Visualization Methods

14 10 2010

I thought this was worth sharing….Periodic Table of Visualization Methods.  This is a nice visualization of the different types of visualization.  It shows some good examples, and some not so good examples of visualization. Make sure you mouse over the different elements.

Rules of visualization designed to create action:

  1. Keep it simple, clear, and concise – with the emphasis on simple.  Don’t use complex charts to explain simple ideas.
  2. Know your audience.  Don’t present glorious details of each step in the analytical process to executives – trust me, they don’t care.
  3. Find a chart style that works well with the data.  Line charts show historical trending, bars charts do a better job of showing relativity.
  4. Don’t use 10 charts when 1 could suffice.
  5. Label well.  Take the time to make sure all of the information is explained.  The last thing you want to happen is for someone to look at it and say “what does it mean?”
  6. Understand there is a difference in analysis and presentation.  If you are trying to convince someone to act, then make sure the data (and you) tell the story.
  7. Start with the big picture, then explain (if necessary) how you got there.  People learn by seeing the picture first, then seeing how the parts go together.
  8. Document your assumptions.
  9. Explain your conclusions, don’t expect your audience to jump to the same answer.
  10. Highlight the relevant points within the data that augment your argument – use a color scheme that calls out the item if you can (red bars vs gray).  Do not be afraid to use the power of a printed report and some hand written notes with arrows to the corresponding areas.
  11. Understand where and why the data does not support your conclusions.  Be prepared to defend against those points, because your audience will likely be looking for ways to contest your conclusions.
  12. Practice what you want to say.  The more proficient you sound the more convincing you will be.




Recession is OVER!!!

27 09 2010

Perhaps not all signs agree with the National Bureau of Economic Research that the recession ended in June 2009. It is pretty clear that the economy is still not as healthy as everyone would like.  Our unemployment rate is still hovering around 10%, and Mass Layoffs is trending in the right direction, but still high.  Looking at the chart below, it is clear that Mass Layoff events are declining (though there could be some other explanations as well) and getting closer to the roughly 1250 average during better times.

Housing starts are on the rise again, yet the DJIA has only recovered a little of the value from the losses from 2008 and early 2009.  While we may still may be feeling the effects of the recession, it is clear that most indications are moving in the right direction.





The end of Blockbusters…

23 09 2010

OK, well it is potentially the end of Blockbuster Inc.  This morning Blockbuster filed for chapter 11 protection.  It is a great example of the Risk of being the market leader.  They owned the market, they were on top of the world.  I am sure during their heyday money was being thrown all over the place.

I would love to hear these questions answered:

The trap of leadership is that you often have to wait and see the result.  You are often not allowed to change your business model until it is too late.  If you change it when you probably need to and a loss occurs, then everyone loses their jobs.  The analysts would quickly call out leadership saying that they lost market share because of the business model shift.  Even it is was a great move that would ultimately save the company, our short term focus is entirely too great.

It is also difficult to understand the nature of the perceived threat.  I am sure there were a couple of times when Management said “what do we do about NetFlix and the changes in the market?”  I would guess that 10% market share did not scare anyone, nor 20%.  Yet, at this point there was too much momentum.

As leaders, when do we act?

If we react too soon, we risk looking prone to panic.  We can always explain it easier after the fact.  Our egos, politics in general, and concern about saving face probably drive more decisions than anyone would ever want to admit.

All to often we push harder on marketing and sales to cover shortfalls in market share.  I would be willing to bet that the company spent more time creating sales spiffs and getting creative in terms of finances, than investing in new business models.  What this leads to is a further entrenchment into the business model, a “we can weather this storm” mentality.

I wonder what would have happened if they would have set hard targets in terms of driving action.  What if they would have said “once our market share slips by 10%, I want a meeting where we come up with 5 new business models”.  We are just not trained to think about creating very specific action.

We ponder and delay (then get out and let someone else handle the mess).





Advanced Analytics

22 03 2010

A major item organizations grapple with is the concept of advanced analytics.  They want it, but have little idea how to use the various tools to make it happen.  Unfortunately too much information often blurs the lines.

For example, I watched a sales presentation on Predictive Analytics where the key outcome showed how to build databases with the tool yet almost completely missed the fact that the real benefit should have been something like “we were able identify two segments to target a marketing program for more effectiveness.  Instead of spending $500k on a generic campaign we were able to identify key attributes that drove increased customer interaction and focus the campaign to only $200k on those segments.”

