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.





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).





Changing Market Place

7 04 2011

Yesterday in the NYTimes was a story about the speed of the changing U.S. race demographic.  As our demographic changes, so will tastes and demand.  Many companies have sat atop their markets feeling they are invincible, yet with these changes many of the companies will find out much too late that they were not as solid as they once felt.

Have you asked yourself any of the following:

  • What percent of our clients come from the majority?
  • Do we have products that meet demands from all sectors?
  • Are we at risk if the legislature, or governing boards, can their ethnicity over time?
  • Where are our biggest threats in this new market?
  • Where are our greatest advantages?
  • What else can we do to capture more in this changing market?
  • Where might new competitors come after our market?

If you are not strategically discussing questions like these, then you elevate your risk of something happening to undermine your position within your market.

 





Analytics: Frequency Distribution & Bell Curves

8 11 2010

A statistical method we often overlook is the distribution curve.  I think most of the time it is dismissed because people get nervous about using statistics if they are uncomfortable with math.  While there are some advanced concepts around using a frequency curve, it can also be used visually as a simple tool to explain results.

A simple stats lesson….

Normal Bell Curve – roughly 68% of the population is within 1 standard deviation (measure of variation) of the average and 95% is within two standard deviations. Below is an example of IQ scores.  The average score is 100 and 68% of the data is between 85 & 115.

While this visualization doesn’t do a tremendous amount for us, this is what we assume when we think of populations, like customers and employees.  And because of our limited statistical training we make a large number of assumptions based on averages.  We love to look at average revenue: average revenue per employee, average revenue per customer, etc.  This thinking also gets us looking into the outliers (that <5% that sits way out to the left or right of the chart).  How much time do you spend on less than 5% of the business?

OK, so back to thinking of this in terms of running a business….

Let’s map out our revenue per customer.  I would be willing to bet it looks something like the following:

If this is the customer revenue distribution, if we use the average number in our analyzes we can quickly generate a number of wrong assumptions.  First and foremost, our typical customer is larger than reality.  It might lead us to think we are serving mid-sized businesses than more likely smaller market customers.  I am also willing to bet our profitability per customer has a similar curve to it.  In this case we are likely spending money on the wrong customers and aligning our better services to a lower profit generating customer (or more likely a profit destroying customer).

Do we need to use it in everything? Of course not, but it might help everyone once in a while to challenge our overuse of the mathematical average to reassess perspectives of our business.  A great place to start is map out the customer base in terms of revenue (profitability is better, but takes a lot longer to do).  It might just lead you to understand your customer (think customer segmentation) better.

Real life example…I was once part of a research project to understand discounting to one side of the outliers (<1% of the business).  The outcome was to focus on reducing discounting to that <1% of the business.  What I argued was to focus on the larger part of the business, where the same efforts would have resulted in millions more in terms of profits.  It was a clear lesson is where to apply process improvement.








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