Are you looking to find out more about data interpretation uses? We have put together a comprehensive article and reviewed the top use cases.
Data has become an extremely valuable asset for business. Companies today wish to get their hands on as much quality data as possible as its power in business and investing has been more than sufficiently proved. But equally as important as getting the data is what we do with it afterward. Only properly handled data is truly valuable as only then this value is extracted. One of the many procedures of turning the potential of data into actual means for success is data interpretation. As this process is made for finding the knowledge coded in data, businesspeople find it to be extremely useful.
What Does the Data Mean?
As we all know, having a lot of data does not already mean knowing a lot as data and knowledge are two different things. Yet, we still want data because we know that it can be turned into knowledge.
Knowledge comes when we process the data and recognize the meaning of it, i.e., what it is telling us. Generally, when we talk about finding the deeper, the non-obvious meaning of something, whether it is art, literature, or ambiguous words in a daily conversation, we tend to refer to interpreting. We interpret something unclear to explain to ourselves and others what it means.
In this sense, data is no different. We interpret the large volumes of at first glance chaotic information to uncover the insights that are meant for business. Of course, unlike with other interpretations that we do, for data interpretation, we cannot read every single data unit before we interpret it. Instead, we process the data methodically to make it more accessible.
For that, we have a step-by-step process that starts with determining the criteria and scope of our interpretation and ends with checking and evaluating the entire process and its results. From this, it is clear that although there are general rules of interpretation, the process can and in a way has to be adjusted for specific needs.
However, the end result always has to be a concise yet comprehensive report of what the data means, usually presented in graphs, charts, and other visual summaries of information.
The Crucial Use Cases of Data Interpretation
The general purpose of data interpretation is to turn raw data into a true asset of business intelligence, that can provide guidance when making important decisions. Here is how data interpretation works out when applying it for decision enhancement in particular cases.
In investing, data interpretation helps to identify the underlying trends and patterns that might have been missed in the sea of raw data. Investors have to deal with a lot of information as there are many types of data that define markets, industries, and particular companies. When all this data is entangled together, it turns into a digital noise, that prevents important patterns from shining through. But when it is interpreted through predefined criteria, investors can confirm or refute their initial assumptions, or notice unexpected trends.
Conducting Marketing and Sales Analysis
When conducting marketing and sales analysis, interpretation allows us to see what works and what does not. The more leads we process, the more deals we make, the more customers we gain or lose, the more data we have. At first, we only know the end result, that all this data leads to the number of customers we currently have. After data interpretation, we can tell much more about who our customers are, what worked for different demographics, what features of customers, or marketing strategies came into play when sales were made or rejected. In short, data interpretation can tell us how and why we got to the kind and size of our clientele.
For performance analysis data interpretation provides clarity. When we collect data about our internal procedures, we aim to understand what sustains our efficiency and what drags us down. However, such data will sooner tell us what reduces our performance than why. And for that reason, it might be misleading, as an apparent issue in one department might be conditioned by a hidden one in another. Data interpretation is meant precisely to separate mere correlation from causation by exposing those hidden issues and their impact.
For many, the word “interpretation” immediately associates with subjectiveness and bias. There can be many interpretations of the same facts, many meanings assigned to the same information.
Some subjectiveness does persist in data interpretation as well, especially if a more qualitative, descriptive approach is chosen instead of the more empirical quantitative approach. However, that is precisely why interpretation involves thorough self-analysis. By reflecting on the possibility of bias, we mitigate its potential harm.
Additionally, data interpretation is not an exclusively singular procedure. Multiple interpretations of the same data with varying pre-defined conditions can be conducted. Then different interpretations can be compared to see how the method affects the results. At the end of the day, some interpretations will conform better to the facts than others and will be recognized as more reliable.