Pharma BI » Sales

Bridging the Dashboard Communication Gap

Christine Muser — Thu, 08 Sep 2011 06:41:16 +0000

The term “dashboard” provides a convenient metaphor because everyone has at least some idea of what a dashboard looks like – and therein lies the problem: our own idea of a dashboard may differ wildly from someone else’s idea of a dashboard. When people talk about dashboards, there may be a huge communications gap and it pays to build a bridge across that gap before taking any action toward developing a dashboard.

There’s a big difference between the dashboard of a car and that of a passenger jet! For one thing, understanding a car dashboard requires significantly less training and experience than a cockpit dashboard. Of course each is designed to meet different needs: a pilot has to worry about many more things than a car driver when ferrying passengers safely from Point A to Point B.

When developing information dashboards for a business we also need to keep user needs in mind: a VP of Marketing will need a much more high-level overview than a Product Director, who in turn has quite different requirements from a sales rep preparing an action plan. In practical terms this means that we first have to figure out who will use the dashboard and how.

Image of a Jet Cockpit

Image credit: http://flysusan.com/cockpits.htm

A dashboard is only useful when it can become an integral part of decision making. Some decisions need to be made on a daily basis, such as which prospects to call or how to follow-up with a client. Other decisions take more time. Those decisions tend to be more organizational and strategic in nature. They require input from many people and data sources, they require observation and a longer term perspective.

The implication here is that a dashboard needs to be capable of providing information at the speed with which decisions are made. A dashboard also needs to provide the appropriate amount of summary and detail. Before we can build anything, we need to define what information is actionable and how soon it needs to be available.

From a technical standpoint, this initial exploration provides the framework for determining how the dashboard should be build. The following questions are just a starting point:

What information is required?
How will the information be used?
What kinds of summaries and calculations make the most sense?
How much detail should be included?
How can we best present the information?
How often does data need to be refreshed?
Which databases and information sources are necessary?
Which software/hardware best meets our needs?

The answers to these questions always involve tradeoffs. Which tradeoffs make sense depends on the impact the dashboard will have and whether we can demonstrate a positive ROI. At some point, the dashboard has to help improve the bottom line.

Drawing a direct line between dashboard and bottom line dollars can be complicated. Dashboard benefits tend to be intangible. To measure ROI, we have to think about how the dashboard enables better decisions, how it helps users focus on profitable actions and whether it helps to save time or other resources. Often we need to offset these intangible benefits against very real budgets and real money that needs to be spent on development, training, software, hardware, and so on.

As we can see, if we are serious about building a dashboard, we also have to be serious about spending some time upfront to map out a plan for getting from the Basic Idea to an Actual Dashboard. To get there, everyone involved needs to develop a common language, that is, a set of common definitions and goals. For instance, if our dashboard is supposed to track progress following a product launch we need a common understanding of what market penetration means. Does it mean “number of actual customers vs. potential customers” or “number of actual customers vs. number of targets” or “number of customers who bought at least X number of widgets.” How we define our key metrics has a direct impact on the usefulness of our dashboard and on the effort required to build it.

Developing common definitions and goals is a key step toward building a bridge across the communication gap. Just because we are using terminology that seems to be well defined doesn’t mean we really are talking about the same thing. For example, we might be talking about lighthouses and we might even have the same general idea of what a light house is and how a typical lighthouse looks. But when it comes to actually building the lighthouse we need something more concrete.

What’s a lighthouse?

The Idea	An Actual Lighthouse	Another Actual Lighthouse

When discussing the concept of a lighthouse, usually this type of image comes to mind.	Most of us would expect an actual lighthouse to look something like this. It meets the typical expectations for a lighthouse.	Sometimes a lighthouse needs to address special situations: when a client needs a special lighthouse and the developer thinks of building a typical lighthouse, the project is in trouble.

Image credits (L to R):

http://www.freeclipartnow.com/buildings/lighthouses/

http://www.destination360.com/north-america/us/maine/images/s/maine-lighthouses.jpg

http://www.longislandlighthouses.com/lv112.htm

The Power of In-House Analytics

Christine Muser — Mon, 26 Jul 2010 22:40:06 +0000

A business associate recently forwarded a white paper by one of the global BI software companies with the comment “… it all sounds so simple, yet we both know the complexities are just under the table.” Like all good marketing materials, this white paper talked about the current pain of the target audience and provided glowing examples of a possible solution. Part of the proposed solution included this: free yourself from expensive consultants by bringing the power of predictive analytics in-house.

Coincidentally, this white paper arrived while I was working through the intricacies of sales transactions for a client who is looking for quick – and accurate – ways to answer questions like “What happened to my sales?” and “What happened to my margin?” Both are high level questions that require a thorough understanding of “low level data” in order to provide meaningful answers. This got me thinking about the complexities of performing predictive analytics.

