Data-based decision-making: tips for managers

Thursday, 04. April 2019

A guest article by Dominic Lindner
 

The hype around data is growing day by day and companies are exploring the potential of this data. From autonomous cars, engineering forecasts and chatbots in customer service, data can change entire industries. A potential is also to take decision based on data in the management. Data can provide Managers important decision-making fundamentals.

What is Data?

Data is a collection of aggregated characters containing information. You can find data anywhere in the world and also in your company.

Examples of data are:

  • Contents of this article
  • Temperature on a thermometer
  • Your sales figures
  • Speed of a car


Preparation of data within the company


The first thing is to identify data in the company and save it. You often have more data in the company than you think. For example, monthly reports or Outlook appointments can bring an indication of utilization. Also, documentations or inventory lists provide with correct evaluation some hints for decision making. Another tip is that you look for paper documentation and digitize it in a targeted way.

After you have identified this data, you have to clean it up and save it. For storage, I recommend the following media:

  • Local storage media (e. g. hard disks)
  • NAS systems (e. g. servers)
  • Databases (e. g. MariaDB)
  • Cloud solutions (e. g. AWS, Storegrid, ...)


Evaluate Data within the company


Data within the companyNIn order to save time and money, you can make numerous initial evaluations in Excel and only start the use to tools such as Tableau for more complex cases. Also, make sure that you do not evaluate data for each use case. Otherwise you risk that working time is not used to create value, which leads to frustration among employees.ow that you have collected a large amount of data in your company, you are faced with the big question: "How can this data help me and what do it want to tell me?" You always need a context for the purposeful evaluation of data. Data itself is useless without context. For example, a temperature of 36 degrees Celsius does not help you if you have no context. For a holiday, this temperature is a bit too hot but just right for the storage of hard drives.

In order to save time and money, you can make numerous initial evaluations in Excel and only start the use to tools such as Tableau for more complex cases. Also, make sure that you do not evaluate data for each use case. Otherwise you risk that working time is not used to create value, which leads to frustration among employees.

In summary, in this step you should formulate clear goals (context) and make them measurable through numbers. For example, employees' workload can be measured using overtime, using calendar entries or shift schedule data. Another tip is the combination of key figures to generate new insights. For example, you can gain new insights by combining layoffs and overtime or revenue per customer with complaints per customer.

If you are aiming for a permanent optimization, then it is important to evaluate the data monthly or weekly and to compare them again and again.

For the evaluation I recommend the following tools:

  • Spreadsheets (e. g. Excel)
  • Data Visualization (e. g. tableau)
  • Machine Learning (e. g. Knime)
  • Programming languages (e. g. Python)


Data-based decision: tips for managers


Data ManagerThe use and analysis of data offers managers potentials to make decisions safer and faster. The initial effort in preparing and evaluating data can be minimized using known and inexpensive methods such as the combination of storage media and Excel.

From the current findings can be summed up some tips for managers. I have taken these from my book with Paul Niebler Datenbasiert entscheiden.

  • Data analytics does not guarantee success, but when applied correctly, it can result in multiple optimizations, a better understanding of the business, and a competitive advantage.
  • You often have more data than you realize. Search in the company for new data sources that may have been unused so far.
  • The evaluation and storage of data costs time and money. Therefore, make sure you have a reasonable level in order to achieve a balanced cost-benefit ratio.
  • Start with simple tools like Excel. For more advanced use cases, it pays off to invest in more specialized tools.
  • Data analysis does not always have to be complex – a new combination of different measures or data can already provide valuable information.


Source


Niebler, P./Lindner, D. (2019): Datenbasiert entscheiden – Ein Leitfaden für Unternehmer und Entscheider. Springer Gabler, Wiesbaden.


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About the Author

Dominic LindnerMSc. Dominic Lindner is a post graduate at the Institute for IT Management (WI3) at the University of Erlangen-Nuremberg and also works as a agile coach for an IT company in the metropolitan area Nuremberg. His main subject of research is leadership and work in SMEs in the context of digitalization. He updates his blog regularly with summaries of his latest research results: https://agile-unternehmen.de.