Despite more advanced analytics tools targeted at small and medium-sized enterprises and lower price points to go with them, many SMEs are still reluctant to give up Excel spreadsheets,
One might think that’s because they struggle more than their larger enterprise counterparts, but that’s not really the case, said Robert Kugel, senior vice president and research director for Ventana.
“Interestingly, small and midsize enterprises are not all that different from large and very large organizations in their lack of maturity,” Kugel said. “That is to say, the same worst and mediocre practices in very large organizations are present in small and midsize organizations, and to the same degree.”
The benchmark research surveyed executives and managers “across a broad range of roles and titles” from small and midsize enterprises. Of the 1,175 qualified surveys gathered, 58% were considered midsize companies, or employing between 100 and 999 employees; the remaining 42% were considered small companies employing fewer than 100 people.
The results “paint a glass half full” scenario, Kugel remarked in a webinar presentation, indicating that a good portion of the small and midsize companies surveyed are relatively immature across the four dimensions it assessed: people, process, information and technology. While some of the findings left Kugel unfazed, others he called “surprising,” including the similarities between SMEs and larger organizations.
“Large organizations, despite all of their resources, find different sets of challenges and are no more likely to step up and meet those challenges or to consider better ways of using analytics,” he said.
For the love of desktop spreadsheets
Kugel noted that the lack of maturity regarding analytics for SMEs extends beyond use to the underlying processes and tools.
Fifteen or 20 years ago, Kugel said, small and midsize enterprises claimed analytics was too expensive, too time-consuming or too cumbersome.
Those types of complaints are not as valid today, Kugel said, as technology becomes easier to manipulate, as systems become more complicated, as data sources grow, and as vendors gear tools and applications to the average line-of-business individual. This is especially the case for BI in midsize companies.
Even so, organizations continue to resist the adoption of and the investment in advanced analytics and instead tend to stick to what they know, namely, desktop spreadsheets, particularly Excel.
The benchmark research reveals 62% of SMEs use desktop spreadsheets as the only analytics tool in their enterprises. In some cases, especially for smaller businesses that do not handle or generate the amount of complex data that midsize organizations do, Excel and other desktop spreadsheets are an invaluable resource for ad hoc analysis and reporting.
“They are an excellent prototyping tool,” Kugel said. “But they were designed for individual productivity use. When they become part of an enterprisewide, repetitive, collaborative analytics tool, they tend to fall apart.”
Yet compared with formal business intelligence (BI) tools on the market, SMEs still regard spreadsheets as simple to set up, easy to use and capable of producing quick results. Kugel doesn’t discount the assessment, but he also believes those benefits can be deceptive.
Inappropriate tools lead to inaccurate data, unfortunate decisions
According to the benchmark research, 52% of respondents believe their data is only somewhat accurate. With Excel and other desktop spreadsheets, errors in data entry and in the formulas are widespread; if the unsound data is used over and over again, the errors simply multiply. That can mean consequences for businesses.
“Ambiguity in data will affect decisions,” he said.
To compensate, Kugel said, businesses -- at one end of the spectrum -- will sit down and try to figure out where the miscalculations lie; while this additional step can help, it’s time-consuming and most likely will not alleviate the issues completely. At the other end of the spectrum, businesses will “make horrendous mistakes” because they’re working with inaccurate data.
“Even more often, and the worst of the two possibilities,” Kugel said, “is people wind up reflexively doubting the validity of the data rather than the conclusions people are making.”
Bad data and poor decisions made because of it can, ironically, create doubt in the benefits of advanced analytics tools. Organizations can come to see such tools as vehicles that get to the garbage faster or as investments in flawed processes, Kugel said. Rather than pursue advanced analytics, some businesses stall, an inaction that tends to say something about the organization as a whole.
“If issues affect one part of the process, issues will be present to some degree in other parts of the process as well,” Kugel said.
In other words, companies that are immature when it comes to data quality, for example, are likely to be immature across the board.
“Poorly run companies tend to have poor analytics, poor data management, an unwillingness to invest in good technology and an inability to attract the best people,” Kugel said.
And vice versa.
Baby steps can turn into adolescence
As technology becomes more sophisticated, it also becomes easier to use. Think of it in terms of the automotive industry, Kugel said.
“When was the last time most people gave any thought to what was going on under the hood of the car?” he asked. “Most people have no idea how these things work, and, increasingly, they just don’t have to.”
While some of the tools out there may still be too difficult to employ beyond the walls of IT, Kugel believes the industry is getting there. This summer alone, both Pentaho and SAP launched their latest versions of analytics software specifically geared to small and medium-sized enterprises.
“There’s a tremendous opportunity as technology becomes more sophisticated to put the increasingly easy-to-use tools into the hands of people just doing their job day to day,” Kugel said.
That doesn’t mean giving up on using desktop spreadsheets and Excel, but it may be time for a basic assessment to determine where the gaps exist preventing a business from reaching its goals, Kugel said.
He suggested beginning the assessment by answering questions like these: What does the business want? What kinds of things would like to have better insight into? Are those things possible today? Do the raw materials across the dimensions of people, process, information and technology exist within the business to do that? If not, how will the business prioritize and address the deficits?
“It may involve purchasing new tools, but it’s probably a combination of people, process, technology and information dimensions,” Kugel said.
And if an organization determines that desktop spreadsheets are an appropriate analytics instrument, Kugel advises using the tool the way it was designed to be used and placing it in the hands of employees who know how to use the tool properly.