Turning Data into Actionable Information

Analyzing data, in general, assumes that the data has already been presented, or “reported” on–in the strict definition of the word. Analyzing literally means “taking apart,” i.e. sifting through something, breaking it down in its components to better understand it. Analysis in business intelligence is therefore the art of understanding data by “taking it apart” and asking it relevant questions.

Or, put an even better way, reporting presents data; analysis turns data into information. Information that, to be useful, can be then acted upon in the interest of the company’s strategy.

We can look at analysis as the simple act of asking your data questions. Take a table of data, for example, showing you a column of sales reps’ names and another column displaying total orders taken. The data is neutral. You can’t immediately make business sense of this simple table, unless you ask it questions. Now, ask the table “who has taken the highest amount of orders?” by sorting the second column, descending. Now the data has turned into information. A simple sort has been your way to ask your data a question, and you are therefore armed with the piece of information that (say), Jones is your top-performing sales rep.

Naturally, analysis can be much more complex than this. It can involve looking at your data from multiple dimensions (OLAP), spotting trends and exceptions, and even predicting future patterns. Regardless, what all these techniques have in common is that they turn neutral data into meaningful information.

The Goal of Analysis

As we have said, analysis turns data into information. In business intelligence, this means asking relevant questions of your data so that you draw the necessary knowledge to make business decisions and take actions that further the company’s strategy.

The Benefits of Analysis

  • Analysis is the step that lets users understand their data, turning it into information; without analysis, data loses its context and much of its meaning.
  • Analysis empowers users to ask questions of their data; this is the main way in which users are said to “interact” with the data. In this sense, the more the analysis interface allows users to obtain meaningful questions of their data, the more it is interactive.
  • Analysis lends the necessary answers that guide business end-users to making the correct decisions and taking appropriate action.
  • Analysis highlights the critical factors and points the end-user towards them. By doing so, it facilitates prioritization and makes the business process more efficient.

Analysis Best Practices

  • Leverage the power of the Web to make data analysis features easy and intuitive to navigate thanks to the familiarity of the Internet
  • Empower as many end-users as possible to analyze the relevant portions of your company’s data. Do so by choosing a Web-based solution that is licensed to be distributed to unlimited end-users without additional cost, as is the case with server-based licensing.
  • Use technology smartly. Technology and the features deriving from it are tools–ask yourself what goal the tools are meant to achieve, and design your analysis interface to attain those goals.
  • Set up your reporting and analysis solutions to point the end-users to the most critical items, using features like dashboards, key performance indicators (KPIs), automated business alerts, etc.
  • Make your analysis actionable, so that the cycle “see, understand and act” is rendered as efficient as possible.