How Data Analytics Relates to Data Analysis

Isn’t data analytics the same as data analysis? Yes—and no. Though most use the terms interchangeably, data analytics  is a subcomponent of data analysis that involves the use of technical tools and data analysis techniques. Analytics tools may include programming languages such as R and Python or BI tools like Tableau. Through the help of these tools, data analysts are making exciting advancements in data analysis. So much so that, these days, there rarely exists data analysis without data analytics. Data analytics tools and techniques are driving new developments across organizations, such as building entire customer 360º profiles or understanding sales forecasting at a granular level with the ability to fold in more and more data. And so, “what is data analytics” could be better rephrased as “what isn’t?” as organizations continue to accelerate their use of analytics tools and techniques.

Types of Data Analytics

To understand data analytics and discover the data analytics tools, it’s important to understand the different types of data analytics. 

  • Descriptive analytics. This type of analytics answers big-picture questions and summarizes big data sets. Descriptive analytics helps businesses discover key information using data visualization and analysis. This type of analytics is one of the most basic, so most data analytics tools can perform descriptive analytics.
  • Diagnostic analytics. This type of analytics digs deeper into the information from descriptive analytics. It can identify anomalies and trends that explain the anomalies using data science. 
  • Advanced analytics. This part of data science uses advanced data analytics tools to extract key information, find trends, and make predictions. The data analytics tools can include anything from statistics to machine learning.

The Foundation of Data Analytics

The question “what is data analytics?” could also be framed a different way. What is the foundation of data analytics? At its core, data analytics depends on data preparation. Without data that has been cleansed, enriched, and structured to fit the objectives of the analytics project, you simply won’t yield accurate results. 

Because data analytics is more closely tied to business results than data preparation, most organizations focus on data analytics more than data preparation. But getting data preparation right is the best way to ensure successful analytics. Data preparation is still considered the most time-consuming part of any analytics projects, which means that most resources who were hired to drive analytics projects are actually spending the majority of their time preparing data. The delay is often due to one of two scenarios. One, analysts are using tools that were built for data analytics, not data preparation (such as SAS) or two, they’re unfamiliar with programming languages and must outsource their data preparation needs to IT teams. In either scenario, their analysts are lacking a data preparation technology that meets the expectations of modern data analytics—one that can handle any size or complexity of data, but was built for the analyst in mind. In other words, a data preparation platform.

Data Analytics’ Key to Success

Trifacta is routinely recognized as the leader in data preparation and a key answer to the question. Its customers are accelerating the process of preparing data in order to generate more insightful outcomes from data analytics projects. One such customer is GlaxoSmithKline (GSK), one of the world’s largest pharmaceutical companies that has conducted thousands of clinical trials in hundreds of different formats. Previously, this historical trial data wasn’t accessible to GSK researchers and scientists because it was siloed throughout the organization and difficult to prepare. With Trifacta, researchers are able to prepare R&D data themselves, which has dramatically reduced analysis cycles, improved drug performance outcomes, and broadened their understanding of data analytics. To learn more about GSK’s use case, click here

Each organization will have a different answer to the question “what is data analytics,” given that each organization has different data and different priorities. But no matter the objective, data preparation is essential. To learn more about how you can use Trifacta for your data analytics initiative, schedule a demo of Trifacta today.