Data analytics not only benefits large organizations that have vast amounts of data, but also smaller businesses that need insights to fuel their growth. In fact, small businesses can experience many of the same challenges when it comes to data analytics, if not more. To understand this, one need only consider the number of resources and capital that a small business has access to, which is almost always significantly less than that of a large organization or corporation.
The role of Automation
Data analysis is achieved through the proper collection, processing, and application of information. It is not just about the right tools and software. It also entails access to experienced and qualified analysts or scientists. You need automation systems and processing solutions, and you also need a structured environment to collect data from sources.
Let’s say you do something as simple as collecting email addresses from your customers. First, you must take care of the entry and accuracy of the data, the reliability of the systems used to collect and store this information, and the proper organization of the stored data. Only when you have the data does the actual “analysis” part of the process begin.
Benefits you can expect
At this point, data analysis seems insurmountable. It begs the question: is it worth the investment? Should a small business even bother setting up and maintaining a data analytics strategy?
The answer is yes – because it offers so many benefits. It is estimated that data analysis will increase US job productivity by 1.5% and increase national income by 30%. To make this clear, here are just a few of the benefits you can expect.
1. It helps you save time
Small business owners have to wear many hats and divide their time between a variety of tasks and responsibilities. They don’t have the same access to resources as large corporations, which means they can’t delegate menial tasks, at least not to the same extent. Therefore, it is crucial that the tools and solutions they employ are effective and not wasteful. Data is only as meaningful as you know how to interpret it. Small businesses often outsource this task or hire consulta nts to do the tedious work. The use of machine learning, the automation of processes, and the attribution of insights into corporate strategy – all of these are crucial for increasing the efficiency of a company.
2. It can open up new insights
Insights and actionable information are gained through data analysis, which requires sifting through information to spot trends and patterns. Moreover, it helps to come to different conclusions. The importance of this process cannot be overstated, especially for small businesses that have limited resources.
With the right data analysis tools, especially with the help of AI and machine learning, these systems can unlock and identify insights that would otherwise be inaccessible. For example, you can suddenly see what the best solution for a future campaign or promotion is based on historical data. You can even predict an outcome based on a variety of factors. Advanced analytics and predictive models helped one manufacturer increase revenue by 55%, according to a McKinsey report.
3. It provides better context
Data analysis can provide answers and a better context, among other things. This is of great benefit in case of problems or mistakes you make. Sometimes you might even discover inconsistencies that you didn’t even know existed.
Data analysis is particularly good at uncovering errors in data entry and management. It’s just looking at the information in different ways and seeing what’s happening where and when. For example, a cashier might have trouble entering customer information at the checkout. Or maybe your system automatically corrects information and even ends up getting it wrong. Thorough analysis can help organizations understand what is going wrong, where, and at what stage. Under Duke Energy, an alert from predictive analytics software alerted employees that something was wrong with the turbines, saving over $4.1 million.
4. It allows for real-time interactions
Conventionally, a company would take action and collect performance data and customer insights to inform its movements, but this would only allow for action after the fact. For example, right before a product launch, a company might see how well the audience is responding, how much it’s selling, or whether or not it’s a failure.