Data analysis for SMEs

A critical analysis

"Data analysis for SMEs" or "data science" should actually be one of the top topics for SMEs. After all, these are times in which it is very difficult to predict the future based on experience. Has the Corona crisis and accelerates a digitalisation that means change on an as yet incalculable scale. How obvious it would be to use the tools of advanced data analysis. Can BI and AI show us the way into the future?

Tentative middle class

Is data analysis a top issue for SMEs? The Medium-sized companies approaches the topic rather tentatively. The approaches often go beyond a simple ABC analysis from customers or suppliers. On the one hand, the reasons for this can be found in the lack of data availability. This is often due to business processes that are still not standardised. Sometimes there is simply a lack of experience in data science. The processing of data is rarely digitalised throughout. Media discontinuities are often the rule and prevent the structuring of data. In addition, around 30 % of the data used by SMEs was not collected by the company itself or does not come from its own sources.

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Tentative providers of data analytics for SMEs

On the other hand, there is still a clearly organised range of suitable tools and advice, especially for medium-sized companies. Of course, there are many data analysis tools and masses of corresponding software. However, the models that are realised with them are rarely aimed at small and medium-sized companies. There is a certain logic to this, as industry and large companies generate a completely different quantity and quality of data. Even young data analysts often lack access to data analysis for SMEs for more "cultural" reasons. Coming from a more scientific approach, it is difficult to find a clear answer to the question: What is the benefit and what does it cost? An understandable dilemma, as you only know whether the data treasure is a treasure after you have analysed it.

A science in itself

As already mentioned: There are many tools that can be used to analyse data for SMEs. The results are usually presented as attractive charts and interactive dashboards. However, the following also applies to data science: "A fool with a tool is still a fool". The advice of several experts may therefore be required here. Some can answer questions such as: How is my data structured and how do I transport it from this structure to a level for data analysis? These are likely to be the consultants who work in the "bowels" of the business software (ERP, CRM, DMS etc.) are at home. How this data is then processed in combination with other data sources of structured and unstructured data into a treasure trove of data that yields new insights is more the responsibility of the data analysts.

Choosing the right approach to data analysis for SMEs

The approach to data analysis for SMEs is usually derived from a question that points to an appropriate approach:

What happened? (Descriptive analysis)

You look at the past and compare it to the present. To gain new insights, this approach to data analysis will not be sufficient for SMEs. However, it will probably almost always be the place to start. Data such as resources used, rejects generated, costs incurred, etc. are determined. It would be nice if these figures could be retrieved from well-integrated business software at the push of a button. Unfortunately, the reality is that this is not always a matter of course for medium-sized companies.

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Why did it happen? (Diagnostic analysis)

This is where data science becomes more interesting. Data is compared and patterns are identified that reveal the reason for a certain experience or event. Answers or at least clues to questions such as: Why are we losing orders to competitors in a certain sector? What is the cause of increased costs or material consumption? etc. In an average medium-sized company, this discipline already requires the consolidation of several data sources.

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What will (can) happen? (Predictive Analyses)

Here we have arrived at the supreme discipline of data analysis for SMEs. This is where the providers promise themselves the innovations and the companies the greatest advantages. Patterns and trends are recognised on the basis of structure-recognising procedures. These insights help with decisions and the definition of strategies. Which customers will buy what in the future? How will costs and profits develop in which market? What influence will which investment have on the company's cash flow. However, the report also asks for indications of new business models and markets. For such statements at the latest, the quality and quantity of data is crucial. Rarely are a medium-sized company's own data sources sufficient for this.

It is about decisions

There are voices that describe the new or recent trends around Data Science as just old wine in new bottles. Other enthusiasts see big dataAnalytics, Mash-up, Data Integration and Co. as the saviours par excellence without which companies will be doomed to fail in the future. None of this is likely to properly address the issue of data analysis for SMEs in absolute terms. Basically, it is true that companies have always faced the challenge of doing the right thing at the right time. And to do this, the entrepreneur must constantly make the right decisions at the right time. The competence to make these decisions has always been the supreme discipline of the entrepreneur. However, as life accelerates and tasks become more and more complex, the experience and decision-making courage of individuals is no longer sufficient.

A matter of people

When dealing with the topic of data analysis for SMEs, you quickly realise that implementing a suitable strategy cannot be a one-off project. Another realisation: it is not a matter for IT. You need expertise in your own company. Employees who constantly support decision-making processes with the background of a data analyst. You still have to ask yourself whether the use of data science makes decision-making easier, more difficult or simply "different". The psychological hurdle for some decision-makers should also not be underestimated. After all, the nimbus of the decision-maker and doer is tarnished if the "data oracle" is consulted before a clear instruction is given by the boss.

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