How SMEs can adopt data science

Data resides everywhere in the business, and SMEs are no exception. However, they still need to be effectively equipped to make full use of data science.

Proper use of data can have a significant impact. This data can be found throughout the company, whether it’s in spreadsheets, online shared folders, or even hosted locally on employees’ computers. Businesses can reap great benefits from data science, the process by which we begin to understand data, using clean, timely information to make quick, informed decisions.

However, SMEs often face obstacles related to the technical aspect of data science. Then, they need to go beyond technology and focus on the basics of data strategy, not just focusing on costly projects or just addressing issues that can lead to significant financial losses. In the early stages, before identifying these issues, data analysis can be done using spreadsheets.

The main advantage of a good use of data is the ability to make informed decisions based on quantifiable and verifiable information. It’s about going beyond hierarchical functioning as we’ve known it for decades and letting the person closest to a problem directly contribute to solving that problem from the data. This motivates employees on two extremely important levels: learning and change.

The evolution of the business data landscape over the last 20 years

When the term scientific data first appeared in 2001, the biggest challenge was how to take advantage of relatively limited data sources. Initially, there was no specific training to prepare people for the daily tasks of a data scientist, there were only specialists in databases, mathematicians, statisticians and physicists who often wrote code from scratch.

In 2012, the profession of data scientist was voted “the sexiest job of the 21st century” by the Harvard Business Review. Everyone rushed to his LinkedIn profile to add the mention of data scientist and thus hope to get a higher salary.

Later, new, simpler computer languages ​​emerged, and we began to see graphical user interfaces that were dragging and dropping making data science accessible to non-coders, but they were still in their infancy.

Back then, big data-like projects we know today consisted of data stored in different departments, in data silos that were difficult to access and controlled by different people. These data projects required a large number of tools and products to turn basic data into something useful for business. You had to talk to database specialists to access the data, learn how to write SQL, or find an expert to extract the data … and then prepare it for analysis. Over time, this process has become much easier.

After analyzing the data, it was necessary to convince the management before implementing anything.

It was then possible to generate real added value from these complex applications, but only with an exceptionally high level of technological expertise and broad coordination among many people. Thanks to these early data workers who laid the groundwork for data science, the model has been proven and the demand for their experience has increased.

Strategies you need to know to develop a data-driven culture and business

Building a data-driven culture requires a solid foundation, including leadership. In fact, the management team must make analysis a standard practice, ready to support and drive change. This is what we see in the most mature companies analytically, and it is something that can be replicated within SMEs.

The adoption of a data-driven approach begins with data literacy programs. To turn this learning into a real culture, we need to integrate analysis into our daily work. It can be a huge investment, but in the long run it pays off. For this to become a reality, new initiatives are needed outside of the usual lunches and trainings, whether it’s offering additional paid learning days, gamifying learning, or career-focused learning for end-to-end staff planning.

People closest to a process know where the problems are, and by amplifying human intelligence to make the most of science and data analysis, they have the context and can see the business impact of the process, the resolution of this issue using the data. There is a big advantage here.

Equip all employees for data analysis, regardless of experience or background

With modern technologies and systems becoming more accessible and easy to use, anyone can become a data citizen scientist, someone who can use data analytics to create knowledge and value. The question of whether employees should be equipped with these tools can only be answered at a micro level by each company. Different use cases require different approaches and levels of governance.

For example, improving the data analytics skills of store ATMs has only a limited benefit, but equipping other back-office employees or store managers with data analytics skills could certainly create value. Possible use cases include the use of computer vision to automate the extraction of data from the time card for spreadsheets or even to automate the extraction of text from receipts and invoices within the supply chain.

Ultimately, while the option of using data analytics is an option for everyone, companies looking to improve the capacity of their people should still follow a standard cost-benefit model.

How organizations can identify the right problems to solve and get there through data analysis

Finding the right problem to solve is a unique challenge for every business, although they may have some points in common, having to navigate the mix of people, processes, legacy technologies, or even geographic location.

Before embarking on data analysis, companies should systematically have an idea of ​​the ideal problem to solve. The trick here is to start the business decision goal and work backwards.

Finding the right problem is often the end result of many smaller-scale victories, as organizations begin to understand not only what they really need, but also the resources and tools they have to get there, while eliminating what is not. it is necessary. These projects, which could be considered intermediate, serve as a basis for achieving the ultimate goal.

State-of-the-art data analysis technologies: an essential lever for SMEs in a competitive market

The ability to integrate new data points into an analytical process, provide real-time information, and quickly adapt to changing market demands is what separates so-called native digital or pure player companies (Netflix, Amazon, etc.). older organizations that are drowning in technique. debt. The automation of the processing of this information allows to release a considerable value, but also to free the time of the collaborators to be able to concentrate in missions of greater added value for the company. It is also for this reason that the employees closest to the data must be included in these projects from their conception as well as throughout the deployment of these technological solutions.

By investing in non-technical tools such as self-service platforms that all employees, from marketing to sales operations, can easily use, experience, and learn new data-related skills. These platforms can help workers figure out how to automate analytical processes to extract powerful knowledge from data, creating a solid skill base for the future.

Automating discovery, analysis, and getting answers helps companies get ahead of the competition. However, one thing is essential: all employees should be able to do it easily, it should no longer be owned by a handful of specialists.

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