Data scientists: how SMEs can turn data into sales

More than 62 percent of small and medium sized enterprises in Germany have set aside their initial scepticism about Big Data and business analytics. Now they are working together with data scientists. These analysts are specialists in machine learning and other data related skills such as processing, preparing and analysing. The findings data analysts come up with help businesses to improve products, optimise processes and reach customer groups as accurately as possible. For example: data science helps companies understand their customer groups and their habits better and produce customer-oriented advertising and customised products.

Important: Data science analyses only succeed if they are based on specifically formulated questions.

How data scientists provide the basis for growth

Purposeful data science provides a systematic foundation for every decision in the value-added process:

• 40 percent of businesses use data-backed analyses to monitor their own business performance.
• 37 percent say they want to understand customers better.
• One in three companies uses the insights acquired to optimise the way personnel are deployed.

Not all SMEs have built up their own expertise in this area of business analytics and employed permanent data analysts. Many employers use outside analysts to supply initial statistics and set up processes, especially at the beginning.

This includes assessing possibilities for grants. The go-Digital scheme run by Germany’s Federal Ministry for Economic Affairs and Energy (BMWi) helps businesses with fewer than 100 employees and less than €20 million annual turnover by giving them up to €16,500 in grants for expertise in data science. This scheme funds 50 percent of consultancy services with a maximum daily consultant rate of €1,100. Grants cover a maximum of 30 days within a period of half a year.

What fields do data scientist work in?

There are a variety of specialisations in the data science discipline. Businesses need skills and expertise in one or more of the following data science areas, depending on the issues and tasks in hand.

• Domain or subject matter expertise
  This involves understanding a company’s most important issues and knowing exactly what the right answers are – and what answers are needed. Data analysts ask: “What are the fundamentals about the problem we have to solve?” and “What exactly interests the consumer/customer about our results?”
• Data engineering
  This is mainly about big data: acquiring, inputting, modifying, storing and accessing data. Metadata – data about data – also plays a role, as does a holistic approach. Analytical questions in this field of data science include: “How do we ask for our data?” and “How can we use the data?”
• Scientific method
  As the name suggests, this field of data science is about the research and academic aspects of the work. Empiricism, hypotheses and reproducible experiments are everyday tasks. The question of data analysts is: “Do I have enough information about the methods and relevant data to repeat the experiment under the same conditions?”
• Mathematics
  This is where masterminds of analytics come in. Applied mathematics helps arrive at quantity, structure, space and change.
• Statistics
  Brainpower needed: this is about the methods, models and algorithms used to get the best out of data.
• Advanced computing
  The bit that needs a lot of patience: includes programming, testing and debugging applications. Programming languages like Python or SQL are required to perform advanced statistical analytics.
• Visualisation

Information’s pretty face: this is about visually preparing data and statistics to make them easy to understand. This field of data analysis incorporates creative processes in handling data abstraction.

• Hacker mentality
  Hacking is a secret recipe in data science. For data scientists it means much more than modifying hardware and software. Instead analysts tenaciously test solutions in the data cosmos until they find the best option in do-it-yourself mode for advanced users.

Budding data scientists usually specialise in one or more of these areas of work while they are still training or at the beginning of their career. If tasks are complex, it can be useful to assemble teams of data analysts who combine different analytical specialities.

 

Infographic of data science, which needs big data

Where can SMEs find good data scientists?

External data analysts work freelance for businesses. They can be identified online through specialised HR agencies and may soon become candidates for job vacancies in the field of data science. Related search terms of employers include data analyst, big data analyst, data engineer and business analyst.

Fourteen German universities, technical colleges, and universities of applied sciences offer bachelor’s and master’s degrees in data science. Their websites explain the curriculums for future analysts.

Non-European universities that specialise in big data and offer data science degrees can be found in countries such as Australia, New Zealand and Canada.

Which skills does a data scientist have to have?

• Critical thinking: Objective data analysis, good knowledge of resources and the ability to consider problems from different angles are basic requirements for business data science and analytics.
• Effective communication: Data scientists have to be able to explain their statistical findings well, think with a view to action and convey processes and research clearly.
• Hands-on problem-solving: Data scientists advise business managers on new business opportunities. They suggest which resources can be used to rectify existing problems. To do this analysts need the investigative skills of a detective to track down the right answers.
• Intellectual curiosity / natural inquisitiveness: “Why?” is a constant question among good data scientists.
• Business sense: Good data scientists aren’t nerds. Understanding the fundamentals of marketing and the workings of business is a core competence. Analysts know the business problems that need to be solved, and what causes them. They understand how to transform large quantities of data into statistical results that are useful to a company.

Other future digital jobs

The volume of global data is doubling every two years. By the year 2020, the world’s data mountain will have grown to 44 zettabytes – because of apps, social media and e-mails. One Zettabyte equals 1,000,000,000,000,000,000,000 bytes. As the statistics suggest the demand for data scientists is likely to increase.

But the digital world is not just creating a need for data science; it is also producing lots of exciting future careers with high salary potential.

• Simulation engineers, for example, illuminate the virtual worlds of algorithms so that we can understand them better.
• Robotics aestheticians develop the technologies used in cosmetic surgery.
• Human-robot interaction specialists are already working as interface designers and psychologists in the building of cars. Read on to find out what developments, careers and innovations await you and all of us.