How to make your business more data savvy

In a business environment clever decision making is key to success. Often decisions are taken based on feelings or emotions but typically data driven decision making results in higher yield. In this article I would like to present some approaches to strenghten your business intelligence capabilities.
The following is a rough guideline on how to make your business and decisions more data driven. As a manager or business leader your are always faced with situations which need a judgment. Clever data tools can simplify decision making and help to become more successful.

1. Build a team

First of all you need the right people for this task. Without the right candidate or team there is obviously a risk to fail and you might not get the most out of it. As you might not be familiar with the area of data science it might be good to get external help. A consultant could help to evaluate whether building a data science team could make sense or not and which type of people you need to achieve your goals. There is a wide range of job titles in this area such as data engineer, data scientist, data analyst and big data developer. Do you know the difference? Do you know how to hire a skilled person in this area? An experienced external consultant could be a very helpful thing to start of data driven decision making in your company. Please contact us for further information.

2. Identify your business questions and related data

You should have an idea of which questions you would like to answer. Typically they arise from situations your are exposed to with your business. Typical examples are related to production processes (yield, cycle time), services, customer (marketing) and suppliers. Once these questions are clear, your data science team might then be able to explore which data could be used and come up with an idea on how to answer your business question.

3. Collect data and develop models

With a plan in place the next step is to collect and analyse relevant data. Your data science team will then develop and validate model to explore predictive performance. This is typically an ongoing process as models often are refined, data sources change or business questions change.

4. Make your data tools available to colleagues

It is nice to have a working model to answer business questions. However, in many cases it makes sense to build a complete data tool and make it available to your colleagues. Quite populare are web-based applications which are made available on the intranet so that othe people could use the data tool. This typically simplifies the work for your colleagues and your data science team will get recognised for their achievement.