in 1st gear and never get to 6th.  Now how fun is that?

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Why Business Intelligence is So Difficult

Reading the buzz on the Jim Davis’s presentation at SAS Global Executive Forum, what it made me realize is that if as an industry we can’t agree on what Business Intelligence is or Business Analytics, how are we supposed to make sense of it in implementation?

business intelligence confusionYou have analytics players, enterprise application vendors, business process consultants, and analysts all trying to sell the ‘hype’ of a better way to analyze your business and makes decisions.    SAS wants to sell their analytic solution that really pioneered data mining in businesses.  Oracle and IBM wants to push dashboard solutions that links to business processes and their enterprise applications.  Gartner that tries to tie together people, process, and technology but is really is focused on what technology to buy.  Then, you have consultants that are trying to help you implement the technology even as they document your processes.  The problem is that it’s all boiling down to the one with the best tool wins.

Enter in the ‘Business’ and now you have a problem.  All they want to know is how they can meet their business objectives.  IT is trying to sell the solution and make them understand the technology, and the business glazes over and can’t figure out what to focus on.  I’ve sat in these discussions where IT tells me, “You tell us what to do, we’ll do it.  Don’t worry about the solution.”  It is open ended.  This leads to IT unable to work towards tangible goals and results.  The business walks away frustrated, projects run from months into years, and original budgets are thrown out the window.  I liken these projects to Boston’s Big Dig.

Neil Raden provided a perfect way to get through the fluff and hype that surrounds analytics and business intelligence. See article From BI to Business Analytics, It’s All Fluff

“I don’t like the term business analytics; it doesn’t tell me anything. Frankly, I think business intelligence as a term is downright laughable, too. What does that mean? Is integrating data intelligence? Is generating reports intelligence? Maybe its informing, but isn’t intelligence something you HAVE not something you do? Does doing what we call BI lead to intelligence, or just some information? A long time ago we called this decision support, and that gets my vote.”

So here’s my take on what steps to take when and how to venture into BI and analytic solutions.

Steps:

  1. What decisions need to be made?
  2. At what point in our business and business processes are these decisions made?
  3. What information is needed at these points?
  4. How should our applications and data provide this information – triggers or visualization?

See the steps?  It starts with the business decion and ends in the technology.  So, when you begin to review vendors and solutions, make sure you have steps 1,2,3 in mind before you determine how to solve step 4.

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