SMBs struggle with data utilization, analytics

While analytics have become a staple of large enterprises, many small and medium-sized businesses struggle to utilize data for growth.

Large corporations can afford to hire teams of data scientists and provide business intelligence software to employees throughout their organizations. While many SMBs collect data that could lead to better decision-making and growth, data utilization is a challenge when there isn't enough cash in the IT budget to invest in the right people and tools.

Sensing that SMBs struggle to use data, Onepath, an IT services vendor based in Kennesaw, Ga., conducted a survey of more than 100 businesses with 100 to 500 employees to gauge their analytics capabilities for the "Onepath 2020 Trends in SMB Data Analytics Report."

Among the most glaring discoveries, the survey revealed that 86% of the companies that invested in personnel and analytics surveyed felt they weren't able to fully exploit their data.

Phil Moore, Onepath's director of applications management services, recently discussed both the findings of the survey and the challenges SMBs face when trying to incorporate analytics into their decision-making process.

In Part II of this Q&A, he talks about what failure to utilize data could ultimately mean for SMBs.

What was Onepath's motivation for conducting the survey about SMBs and their data utilization efforts?

Phil Moore

Phil Moore: For me, the key finding was that we had a premise, a hypothesis, and this survey helped us validate our thesis. Our thesis is that analytics has always been a deep pockets game -- people want it, but it's out of reach financially. That's talking about the proverbial $50,000 to $200,000 analytics project… Our goal and our mission is to bring that analytics down to the SMB market. We just had to prove our thesis, and this survey proves that thesis.

It tells us that clients want it -- they know about analytics and they want it.

What were some of the key findings of the survey?

Moore: Fifty-nine percent said that if they don't have analytics, it's going to take them longer to go to market. Fifty-six percent said it will take them longer to service their clients without analytics capabilities. Fifty-four percent, a little over half, said if they didn't have analytics, or when they don't have analytics, they run the risk of making a harmful business decision.

We have people trying analytics -- 67% are spending $10,000 a year or more, and 75% spent at least 132 hours of labor maintaining their systems -- but they're not getting what they need.

That tells us people want it… We have people trying analytics -- 67% are spending $10,000 a year or more, and 75% spent at least 132 hours of labor maintaining their systems -- but they're not getting what they need. A full 86 % said they're underachieving when they're taking a swing with their analytics solution.

What are the key resources these businesses lack in order to fully utilize data? Is it strictly financial or are there other things as well?

Moore: We weren't surprised, but what we hadn't thought about is that the SMB market just doesn't have the in-house skills. One in five said they just don't have the people in the company to create the systems.

Might new technologies help SMBs eventually exploit data to its full extent?

Moore: The technologies have emerged and have matured, and one of the biggest things in the technology arena that helps bring the price down, or make it more available, is simply moving to the cloud. An on-premises analytics solution requires hardware, and it's just an expensive footprint to get off the ground. But with Microsoft and their Azure Cloud and their Office 365, or their Azure Synapse Analytics offering, people can actually get to the technology at a far cheaper price point.

That one technology right there makes it far more affordable for the SMB market.

What about things like low-code/no-code platforms, natural language query, embedded analytics -- will those play a role in helping SMBs improve data utilization for growth?

Moore: In the SMB market, they're aware of things like machine learning, but they're closer to the core blocking and tackling of looking at [key performance indicators], looking at cash dashboards so they know how much cash they have in the bank, looking at their service dashboard and finding the clients they're ignoring.

The first and easiest one that's going to apply to SMBs is low-code/no-code, particularly in grabbing their source data, transforming it and making it available for analytics. Prior to low-code/no-code, it's really a high-code alternative, and that's where it takes an army of programmers and all they're doing is moving data -- the data pipeline.

But there will be a set of the SMB market that goes after some of the other technologies like machine learning -- we've seen some people be really excited about it. One example was looking at [IT help] tickets that are being worked in the service industry and comparing it with customer satisfaction. What they were measuring was ticket staleness, how many tickets their service team were ignoring, and as they were getting stale, their clients would be getting angry for lack of service. With machine learning, they were able to find that if they ignored a printer ticket for two weeks, that is far different than ignoring an email problem for two weeks. Ignoring an email problem for two days leads to a horrible customer satisfaction score. Machine learning goes in and relates that stuff, and that's very powerful. The small and medium-sized business market will get there, but they're starting at earlier and more basic steps.

Editor's note: This Q&A has been edited for brevity and clarity.