Big data sounds like a topic for large companies and that is practically the case. The small and medium business can however also benefit from a data analysis, including in large volumes. Any company will enjoy benefits if it manages to discover ways of reducing its expenses, increasing its profit and enhancing the customer satisfaction. Data analysis helps us find the right solutions for each one of these targets.
Why do the results of data analyses lead to better and more precise decisions?
Because they reduce the general uncertainty of the environment in which the company functions; the management decisions are based on more precise information; the use of evidence increases the operational effectiveness and, last but not least, we can better understand our customers’’ behaviour.
What kind of data are we talking about?
Let’s start with the financial data: all those indicators disclosing a company’s financial health like operational costs, sales and sale revenues, profit margin, the products’ market performance and many others.
Let’s move on to the marketing: who are our current and potential clients, what content can influence them when they take consumer decisions, which message resonate with their needs and desires.
These values will not mean much by themselves though if we have nothing to compare them with. In other words, if we do not know what our business goals are and which indicators to use to measure the progress towards their achievement. Every company has a set of data sources. For instance, the accounting information can help us distribute our resources per business goals more effectively to be able to manage the risks, to discover inaccuracies and make corrections fast before they have turned into a problem.
One of the most significant benefits of big data operational analyses is that they help us discover the points of ineffectiveness in the distribution of the resources, as well as in the workflows. So where is the catch? We have to be sure in the quality of the data we use to produce practicable insights. Errors, inaccuracies and duplications in the data distort the results and therefore the decisions that they lead to may not produce the expected results. The same effect is also possible if the data do not correspond to the indicators we want to measure.
But even with good quality data extracting applicable conclusions from raw data is a hard and highly specialised undertaking. The role of a “translator” is played by the visualisation, which transforms endless data rows into various graphs and diagrams. They are much easier to read; they display trends and disclose significant relationships between indicators.
And these are not all the challenges that a small business must overcome so that the benefits of the analysis get realized. One of the most frequent barriers is budget constraints. Complicated analytical operations require investments in technology. Without its support extracting operational results from big data processing is very difficult and, more often than not, contains consequential omissions. Furthermore, there are not many small companies which can afford trained experts with the necessary skills.
How can we handle these challenges?
By using cost efficient information and consultancy services that are emerging on the market. There is dynamic development on this field and a forward thinking demands that we keep an eye on them if we want to remain competitive.