What Is Data Analytics? 4 Types of Data Analytics Defined

As a field, data analytics is a branch of data science prominently used in business operations to draw conclusions from complex data to facilitate decision-making. Data analysts use their skills in computer science, business analytics, and statistical analysis to help their employers increase business intelligence about data and thus make informed decisions about a variety of subjects.

The use of data analytics leads to better business decisions on targeting demographics, analyzing customer trends, discovering important correlations in historical data sources, designing marketing campaigns, and determining future outcomes in general. It can provide a real-time and granular look at how a business can better excel in the future and why it has struggled in the past according to a host of different metrics.

Data analytics can help you analyze past outcomes or possible future outcomes within a set of certain parameters. Here are the four main types of data analytics:

1. 1. Descriptive analytics: Descriptive analytics take a past dataset and explain what happened and why in laypeople’s terms. This process is often used by businesses looking to understand how, rather than why, they performed a certain way.
2. 2. Diagnostic analytics: Diagnostic analytics look backward to understand how or why a business either exceeded or failed to meet its goals. These analysts comb through data to see where the points of strength and weakness were.
3. 3. Predictive analytics: Predictive analytics involve the use of predictive modeling to determine how businesses or projects will perform in the future if nothing changes about their current strategies.
4. 4. Prescriptive analytics: Prescriptive analytics advise as to how a business can perform better in the future on any given metric. This advanced analytics type is perhaps the most complex of the four main types of data analytics.

While data analysis is complex, there are four general areas in a core dataset analytics program. These analytics techniques are the cornerstones of the data analysis process and can occur in this order or simultaneously:

1. 1. Data mining: The process of ushering large amounts of data into a more accessible, concise form is data mining. It’s important to ensure the raw data quality is sound before combing through large datasets—if the jumping-off point doesn’t contain what you need, there’s no reason to go through it in the first place. Once you’ve determined what quantitative data points you need for a given project, you can begin the digital transformation of mining it from the vast amounts of unstructured data you’ve compiled.
2. 2. Data management: Data management is largely an exercise in building interactive data warehouses (easily searched and understood databases). Managing these warehouses means keeping them from becoming too overcrowded or indiscernible, as well as ensuring everyone who needs to access them for information can do so at any time. These databases are often maintained in the SQL (structured query language) programming language.
3. 3. Statistical analysis: Once you condense datasets and make them into interactive warehouses, real analysis can begin. Depending on the data and goal of analysis, you can use algorithms to comb through and analyze, for example, how an e-commerce marketing strategy is performing; the ways automation could help or hurt retailers; areas where healthcare is inaccessible; how a new product is performing in focus groups across the country; or something else entirely. Big data analytics tools allow for a broad look at these sorts of business questions.
4. 4. Data visualization: Ultimately, data scientists and analysts must present their findings visually to other business analysts at their companies who may not be as well-versed in technology and computer science. Data visualization usually takes the form of slideshow presentations in which the hard data winds up in an easy-to-understand, graphed-out format.

The importance of data analytics is incalculable to the future of work in general. Here are three things you can do if you’re interested in the field:

1. 1. Learn programming languages. While data analytics isn’t as code-focused as software engineering, it still requires a robust understanding of how to think like a programmer and write instructions a computer will understand. Python and R are the two most prominent coding languages used in data analytics, so learning both would give you a head start in your studies.
2. 2. Educate yourself on statistics. Statistics form the backbone of data analytics—the technological tools are just a sophisticated, far-reaching means to perform statistical work. Understanding statistics and how you can use them to gain important insights about any given subject is fundamental to data analysis.
3. 3. Study other necessary technologies. Whether through online courses, a certificate program, or a more traditional university education, it’s important to keep abreast of all the other technologies that go into modern data analytics. Artificial intelligence, natural language processing, and machine learning are all subjects you’ll need to deeply understand to succeed as a contemporary data analyst.

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