Analytics-blog

I think we’ve all heard about how statistics can be made to support “any argument you want”.  I think maybe that phrase is wrong…it should say that statistics can be made to support “any argument you want if the recipient isn’t paying attention”.

That’s a fact.  It’s really all about the recipient or the audience.  If they don’t thirst for the truth, then they will find themselves fooled time and time again.

For analytics it’s exactly the same.  You can have the best system in the world, but if you do not have the time to seriously contemplate the numbers, then you probably are better off not spending any time with analytics at all.   Just run with your gut.

After all, drawing the wrong conclusion is sometimes worse than no conclusions….and ignorance is bliss.  In addition, for a lot of people, companies etc…inertia is the ONLY force.  So you can join that crowd.  Enjoy.  Don’t call me.

But if you are the type that challenges numbers and understands that the real work begins AFTER reading a graph, chart or table, then by all means you should invest in some sort of analytics or business intelligence system.

Of course, it is equally important that you approach your analysis with discipline and consistency.  Speaking personally, it’s the consistency that is the struggle for me.  It’s very tempting to go down one path, learn a little bit, get distracted….and suddenly end up somewhere else.  Obviously, you need to clean you plate before asking for seconds.

So that’s the trick with analytics.  You must understand the challenge at hand and have the discipline and consistency to utilize your tools effectively.  (or hire someone to help you)

There is no analytics system in the world that can “tell” you what to do.  (If you have such a system in your control, it’s actually not under your control and you are probably headed for bankruptcy or imminent disaster at some point)

I’m going to speak out today against the incessant march toward simplicity.

YES – it’s good to start simple…especially when it comes to analytics.

NO – that’s not where you should stop.  ESPECIALLY if you are in management.

I read a post today about various “brilliant” people talking about human nature and applying those learnings toward how to manage a business.

The end goal was “let’s dumb it down”.

Really?

I seem to recall thinking that my schooling was “sufficient” sometime around the first grade.  Thankfully, some adults around me thought differently.  As it turns out, it was a good thing they kept me going.  ”Graduated with honors from 1st grade” just doesn’t pay well.  (unless you work in Hollywood)

And that’s what I’m saying right now.  If you don’t like thinking and if you don’t want to challenge yourself…then I would suggest you remove yourself from the business world before it is done for you.

Enough of this catering to the undisciplined.  Most business intelligence software and analytics software providers offer dashboards and KPI’s.  But they are just a starting point for analysis.

We also provide tools that allow for more detailed analysis.

Use them.  Please.

The folks at Gartner see a bright future for business intelligence.  It’s going to be embedded in everything- it appears- including social networking.

Do they see it making you a burrito?  Unfortunately no.  And that’s a shame because I really like a good burrito.  (especially for breakfast)  I also think the term “BI Burrito” has an nice ring to it.

Now back to the topic at hand.  Is Gartner alone in their burrito-less vision?

Nope.  The CTO at LogiXML also seems to buy into this vision.

And so do I.

But frankly, I still don’t see many companies using business intelligence in an efficient manner.  It’s difficult to me to contemplate all the other applications for business intelligence when the corporate market is STILL learning how to utilize BI across the enterprise.

In addition, I’m not sure what the revenue model looks like for a social media business intelligence application provider.  But, I do know that there are serious costs involved in providing any sort of analytics or business intelligence.

So yes, I’m optimistic about the future of analytics and business intelligence…but I’m not sold that the future has arrived in any meaningful way that can impact the bottom line.  And, I’m sure not interested in the “build an audience and somehow you will monetize it” approach.

Those days are at an end with all the financial issues out there.

So I guess we’ll see how long it takes before the future arrives.

I want to continue to address the concept of making sure you mix qualitative in with the quantitative when making decisions.

Suppose you worked for a large company that rolled out a product in their home country that was a massive flop.   Suppose you were not the only failure in that industry…instead everyone else who tried to bring that particular business model to life also flailed.

Would you stick with it or give up?

If you just looked at the sales numbers, reviewed all the various doomsday reports about the lack of growth projected for the industry and sifted through the negative news that resulted from the previous failure….then you would likely want to give up.

In my direct experience, that would be a many-billion dollar mistake.

In spite of all the negative press and sales numbers, your problem was not a product/service that no one wanted.  Your problem was a marketing problem.  Wrong price.  Wrong message.  Wrong distribution…but the right product.

This is a situation where your business intelligence system is perfectly useless.  It’s rare, but it happens.  As I stated in the previous post, this is where the quantitative can let you down.

Empirical data can be useless when you are trying to start a revolution.  Something to keep in mind.

