Data interpretation is the most crucial and vital step when you’re using data to make decisions regarding the growth of your business. Today we can analyse everything from upcoming trends to customer feedback and job performance, which opens the door to more quality management based on statistical research and analytical reports.
However, as with any intelligence business tool, the numbers itself don’t tell you much. What is truly important is the interpretation of those numbers and how the insights you gather from them can aid you in reaching the maximum potential of your company on the market.
The best way to explain the amount of data you can analyse today is through a study made by Digital Universe Study, where they found that in 2012 the total data supply was 2.8 trillion gigabytes, and just imagine how much more data we compiled in the following six years. Using data analysis and data interpretation has become a “must-have” tool for companies.
Being competitive in the market is depended on insights and findings that emerge from the numbers on the report. The critical issue regarding interpretation is understanding the presented patterns and reading them right. It is advisable you hire professionals, like the experts working at Minerra, to help you with the analysis.
The data integration services, which Minerra can provide, are going to automate the manual processes incorporating data from multiple systems in one place, where each of your colleges can have access, too. This will speed up the process of joint decision making, and it will give you a complete preview of the data you want to process with our software.
The data interpretation definition
The data interpretation definition in business terms is the implementation of different processes in which data is analysed and revised with the purpose of gaining insights and recognising emerging patterns and behaviours. These conclusions are helpful for you as a manager to make an informed decision, backed by numbers while having all the facts at your disposal. Data doesn’t come from a single source but multiple diverse sources.
Data analysis is always subjective and connected to the goals and the nature of the industry. For example, even if you’re using the same amount and the same type of data, a food chain and a fashion store will analyse it differently and gain a different awareness from it. Additionally, every business has its objective, and the interpretation of the results is going to produce a report that is uniquely qualified for them.
Data analysis has two main classifications, which we are going to elaborate on in this article: the “quantitative analysis” and the “qualitative analysis.” However, before we continue, we also have to take a look at the scales of measurement because they decide the continuing effect the data is going to have on your decision making.
- Nominal scale – It incorporates variables, which are exclusive and You can’t rank or compare with non-numeric classifications.
- Ordinal scale – It incorporates variables which are exclusive and extensive but in a logical order (online quizzes and surveys are usually done in an ordinal scale).
- Interval scale – With an arbitrary zero point, which systematically integrates the categories and groups them in equal distances.
- Ratio – A combination of all of the above.
Qualitative data interpretation
A qualitative analysis is not done by running numbers but by classifying data in categories. It’s mostly used for observations, interviews, documents, surveys, etc. This type of research is intended to gain insights by using a person-to-person method for data gathering. For example, when you complete an online survey, the answers are rated in the following manner:
- I strongly agree
- I agree
- I’m neutral
- I disagree
- I strongly disagree
The best feature about the qualitative data interpretation is that the findings are grouped into topics and categories, which makes it easier to notice trends and collect the data. A report created from a qualitative analysis tends to be more readable for the layman.
Quantitative data interpretation
If the qualitative analysis was contextual, the quantitative analysis is numerical. With quantitative data interpretation, you analyse the numbers in the data to gain insights, and that is achieved with statistical modelling. There are three types of statistical modelling:
- Standard deviation
- Frequency distribution
The most common technique of quantitative analysis is running tests on two or more significant variables, which are later processed together or separately and in the end, are compared to one another to sum up a report.
Benefits of the interpretation of data
The benefits of the interpretation of data are numerous and can affect your business in a number of ways, but they are mostly used for informed decision making and predicting upcoming trends and behaviours. Another invaluable resource you can tap into with data interpretation is identifying problems and solutions.
If you want to enhance work productivity, the most prudent thing to do is to gather data and make an analysis instead of following general policies that are not backed up by numbers.
Another area where data analysis is helpful is reducing costs. Deloitte did a study in which they proved that data analysis ROI could prepare management to cost-reduction opportunities and without damage to the human capital. Intel saved over $3 million in manufacturing costs because of data analysis in 2012.
However, the most prominent example of this is Norfolk Southern, a train company that saved over $200 million by making trains run one mph faster, and they managed to do that because of customised software, which monitors the rail traffic.
However, one thing you should be careful about is how statistics can be misleading and that only proper data interpretation can benefit your business. You should be careful not to make causation and correlations, which are feasible just because enough data was analysed in the process. This is one of the common mistakes managers make when it comes to interpreting data results.