Tag Archives: Data

Working from the outside in

We’re drowning in a sea of data and ideas, with huge volumes of untapped information available both inside and outside our organization. There is so much information at our disposal that it’s hard to discern Arthur from Martha, let alone optimize the data set we’re using. How can we make sense of the chaos around us? How can we find the useful signals which will drive us to the next level of business performance, from amongst all this noise?

I’ve spent some time recently, thinking about how the decisions our knowledge workers make in planning and managing business exceptions can have a greater impact on our business performance than the logic reified in the applications themselves. And how the quality of information we feed into their decision making processes can have an even bigger impact, as the data’s impact is effectively amplified by the decision making process. Not all data is of equal value and, as is often said, if you put rubbish in then you get rubbish out.

Traditional Business Intelligence (BI) tackles this problem by enabling us to mine for correlations in the data tucked away in our data warehouse. These correlations provide us with signals to help drive better decisions. Managing stock levels based on historical trends (Christmas rush, BBQs in summer …) is good, but connecting these trends to local demographic shifts is better.

Unfortunately this approach is inherently limited. Not matter how powerful your analytical tools, you can only find correlations within and between the data sets you have in the data warehouse, and this is only a small subset of the total data available to us. We can load additional data sets into the warehouse (such as demographic data bought from a research firm), but in a world awash with (potentially useful) data, the real challenge is deciding on which data sets to load, and not in finding the correlations once they are loaded.

What we really need is a tool to help scan across all available data sets and find the data which will provide the best signals to drive the outcome we’re looking for. An outside-in approach, working from the outcome we want to the data we need, rather than an inside-out approach, working from the data we have to the outcomes it might support. This will provide us with a repeatable method, a system, for finding the signals needed to drive us to the next level of performance, rather than the creative, hit-and-miss approach we currently use. Or, in geekier terms, a methodology which enables us to proactively manage our information portfolio and derive the greatest value from it.

I was doodling on the tram the other day, playing with the figure I created for the Inside vs. Outside post, when I had a thought. The figure was created as a heat map showing how the value of information is modulated by time (new vs. old) and distance (inside vs. outside). What if we used it the other way around? (Kind of obvious in hindsight, I know, but these things usually are.) We might use the figure to map from the type of outcome we’re trying to achieve back to the signals required to drive us to that outcome.

Time and distance drive the value of information
Time and distance drive the value of information

This addresses an interesting comment (in email) by a U.K. colleague of mine. (Jon, stand up and be counted.) As Andy Mulholland pointed out, the upper right represents weak confusing signals, while the lower left represents strong, coherent signals. Being a delivery guy, Jon’s first though was how to manage the dangers in excessively focusing on the upper right corner of the figure. Sweeping a plane’s wings forward increases its maneuverability, but at the cost of decreasing it’s stability. Relying too heavily on external, early signals can, in a similar fashion, could push an organization into a danger zone. If we want to use these types of these signals to drive crucial business decisions, then we need to understand the tipping point and balance the risks.

My tram-doodle was a simple thing, converting a heat map to a mud map. For a given business decision, such as planning tomorrow’s stock levels for a FMCG category, we can outline the required performance envelope on the figure. This outline shows us the sort of signals we should be looking for (inside good, outside bad), while the shape of the outlines provides us with an understanding (and way of balancing) the overall maneuverability and stability of the outcome the signals will support. More external predictive scope in the outline (i.e. more area inside the outline in the upper-right quadrant) will provide a more responsive outcome, but at the cost of less stability. Increasing internal scope will provide a more stable outcome, but at the cost of responsiveness. Less stability might translate to more (potentially unnecessary) logistics movements, while more stability would represent missed sales opportunities. (This all creates a little deja vu, with a strong feeling of computing Q values for non-linear control theory back in university, so I’ve started formalizing how to create and measure these outlines, as well as how to determine the relative weights of signals in each area of the map, but that’s another blog post.)

An information performance mud map
An information performance mud map

Given a performance outline we can go spelunking for signals which fit inside the outline.

Luckily the mud map provides us with guidance on where to look. An internal-historical signal is, by definition driven by historical data generated inside the organization. Past till data? An external-reactive signal is, by definition external and reactive. A short term (i.e. tomorrow’s) weather forecast, perhaps? Casting our net as widely as possible, we can gather all the signals which have the potential to drive us toward to the desired outcome.

