Data analytics have been around for a long time, hiding under a term most of us hated in our science courses: “statistics.” Today, data analytics combine statistics and other techniques, such as artificial intelligence and neural networks, with a specific goal of making informed business decisions based on real data.
Yet many small businesses remain skittish. They think meaningful analytics demand massive data sets pored over by Ph.D.s, while in reality, just about every business is already generating all the data it needs to gain real insights from analytics. And businesses can’t compete in a modern world making decisions by instinct alone. It’s time to break down a few of the myths surrounding this crucial business tool.
Fallacy: Only Big Businesses Can Use Data Analytics
Data analytics require data, which means that during the past century, only the largest businesses could use these tools and techniques. Why? Because only large businesses had collected enough data in computing systems to make meaningful conclusions.
Today, even the smallest business can use software that gathers reams of data: In 2018, for example, Intuit reported that it has 4.5 million paying customers for its QuickBooks accounting software. That’s a lot of data readily available for analysis and decision-making.
Likewise, businesses that deploy online marketing solutions have many metrics and reports available, delivering detailed information on who saw their message. The same is true of HR automation platforms, customer relationship management systems, Google Analytics for websites and much more. There’s no shortage of data for businesses of every size.
Fact: Data Analytics Can Lead Small Businesses Astray
Every IT professional has heard the phrase “garbage in, garbage out.” It goes for analytics too. Getting meaningful insights depends on having real data on what is happening in the business, no matter how large or small. If the data going into the analytics tools are unreliable, poorly understood or inconsistent, then the results that come out will be equally unreliable.
Before making decisions based on the results of any data analytics exercise, businesses should make sure they understand where the data came from and whether those sources can be trusted. At the same time, step back for a sanity check: Does this result make sense?
If something looks obviously wrong or skewed, double-check the data and the actual analysis before diving headfirst into what could be a bad decision.
Fact: Data Analytics Beat Business Hunches
Intuition, which for most successful business leaders is informed by experience, certainly has its place. And it’s important for entrepreneurs to include their own informed intuition along with quality data in the decision-making mix. Yet data can support or contradict that intuition and provide meaningful metrics along the way.
Analytics are also useful at many levels: It’s not just the big decisions that take executive insight.
You can use data analytics to explore small changes with “what-if” analysis. What if we extended our warranty? What if we increased the price of an accessory? What if we changed how we charge for certain services? Analytics often use correlations among different data sets to help draw operational conclusions: Which warehouses need what stocking level on which products during Christmas season to reduce time to delivery? Which advertising strategies deliver the best sales results?
Sometimes the best decision can start with a hunch. But the best decision-making process includes data analysis.
Fallacy: Data Analytics Are Expensive
Data analytics simply involve using the data a business already has to help it make better business decisions. Sure, it can be expensive to hire a specialist with a background in programming, data science and business processes. But small businesses in particular can start with basic analytics without spending much money at all.
For most small businesses, the place to start is with the software already in place. If the business has been delaying an upgrade to its accounting or customer relationship management tools, this might be a good reason to go ahead; the most recent versions of every tool have more reports and analysis tools built in.
Next, check out cloud-based add-ons. Rather than deliver a specialized tool for on-premises use, many data analytics and business intelligence tools are offered in the cloud. Why? Because it’s rarely necessary to install new servers, databases and software just to ask a few questions and run a few reports. With cloud-based tools, the business can cost-effectively and quickly get into the world of analytics and focus on what the data is saying, freeing up IT resources for more strategic tasks.
Data analytics isn’t some new magic bullet. It’s a way of leveraging the data that most every business has been quietly accumulating for years to deliver insights that lead to better decisions.