With tech currently being all the rave, two terms we often come across are data analytics vs. data analysis. But how are they different or related? Or are they two terms that represent the same concept? From research, we’ve come to realize that they are, in fact, two separate entities, with the idea of them being the same a mere misconception. Our article explains both concepts further in an attempt to clear the confusion.
What is data analytics?
Data analytics is the science and art of all activities directly related to data. The sole aim of this concept is to provide data in a manner that makes it easy for businesses to access, understand, and derive workable conditions.
With data analytics, you undertake several machine-enabled and human-enabled steps that allow you to discover, interpret, visualize, and narrate the data patterns to help drive business outcomes. You should uncover opportunities, find trends, make decisions, and predict triggers, actions, or events with data analytics. The systematic nature of data analytics opens it up too many areas such as data science, applied statistics, and machine learning.
What is data analysis?
Data analysis is a little portion of the data analytics pie. This pie slice comprises the cleaning, transforming, modeling, and questioning of all given data to find information that can add value to your business. Data analysis is often limited to already prepared data sets, and it employs the use of software automation or data analysis services in its first round of analysis.
The second analysis process features human assistance, whereby the human interrogates and investigates the data within a specific context. So, in short, data analysis aims to present data, suggest actions based on said data, and give others access to the processed data.
Data analysis also uses different types of techniques, but the well-known five include text, diagnostic, statistical, predictive, and prescriptive. Text analysis is also known as data mining. This concept uses databases and data-mining tools to discover patterns in large-form data sets. Next is diagnostic analysis, which is used mostly to identify behavioral patterns of data. It uses insights discovered during statistical analysis to seek the cause of pattern changes.
Statistical analysis, on the other hand, collects, analyzes, interprets, and presents data and helps answer the question “What happened?” Predictive analysis uses previous data to predict future outcomes and how they will affect businesses. Lastly, prescriptive analysis is the last common data analysis type, and it combines the insights derived from all the above to help influence business decisions.
Which one is better?
When it comes to Data Analytics vs. Data Analysis, several experienced marketing professionals believe that the output of data analysis alone doesn’t come close to the outcome of data analytics. The latter results are seen as more encompassing, making it more beneficial when making decisions.
If you’re struggling to grasp the concept, consider these two scenarios:
- A business analyst creates a dashboard where users can interact with descriptive analysis of data. Received instead of sending the user a spreadsheet of numbers.
- A business user is gaining access to a web app that allows the user to interact with predictive analysis. While showing the forecast, as opposed to receiving a report with the live value of a marketing campaign.
In both scenarios, the former options sound more encompassing. As they allow the business user to see the data analytics in action.
All in all, data analysis is a set of specific actions that fall under the broad subject of data analytics. On the other hand, data analytics is an expansive field that looks at how data and data tools are used in making informed business decisions. For this reason, one can conclude that raw data that hasn’t gone through any process has no value. The value of data depends on what it is used for, which makes data analytics more valuable. Data Analytics vs. Data Analysis so forth is concluded here!