Next, we balance the information portfolio for this decision, identifying the minimum set of signals required to drive the decision. We can do this by grouping the signals by type (internal-historical, …) and then charting them against cost and value. Cost is the acquisition cost, and might represent a commercial transaction (buying access to another organizations near-term weather forecast), the development and consulting effort required to create the data set (forming your own weather forecasting function), or a combination of the two, heavily influenced by an architectural view of the solution (as Rod outlined). Value is a measure of the potency and quality of the signal, which will be determined by existing BI analytics methodologies.

Plotting value against cost on a new chart creates a handy tool for finding the data sets to use. We want to pick from the lower right – high value but low cost.

An information mud map
An information mud map

It’s interesting to tie this back to the Tesco example. Global warming is making the weather more variable, resulting in unseasonable hot and cold spells. This was, in turn, driving short-term consumer demand in directions not predicted by existing planning models. These changes in demand represented cost, in the from of stock left on the shelves past it’s use-by date, or missed opportunities, by not being able to service the demand when and where it arises.

The solution was to expand the information footprint, pulling in more predictive signals from outside the business: changing the outline on the mud map to improve closed-loop performance. The decision to create an in-house weather bureau represents a straight forward cost-value trade-off in delivering an operational solution.

These two tools provide us with an interesting approach to tackling a number of challenges I’m seeing inside companies today. We’re a lot more externally driven now than we were even just a few years ago. The challenge is to identify customer problems we can solve and tie them back to what our organization does, rather than trying to conceive offerings in isolation and push them out into the market. These tools enable us to sketch the customer challenges (the decisions our customers need to make) and map them back to the portfolio of signals that we can (or might like to) provide to them. It’s outcome-centric, rather than asset-centric, which provides us with more freedom to be creative in how we approach the market, and has the potential to foster a more intimate approach to serving customer demand.

Inside vs. Outside

As Andy Mullholland pointed out in a recent post, all too often we manage our businesses by looking out the rear window to see where we’ve been, rather than looking forward to see where we’re going. How we use information too drive informed business decisions has a significant impact on our competitiveness.

I’ve made the point previously (which Andy built on) that not all information is of equal value. Success in today’s rapidly changing and uncertain business environment rests on our ability to make timely, appropriate and decisive action in response to new insights. Execution speed or organizational intelligence are not enough on their own: we need an intimate connection to the environment we operate in. Simply collecting more historical data will not solve the problem. If we want to look out the front window and see where we’re going, then we need to consider external market information, and not just internal historical information, or predictions derived from this information.

A little while ago I wrote about the value of information. My main point was that we tend to think of most information in one of two modes—either transactionally, with the information part of current business operations; or historically, when the information represents past business performance—where it’s more productive to think of an information age continuum.

The value of information
The value of information

Andy Mulholland posted an interesting build on this idea on the Capgemini CTO blog, adding the idea that information from our external environment provides mixed and weak signals, while internal, historical information provides focused and strong signals.

The value of information and internal vs. external drivers
The value of information and internal vs. external drivers

Andy’s major point was that traditional approaches to Business Intelligence (BI) focus on these strong, historical signals, which is much like driving a car by looking out the back window. While this works in a (relatively) unchanging environment (if the road was curving right, then keep turning right), it’s less useful in a rapidly changing environment as we won’t see the unexpected speed bump until we hit it. As Andy commented:

Unfortunately stability and lack of change are two elements that are conspicuously lacking in the global markets of today. Added to which, social and technology changes are creating new ideas, waves, and markets – almost overnight in some cases. These are the ‘opportunities’ to achieve ‘stretch targets’, or even to adjust positioning and the current business plan and budget. But the information is difficult to understand and use, as it is comprised of ‘mixed and weak signals’. As an example, we can look to what signals did the rise of the iPod and iTunes send to the music industry. There were definite signals in the market that change was occurring, but the BI of the music industry was monitoring its sales of CDs and didn’t react until these were impacted, by which point it was probably too late. Too late meaning the market had chosen to change and the new arrival had the strength to fight off the late actions of the previous established players.

We’ve become quite sophisticated at looking out the back window to manage moving forward. A whole class of enterprise applications, Enterprise Performance Management (EPM), has been created to harvest and analyze this data, aligning it with enterprise strategies and targets. With our own quants, we can create sophisticated models of our business, market, competitors and clients to predict where they’ll go next.

