BI

It’s not Rocket Science – What you Measure Defines how People Behave

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A while back I wrote a post titled, To Measure is to Know.  That is only part of the story, so please read on.

The other side of the coin is that what you measure defines how people behave. This is an often forgotten aspect of Business Intelligence, Compensation Plans, Performance reviews, and other key areas in business. While many people view this topic as “common sense,” based on the numerous incentive plans that you run across as a consultant it seems that is not the case. Picture showing an astronaut floating in space above Earth

Is it a bad thing to have people respond by focusing on specific aspects of their job that they are being measured on? That is a tough question. This simple answer is, “sometimes.” This is ultimately the desired outcome of implementing specific key performance indicators (KPIs), but it doesn’t always work. So, let’s dig into this a bit deeper.

One prime example is something seemingly easy yet often anything but – Compensation Plans. When properly implemented these plans drive organic business growth through increased sales and revenue (both likely items being measured), as well as drive steady cash flow by constantly closing within certain periods (usually months or quarters). What could be better than that?

Salespeople focus on the areas where they have the greatest opportunity to make money. Presumably they are selling the products or services that you want them to based on their comp plans. Additionally, certain MBO (management by objective) goals are presumably focused on positive outcomes that are important to the business, such as bringing-on new reference accounts. Those are forward looking goals that increase future (as opposed to immediate) revenue. In a perfect world, with perfect comp plans, all of these business goals are codified and supported by motivational financial incentives.

Some of the most successful salespeople are the ones that primarily care only about themselves. They are in the game for one reason – to make money. Give them a plan that is well constructed and allows them to win and they will do so in a predictable manner. Paying large commission checks should be a goal for every business, because when they are doing that their own business is prospering.

But, give a salesperson a plan that is poorly constructed and they will likely find ways to personally win in ways that are inconsistent with company growth goals (e.g., paying commission based on deal size, but not factoring in the overall impact of excessive discounts). Even worse, give them a plan that doesn’t provide a chance to win and the results will be uncertain at best.

Just as most tasks tend to expand to use all time available, salespeople tend to book most of their deals at the end of whatever period is being used. With quarterly cycles most of the business tends to book in the final week or two of the quarter – something that is not ideal from a cash flow perspective. Using shorter monthly periods may increase business overhead, but the potential to significantly increase business from salespeople working harder for that immediate benefit will likely be a very worthwhile tradeoff.

What about motiving Services teams? What I did with my company was to provide quarterly bonuses based on overall company profitability and each individual’s contribution to our success that quarter. Most of our projects used task oriented billing where we billed 50% up-front and 50% at the time of the final deliverables. You needed to both start and complete a task within a quarter to maximize your personal financial contribution, so there was plenty of incentive to deliver and quickly move to the next task. As long as quality remains high this is a good thing.

We also factored-in salary costs (i.e., if you make more than you should be bringing-in more value to the company), the cost of re-work, and non-financial items that were beneficial to the company. For example, writing a white paper, giving a presentation, helping others, or even providing formal documentation on lessons learned added business value and would be rewarded.  Everyone had the right motivation, performed work and delivered quality work products as needed, and made good money doing so.

This approach worked very well for me, and was continually validated over the course of several years. It also fostered innovation, because the team was always looking for ways to increase their value and earn more money. Many tools, processes and procedures came out of what would otherwise be routine engagements.

Mistakes with comp plans can be costly – due to excessive payouts and/or because they are not generating the expected results. Back testing is one form of validation as you build a plan. Short-term incentive programs are another. Remember, where there is not risk there is little reward, so accept the fact that some risk must be taken to find the point where the optimal behavior is fostered.

It can be challenging and time consuming to identify the right things to measure, the right number of things (measuring too many or too few will likely fall short of goals), and provide the incentives that will motivate people to do what you want or need. But it is definitely worthwhile work if you want your business to grow and be healthy.

This type of work isn’t rocket science, and therefore is well within everyone’s reach.

To Measure is to Know

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Lord William Thomson Kelvin was a pretty smart guy in the 1800’s. He didn’t get everything right (e.g., he supposedly stated, “X-rays will prove to be a hoax.”), but his success ratio was far better than most so he did have useful insight. I’m personally a fan of his quote, “If you can not measure it, you can not improve it.”

Business Intelligence (BI) systems can be very powerful, but only when they are embraced as a catalyst for change. What you often find in practice is that the systems are not actively used, or do not track the “right” metrics (i.e., those that provide insight into something important that you have the ability to adjust and impact the results), or provide the right information – only too late to make a difference.

