The emerging role of the analyst…

6 10 2011

A few days ago, I wrote about the analyst function being dead, which spurred conversations about the emergence of a new breed of analysts. Organizations, with all of their investment in data capture, data generation, and business intelligence, still struggle to use data effectively to make decisions. With the explosion of data over the last couple of decades, the analyst moved away from business and morphed into an IT role.  The role became more about writing business requirements and providing reports than understanding data and helping the organization digest meaning from it.

Now the analyst needs to move back to a business role, but with more of a mathematics and statistics background.  They have to be curious about improving the business and have the acumen to do it.  They need to know how to blend traditional data with today’s non-traditional data feeds from blogs, social media, video, etc.  The value of the analyst is back in creating business value through relevance, context, and timeliness.

To achieve this, the emerging role of the analyst requires a new skill set and must:

  • Understand how to derive information out of data and present it in business terms – this is perhaps the most important of all of the new skills.  The analyst must be able to take a tremendous amount of information and coalesce that information into business terms leading to action.
  • Integrate various types of information – as data is coming from all different places and in new forms, it is increasingly important to understand how and when to leverage potentially rich data, and decipher what is irrelevant quickly.
  • Design problems with various concepts – the analyst needs a consultative style in which different models are applied to solve ever more complex issues.
  • Use technology – with Business Intelligence, Planning, and Predictive Analytic style software becoming easier to use, the analyst needs to know not only how to use these tools, but when to use them.
  • Delegate – traditionally the analyst needed to do everything.  Now as technology, data sources, and businesses have become more diverse, the analyst needs to know how to farm out some of the analytics to specific expertise at the right time, guide the project, and integrate the results.

The analyst also needs to transform informational projects into a process where requests for information are appropriately managed. This includes breaking down information into four areas: persistent information or basic reporting of facts on a regular timeframe; performance measures that have higher level KPIs; problem analysis; and data exploration.

Gone are the days where the analyst was a report writer, spending too much time on data acquisition.  They must now know how to enhance data to get more out of it in a timely and fashion and present that back to the business in a manner that drives value creation.





Analytics Blogarama

29 09 2011

You’re invited to join in the Analytics Blogarama!

Theme: The Emerging Role of the Analyst
When: October 6
Where to post: Your own blog
Who’s invited: All bloggers with an interest in analytics
How to get in on the link promotion friendliness: Send an email with title and url of your post to cliff@socialmediatoday.com.

Analytics bloggers Michael Ensley and Meta S. Brown invite you to join us for the very first Analytics Blogarama – one day when we share our individual views on a common theme. Smart Data Collective will be spreading the word and linking to all participants’ posts. All bloggers with an interest in the theme are welcome to participate, so please share this invitation with your blogging pals.

Why participate? Build your readership! Everybody gets a link from the Analytics Blogarama page. Collaboration among participants (exchange ideas, comment on posts, link to one another) is encouraged!

So, on October 6, post your take on The Emerging Role of the Analyst on your blog. Please include a link to the blogarama navigation page, so your readers can find their way to other viewpoints. And send an email with title and url of your post to cliff@socialmediatoday.com, so your post can be listed there, too!

Blogarama navigation page url: http://smartdatacollective.com/40832/analytics-blogarama-october-6-2011





The analyst function is dead

8 09 2011

The role of the operational analyst has moved from the business into both Finance and into IT.  The Finance team typically focuses only upon the financial outcomes of the business and has left the operational side of the business to the IT team.

Here is a conversation a client of mine recently had with their analyst…

ANALYST: ” Here is the report on units sold this year.”

BUSINESS:  “What happened here?”

ANALYST:  “That is a spike in the data.”

BUSINESS:  “Right.  But what happened?”

ANALYST:  “That is what the data is showing.”

Sadly, this is not uncommon in the business world today.  Billions of dollars are spent every year on Business Intelligence software to help us visualize what is happening within the business, yet we are really no better off in terms of insight.

WHY is this happening?

  1. The biggest reason why this is happening is we have changed the role of the analyst.  It used to be a marketing person looking at marketing data, or operations looking at manufacturing information.  We have now moved that role to IT, or IT has promised that that can do it better with their understanding of data structures.
  2. We have wrongly assumed that a picture is worth a thousand words.  In BI terms, a chart is worth a handful of questions. IT can not predict that next series of questions and is then left to prioritize what questions to tackle next.
  3. The pace of business, or at least the pace and variety of business questions (like the data we collect) has risen exponentially and scaled faster than our ability to respond.
  4. IT is over burdened and lacks the political power and will to say “no.”  They are in complete reaction mode and lack the resources to cover the demand.