Robert K. Merton: Father of Quants
Robert K. Merton: Father of Quants

Despite EPM’s impressive theories and product sheets, it cannot, on its own, help us leverage these new market opportunities. These tools simply cannot predict where the speed bumps in the market, no matter how sophisticated they are.

There’s a simple thought experiment economists use to show the inherent limitations in using mathematical models to simulate the market. (A topical subject given the recent global financial crisis.) Imagine, for a moment, that you have a perfect model of the market; you can predict when and where the market will move with startling accuracy. However, as Sun likes to point out, statistically, the smartest people in your field do not work for your company; the resources in the general market are too big when compared to your company. If you have a perfect model, then you must assume that your competitors also have a perfect model. Assuming you’ll both use these models as triggers for action, you’ll both act earlier, and in possibly the same way, changing the state of the market. The fact that you’ve invented a tool to predicts the speed bumps causes the speed bumps to move. Scary!

Enterprise Performance Management is firmly in the grasp of the law of diminishing returns. Once you have the critical mass of data required to create a reasonable prediction, collecting additional data will have a negligible impact on the quality of this prediction. The harder your quants work, the more sophisticated your models, the larger the volume of data you collect and trawl, the lower the incremental impact will be on your business.

Andy’s point is a big one. It’s not possible to accurately predict future market disruptions with on historical data alone. Real insight is dependent on data sourced from outside the organization, not inside. This is not to diminish the important role BI and EPM play in modern business management, but to highlight that we need to look outside the organization if we are to deliver the next step change in performance.

Zara, a fashion retailer, is an interesting example of this. Rather than attempt to predict or create demand on a seasonal fashion cycle, and deliver product appropriately (an internally driven approach), Zara tracks customer preferences and trends as they happen in the stores and tries to deliver an appropriate design as rapidly as possible (an externally driven approach). This approach has made Zara the most profitable arm of Inditex, a holding company of eight retail brands, and one of the biggest success stories in Spanish business. You could say that Quants are out, and Blink is in.

At this point we can return to my original goal: creating a simple graphic that captures and communicates what drives the value of information. Building on both my own and Andy’s ideas we can create a new chart. This chart needs to capture how the value of information is effected by age, as well as the impact of externally vs. internally sourced. Using these two factors as dimensions, we can create a heat map capturing information value, as shown below.

Time and distance drive the value of information
Time and distance drive the value of information

Vertically we have the divide between inside and outside: internally created from processes; though information at the surface of our organization, sourced from current customers and partners; to information sourced from the general market and environment outside the organization. Horizontally we have information age, from information we obtain proactively (we think that customer might want a product), through reactively (the customer has indicated that they want a product) to historical (we sold a product to a customer). Highest value, in the top right corner, represents the external market disruption that we can tap into. Lowest value (though still important) represents an internal transactional processes.

As an acid test, I’ve plotted some of the case studies mentioned in to the conversation so far on a copy of this diagram.

  • The maintenance story I used in my original post. Internal, historical data lets us do predictive maintenance on equipment, while  external data enables us to maintain just before (detected) failure. Note: This also applies tasks like vegetation management (trimming trees to avoid power lines), as real time data and be used to determine where vegetation is a problem, rather than simply eyeballing the entire power network.
  • The Walkman and iPod examples from Andy’s follow-up post. Check out Snake Coffee for a discussion on how information driven the evolution of the Walkman.
  • The Walmart Telxon story, using floor staff to capture word of mouth sales.
  • The example from my follow-up (of Andy’s follow-up), of Albert Heijn (a Dutch Supermarket group) lifting the pricing of ice cream and certain drinks when the temperature goes above 25° C.
  • Netflix vs. (traditional) Blockbuster (via. Nigel Walsh in the comments), where Netflix helps you maintain a list of files you would like to see, rather than a more traditional brick-and-morter store which reacts to your desire to see a film.

Send me any examples that you know of (or think of) and I’ll add them to the acid test chart.

An acid test for our chart
An acid test for our chart

An interesting exercise left to the reader is to map Peter Drucker’s Seven Drivers for change onto the same figure.

Update: A discussion with a different take on the value of information is happening over at the Information Architects.

Update: The latest instalment in this thread is Working from the outside in.

Update: MIT Sloan Management Review weighs in with an interesting article on How to make sense of weak signals.