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Data analysis - Six steps to better business decisions

Data analysis - Six steps to better business decisions



ByJan Watermann

...

September 26, 2019

Wrong business decisions can have devastating effects. Therefore, many companies rely on their business data when it comes to balancing important decisions. With this article, we explain how even small companies can use data analysis to make better business decisions in the long run.

There is no data shortage in most companies. In fact, many companies have too much data that makes it impossible to get a quick overview of important aspects. In order to make better business decisions with the help of data, clarity is key. Data analytics is a way to merge different data sources and identify a business’s relevant aspects - the basis to make smarter business decisions.


Datenanalysis with Tableau and Datapony

To avoid losing resources when performing data analysis, your company should establish the following six steps:

1) Defining questions for the data analysis

Clearly defined questions stand at the beginning of efficient data analysis. How would you analyze data without knowing what you are looking for? The answers to the questions should be clearly and precisely extractable from the available data. Ideally, problem-related questions are set.



Example:A company is noticing drastically rising costs in a particular region in its area of operation. One of the many related questions would be: When would the rising costs make the company unprofitable?

We recommend initially focusing on a handful of relevant questions. In the first step, find some key questions for your company to answer in a later analysis.

2) Preparing evaluations

Once you defined first questions, the preparations for the evaluation are your next step. This is primarily about clarifying how to develop a solution for the respective questions.

In the example above, officials should take a close look at sales and profits. But that's not all. To find a solution to the situation, the triggers must be nipped in the bud. So in our example, it's about understanding where you can find answers that explain the cost increase - is it a local problem? Or does a certain business step increase costs?

The ancillary aspects are equally important in this case, if not more important than the core question. For most companies, the preparations define how successful the later data analysis will be.

3) Collecting data

Merging all the necessary data is primarily about selection.

Not all available data sources are suitable for solving the defined problem. Your company should first access internal data sources - this saves costs and speeds up the entire process.

Then you should take a look at external data sources. For example, these can be technical tools that are regularly used by a company.

Collecting includes the preparation and correction of data. Often, companies have data that is inaccurate or incorrectly formatted. It’s usually a data scientists task to prepare the data in a way that analyzations later can be carried out successfully.

4) Clarifying the „HOW"

Many different business intelligence tools in the market can meet different requirements. To successfully perform data analysis and then make better business decisions, companies should commit to a tool. Excel may already be enough for simple data analysis. Especially if your data is primarily stored in Excel, this is a good solution. Here are some insights about the analysis-features within Excel. For data scientists and medium-sized companies, however, we generally recommend Tableau. Tableau is a cross-platform-solution and has become well established in the business intelligence field. If you’re not sure whether Tableau is the right tool for you, have a look at our article about whether Tableau is the best business intelligence tool on the market. While selecting a business intelligence software, your primary focus should be that it perfectly fits the technology your business uses.

5) Analyzing data

After the first four steps have been carried out, the fifth step deals with the actual analysis. In the first place, it is important to find links between individual data sets that could provide a solution to the question defined in step 1.

In practice, analyzing the combination of two coefficients in Tableau looks like this: https://www.thedataschool.co.uk/emily-dowling/calculate-correlation-coefficient-tableau/

When it comes to the question asked at the beginning of this article, connections between sales and earnings should be found. To do this, we recommend entrepreneurs tto repeatedly review existing records before they find their impact on the analysis. When using online data sources for analysis, live connections to these data sources may also be useful.

These not only help to find the solution to acute problems but also contribute to better business decisions in the long term. When creating a dashboard with live connections, updating rhythms of individual data sources is particularly important.

Are you not sure if you need external help for that? Have a look at our article about Tableau consultants.

6) Evaluation

The sixth step is to evaluate the findings and then use the gained knowledge. You should ask yourself the following questions to determine the success of data analysis:


  • Was the question asked at the beginning answered?

If not - why? Can you answer the question in a new analysis?


  • Can the problem be corrected with the newly gained insights?

If not - what do you need to solve the problem once and for all?


  • Have all aspects been taken into account in the data analysis?

If not - Are there any data, situations or other influences that could falsify the result?

With these six steps, you can make long-term data-driven and better business decisions. But do not forget that data analysis can only be as good as the data behind it. As a business owner, you should therefore extensively document all processes in your company.

Are you interested in learning more about data analysis for your company?

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