WHAT can we do to fix this?

  • First off, we need to understand the analytical gap within the organization.  IT can manage the data and needs to partner with the business, but the business needs to own the intelligence.  It is easier to teach the business a little about technology, than teach the IT resources about the business.  The business side needs to find that type of person who understands a little about technology, but has a solid mathematical or statistical mind with a curiosity about improving the business.
  • The organization needs to find a better way to integrate better analysis back into the management process.  We need to give the analysts a frame of reference in which to explore ideas and present results.  Some of this will follow reporting upon weekly/monthly operational outcomes, while most will likely by ad-hoc hypothesis or what-if scenarios about some aspect of the business.
  • The culture has to reward critical thinking.  This is not true in most corporate cultures.  All to often, the analyst is criticized for not “going along” with the current belief.  If the culture does not reward new thinking, then the analysis will quickly fall in line with visualizations that support the status quo.
  • Invest in tools and training beyond just the core cubes and reports of the BI market.  While a good portion of analysis can be done with Microsoft Excel and a data dump, the more we want out of our analysts, the more we need to give them.  We need them to look at market baskets, threshold containment, frequency curves, optimization models, assumption testing, correlations, and many other types of analytical tools.

 





When More is much, much Less

2 08 2011

Recently I was cleaning up my Gmail inbox and it was clear to me that some people treat email like free marketing.  For example, Dick’s Sporting Goods was sending me 3-4 emails a week.  While I shop at Dick’s Sporting Goods and like the brand, it was very clear to me that they really weren’t paying attention.  My lack of response, nor opening of any emails should have been a trigger to them.  More was much, much less.  They were not alone, but one of the worst examples of over-communication.

Thoughts for email marketing:

  • Use the information effectively.  Not only have I asked them to stop emailing me all together, they have hurt their brand standing with me.
  • Test your campaigns.  Because they are free doesn’t mean everyone should get everything.  That’s just laziness.  There are too many tools out there not to be able to do some type of segmentation based upon gender, usage patterns, social, and economic demographics.
  • Learn! This is probably the most important aspect.  If a customer gives you their email address, then treat it like a valuable asset and learn from it.  It is not a resource to be used up.  Offer different things at different times, send emails in different patterns, send different offers and test the response.  And if they don’t respond to anything, pull back and wait.

I know this sounds way too obvious, but here is an example from someone with the size and clout to know better.  Chances are your marketing organization is overusing their free marketing channel and just don’t know it yet.  Go ask them for an analysis of how many emails are being sent out to each customer segment each week.  Ask them how often they clean up their contact list to trim out people who have never responded. And wait for the dreaded, “we don’t want to skip anyone in case this is the campaign that will get their attention.”  Trust me, there is a breaking point.





The enemy of my enemy is my friend

19 07 2011

Strange what a few years means to the technology sector.  Google, once champion of the little guy, the individual, the anti-Mircosoft, now becomes the problem.  The Michigan – Ohio State rivalry of the tech industry was supposed to be Apple and Microsoft.  They have spanned great battles over the years – and better commercials…

Yet, all of a sudden Google is the evil invader in the space.  What else could make Apple and Microsoft consortium partners?

WHAT!!  Wait a second…

Nortel Networks, one of the great patent holders, is watching its power, influence, and ultimately its profits dwindle away.  Up for auction were a sizable number of its patents.  While Google was the early favorite, Apple and Microsoft teamed up with Ericsson, EMC, RIM, and Sony teamed up with each other to outspend Google.

While we get to wait and see what this means for Google, we can wonder what our competition might be willing to do to us given the opportunity?

  • How do external opportunities trigger discussions within the organization?
  • Who monitors the external market for us?
  • How do we leverage information to make timely decisions?
  • How well do we gamemanship our competition?  Are they better at it than us?

 





Obesity in the US

29 04 2011

This is again perhaps a little off topic for me, but it does pose some really interesting strategic points for consideration…

The cigarette of today’s generation is fast food, sodas, and poor eating habits in general.  Obesity in the US is projected to be about 20% of our annual health spending – or roughly $350 billion (USA Today) by 2018.  This means the number will double from 10% of the spending to 20% by 2018.  Food related deaths account for more than half of our causes of death (CDC) and we focus very little attention to it.  And for the first time in decades the US life expectancy is projected to decline by 5 years (National Institute of Health) with this generation.

So from the viewpoint of Strategy, this poses a wild number of potentials.  Depending upon your industry this either opens you to a tremendous opportunity, or a concerning level of risk.

