Some of you may use these terms interchangeably, but they aren’t the same thing. So let’s first get aligned on the terminology.
Reporting is the gathering of data and the organization of it consistently for comparing it in a meaningful way. 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.
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.
Analytics 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.
Curious to learn more? Contact your local managed IT service provider?
The three sectors with the biggest spending on cybersecurity are banking, manufacturing, and the central/federal government, accounting for 30% of overall spending (IDC).
The average cost of a data breach in the United States is $8.64 million, which is the highest in the world, while the most expensive sector for data breach costs is the healthcare industry, with an average of $7.13 million (IBM).
Forty-three percent of attacks are aimed at SMBs, but only 14% are prepared to defend themselves (Accenture).
The internal team was energized. With the Level 1 work off its plate, the team turned its attention to the work that fueled company growth and gave them job satisfaction.
The cost of cybercrime is predicted to hit $10.5 trillion by 2025, according to the latest version of the Cisco/Cybersecurity Ventures “2022 Cybersecurity Almanac.”.
40% of businesses will incorporate the anywhere operations model to accommodate the physical and digital experiences of both customers and employees (Techvera).
It takes an average of 287 days for security teams to identify and contain a data breach, according to the “Cost of a Data Breach 2021” report released by IBM and Ponemon Institute.
More than 33 billion records will be stolen by cybercriminals by 2023, an increase of 175% from 2018.
We did a proof of concept that met every requirement that our customer might have. In fact, we saw a substantial improvement.
We did everything that we needed to do, financially speaking. We got our invoices out to customers, we deposited checks, all the things we needed to do to keep our business running, and our customers had no idea about the tragedy. It didn’t impact them at all.
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