The goal of any business is developing a profitable business model and then executing extremely well. So, you need to have something that people want, then need to be able to deliver high quality goods and/or services, and finally need to make sure that you can do that profitably (it’s amazing how many businesses fail to understand this last part).  Developing a systematic approach that allows for repeatable success is important. Pricing at a level that is competitive and provides a healthy profit margin provides the means for growth and sustainability.

Every business is systemic in nature. Outputs from one area (such as a steady flow of qualified leads from Marketing) become inputs to another (Sales). Closed deals feed project teams, development teams, support teams, etc. Great jobs by those teams will generate referrals, expansion, and other growth – and the cycle continues. This is an important concept to understand because problems or deficiencies in one area can manifest themselves in other areas.

Next, understanding of cause and effect is important. For example, if your website is not getting traffic is it because of poor search engine optimization, or is it bad messaging and/or presentation? If people come to your website but don’t stay long do you know what they are doing? Some formatting is better for printing than reading on a screen (such as multi-column pages), so people tend to print and go. And, external links that do not open in a new window can hurt the “stickiness” of a website.  Cause and effect is not always as simple as it would seem, but having data on as many areas as possible will help you understand which ones are really important.

When I had my company we gathered metrics on everything. We even had “efficiency factors” for every Consultant. That helped with estimating, pricing, and scheduling. We would break work down into repeatable components for estimating purposes. Over time we found that our estimates ranged between 4% under and 5% over the actual time required for nearly every work package within a project. This allowed us to fix bid projects to create confidence, and price at a level that was lean (we usually came-in about the middle of the pack from a price perspective, but the difference was that we could guarantee delivery for that price). More importantly, it allowed us to maintain a healthy profit margin that let us hire the best people, treat them well, invest in our business, and take some profit as well.

There are many standard metrics for all aspects of a business. Getting started can be as simple as creating some sample data based on estimates, “working the model” with that data, and seeing if this provides additional insight into business processes. Then ask, “When and where could I have made a change to positively impact the results?” Keep working and when you have something that seems to work gather some real data and re-work the model. You don’t need fancy dashboards (yet).

Within a few days it is often possible to identify the Key Performance Indicators (KPIs) that are most relevant for your business. Then, start consistently gathering data, systematically analyzing it, and present it in a way that is easy to understand and drill-into in a timely manner.  To measure the right things really is to know.

 

Spurious Correlations – What they are and Why they Matter

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In an earlier post I mentioned that one of the big benefits of geospatial technology is its ability to show connections between complex and often disparate data sets. As you work with Big Data you tend to see the value of these multi-layered and often multi-dimensional perspectives of a trend or event. While that can lead to incredible results, it can also lead to spurious correlations of data. Image containing the word "Technology"

First, let me state that I am not a Data Scientist or Statistician, and there are definitely people far more expert on this topic than myself.  But, if you are like the majority of companies out there experimenting with geospatial and big data it is likely that your company doesn’t have these experts on-staff. So, a little awareness, understanding, and caution can go a long way in this type of scenario.

Before we dig into that more, let’s think about what your goal is. Do you want to be able to identify and understand a particular trend (reinforcing actions and/or behavior), or do you want to understand what triggers a specific event (initiating a specific behavior). Both are important, but they are both different. My personal focus has been on identification of trends so that you can leverage or exploit them for commercial gain. While that may sound a big ominous, it is really what business is all about.

There is a common saying that goes, “Correlation does not imply causation.”  A common example is that for a large fire you may see a large number of fire trucks.  There is a correlation, but it does not imply that fire trucks cause fires. Now, extending this analogy, let’s assume that in a major city the probability of multi-tenant buildings starting on fire is relatively high. Since they are a big city, it is likely that most of those apartments or condos have WiFi hotspots. A spurious correlation would be to imply that WiFi hotspots cause fires.

As you can see, there is definitely potential to misunderstand the results of correlated data. More logical analysis would lead you to see the relationships between the type of building (multi-tenant residential housing) and technology (WiFi) or income (middle-class or higher). Taking the next step to understand the findings, rather than accepting them at face value, is very important.

Once you have what looks to be an interesting correlation there are many fun and interesting things you can do to validate, refine, or refute your hypothesis. It is likely that even without high-caliber data experts and specialists you will be able to identify correlations and trends that can provide you and your company with a competitive advantage.  Don’t let the potential complexity become an excuse for not getting started, because as you can see above it is possible to gain insight and create value with a little effort and simple analysis.