Opportunities:

  • Food industry – being an early mover to healthier versions of your food may attract more customers
  • Education – providing content for school, churches, communities, etc may open more doors for you
  • Healthcare – with increasing costs, providers that can target care to show health gains with children, or keep their clients healthier may see improved demand for their products while at the same time controller their costs.
  • Marketing – Branding your self as a healthy alternative
  • HR – being seen as a healthier employer may improve your retainment and attraction to new employees.  You may also see a reduction in your health care costs over time.

Risks:

  • Fast food – This entire industry may be about to come under ever increasing levels of attack.  The attacks will likely be on menu, ingredients, nutritional labeling, and potentially lawsuits.
  • Sports drinks – As parents become more aware of the level of sugar in these drinks, demand is certainly at risk.  As one of their core segments is children, it is also possible that even the marketing placement will be called into question.
  • Education – As Jamie Oliver’s Food Revolution has clearly pointed out, he is certainly targeting the school system menu.  Once the parents get involved school district lunch menus will likely need to change dramatically.
  • Healthcare – spiraling costs will force most healthcare companies to make very difficult decisions to remain profitable.

Here is Jamie Oliver’s presentation on TED.

Here you can see the growing obesity problem in the us (CDC).





Zombie Initiatives and Tasks

5 01 2011

Over the holidays I heard a story on Zombie Processes.  It reminded me of the number of these I have come across in business.  One of the luxuries of being a consultant is you get to ask “why do you do that” or better yet “what would happen if you didn’t do that anymore”.  As businesses grow and scale we often pick up a number of new initiatives, or increase the subtasks, and never kill off old ones.  We also inherit more and more “stuff” that people do that does not necessarily add value.

Zombies: A Zombie initiative/task is something that continues on because no one has done the favor of saying it is either over or complete.  It can also be a task that exists that no longer needs to exist.   Basically it is inefficient effort and time.

Do these exist in your organization? Absolutely and everywhere.  The key is not trying to fix them all at once – this will get you nowhere.  What makes the most sense is to identify your strategic goals and initiatives and start with the processes that support those goals.

Where do you start? Take a look at your critical initiatives across the organization.  Ask yourself which ones are going to provide the most strategic value over the next 12 months.  Pick 3 and define the value of those initiatives.  Are they about increasing/decreasing time, revenue growth, cost cutting, elevating customer value?  Figure out how improvements should be measured.  Set up serious targets and a process to manage improvements.  Roll up your sleeves and get rid of the Zombies.  And while again this is self serving, it does not make it less true – hire a consultant.  Have someone independent to the organization ask the questions.  Especially if this is a new concept inside the organization.  People don’t like change, they fear it will expose them or put them at risk.  This can lead to the wrong motivation for process improvement.

 

Stuff:  This can be projects, tasks, subtasks, processes, or simply job justification work.





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





Employment Situation Sept 2009

6 10 2009

Statement of Keith Hall, Commissioner, Bureau of Labor Statistics before the Joint Economic Committee UNITED STATES CONGRESS (PDF of his speech, or PDF of the actual report)

Job losses continued in September, and the unemployment
rate continued to trend up, reaching 9.8 percent. Nonfarm
payroll employment fell by 263,000 over the month, and losses
have averaged 307,000 per month since May. Payroll employment
has fallen for 21 consecutive months, with declines totaling 7.2
million. In September, notable job losses occurred in
construction, manufacturing, government, and retail trade.
Construction employment decreased by 64,000 in September.
Job losses averaged 66,000 per month from May through September,
2
compared with an average of 117,000 per month from November 2008
through April.

“Job losses continued in September, and the unemployment rate continued to trend up, reaching 9.8 percent. Nonfarm payroll employment fell by 263,000 over the month, and losses have averaged 307,000 per month since May. Payroll employment has fallen for 21 consecutive months, with declines totaling 7.2 million. In September, notable job losses occurred in construction, manufacturing, government, and retail trade.

Construction employment decreased by 64,000 in September. Job losses averaged 66,000 per month from May through September, compared with an average of 117,000 per month from November 2008 through April.”





Analytics Competency Center

28 09 2009

We spend a lot of time on Business Intelligence, Master Data Management, Data Governance, Standardization, off-shoring, etc., yet I rarely hear organizations spending time and energy on analyzing the data.  We have cubes, we can do all sorts of things with reports and dashboards, yet I still hear people say “I need more information!”

It is impossible that we are short on data!

  • How then are we not getting enough information out to the organization?
  • Is it possible that we are spending all of our time and energy on data preparation and data movement?
  • Are we creating value, or just planning to create value?
  • What about creating a center of excellence around the business user?
  • Or something around the levers of the business?




