The Harvard Business Review had an excellent blog post this week on how to “Make Data Work Throughout Your Organization". One of the points they made was that in order to put data to work it has to be clearly defined and have high quality. We couldn’t agree more. The challenge we see is that heart of the information quality issue is beyond simple technical measurements of data quality. For example, we frequently speak with our clients about technical aspects such as uniqueness, relational quality and domain integrity.

But there can be no quality without the ability for humans to communicate about it. If an organization lacks the vocabulary, process and experience to communicate about basic aspects of information such as uniqueness, hierarchies and sets – can they ever really have trust in their information? If two people cannot quickly and efficiently determine that they are both talking about the same set of customers are they prepared to have a conversation about customer segmentation?

In our Making Information Powerful approach we believe that we have to teach more than just data manipulation technology. We have to provide a language and a vocabulary. Only with an ability to communicate can we achieve information quality.

Would you believe that a company that prided itself on customer service would have only the bare minimum of information on customers in its data warehouse? Would a company that focused on wide product selection not capture all of the available attributes of its products?

Every organization has a unique strategy, a unique answer to the question – why do we exist? We believe that our most successful clients use information in a way that reinforces that unique strength. They do this by having more than just the same generic data from their business systems that all their competitors have. They’ve supplemented that with measures and dimensional information that applies directly to their unique strategy.

One of our clients is a retailer that focuses on customer service and the expertise of their staff. Their data warehouse therefore contains not just the names and current positions of each staff member but also their position history over time, the number of touches they’ve had with customers and the detailed information on their activities on the floor and in the stock room.

This gives them more than just a minimum set of data from their business systems coupled with some shiny business intelligence software tools. They have a unique and powerful perspective on their unique strategy and how well they are executing on that strategy.

The dream of business forecasting would be to find a single easily accessible indicator that would predict your company’s future business demand with perfect accuracy. In the last few years we’ve been able to get more prospect behavior data than ever. Examples include whether email campaigns were actually opened and patterns of customer behavior on websites. These are certainly great leading indicators of potential customer interest in products or services.

But what if we could get these same indicators of customer interest without the effort and expense of promotional campaigns? A recent article explained how using Google Correlate helped an auto manufacturer create a forecast that was 18% more accurate. This toolset allows anyone to analyze the correlation between web searches for particular words or phrases and their business performance. This in turn can be used as an input to a more accurate fitted model for forecasting.

Our best demand forecasting models include broad indicators like Google Correlate as well as more geographically specific indicators. This combined with sophisticated statistical models of historical performance to capture trends and seasonality let us get closer and closer to the dream of business forecasting.

The Economist magazine recently published a fun article about reader suggested ideas for new leading indicators of the state of the economy. Their readers suggested ideas ranging from the use of veterinary services—which fall in a down economy--to the sales of stress related healthcare products such as antacids—which rise.

At Perkins, we encourage our clients to think about leading indicators that are more directly related to their business. Traditionally, most organizations have looked at their sales pipeline as their key leading indicator, but our clients have found that thinking outside the box leads them to valuable insights at different time horizons. We’ve helped them look at demographic change data by Metropolitan Statistical Area (MSA), local weather forecasts and building permit counts--all useful leading indicators at relatively short time horizons.

Another example might be to use easily accessible public data to track specific financial indicators for a basket of public stocks representing your customers. Seeing their inventories go down while their sales go up might be a valid leading indicator that they will need to place additional orders. As always, the best way to build demand models is by doing low-cost testing. At Perkins we are always testing for better leading indicators of unconstrained demand for our clients products and services.

Performance analytics goes Hollywood... Moneyball, starring Brad Pitt, is coming out next month and it seems performance analytics will finally get the red carpet treatment it deserves.  The movie is based on the story of Billy Beane,  the general manager of the Oakland Athletics,  a Major League Baseball team. Beane turned  baseball on its head by throwing out a century of hallowed collective wisdom about baseball statistics, instead using performance analytics to assess players and put together a winning team on a not-so-winning budget.
Enshrined statistics, such as stolen bases, runs batted-in, and batting average were tossed aside in favor of demonstrated statistical indicators of success, like on-base percentage and slugging percentage. Other teams were looking for players with speed and style, but Beane stuck to his analysis and the players that the data told him to recruit. It turned out to be a winning strategy—in 2002 the Athletics remained competitive with other MLB teams who spent three times as much on player salaries.

Baseball teams aren’t the only organizations that fall victim to chasing old and out-dated statistical measures of employee performance.  A lot of organizations are still measuring employees by their attendance and how many internal training classes they attend. These metrics measure only the most routine aspects of individual performance and don’t capture how strategic individual efforts can move the entire company forward. Just as on-base percentage can capture the critical first step of getting on a base – a simple metric like customer calls returned can capture a critical driver of lifetime customer value. There are so many other and better ways to measure employee performance, and companies that learn to use them can be just as successful at winning at their own industry’s Moneyball as Billy Beane was in his.

There’s a scene in the film Minority Report where the character played by Tom Cruise enters a Gap store and is greeted by a digital display that scans his retinas, identifies him, and presents him with a personalized welcome.  This sort of scene is not so futuristic - facial recognition is already being used to identify terrorists in large crowds.  Right now personalized tracking is mostly tied to cell phones and not to retinas - when I walk down the street my cell phone broadcasts where I am and applications like Foursquare and Google Maps can suggest places to get a bite to eat or products for sale nearby that I might be interested in. So the business opportunity is there to deliver a targeted marketing message based on the proximity of a potential customer to your product or service.