Can business intelligence software and analytics replace human beings?  In other words, can we automate decision making using algorithms.

Well…let’s talk about the financial industry and their “success” with automating their decisions.

Long Term Capital Management.  Oops.

The entire mortgage securitization business.  Massive Oops.

What went wrong?  In all cases they mistook trends for truth.  Say it out loud with me….”Past performance is no guarantee of future results”.

And that’s it.  They forgot the human element.  They forgot that humans say one thing and mean another.  They are emotional.

Those algorithms just don’t have a lick of common sense.

Now I’m a big fan of Ray Kurzweil and his various predictions regarding the merger of man and machine.  His track record is solid.  He thinks that over time mankind and machines will merge.

Now maybe at some point humans/machines/borgs will someday think in a more logical fashion.  Then we can eliminate all the manias, the panics…all the emotion that accompanies human nature.

Sounds boring as hell.

However, for now we need to understand that while business intelligence tools and complex statistical formulas can help inform our decisions, they cannot make decisions for us.

It’s still critical to get out there and understand socioeconomic trends that lie outside of your business intelligence system.

For example:

-Talk to your customers.

-Talk to your non-customers.

-Study macroeconomic indicators.

-Read a book or two that have different points of view on the same topic.

-Spend a little less time listening to the hyperbole of the major news networks.

Then you can see all sides of the story and have a better chance of making the optimal decision.

Better chance than any robot at least.

IBM recently interviewed over 2,500 CIO’s to learn how their roles are evolving, and what they view as important. It’s an interesting report and an easy read.

So here’s one of the stunning results: 83% of those surveyed stated that business intelligence was their top priority.

83%! Seriously…have 83% of people ever agreed on anything? This is either a great sign or a sign of Groupthink Gone Wild ™.

I’ll side with the former and say that these C-level folks do have a clue about what is important.

Second on their list was virtualization at 76%. Virtualization came in second? Is anyone else surprised by this? Virtualization is primairily about cutting costs. Business Intelligence is about decision making. This is a big deal for CIO’s. Now, CIO’s are thinking strategically instead of thinking just in very simple terms of how to cut costs. (which was one of the major points made by IBM in the report)

Now the reality is that business intelligence, when properly utilized, will lead the CIO’s, and their minions, to make better decisions. And that will likely save them more $$$ than any virtualization exercise.

Nonetheless, it’s a little stunning to see the C-level thinking a little more on the abstract level…especially given the economic circumstances.

My day has been made.

Because I said so.

Oh that doesn’t work for you? Well that’s a shame. I just tried to spare you a page of reading.

But…since you are a hardheaded “logical” type person read below and knock yourself out.

Almost every day I see a discussion on the social networks where someone posts “what is the difference between SaaS Business Intelligence and regular ol’ Business Intelligence?”

After I got past the initial shock that the entire world “doesn’t just get it,” I realized it’s a pretty legitimate question.

So I’ve compiled a short list of the “Big Six” differences between SaaS business intelligence and traditional business intelligence.

1.) No hardware required: SaaS means you don’t host the software within your company. That means you don’t need to buy any servers, procure any cold rooms, or talk to anyone in IT about anything hardware related.

2.) No software integration expertise required: You don’t need to hire a consultant or staff up with “expert” business intelligence employees. Instead, you just need a web browser with a web connection.

3.) Requirements Document: You can throw it away. You can cancel your committee meeting to help write that document. 90% of what you need is likely in the SaaS system already. The other 10% will likely be in the system by the time you’ve mastered the first 90%.

4.) IT: You may not need them at all. It just depends where your data resides. But I can tell you that you probably don’t need very much of their time.

5.) Cost: SaaS vendors charge thousands per month with very few upfront costs. Traditional business intelligence requires a substantial capital investment up front, and sometimes their solutions end up costing millions.

6.) And finally, the most important thing here is SPEED: So the SaaS solution just eliminated most of your discussions with finance, eliminated any hiring decisions, reduced or eliminated time spent with IT…. and will actually deliver reports and analytics you can use within weeks.

It’s awesome. Just like I said…one page earlier….

The Mayor suggested we blog about the much ballyhooed “single version of the truth.”  To be clear, this is not a blog on theology.  So if you were getting nervous…it’s okay.  We are talking about business intelligence and analytics here.

Once again we will channel Wikipedia for a semi-formal definition of the the Single Version of the Truth:

Single Version of the Truth: In computerized business management, svot, or Single Version of the Truth, is a technical concept describing the data warehousing ideal of having either a single centralised database, or at least a distributed synchronised database, which stores all of an organisation’s data in a consistent and non-redundant form.