Indicators & KPIs

9 09 2009

In a recent Wired magazine article “American Vice: Mapping the 7 Deadly Sins,” (The original was in the Las Vegas Sun’s One Nation, Seven Sins) a group from KSU students did a great job mapping data geographically.  While in no way is the data perfectly accurate, but in the same way it is a logical indication of behavior.  You can spend time arguing the merit of the work, or spend that same time debating the implications of the information.  Either way, it is a rather entertaining visual display of information.

In the business world, we struggle from trying to be perfect, or perhaps afraid of not being completely accurate.  Indicators do not need to be perfect, they just need to trigger a discussion by highlighting the potential of an issue.  The downside is that when we try, we often try to find indicators for everything (spandex rule) and “a point in every direction is like no point at all” (Harry Nilsson, for those who like eccentic music).  Too many indicators and we spend too much time on data collection and visualiztion with no time for analysis and discussion.  The point is to discuss information.





Snowblowers and investing in tools

24 08 2009

How often do parents buy snowblowers right after their kids leave the nest?

Good employees often understand the tools they need to best do their jobs.  Good analytical minds usually look for different ways to extract new information out of large data sets.  This requires access to new tools, frameworks, and methodologies.  If we are not reinvesting in improving our analyical capabilities, we risk losing our best people as they quest other ways to challenge themselves.  We are then required to invest in the tools we declined to compensate for a loss of analytical brain power.

Instead of purchasing tools after a star analyst leaves, we need to find fresh ways to challenge analytical minds.  We can do a much better job of pointing people in new directions, or finding new ways to derive value.  Otherwise, we’re left investing in expensive equipment to do necessary tasks.





Design for Information

4 08 2009

All to often reports are designed to provide data, not information.  There are charts and tables with little intrepretation, or description.  While I am not great fan of PowerPoint, it can often make up for Enterprise BI limitations.  We can call out certain areas within the charts and graphs, as well as add the commentary to help us communicate our point.

A safe assumption is that the person reading the report will not have the same understanding of the material as the report designer, or analyst.  It is then our job to make sure that the report communicates the point clearly.  The last thing you want is to hear “what are you trying to show me?”

Below is a good example of presenting data, while not telling us much.  Here we see that he/she has a few fans that are frequent contributors, and that tweet volume picks up around the lunch hour.  There is not much variation for the days of the week, with a little drop off for the weekend.  August is also the most popular month.

twitter2008-1

What would be helpful to know is why this data is important to us.  What perhaps would be the most important is to know the subject material, so we could do things like tweet just before lunch as that seems to be the most popular time to inspire reaction.  Or that August tweets were up due to an embarassing grammatical error.

As we are designing reports, make sure that the information has a purpose.  Most specifically, know the audience and know the potential actions the information is going to inspire.





Perfection to Value

16 07 2009

One of the areas where performance takes a giant hit is in the area of project initiaition or closure.  And this is further complicated by personal preferences, politicing, and portfolio management.

In the diagram below there are three lines.  Line A is Corporate or Organization expectation of the trade off between speed and perfection.  Projects or tasks with little value (lower left corner) should require lower expectations of research, analytical thought, and discussion.  While projects that are higher in value (farther up to the right) should have higher expectations on quality of thought and preparation.

Perfection to Value Trends2

What happens all too often is we see line C where people don’t have the capacity or time to do the right job and throw something together.  We see that in the end we deliver far less than desired while wasting resources.  The small blue box is the value received, the red box is the wasted resources, and the green box was the original expected value of the project.  The arc is the value frontier, which demonstrates the trade off value between speed and quantity – or what we expect in terms value created from a combination of speed and quality.

Quality vs Speed - Speed

Or we have line B where we basically have a failure to launch because we spend all of our time debating how to be perfect.  Very similar to the situation with line C where we deliver far less than originally desired while wasting similar resources.

Quality vs Speed - Quality

Portfolio Management

Is this an individual issue, or a management issue?  If we were to plot out the results of the individual projects how would your organization look?

Perfection to Value Management2

If we were to see trends like the circles above, this would indicate a management problem.  As management either did not get the individual(s) to move back to the expected line, or management places to high a premium on either speed or perfection thus artificially altering expecations.

What I have witnessed is that line B is more often the norm.  Line C typically causes painful exposure, which causes people to be more fearful, thus needing more inputs and more support.  This creates more meetings, more approvals, more time, more people, which again causes more information, more analysis, more debate.  It is a vicious circle.

Failure to Act is a companion blog.








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