What’s the prize if I win?

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In consulting and in business there is a tendency to believe that if you show someone how to find that proverbial “pot of gold at the end of the rainbow” that they will be motivated to do so.  Seasoned professionals will tend to ask, “What problem are you trying to solve?” to understand if there is a real opportunity or not. If you are unable to quickly, clearly and concisely articulate both the problem and why this helps solve that problem it is often game over then and there (N.B.  It pays to be prepared). But, having the right answer is not a guarantee of moving forward.

Unfortunately, sometimes a mere pot of gold just isn’t enough to motivate. Sometimes it takes something different, and usually something personal. It’s more, “What’s in this for me?” No, I am not talking about bribes, kickbacks or anything illegal or unethical. This is about finding out what is really important to the decision maker and helping demonstrate that this will bring them closer to achieving their personal goals.

Case in point. Several years ago I was trying to sell a packaged Business Intelligence (BI) system developed on our database platform to customers most likely to have a need. Qualification performed – check. Interested – check. Proof of value – check. Close the deal – not so fast…

This application was a set of dashboards with 150-200 predefined KPIs (key performance indicators). The premise was that you could quickly tailor and deploy the new BI system with little risk (finding and validating the data needed was available to support the KPI was the biggest risk, but one that could be identified up-front) and about half the cost of what a similar typical implementation would cost. Who wouldn’t want one?

I spent several days onsite with our client, identified areas of concern and opportunity, and used their own data to quantify the potential benefit. Before the end of the week I was able to show the potential to get an 8x ROI in the first year. Remember, this was estimated using their data – not figures that I just created. Being somewhat conservative I suggested that even half that amount would be a big success.  Look – we found the pot of gold!

Despite this the deal never closed. This company had a lot of money, and this CIO had a huge budget. Saving $500K+ would be nice but was not essential. What I learned later was that this person was pushing forward an initiative of his own that was highly visible. This new system had the potential to become a distraction and he did not need that. Had I been able to make this determination sooner I could have easily repositioned it to be in alignment with his agenda.

For example, the focus of the system could have shifted from financial savings to project and risk management for his higher priority initiative. The KPIs could be on earned value, scheduling, and deliverables.  This probably would have sold as it would have been far more appealing to this CIO and supported what was important to him (i.e., his prize if he wins).  The additional financial savings initially identified would just be the icing on the cake, to be applied at a later time.

There were several lessons learned on this effort. In this instance I was focused on my own personal pot of gold (based on logic and common sense), rather than on my customer’s prize for winning. That mistake cost me this deal, but is one I have not made since (which has helped me win many other deals).

What’s so special about Spatial?

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Two years ago I was assigned some of the product management and product marketing work for a new version of a database product we were releasing. To me this was the trifecta of bad fortune. I didn’t mind product marketing but knew it took a lot of work to do it well. I didn’t feel that product management was a real challenge (I was so wrong here), and I really didn’t want to have anything to do with maps.

Boy, was I wrong in so many ways. I didn’t realize that real product management was just as much work as product marketing. And, I learned that spatial was far more than just maps. It was quite an eye-opening experience for me; one that turned out to be very valuable as well.

First, let me start by saying that I now have a huge appreciation for Cartography. I never realized how complex mapmaking really is, and how there just as much art as there is science (a lot like programming). Maps can be so much more than just simple drawings.

I had a great teacher when it came to geospatial – Tyler Mitchell (@spatialguru). He showed me the power of overlaying tabular business data with common spatial data (addresses, zip / postal codes, coordinates) and presenting the “conglomeration of data” in layers that made things easier to understand. People buy easy, so that is good in my book.

The more I thought about this technology – simple points, lines, and area combined with powerful functions, the more I began to think about other uses. I realized that you could use it to correlate very different data sets and graphically show relationships that would otherwise extremely difficult to make.

Think about having access to population data, demographic data, business and housing data, crime data, health / disease data, etc.  Now, think about a simple and easy to use graphical dashboard that lets you overlay as many of those data sets as you wanted. Within seconds you see very specific clusters of data that is correlated geographically. Some data may only be granular to a zip code or city, but other data will allow you to identify patterns down to specific streets and neighborhoods. Just think of how something so simple can help you make decisions that are so much better. The interesting thing is how few businesses are really taking advantage of this cost-effective technology.