Swap out the “s” for “z” on some of those words if you are American and hopelessly confused. Apparently the author was taught British English.

So much like many of my college textbooks, the formal definition is abstract and semi-useless.  So let’s talk about some examples that dummies like myself can understand.

One of the first steps that must be taken is to create definitions that are the same across the enterprise.

For example, if we are looking to answer the question “What are the sales for the past week?”; then we must standardize the how we derive the sales numbers.

Various departments, such as finance and operations,  might view the calculation method from different viewpoints. For example, Operations might include shipping and handling in their sales calculation,  Finance might prefer inclusion of the taxes collected in their sales calculation, and Marketing most likely has a different definition of when the calculation should start and end.

More variables on that simple question include:   What is defined as a sale? Is it considered a sale when it leaves the fulfillment center? When the customer orders it? When the customers pays for it? Does it include tax, which tax? Is the tax different from state to state…

So what sounds simple on the surface is not.  Sort of like marriage.  .

Getting those definitions together will not be a small task in a large organization.  Just that process alone can take weeks or even months depending on how “controversial” those definitions may seem.  It may turn out that your sales organization wants the numbers to look as big as possible and your operations team disagrees.  Good luck.

Once you’ve addressed definitions, then you get to ponder the accuracy of the data itself.  Manual data entry, missing records, and allocations based on the time dimension.  This is where you really earn your salary.

After that, then you get to tackle interesting questions like “how many unique customers do you really have”?  Is clint@analytixondemand.com the same as clint.brauer@analytixondemand.com?  If there is yet another “Clint Brauer” at yet another shipping address….is that the same Clint?

All something to ponder.

Then there are all the technical implications of wanting to have “one version of the truth.”  Do you have multiple databases?  If so, how and when do they synch and what are the rules for updates to various fields?  How do you validate and continue to monitor your data for accuracy purposes?

As the Mayor called out to me, this is yet another case where it’s more about the journey and less about the destination.  Chances are you will constantly be making adjustments to your business rules and your technical systems.

As always, the key is to communicate and make sure everyone is drinking the same truth serum.  As long as everyone understands the definitions and the limitations at any given point, then your organization can go about making constructive decisions based on quantitative data instead of pure speculation.

A real Tree of Discouragement. So sad.

Tell me if you’ve had this experience.  (actually, I already know you have)

You’ve given business for years without complaint to some firm or company.  You were not a coupon/discount shopper.  You rarely, if ever, called their support team.  You just forgave them for small annoyances.  You provided referrals through your social interactions.  You paid on time.

Then one day something goes awry.  So you pick up the phone and call support.

After you work your way through the Phone Tree of Discouragement, they put you on hold.

For a long time.

Then they re-route you because you couldn’t quite pick the right option from the Phone Tree of Discouragement.  You dummy.

Back on hold.

Re-routed.

Insulted.

Flipping out.

Frankly, this describes about 80% of my experiences.  It’s why I’ve made a rule to transact as little as possible.  It’s just not worth it.

So why does this happen to a valuable customer like yourself?  It’s certainly not a wise business decision to anger your best customers.

I’ll tell you exactly why.

Companies don’t know the value of their best customers.

And even if they do, they have no clue if a valuable one just bought something from them, if they are on the phone or just sent an email.

Honestly, there is no excuse for this gibberish.

What needs to happen is as follows in no particular order:

1.)  You dump your call center data into a database

2.)  You dump your ecommerce and retail data into that same database

3.)  You dump your loyalty data into that same database

4.)  You make sure that you can connect the data points at the customer level

5.)  You do some really simple segmentation using some nice business intelligence and analytics tools (or you can do complex segments..your choice)

6.)  You pipe those segments into your call center, commerce and retail systems

7.)  You program those systems to react accordingly based on the segments

Voila!  When your high value customer calls in…they go to the top of the queue.

Congrats!  You have a 360 degree view of your customer!   You are in the top 10% or all companies.  (just an estimate)

Honestly, this isn’t that tough.  I did this over five years ago with some bailing wire, conviction and multiple low-cost SaaS analytics, CRM and email providers and a very small team of infighters.

So put on your big boy pants and look out for your top customers…before they look for someone else.

Now that the total ownership costs of business intelligence systems are being driven down by SaaS, we’ll likely see the business intelligence market grow like a California cash crop over the next years; regardless of economic conditions.

In other words, the chart below might be about to “elbow” or “hockeystick”.

So braid my hair Mary Jane, it’s going to be a fun ride.

Source: The OLAP Report 2009