Now that we have figured out how to get machines to monitor business processes and then gush data like a geyser, the next question is: What do we do with all this stuff?
Many companies invest a lot of time and resources gathering and consolidating data, only to stumble when it comes to transmuting that data into valuable intelligence. In this all-too-common scenario, we see information not only wasted, but actually stalling forward momentum.
Deriving actionable insights from data is the only thing that justifies the considerable cost of collecting it.
Case after case shows that business intelligence (BI) can drive great decisions. If you’re a retailer, for example, how do you know when to discount, and how much? Data can tell you when you are discounting unnecessarily, or when to advertise a loss leader to get shoppers on your floor rather than your competitors’.
A company could solve complicated scheduling and shipping problems, speeding up response time and reducing fuel costs. Another could devise the most efficient staffing schedule for a call center. Some of the most powerful uses of BI involve predicting needs in order to better plan for change.
What follows are steps to gathering and exploiting data, and problems to avoid at each step. If you believe your company could make better use of data (and whose doesn’t?), review these steps to help you identify which analytics you can reasonably accomplish in-house and which ones are a bit tougher and may require investment in some outside BI support. Some companies can do it all, although they tend to be on the whale-sized side of the spectrum. If you are one of those companies, feel free to stop reading. If you aren’t one of those companies, read on.
Some of you may use these terms interchangeably, but they aren’t the same thing. So let’s first get aligned on the terminology.
Put simply, reporting is the gathering of data and the organizing of it consistently for the sake of comparing it in a meaningful way or observing shifts over a scale (such as time). Done well, a report can reveal the current performance of different business areas. Key words to remember when you think of reporting: data, gathering, organizing. Though the goal of reporting is to enable us to look at data in an apples-to-apples format so we can see differences, spikes, anomalies, etc., the report itself doesn’t derive insights, it just shows us data in an organized way. The insights come from analysis.
If reporting is just the gathering, organization and display of like-data, analysis is the layering in of data calculations, rules or judgment criteria to generate the insights that are hidden within the data. These calculations fold in related data, measures, hueristics or thresholds to drive new and business objective-specific and valid insights. When leveraged, the valid insights generate valuable change.
Putting those steps into a broader context, the full process process can be described like this:
The first step in this process points to the gathering of data. Success downstream in the process hinges on good, reliable data upstream at this step — and right away we’re talking about a very common problem. While most companies collect and store data, many are not careful enough about ensuring the integrity of the data. Has it been entered consistently? Have any steps been missed? Without a strict data collection process, a lot of time can be wasted generating data of little value—illustrating the familiar principle of “garbage in, garbage out.”
Reporting is can be as simple and unsophisticated as a basic, run-of-the-mill columnar spreadsheet, however more often than not reports leverage spreadsheet or database data to create graphs and charts designed to present data visually and hopefully meaningfully. That said, not all graphs and charts are equally useful. A good graph or chart leverages format to best accommodate the information it is presenting. Consistently meaningful and visually representative reports can be as much art as it is science, so reviewing different chart types in the database, statistics or spreadsheet applications and reading the helpful tips there can usually provide a great deal of direction on the types that would best fit your needs.
Once you’ve got revealing and hopefully meaningful reports, it’s time to analyze the data therein—and this is where it gets tricky. Large companies can apply analytical applications to vast data sets to provide actionable insights. But doing so requires the appropriate and necessary infrastructure, consistency of processes, automation of analytics to data sets, and outputs of that analytics to meet the many multifaceted needs of these giants, but mostly this boils down to resources. Small and medium-sized companies with fewer resources must be critical and choosy, pinpointing the specific business objectives or critical painpoints to target rather than the measuring and analyzing all data flowing in broadly and creating analytics programs to address all things. Strategic decisions should be made about exactly what data to collect, how best to report it, and how to drive the most productive decisions from what the reports reveal.
The best analytics clearly measure success or failure against defined business thresholds and standards. All business intelligence should speak to business goals or needs or KPIs in some way.
Let’s look at an example. Say we want to measure increases in business revenue, and our goal is to earn $100,000 in Q1 for a new product line. Here’s how this might look in graphic form:
In our example, the business goal of earning $100,000 in Q1 for the new product line can be accomplished in two ways: by converting enough leads and by generating enough leads. But if sales are slow, how do we know which part of the cycle isn’t pulling its weight? Are we not catching the attention of new customers? Or are we simply not converting those leads into cash?
By breaking up our wealth of collected data into finer and finer granules, we can begin to see how each individual avenue contributes to achieving our business goals. Perhaps we’re getting mega leads from the website and direct mail, but telemarketing is dead. The capital and energy put into cold calling could be redoubled into more fruitful marketing efforts. Maybe we’re getting TONS of in-store leads, but our sales team isn’t following up properly. Would cleaning house and hiring new sales reps help? Well, that’s for the BI gurus to find out.
When beginning your own Business Intelligence analysis, it is often helpful to consult a partner who can provide expertise and a fresh, outside perspective. Be forewarned that, while your firm may have easy access to a lot of basic information from suppliers, clients, or other groups of interest, you may have to dig a little deeper to get at those elusive insights that can really reshape your business.
Whether you’re starting with a business problem or a annual goal, the first thing you need is reliable data (and lots of it). Identify and then lean on the expertise of employees with strong training in statistics, process improvement, regression analysis and so on to help you collect, sort, and make sense of the wealth of information before you. If the insights and answers you are hoping for still elude you (or you suspect there’s room to dig deeper), consider working with an outside BI/Analytics partner for help. It typically takes both variety and volume of data to draw good conclusions. A smart analytics partner can not only give you perspective on tracking and measurement, but also ratchet up the value of the insights you need to drive success.
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