Why is this? The primary reason is we do not truly understand the tools and how best to use them.  A Swiss army knife is not good for home repair, but is the perfect tool to throw in a hockey bag, or car trunk for occasional use as a widget to get you out of a jam – a screw needs to be tightened, a shoelace needs to be cut, or an apple peeled.  We need to understand which tool to use in the most appropriate situation instead of thinking of various tools as universal.

Business Intelligence, Planning, What-If Scenario Tools, Optimization, Dashboarding, Scorecarding, Cubes, Cluster Analysis, Predictive Analytics are all different tools for vastly separate purposes yet have similar uses.

Advanced Analytical Tools

Here are the core elements of Advanced Analytical tools:

  • Business Intelligence – great for creating an enterprise-wide, data visualization platform.   If you do this right, you should create a single version of the truth for various terms within an organization.  It should enable better reporting consistency standards for the organization.  In the end, it reports what the data says.
    • Scorecard & Dashboards – These are primarily BI tools that have a more organized or structured methodology for presenting ideally the Key Performance Indicators.  These are great tools, but to be most effective, they need a specific purpose that is highly integrated into a management process.
  • Enterprise Scenario Planning – Most enterprise planning exercises are giant what-if scenarios that try to plan out financial outcomes based on a series of drivers (employees, widgets, sales reps, etc.).  We build out plans based on a number of assumptions, like the average sales rep drives $2mil in business, or benefit costs for the year are going to be #of employees * average salary * 2.  We do this primarily to lay out a game plan for the year and we do it as part of an annual or rolling cycle.
  • Tactical or Ad-Hoc What-if Scenario Analysis – Besides the full scale project we do to plan out the company’s cash outlays, we also do a significant amount of smaller, typically tactical “what-if” scenario tests.  This is traditionally done in Microsoft Excel.  We dump a bit of data into excel, make a number of assumptions and try to build out likely scenarios.  For example, “if we were to create a customer loyalty program, what would be the cost and a likely reward.”  We are doing this to test ideas, so yes it might be ideal to bolt those into the Enterprise planning tool, but it typically takes too much overhead.  It is easier to just get something done quickly, then make a go/no go decision.
    • Data Visualization can also be a great help with this – to bolt on a couple of reports to see the data and how different scenarios impact the various facts and dimensions.  This can help us with our conclusions and recommendations.
  • Predictive Analytics – This tool is best used when we have historical data, or representative data set and we want to make a conclusion based on mathematics.   The key is math.  This is not guessing, it is improving the chances of being right with math, or a structured approach to remove risk from decision making.  With a planning tool, we primarily use assumptions to create plans.  We cannot use predictive analytics for all decisions, but for a few specific types of decisions:
    • What transaction details and customer insight can we use to determine credit card fraud?
    • What customer attributes create our buying segments?
    • Which customers are most likely to abandon our offering?
    • What products are most often purchased together?
    • Which taxpayers most likely need to be audited?
  • Optimization Analytics – This is perhaps the most specific advanced analytics tool when looking to solve the specific business question: “With the given parameters of these trade-offs, which mix of resources creates the most effective (or efficient) use of those resources?” This helps make decisions around production locations and product investment.  Like predicative analytics, it is mathematically based (though you may need to make a couple of assumptions as well) in how it determines the answer.

Advanced Analysts

Another reason we lack understanding is analysts.  Our analysts are commonly from the IT team, trained in data structures, or from the finance team, trained in accounting.  Neither is wrong, they just have a default mindset that falls back on using the tool they best know.  This lacks the business/statistical trained person who can both layout the hypothesis and, more importantly, explain the results.

We do not want correlation explained in R-squared values, “63% of the variation of the data is explained by our independent variables.”  While this may make sense to other statisticians and mathematicians, it is lost on the business.   One key value of using a math-based concept is that the explanation should sound more like, “We have found a way to decrease fraud by 3.2%, which should result in a $576K return to the business every quarter” or “We have tested our marketing campaigns and have found three segments that are 25% more likely to purchase based on the campaign, which should result in a payback period of 3 months.”

The right tool with the right skill set is imperative to successfully using advanced analytics.  We also need the discipline to have the right people using the right tools for the right information to drive action.  If you have an algorithm that predicts customer defection, you need to use it and test the results.  It is never going to be perfect, but in most cases, you can bet it will be better than not using it at all.





