Wednesday, December 1, 2010

Data vs. Information

What is the difference between data and information?  Data are dormant.  Data are passive.  Data are numbers, words and phrases.  Alone, data don’t mean anything (John Smith, MA, 21, orange).  Information is active, insightful and can drive action and decisions.
A structured collection of data may describe the characteristics of an object of interest (client name: John Smith, client state: MA, client age:21, client favorite color: orange).  This collection for a single subject is called a record.
Data become information when they are put into a context.  A context can be defined within a point of reference (a list of customers that like orange), or as an aggregation (percentage of customers that like orange).  When data are transformed into information with a context, it may be used to make relevant and evidenced based decisions. 
The process of transforming data to information may be generalized as analytics.  Analytics can be a simple aggregation (total number of clients that like orange), or sophisticated statistical processes (character profiles and clusters of the best customers).  Analytics are not trivial, and are best applied by resources that are trained in quantitative and statistical techniques, yet have a strong business acumen and knowledge of business objectives and constraints (how can we best change the way we sell to our current customers such that sales are maximized, yet fit within the current – or a different – sales resource and commission structure?  How can data inform this?).
The field that offers a structured methodology to this approach is called Business Analytics.  Business Analytics is not simply “data analysis”, but is instead a holistic perspective that aims to understand and define the people, processes, tools and data that are necessary to support informed decision making for particular business objectives.  Only with this broad perspective can the right quantitative techniques be applied to the business objective at hand.
The process converting data through analytics to information is a powerful tool that enables smart, evidenced based decisions.   This approach allows for the transformation of an organization from one that is managed reactively on gut feel, to one that succeeds with proactive and informed decision making.
Next BLOG: Trends in Business Analytics

Monday, November 1, 2010

Techniques for Data Analysis

So now that you have data, what do you do with them?  Data analysis, right?  But what is that anyway?  A useful way to think about data analysis is to relate it to driving a car. 

In a car, the rear view mirror tells the driver what is behind them.  The data equivalent for this is a historical report.  Typically historical reports display past trends and give a sense of how a metric of interest is changing.  The most easily interpreted analytic reports are those that display summary (i.e. rolled up) information that has an option for "drill down" into detail.  For example, a historical trend of summary sales results shows how they have changed over time, but yet allow for filtering and drill down to the detail behind the numbers.

You can't drive a car by looking only in the rear view mirror though!  Another useful tool to monitor your driving experience is the ever present dashboard.  This gives information about your current state such as speed, RMP, time, temperature, trip distance and fuel.  The analogy for this in the data world is its namesake, the data dashboard.  These data dashboards allow the business user to get a snapshot of the current state of affairs within a specific business function.  Like historical reports, the best dashboards display summary information with the option for filtering and drill down.  For example, a sales performance dashboard will summarize bookings, pipeline and backlog to date, with a comparison to a specific baseline or goal (think speed limit).  These dashboards are designed in a way that make for easy interpretation "at-a-glance", and are tailored for specific business roles.

A relatively new tool for drivers is the GPS.  This is the tool that allows the driver to specify their destination goal, such that they be provided step by step directions for reaching that destination.  The added benefit that the GPS provides is a fresh set of directions should the driver get off course or if conditions change.  A data analysis equivalent to the GPS are decision support tools.  These tools allow the business manager to specify their business goals (i.e. a sales target), and then indicate the specific decisions necessary to achieve these goals (i.e. sales staff to hire, prospects to contact, etc.).  Decision support tools are typically driven by data mining techniques such predictive analytics and optimization.

When the rear view mirror, dashboard and GPS fail, what do good (non-male) drivers do?  Stop and ask for directions, of course!  When a business user can not get the answers they need from reports, dashboards and decision support tools, ad hoc analysis can provide custom answers to these custom questions.  Ad hoc analysis by definition is customized to meet a specific need and as such requires specialized resources to extract, organize and analyze the data.  These analyses generally are the exception not the rule.  If they do become frequent, then consideration must be given to the usefulness of existing reports, dashboards and decision support tools.

While each of these tools have their use in supporting business operations, no one can claim dominance over the others.  Each are equally important (yes, just like their auto equivalents).  So get that car out of the garage and take your data for a spin!

Wednesday, September 29, 2010

What's in Your Data?

You've heard the old addage "garbage in, garbage out" as it relates to data quality and the quality of subsequent analytics, right?  It is true that numbers that mis-represent reality will not tell an accurate story when they are crunched, but data quality is easier said than done.  And how bad do bad data need to be before they become really bad?  Afterall, unless there are systemic issues or consistent biases with a dataset and how it is collected, the "averages" will tend to give a pretty good representation of, well, the average - good enough anyway to gleen some business insight into a subject of study.  The loser in this scenario, however, is a crisp understanding of the underlying business variability, which oftentimes (unfortunately in this case) ends up being more important for business planning and decision making than the average.  But still, this is no reason to discard a dataset that is suspected to be unclean.

