Big data for small business

rain_pineArticle published in MonitorPro 04/14, p. 39-40.

Do small businesses have big data and if so, how can they take advantage of big data analytics?

The perception that small businesses have too little data or that there is no time or interest to perform data analytics or that it is too expensive is simply not true. They can still gain valuable insights from data, they just have to apply analytics in the right scope.

Big data is relative. What was perceived as big data some years ago is not big data today. Data volumes and storage capacity are constantly increasing. What we consider big data today may not be big data tomorrow. Big data may be considered as data that reaches the limits of the technical storage capacity that is available. If a small business has so much data that it fills up its disk space, this could be perceived as big data within their scope.

Big data and analytics

We often interchange the terms big data and analytics. Or we use the combined term big data analytics. Analytics means that we look at all the information that the business generates in order to discover trends or patterns, such as customer behavior, trends in the market, or other highlights and hidden knowledge. It can be applied to any size data. On one hand, it may be more insightful when applied to large volumes rather than small volumes of data. On the other hand, it may give more accurate results when applied on smaller, more structured data sets.

Traditionally, there was no need for data analytics. A small business owner knew all of their customers personally and was always able to help them to their maximum satisfaction. But even a small list of customers (for example, 200 or 500 or 1000 customers) can hold valuable information that would not be easy to discern by just making a guess. We could still apply simple mathematical functions or even algorithms to this data and find out if anything stands out.

A simple example might be Google Analytics on a small business website. There may not be very much information, but even with what we have, we could still look at what pages are most interesting to our visitors, what search terms the visitors use to reach our site, which referrers have sent these visitors, where are they coming from geographically and so on. This would allow us to enhance our website with better landing pages or restructure the navigation to drive more sales.

Steps to a successful result

Small businesses might not want to invest heavily in technology and storage for collecting and analyzing data. But it doesn’t have to start with costs. There are plenty of free or inexpensive options available, such as cloud and open source. Many solutions are available for free or for a very small fee for entry level users, which small businesses indeed are. Here is a list of typical steps that may be followed when performing data analysis.

1. Have the goal in mind

The first step in starting data analysis is to have the goal in mind. Sometimes it is not just the data and the technology and the analysis that makes a difference. We need an idea. We want to know what to look for, rather than just examining the data randomly and waiting for patterns to emerge. For example, we may want to look for typical buying patterns of existing customers, we may want to increase profitability or find out what is blocking us from making more sales.

2. Gather data

The next step is to find out whether we have the necessary data at our disposal. If not, can we get data from somewhere? If yes, is it in a format that is easily accessible or do we need to apply any intermediate steps in order to gather the data? For example, depending on the goal of our analysis, we may want to access data in our customer databases, web server traffic or data from social media if we use it to promote our business. We may also want to combine our own data with external data sources such as weather patterns, demographic information, market share data, and so on.

3. Combine and transform the data

The most efficient way to gather data is to find a solution that enables pulling data from the sources that were identified. Data can then be visualized in the form of dashboards or presented in simple reports which give a quick overview of the data that is at our disposal. We do a preliminary data profiling to ensure that the data is relevant and complete. We may want to do some filtering, such as excluding data that is not relevant or appears to be of a poor quality. We may also want to do some transformations, especially when we want to combine data from different sources.

4. Do the analysis

After we have gathered and prepared all the data that we want, we do the actual analysis. We may start by doing visualizations and creating reports. This would give us a high level overview of the data and may already identify trends or outliers. Additionally, we may apply machine learning algorithms to identify patterns or to predict future trends.

5. Apply the analysis results

Last but not least, we should apply the results of the analysis in our own business processes, otherwise any analysis that we have done or any results that we may have reached would not drive real business outcomes.

 

Although the perception may be that the more data we have, the better analyses we can do, this is not the general rule. It is much more important to know what questions to ask of our data, do the right analyses and apply what we have learned from the data in real business processes. The data that a business generates is full of potential from which we gain valuable insights. These insights help us to stay ahead of the competition. This applies to any business, big or small.

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