Telling a Story

28 12 2009

“What we’ve got here is a failure to communicate” Luke in Cool Hand Luke (played by Paul Newman)

A friend of mine sent this video along to a number of friends in the Business Intelligence space, saying we need to be better story tellers (Thanks Katie McCray).  We do spend an enormous amount of time talking about data structures, common data dictionaries, ease of use, speed, consistency, etc.  What we typically fail to do is tell our clients how to create information, to tell the story in a convincing enough manner to create attention, and more importantly, enable action.

As analysts we typically spend more time talking about data discovery, and the calculations we used than starting off by making our point.  We try to create 50 charts to explain everything, and not the one chart that most simply illustrates our point.  This not only wastes time, but we lose our audience.

Watch the next couple of presentations you sit through and watch the number of slides that build up to the point trying to be made.  What happens is that with each slide our listeners pay less and less attention as they have lost the point trying to be made.  As learners, we need the point to be made first.  We need to see how it all comes together, then have it explained how to get there.  It provides the context for the point to be made.  People now understand what to listen for and why they are listening.

On a slightly different note, last week I wrote about the housing market and the Dangers of Leading Indicators.  I had to update the post due to a new story with a different viewpoint that ran in the Globe on the 23rd.  Amazing how story tellers can tell such dramatically different things.





Predictive Analytics, Business Intelligence, and Strategy Management

9 12 2009

I was having a discussion with one of my clients this week and I thought he did a nice job summing up Predicative Analytics.

So in the World According to Reed (WOTR) – “queries answer questions, analytics creates questions.” My response was “and Strategy Management helps us to focus on which questions to answer.”

Reed Blalock is exactly right, traditional BI is about answering the questions we know. Analytics is really what we create with data mining – we look for nuances, things that might give us new insight into old problems. We use human intellect to explore and test. And yes, there is a little overlap. But what is really happening is that we have a different level of human interaction with the data.

BI is about history, analytics attempts to get us to think, to change, and idealistically to act.

The danger with both of these is that they can be resource intensive. Neither tool, or mindset should be left to their own devices. What is needed is a filter to identify the priority and purpose. This is where strategy management and scorecarding comes into play. We have built out massive informational assets without understanding where, when, and how to use it. We have pushed out enormous reporting structures and said “it’s all there, you can find anything you need” yet we scratch our heads when we see adoptions levels are low.

What we have typically not done all that well is build out that informational asset by how it helps us be more productive along product lines, divisions, sales region, etc. We have treated all dimensionality the same. Why, because it was easy. The BI tools are tremendous in how quickly you can add any and all dimensions.

“But because you can, doesn’t mean you should”

As we built out these data assets, we did not align them to performance themes.  We have gotten better with some key themes, like supply chain management, and human resource management, but what about customer performance?  We might look at sales performance, but that is a completely different lens than customer performance.

How do we determine which assets to start with…what assets do we need to be successful 3-5 years from now, or what are our biggest gaps to close today.  Think about customer value, or employee satisfaction (and that doesn’t mean more HR assets).  Think about your gaps in Strategy.

How often do we discuss…

  • Are our customers buying more or less frequently?
  • What are our best, and better customers doing?
  • What are the costs associated with serving our least profitable customers?
  • Where are our biggest holes in understanding?




Analytics Process

23 11 2009

Over the last couple of months I have been writing about a handful of US Economic Indicators.  While I have reviewed these over the last few years of my life, I had not done so on a regular basis.  This inconsistent and let’s call it a casual curiosity lead to never really understanding the implications behind the numbers.  Sure I could talk about them, but I could not leverage them.  While not an expert by any means, I can see a lot more now than I did when I started this blog series.

This is similar to ad-hoc analysis without purpose.  We do something once and create a little hype.  When we don’t have any vehicle to take advantage of the newly found ideas, the idea dies as does the learning.

Think about the process of how you handle ad-hoc analytics within your organization:

  • Do you have the right minds constantly looking for new issues?
  • Or, do you put the right minds on solving issues when they arise?
  • Can you name your best analytical minds?  Are they assigned to thought leadership and problem solving?
  • Do you use your analytical minds to challenge the knowledge levels of others?
  • How do you foster new thinking?

 

Consistency breeds familiarity, and familiarity breeds knowledge