Complexities lurking under the predictive analytics table include issues such as data quality. For instance,

Customer IDs and customer names not always matching
Customer ratings changing over time
Master invoices being used to track transactions over an extended period of time
Inconsistent data entry – for instance, credits sometimes showing up as negative numbers and sometimes as positive numbers – depending on how the data entry person coded the transaction.

More important than data quality is the question of “how do we interpret what we see?” Statistical outliers serve as an example here, since they do not require a lot of explanation and their meaning is open to interpretation. They could be the first sign of a new trend, a fluke, a data error, or the result of factors beyond our control. How we deal with outliers when building our predictive model depends on what caused them.

Non-repeatable exceptions, a.k.a. flukes, are meaningless when we are trying to build a model of the future. Usually they are noise and become part of our margin for error rather than a factor we would include in our model. In order to separate meaningful facts from flukes, we need to dig further into the details and determine their influence on the big picture.

For example, the chart below shows an “Outlier Territory” that performed particularly well in terms of achieving sales goals.

Graph showing territory performance, including a statistical outlier.

As we refine our bonus plan for the next pay period, how should we proceed? Should we assume this territory will continue to have high sales and therefore raise its quota? The answer depends in part on whether we are dealing with

A real issue, such as our bonus model not working for that territory, or
A fluke, like a one-time-only buy in by a major customer, or
Data errors, as in “somehow we summed up the sales data incorrectly,” or
Factors beyond our control, like an uptick in demand because of an unexpected and short-lived emergency.

Sometimes the sales rep can provide the insight we need to understand what caused the outlier. Usually, though, we need to look for likely causes using the data we already have and relating it to information from other sources.

As we can see, our crystal ball is only as good as the answers we derive from data collected in the past. Building it also requires us to make assumptions about how pieces fit together, how they influence each other and how important they are in shaping the future. We can improve our assumptions using statistical tools like t-Tests, ANOVAs and various regression models. We can look to proxies and draw on our understanding of the market place. No matter how we develop our assumptions, we need to understand their limitations or they might turn us into Jacks and Jennys down the road.

Long story short: to build a crystal ball we need more than powerful tools. We need skilled and experienced people, good data and the commitment to adapt over time.

Decision Making During Turmoil: Are We Prepared?

Christine Muser — Thu, 11 Mar 2010 00:34:56 +0000

In order to make profitable decisions, we need good information. Whether we base our decisions on sales, customer perceptions or the number of widgets we shipped last month, our information comes from some system that collects and measures relevant data for us.

In my Six Sigma Black Belt class we recently discussed the challenges of developing a meaningful measurement system. As usual, the theory sounds easy – until it hits the road of reality. A very simple class room exercise illustrated that point neatly: our instructor had gone through the effort of individually placing twenty M&M candies into twenty numbered plastic bags and then asked us to “accept” or “reject” each M&M based on three criteria. The criteria were written down and no additional verbal cues were given nor did we have a “master” M&M on which to base our judgment.

We realized very quickly that these criteria were not nearly as clear cut as they appeared to be. For example, one criterion specified that the letter “m” on the candy should be “100% visible.” Sounds clear cut, right? After all, is has a numeric qualifier to help us make our decision! Reality check: have you ever looked at an M&M up close? The next time you do, look for tiny spots where the white ink is thin enough for the underlying color of the candy to bleed through the letter “m.” Question: if the entire outline of the letter “m” appears on the candy but these little flecks of color are bleeding through, does this mean that the “m” is no longer 100% visible?

The graph below shows the result of the M&M exercise. It illustrates just how far apart the judgment of perfectly reasonable people can be when they are asked to interpret someone else’s instructions. The left hand graph shows how much each team agreed with itself after reviewing all 20 candies twice in a row. The right hand graph shows how much each team agreed with an external standard for evaluating the candies. The fact that the two red lines barely line up with each other illustrates just how far apart the two teams were with their assessment of the same group of M&Ms.

M&M Attribute Agreement Analysis - click the picture to enlarge it.

The real issue, of course, has nothing to do with the candy and how it looks. The bigger point lies in something the Six Sigma folks call “operational definitions” and how we use them. The M&M example illustrates just how unpredictable individual judgments can be and how much training and feedback may be required before team members reach similar conclusions – which, in turn, will allow the team to work toward a common goal.

As the M&M example shows, developing operational definitions can be tricky. Definitions may be less clear cut than we think. We have a limited amount of time in which to develop them. In group settings, we also have to figure in personalities and hidden agendas. Good leadership and negotiation skills are needed to keep everyone focused without suppressing critical input. In the world of sales and marketing we have the additional challenge of dealing with missing and incomplete data. While statistical models go a long way toward filling in the picture, they are difficult to explain and are not always accepted by those whose paycheck depends on them or by those whose experience seems to indicate something else.

