Studies by data analysis specialists and market analysis companies such as IDC show the sales and profit potential that companies are missing out on due to the inadequate use of data. Around three quarters of respondents to a recent survey stated that their operational efficiency had improved by an average of 17% thanks to data science. In terms of turnover and profit, they also reported an average increase of 17%. According to the study results, German companies also see the increase in customer satisfaction as a particularly important argument for investing in data analysis. However, according to another survey, only 5% of German companies currently have sufficient data expertise.

According to the definition, the term 'data literacy' refers to the ability to read, work with, analyze and argue with data. Ideally, employees at all levels should be able to use these skills to ask targeted questions about data and its meaning, handle data analytically, build knowledge, make data-based decisions and communicate with other people.
With the following five steps, companies can improve the internal handling of data to unlock the potential of data science.

1. reduce data contamination

Today, almost every employee contributes to the large data streams. To minimize data contamination, employees need to be better informed about downstream data processes. Some information is business-critical, but a lot of data is also generated in an unstructured and undefined way as a by-product of daily office work as so-called 'dark data'. This includes, for example, data from spreadsheets, email archives and attachments, different versions of documents, data on former employees and reports or survey data that are not assigned to any specific processes and structures. In order to increase the benefits, data must be better cleansed, relevant data defined and freed from its silos and made accessible for analysis. Without qualified employees and the creation of suitable roles, however, this is a rather hopeless task.

2. use transformative technology

Insights from past business processes and decisions can provide a valuable basis for future business decisions - if the corresponding data is usable. In this way, companies can better understand their customers, develop meaningful products, offer innovative services and optimize processes to improve ROI. An important step towards this is to use technologies such as machine learning and artificial intelligence for data analysis. Algorithms and platforms for 'machine learning' and 'deep learning' help to evaluate the huge amounts of data - the keyword here is 'big data'. Investing in the appropriate tools, technologies and employee skills is therefore an important strategic decision.

3. set incentives for data science roles

Andreas Rothkamp is Vice President DACH Region at Skillsoft.

© Skillsoft

According to Gartner, the skills shortage in many sectors has encouraged companies to view employees as internal customers. Top employers are formulating specific offers with clear value propositions and career prospects rather than 'jobs' to attract and retain data professionals.

4. diversify data roles

According to McKinsey forecasts, a skills shortage of 250,000 data scientists is expected by 2024 in the US alone. Due to the rapid pace of technological development and the mix of technical skills, business acumen and communication skills required of data professionals, it is a major challenge to fill all the roles required. However, as specialists with many years of training are not required for every task, it makes sense to specify the tasks and roles at an early stage in order to build up the relevant skills internally. A forward-looking strategy is therefore to identify promising employees or candidates at an early stage and to qualify them for the further development of their career in the field of data science with the help of training courses.

5. show career opportunities

The incentives mentioned above can be used not only for external recruitment, but also to communicate internally the career and promotion opportunities offered by building skills and expertise in data science. By offering upskilling, pre-qualification and retraining opportunities for employees, organizations also develop skills internally that are needed now or in the future. Promoting employees internally also preserves valuable institutional knowledge instead of losing career-oriented employees to other employers.