If that wasn’t enough, just think about location aware applications, the proliferation of smart devices that completely lend themselves to so many helpful and lucrative mobile applications. Even more than that, they make those devices more helpful and user friendly. Just think about how easy it is to find the nearest Indian restaurant when the thought of curry for lunch hits you.  And these things are just the tip of the iceberg.

What a lucky day it was for me when I was assigned this work that I did not want. Little did I know that it would change the way that I think about so many things. That’s just the way things work out sometimes.

My perspective on Big Data

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Ever since I worked on redesigning a risk management system at an insurance company (1994-1995) I was impressed at how you could make better decisions with more data – assuming it was the right data.  The concept of, “What is the right data?” has intrigued me for years, as what may seem common sense today could have been unknown 5-10 years ago, and may be completely passe 5-10 years from now. Context becomes very important because of the variability of data over time.

And this is what makes Big Data interesting. There really is no right or wrong answer or definition. Having a framework to define, categorize, and use that data is important. And at some point being able to refer to the data in-context will be very important as well. Just think about how challenging it could be to compare scenarios or events from 5 years ago with those of today. It’s not apples-to-apples but could certainly be done. It is pretty cool stuff.

The way I think of Big Data is similar to a water tributary system. Water gets into the system many ways – rains from the clouds, sprinkles from private and public supplies, runoff and overflow, etc.  It also has many interesting dimensions, such as quality / purity (not necessarily the same due to different aspects of need), velocity, depth, capacity, and so forth. Not all water gets into the tributary system (e.g., some is absorbed into the groundwater tables, and some evaporates), so data loss is expected. If you think in terms of streams, ponds, rivers, lakes, reservoirs, deltas, etc. there are many relevant analogies that can be made. And just like the course of a river may change over time, data in our water tributary system could also change over time.

Another part of my thinking is based on an experience I had about a decade ago working on a project for a Nanotech company. In their labs they were testing various things. There were particles that changed reflectivity based on temperature that were embedded in shingles and paint. There were very small batteries that could be recharged tens of thousands of times, were light, and had more capacity than a 12-volt car battery. And, there was a section where they were doing “biometric testing” for the military. I have since read articles about things like smart fabrics that could monitor the health of a soldier, and do things like apply basic first aid when a problem was detected.  This company felt that by 2020 advanced nanotechnology would be widely used by the military, and by 2025 it would be in wide commercial use.  Is that still a possibility? Who knows…

Much of what you read today is about the exponential growth of data. I agree with that, but also believe that the nature of the source of that data will change.  For example, nano-particles in engine oil will provide information about temperature, engine speed and load, and even things like rapid changes in movement (fast take-off or stops, quick turns). The nano-particles in the paint will provide weather conditions. The nano-particles on the seat upholstery will provide information about occupants (number, size, weight). Sort of like the “sensor web,” from the original Kevin Delin perspective. A lot of data will be generated, but then what?

I believe that time will be an important aspect of every piece of data, but I also feel that location (X, Y, and Z coordinates) will be just as important. But, not every sensor will collect location. I believe there will be multiple data aggregators in common use at common points (your car, your house, your watch). Those aggregators will package the available data in something akin to an XML object, which allows flexibility.  And, from my perspective, this is where things get real interesting.

Currently we have companies like Google that make a lot of money from aggregating data. I believe that there will be opportunities for individuals to place their anonymized data to a data exchange for sale. The more interesting their data, the more value it has and the more benefit it provides to the person selling it. This could have a huge economic impact, and that would foster both the use and expansion of various commercial ecosystems required to manage the commercial aspects of this technology.

The next logical step in this vision is “smart everything.” For example, you could buy a shirt that is just a shirt. But, for an extra cost you could turn-on medical monitoring or refractive heating / cooling. And, if you felt there was a market for extra dimensions of data that could benefit you financially, then you could enable those sensors as well. Just think of the potential impact that technology would make to commerce in this scenario.

Anyway, that is what I personally think will happen within the next decade or so. This won’t be the only type or use of big data. Rather, there will be many valid types and uses of data – some complementary and some completely discrete. What is common is that someone will find potential value in that data, today or someday in the future, and decide to store it. Someone else will see this data as a competitive advantage and do something interesting with it. Who knows what we will view as valuable data 5-10 years from now.

So, what are your thoughts? Can we predict the future, or simply create platforms that are powerful enough, flexible enough, and extensible enough to change as our perspective of what is important changes? Either way it will be fun!