Some ideas for dealing with all this will be the subject of future posts. For today I simply want to ask these questions: with so many changes in the health care marketplace, how well are we prepared to make decisions? Which operational definitions do we need to add, update or toss out in order to ensure good decisions for the future?

P.S.: Additional Information About The M&M Graph

This data mimics the results from a Measuring System Audit (MSA) project with M&M candies. The assignment was to inspect 20 pieces of candy and to determine whether each met these three criteria:

1: the letter ‘m’ is 100% visible
2: the ink for the letter ‘m’ is not smudged
3: there are no chips

Only these written criteria were given. Neither team received additional instructions nor a “Master” against which to evaluate the candy. Each team was asked to review the candies in two rounds. During the first round, Team 2 decided to fail all 20 pieces of candy, hence that team’s low rate of agreement.

Conclusion: gaining agreement about operational definitions is critical. Make sure that everyone has the same training and verify that everyone in a decision making role can reach decisions that support the established goal. Repeat training and offer opportunities for feedback & refinement of criteria.

Looking Ahead: More Data, More Details – Less Work?

Christine Muser — Sat, 07 Mar 2009 03:12:48 +0000

As companies grow, so do the demands on their Sales Operations and Marketing Research teams. As new products are launched and sales forces grow, BI managers need to answer ever more questions from ever more people about ever more data and ever more details. The “working smarter” solution to deal with this increasing demand usually involves new skills or new technology or additional resources – and the time and resources needed to implement them.

Working smarter can be accomplished by

Streamlining processes
Automating routine tasks
Reducing errors
Letting go of less important tasks
Finding someone with the right skills to do the job.

In this article we will take a look at how to meet increasing demands without simply doing more work.

Streamlining Processes

Most of us easily recognize this scenario: a BI manager is asked to provide a “quick analysis” to answer a burning business question. Sometime later the manager is asked to update the results of this “quick analysis” and before you know it the one-time “quick analysis” turns into a regular report.

Chances are good that the manager initially did not worry too much about how efficiently the analysis was done, as long as the results could be delivered quickly. Chances are equally good, that the process for performing this “quick analysis” can be streamlined to consume less time and resources.

Some ways in which we can streamline processes include:

Having data in a more useful format
Pre-calculating certain results and storing them where they can be easily retrieved
Employing technologies like dashboards and pivot tables to quickly retrieve different views of the data.

If the requested analysis requires significant effort, one should also review the entire process from data collection to processing to producing output. Chances are good that some tasks in that process can be done more efficiently or that by reorganizing the process itself we can save time and effort. If certain tasks have to be repeated over and over again they become a candidate for automation.

Automating Routine Tasks

Our article about comparing a product against competitors with multiple indications provides an excellent case study for automating routine tasks. In that scenario, product units have to be converted into more useful information, taking into account how and where products are used. To account for all this, raw units have to be factored each time new unit data becomes available – a tedious task which is also prone to human error.

Unlike humans, computers excel at processing routine and tedious tasks, so why not give them that job?

Reducing Errors

The more critical the information, the more questions will be asked. This axiom lies at the root of many BI headaches and its impact snowballs when errors occur. Each error we find chips away at the level of trust we have in the information. As BI managers we need to provide the most accurate information we can, given the resources at our disposal.

Automation represents one way in which to reduce errors, but we can do more. Spot checking, quality control steps like tying out sub-totals and checking whether the results “make sense” should be an integral part of every information delivery. Even better, we usually can automate some of these QC steps.

Letting go of less important tasks

The article “Making Time For The Future” addresses this topic in more detail. Suffice it to say here that letting go of less important tasks allows us to spend more time on the more important ones.

Finding someone with the right skills to do the job.

This brings us to the last item on this list: to get all of this done, we sometimes have to look for someone else to do the job – either because we do not have the time or the necessary skills. This raises questions like:

Can we afford additional headcount or a consultant?
Is this a long term need or a short term need?
What exactly should this person do?
What skills, knowledge and experience do they need to bring to the table?

Answers to these questions depend on many factors, including:

Budget
Expected growth of the organization
To what extent the BI department wishes to combine business skills with IT skills
Which skills are already available in house
Whether the organization prefers to do all the work in-house or work with external partners
Whether the right person can be hired as an employee or a consultant.

The Bottom Line

Successful organizations will require ever more complex information to stay competitive. As the industry changes and sales reps need to blend more and more sales, medical science and account management skills, we have to integrate data from ever more sources. While technologies like dashboards make the presentation of information easier, we are still faced with the tricky task of determining which information needs to be available when and to whom.