It turns out that there are a whole host of quantitative techniques for identifying and dealing with "bad" data.  No technique will turn garbage into gold, however.  One must eventually adopt a philosophy of sacrificing data quality for the act of getting down to business and actually doing something with the data.  Far too often there is an inordinate amount of effort put into data quality (rightfully so in some cases - like regulatory reporting), with analytics becoming an afterthought.  If data are to be leverage to make better, smarter decisions - with limited resources - there needs to be a relaxation of the high expectations for data quality. 

We can have it both ways though, if we reconsider how we capture data: Any data that requires a human to use any kind of judgement, and that additionally requires that same (or another) human to input those data into a computer, is right off the bat, "bad" (think surveys).  This can be mitigated somewhat by carefully controlled data forms, but the point is that no two humans will look at the same data call identically, and even the same human might look at it differently at different points in time.  A far better way to capture data is through the machine capture of human activity.  This is the "data exhaust" that gets emitted when humans interact with machines - login frequency, clicks, emails, web searches, non-cash purchases, monitoring systems, etc.  Let's admit it, most of our business activity one way or another interacts with machines, and those machines do (or could) capture the who, what, where and when of this activity (it is up to business analytics to figure out the "why").  Afterall, business analytics at its core is all about trying to understand specific human behaviors that relate to one's business (sales, marketing, operations, etc.).  So why not use a data source that specifically and automatically tracks that, rather than trying to replicate it with a subjective surveys?

Whatever the quality of your data, your means of capturing them or your philosophy and tolerance towards data quality, think about ways to improve your data, but don't obsess over it.  Instead, understand the shortcomings of what you've got, take advantage of what's good, and get out there and use your data!

Sunday, September 5, 2010

Models for Decision Making

What are decision making models? Generally they are tools that allow senior managers to prospectively evaluate management decisions they plan to make for their organization. Decision making models are classified as predictive models and optimization models. Predictive models focus on the likely outcome of a decision over some future time period. For example, a business development manager may wish to understand the impact over time of a marketing campaign prior to initiating it. Predictive decision models in this case would draw on historical data of past marketing campaigns and on professional judgment to forecast the impact of a prospective campaign, given its characteristics. These types of models allow for what is often referred to as "what-if" analysis, allowing the business development manager to "turn the dials" of the base assumptions for the campaign (i.e. target prospects).

Optimization models, on the other hand, take "what-if" analysis of predictive models to the next level. They allow the business manager to understand the "best" set of decisions for a particular business strategy. For the marketing campaign example, an optimization model would suggest target prospects that would generate the best return on investment for the campaign.

For many small and medium sized businesses, these types of decision making tools seem out of reach either because of lack of quantitative resources, or because of the high cost of commercially available decision making tools. These barriers need not prevent the adoption of this type of business strategy, however. Spreadsheet models or spreadsheet simulation provide the necessary technology to deliver decision making tools, at a low cost. Coupled with that, decision modeling consultants can offer quantitative resources on a contract basis that deliver business specific tools to the hands of the business manager.

Historical vs. Predictive Analysis

Oftentimes consumers of business information wish to summarize available data through data summaries and data visualizations (pivot tables, charts, graphs, etc.). While these tools are incredibly important to provide insight to past business outcomes, they are somewhat limited in telling a complete and forward looking story. They rely on a restricted set of past experiences and business scenarios (i.e. what have been sales results with current and past staff levels) and limit the ability to deduce potential future outcomes.

Predictive modeling, on the other hand, uses historical results to drive statistical models which can forecast outcomes for business scenarios that have not yet occurred (i.e. what will sales results be if we increase staff levels). This interpolation and extrapolation of allows for “what-if” analysis that informs the decision making process. Also, if correctly designed, predictive analysis will not only offer point estimates, but also expected outcome ranges that consider inherent business variability and uncertainty.

Monday, August 23, 2010


Dashboards are a great way to monitor metrics and key performance indicators (KPIs) that are important to your business. There is more to them than pretty charts and graphs, however. Like their automotive counterpart, they must relay real-time information that is relevant to the operation at hand. Typically a dashboard does not display historical trends (save that for historical reporting); instead the current state should be compared to some historical baseline (i.e. previous period or YTD average).

When creating a dashboard, consider three things: 1.) the business process being monitored, 2.) the audience being targeted, and 3.) the frequency with which the dashboard should be viewed. With these factors identified, the data and calculations necessary for the dashboard can be determined to ensure 1.) a relevant message that 2.) informs the right people and 3.) displays information that changes each time the dashboard is viewed. There is nothing wrong with creating different dashboards for different processes, people and time frames!

To learn more about how to build relevant dashboards, contact us!