How SMEs can embrace data science

Data resides everywhere within the company, and SMBs are no exception. However, they still need to be properly equipped to take full advantage of data science.

Proper use of data can have a significant impact. This data is everywhere within the company, in spreadsheets, shared folders online, or even hosted locally on employee computers. Businesses can greatly benefit from data science, the process by which we begin to understand data, using clean and timely information to make quick and informed decisions.

However, SMEs often face obstacles related to the technical aspect of data science. They must therefore go beyond technology and focus on the fundamentals of data strategy, not just dwelling on expensive projects or only addressing issues that could lead to significant financial losses. In the very early stages, before these problems are identified, data analysis can be performed using spreadsheets.

The main benefit of good use of data is the ability to make informed decisions based on quantifiable and verifiable information. It is about going beyond hierarchical functioning as we have known it for decades and letting the person closest to a problem have a direct contribution to solving this problem based on the data. This motivates employees on two extremely important levels: learning and change.

The evolution of the enterprise data landscape over the past 20 years

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

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

Subsequently, new simpler computer languages ​​emerged, and we began to see drag-and-drop GUIs that made data science accessible to non-programmers, but these were still in its infancy.

At the time, 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 the business. You had to talk to database specialists to gain access to 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 simpler.

After analyzing the data, management had to be persuaded before implementing anything.

It was therefore possible to generate real added value from these complex applications, but only with an exceptionally high level of technological competence and extensive coordination among many people. Thanks to these early data workers who laid the foundations of data science, the model has proven itself and the demand for their skills 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 at the leadership level. Indeed, the management team must make analysis a standard practice, ready to support and guide change. This is what we see in analytically more mature companies, and it is something that can be replicated within SMEs.

Adopting a data-driven approach starts with data literacy programs. To transform this learning into a true culture, it is necessary to integrate analysis into our daily work. It can be a huge investment, but it pays off in the long run. To make this a reality, new initiatives are needed outside of the usual lunches and training courses, whether it be offering extra paid learning days, gamification of learning or career-focused learning for end-planning. to-end of the workforce.

The people closest to a process know where problems are, and by amplifying human intelligence to make the most of data science and analytics, they have context and can see the business impact of the process and the resolution of this question from the data. There is a huge benefit to be found here.

Provide all employees with data analytics, regardless of experience or background

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

For example, improving the data analytics capabilities of store cashiers has only a limited benefit, but equipping other back-office employees or store managers with data analytics capabilities could definitely create value. Possible use cases include using computer vision to automate the extraction of timesheet data for timesheets 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 their skills 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 it may have some commonalities, which must navigate the mix of people, processes, legacy technologies, or even geographic location.

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

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

Cutting-edge technologies for data analysis: an essential lever for SMEs in a competitive market

The ability to integrate new data points into an analytical process, provide information in real time and adapt quickly to changing market demands is what separates so-called digital native companies or pure players (Netflix, Amazon, etc.) from debt. The automation of the processing of this information allows to release a considerable value, but also to free up the time of the collaborators to concentrate on missions with 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 and during the implementation of these technological solutions.

By investing in non-technical tools such as self-service platforms that every employee, from marketing to sales operations, can easily use, experience and learn new data skills. These platforms can help workers discover how to automate analytical processes to extract powerful insights from data, creating a strong skill base for the future.

Automating discovery, analysis, and response obtaining helps companies outpace the competition. However, one thing is fundamental: all employees must be able to do it easily, it must no longer be the preserve of a handful of specialists.

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