This article provides some ideas about how to meet these increasing demands without simply doing more work. It is the second in a three-part series of articles looking at Three Steps To A Better BI Future. In the last article we will take a closer look at how to provide the right information to the right people at the right time.

The three articles in this series are:

Step 1: Making Time For The Future
Step 2: Looking Ahead – More Data, More Details – Less Work? – this article
Step 3: Getting There – The Right Blend Of People, Skills And Goals

Making Time For The Future

Christine Muser — Tue, 03 Mar 2009 08:45:30 +0000

Time acts like money: we have to invest a little up front to get much more of it down the road. The initial time investment involves paper, pencil and some brainstorming. We build on this investment with a little bit of research and use the research results to start building a case for stopping things that no longer provide the value they once did. As we stop doing some things we make time for building a better future.

Paper, Pencil and Brainstorming

Let us start the brainstorming session with a simple question: “What keeps us busy every day?” The answer should not be a detailed list of every report the department produces, nor of every research project being conducted. Rather the goal here is to identify where the big chunks of time are going, yet with enough detail so that we can later group our thoughts in meaningful ways. Some meaningful groupings might be:

by department
by brand
by budget line item
by the type of data being used
by cost
by the amount of effort involved.

We may want to start with any or all of these bullets to get the brainstorm going. However, since we are in the process of trying to find TIME for the future, we eventually want to focus on the EFFORT involved.

A Little Bit Of Research

Since perceptions can be deceiving, we need to validate our impressions. At this stage we need to explore whether individual projects really consume as much time as we think they do. This is also a good place to ask whether something still is being used or whether it has the same importance it once did. Some of the more tricky items to review are all the “favors” that we inevitably do for others. At this stage of the game we are trying to:

verify how much effort is required
look for low hanging fruit
search for items that promise big returns.

With some projects it will be obvious that they are important and need to get done, yet we can always explore whether they can be done faster and more efficiently. Options include finding new technology, outsourcing, procuring the co-operation of other departments or hiring someone with more appropriate skills.

For other projects it will be just as obvious that they can be stopped or reduced in size. For everything else, we need the next step.

Building A Case For Stopping Things

Chances are good that eventually we will have picked all the low hanging fruit. Budget constraints, changing business priorities and additional demands may make it necessary to cut back on existing services. And of course, we are still looking to find time for the future!

At this stage we have to weigh the benefits of one project against those of another. We have to weigh the impact each project has on the company overall and we have to parse perceived need from real need. Key questions to ask include:

How does this make you more effective?
How does it help you make better business decisions?
How would it hurt your effectiveness if the information was updated less frequently?
Without this information, how would you get your job done?

Notice that these questions do not allow for “yes” or “no” answers. They are the starting point for exploring the true value of something that is perceived as a needed service. Only if we and our information customers explore these questions, can we determine whether other options are available or whether the need is as great as we initially thought.

Building a case takes time and effort – and if we reach this stage, we should also weigh the needs of our own department and not just the needs of those we support. To effectively support our information clients we need to also look at how our own service offering needs to evolve to meet changing needs.

Needing Time For The Future

Successful business information managers constantly engage in their own form of R&D, researching and developing solutions that provide timely and accurate information on which to base profitable business decisions. To meet future demands it is vital that BI managers

Develop expertise with relevant business issues
Follow market trends
Learn about new data sources
Explore new technologies

To be able to do these things, BI managers need to find time in their schedule – or better yet, they need to make time for the future. The tips outlined here should provide a good starting point.

This is the first in a three-part series of articles looking at Three Steps To A Better BI Future. In the next article we will take a look at how to meet increasing demands without simply doing more work.

The three articles in the series are:

Step 1: Making Time For The Future – this article
Step 2: Looking Ahead – More Data, More Details – Less Work?
Step 3: Getting There – The Right Blend Of People, Skills And Goals

Three Steps To A Better BI Future

Christine Muser — Tue, 24 Feb 2009 18:58:50 +0000

Managers who provide information to their sales and marketing organizations walk a tight line between drowning in data details and making sure that the information they provide leads to profitable decisions. The daily fire fights do not leave much time for planning ahead, let alone for envisioning the future. Yet, all successful managers know that the future waits for no one – and that it’s better to be prepared than to catch up. Thus they periodically make time to determine which activities need to:

Stop
Continue
Start.

Of course we all know that this traffic light approach to the future sounds more simple than it is to implement. Implementing “stop-continue-start” often requires multiple doses of diplomacy and the stamina and steadfastness of a referee. Over the course of three articles we will take a look at what it takes to get there.