An even bigger opportunity is to use not just the location of an individual right now but the movement of many individuals as a statistical sample. Have you ever wondered how GPS mapping applications know where the traffic on your morning commute is backed up? They purchase aggregate data about how fast cell phones are moving along various streets and highways. Access to this same aggregated movement data can be invaluable in locating a new business. You can build the coffee shop on the morning’s busiest street corner; you can offer the lunch special at the day and time customers are most likely to be drawn by it. At a more micro level you can put the display of new stylish khaki pants in the part of the store that men aged 20 to 40 are most likely to walk through. As this technology advances, maybe someday you’ll even be able to tune your offering so that Tom Cruise himself stops by…

A lot of companies fail because they get either too focused on the short- term (hitting the quarterly results) or too focused on the long-term (endlessly creating strategic plans but never executing). It’s easy to get caught up in the crisis of the moment, or the optimism of the future, but to be successful, organizations need to be able to focus on both the short- term, the long-term and  all the points in between. Our most successful clients are the ones who can manage multiple time horizons.

Done right, business intelligence imposes the discipline to continually look at different distances in the future. For example, a dashboard detailing sales information is used for the here and now. A scorecard of metrics measured against goals is a good mid-term tool. A rolling budget which is revised monthly or quarterly but forecasts a year or two into the future can keep everyone involved and focused on the future. Budgeting this way forces people to focus on this month, next year, and all the points in between.

The best business intelligence solutions create metrics and report mechanisms so that organizations can measure and monitor their achievement of the long-term strategic plan on a day-by-day, month-to-month, quarter-to-quarter, and year-to-year basis. For an organization to thrive, they need to understand and maximize their performance in each of these timelines.

One of the most difficult things to measure is employee performance. Most organizations focus on the easily quantifiable aspects of performance, like attendance and technical training attendance, yet there are so many fuzzier parts of performance that are just as important to the success of an organization. Take for example, commitment. Employee commitment is important because it influences turnover and team performance, but how do you measure an employee’s commitment to a company? Is a supervisor’s assessment really an accurate gauge of an employee’s commitment to an organization or is it more likely to be an assessment of how successful that employee is in sucking-up?

An article in a recent Harvard Business Review by Francis J. Flynn seems to indicate that guilt may be a good proxy for employee commitment. 

Flynn gathered her research on guilt from a standard psychological test, not unlike the personality tests many organizations already use. Other possible indicators of commitment might be how quickly and reliably an employee responds to emails or how often they volunteer for mundane tasks. Most organizations are collecting lots of data on their employees that they could be using to get outside the traditional HR box when evaluating employee performance. With the right business intelligence tools and some creative thinking, they can begin to measure and reward those aspects of employee performance, like commitment, that aren’t easily quantified yet highly valued. We’ll have much more to come on using “data exhaust” to examine employee performance.

I don’t think of myself as an artistic person (I got a “needs improvement” in first grade art), but a lot of our work is about turning data into actionable information and a lot of times the best way to do that is to make data visual.  For instance, I could show you an enormous table of profitability rates by product or I could show a simple bar chart (see the example at the bottom of this page.)

Clearly the picture is worth a lot more than the 50 words. Intellectually I get it—that pictures often beat words in communicating information, but how do I learn how to think more visually? Since I was educated primarily in words so that’s how I’ve learned to think.  Except for the occasional art class, we aren’t taught to visualize, we are taught to read; we aren’t taught to draw, we are taught to write. To a large degree that‘s because of the technology of the time—keyboards—allows us to communicate with lots of people over lots of different time frames, but always with words.

But the world is changing fast and it is rapidly evolving away from mere words.  We live in a world of 32 bit color, video games, mind maps and interactive visualizations. The world is exploding with technologies that allow us to communicate things with pictures and the possibilities are exciting and endless. So how do I explore them—how do we break the chains of our word based thinking and learn to think and communicate in pictures? By practice and habit. And there are several great sources to encourage new visual habits. A few of my favorites:

Blogs:

Flowing Data, Information Aesthetics

Books:

Information Dashboard Design: The Effective Visual Communication of Data,  Beautiful Evidence, The Back of the Napkin: Solving Problems and Selling Ideas with Pictures

Last week I had an opportunity to attend the Strata conference in Santa Clara. This was a technology conference to bring together data scientists and others who work with “big” data. But my reasons for attending were to see how different types of organizations are staffing and managing their use of information.

At Perkins Consulting we are constantly working to help our clients make their information powerful. The technology aspect of this has always been easier than the people and process aspects. Over the last few years new popular books such as The Fourth Paradigm, Moneyball and Supercrunchers have made the world aware of the opportunities in data. A new breed of data-centric startup company has sprung to life. Is there anything in this new literature or in these new companies that might help our clients? I went to Strata to find out.

Some interesting tidbits that I picked up include:

• Data by itself is meaningless. It is not just turning data into Information that adds power but also providing a contextual frame. Telling stories with the data is one way to add this frame.
• We need to understand clearly that we are providing information to assist decisions – not make them. Sometimes we try too hard to frame information in such a way that the decision is completely obvious. This doesn’t take advantage of the capabilities of our people and it is too rigid for an ever changing business environment.
• To be successful with information you need a quantitative culture.
• It is really important to have a culture that encourages exploration and testing if you want to build an analytical community. A culture that focuses on pinning blame for bad decisions can’t be analytical.
• 70% of the work is data preparation
• The metrics that you don’t have are expensive but have a high Return on Investment. We see this a lot. Simple metrics like total sales are not highly valued but cross-process metrics like customer profitability are.
• I saw an interesting Barnes and Noble presentation on how they use information. They are moving from product-centric analysis to customer-centric. Focusing on customer profitability rather than product profitability.
• Good process is better than great technology. How true!

There was a lot of good details that I’ll be adding into our data mart and business intelligence reference project plans, deployment questionnaires and staffing plan documents. But the overall message I took away was a simple confirmation of my existing understanding:

It’s all about how people are prepared to use information. It’s not about the technology.