How small and medium-sized enterprises can use big data to their advantage
Not just big players such as Google or Amazon can benefit from the seemingly endless potential of big data. Small and medium-sized enterprises should just as much be aware of the power of their data. In practice, however, many SMEs do not tap their data resources at all. Prof. Axel Polleres, Academic Director of the Professional MBA Digital Transformation & Data Science at the WU Executive Academy, illustrates how SMEs can use big data to their benefit even without a large budget and an entire data department assigned to the task.
“Who will mine the data gold?” was the headline of a Zeit Online article already in 2013. Ever since, the gold miners have been on the rise, including disruptive start-ups that dismantle markets through their platforms as well as digital giants such as Facebook and Google, which have been at it since the aughts. Corporations and authorities have whole departments that analyze the data streams of customers and citizens.
Big data is not just for the mighty and cool, says Prof. Axel Polleres, head of the Institute for Information Business at WU Vienna. In the age of the internet, data is a valuable resource and a potential key to success for companies across virtually all industries, and especially for SMEs. The term “big data” often does not resonate with small companies.
Prof. Axel Polleres
Everything is relative. Small enterprises also possess data which they fail to exploit, either because it is too large to process for them or for other reasons not related to technology. Big data is not so much characterized by its gigantic size than by being “dynamic, challenging, and complex.”
Small and medium-sized enterprises are frequently oblivious to the vast economic potential of their data. Failing to tap this precious resource, they fall behind their competition. Axel Polleres has summarized the five most important learnings for SMEs when dealing with big data.
A company’s data can be substantial, but very rarely are such datasets systematically processed. “Especially among SMEs, this is, unfortunately, rampant. It just does not make sense to randomly collect data for a future application if you have no plan for its use and do not process it accordingly,” says Axel Polleres. The consequence is “garbage in, garbage out” – low-quality basic data cannot be used for high-quality forecasts and analyses. “It is important to clearly define quality criteria and the purpose of the data collection in the run-up,” Polleres advises. For the WU expert, a targeted collection and adequate processing of data are crucial. From an SME perspective, this means that there should be at least one and ideally several staff members in key positions with basic data governance skills. Otherwise it will be impossible for the company to use data systematically and to its advantage.
Tracking customer behavior online and on social media is an extremely valuable marketing tool, helping companies to better understand their customers, develop new services, and optimize pricing. Such an approach can be very effective for SMEs when it comes to developing new strategies ahead of the competition. At the same time, legal aspects as well as technological possibilities remain uncertain factors. It is not just about the personal data of customers, an issue often brought up in discussions about data protection. Large datasets are crucial in fields beyond marketing and sales but can also be used for production, processing, and process optimization in any business area to avoid downtimes and bottlenecks or simply save time. Machine data could, for example, serve to prevent machine failure in the future. SMEs will, however, be well advised to not just focus on big data: also data that structures company knowledge and makes it easily applicable, so-called “smart data,” is valuable and likely to contain important information. This could include employee experience in dealing with machines, for example.
The fact that start-ups disrupt entire industries has long ceased to be a secret. It is also old news that companies can use data on customers, work processes, or the value chain to gain an edge over their competitors: “What interests us are disruptive, data-driven business models that go about processing, analyzing, and interpreting this data with innovative methods unlike anything else before.” These models could jeopardize SMEs as well, Polleres adds. “We have seen disruptive business models access markets through the back door by offering apps and systems that are new to an industry. What they actually do, however, is siphon off data, gaining a competitive edge and, possibly, even control of the concerned domains,” Polleres warns. He advises small and medium-sized enterprises to become independent of innovative app developers: “It makes more sense to join forces with other companies and across industries to develop apps together, for example farmers creating an app to collect data on tilling the land.”
In this model, the companies would maintain control and ownership of their own data. Such decentralized systems have proven more resilient. To create more exact forecasts based on larger quantities of data, which can be used to train artificial intelligence such as algorithms, it is necessary to cooperate with others. Open data, an approach in which data is shared in order to create a mutual benefit, has much more to offer than many enterprises currently realize: “It would be a start to share and dovetail opening hours or other non-sensitive basic data in retail,” Polleres says. The Austrian government issued a report presenting findings of considerations with regard to drafting a strategic plan for artificial intelligence, recommending that domestic businesses establish marketplaces for digital AI solutions and data hubs for sharing data and cross-company cooperation.
The hottest new profession is that of a data scientist. These experts deal with the structure, analysis, and interpretation of data flows. But they are not the only ones with this kind of know-how. Technology giant Google requires all employees to have some basic knowledge of data and IT. Especially small and medium-sized companies should make sure that several staff members can provide data expertise, Axel Polleres recommends. “Everyone needs to step out of their silos. All employees should have a basic understanding of and basic skills in handling data.” To make the right decisions in marketing and sales as well as executive positions, these skills are non-negotiable. “In our short program Data Science, participants learn what to look out for regarding data quality, how to analyze larger data streams, and which tools to use to set up data pipelines and data processes,” Polleres says.
Prof. Axel Polleres
Any competence-building measures regarding data and digitalization should definitely start with the middle and higher management, as the transformation into a data-driven company is almost always contingent on the company culture imposed by the management. This is especially true for SMEs.
SMEs are often reluctant to use data flows for their governance and optimizing customer relationship management. Negative customer reactions are a cause of concern, but: “Customers do not per se have a problem with companies using their data; they worry about the abuse of sensitive data,” Axel Polleres clarifies. This is why it is crucial to stay abreast of the legal conditions, the type of data collection, and the purpose of their use, and “be able to inform the customers how their data is used at any given time. If you make use of AI, you need to be able to explain what for.”
For more information about the Professional MBA Digital Transformation & Data Science, please click here.
For more information about the short program Data Science, please click here.