Data Analytics

Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is sometimes also referred to as data analysis.[1]

Data analytics is the pursuit of extracting meaning from raw data using specialized computer systems. These systems transform, organize, and model the data to draw conclusions and identify patterns. While data analytics can be simple, today the term is most often used to describe the analysis of large volumes of data and/or high-velocity data, which presents unique computational and data-handling challenges. Skilled data analytics professionals, who generally have a strong expertise in statistics, are called data scientists. The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of big data cost-effectively. While data warehouses are certainly a relevant form of data analytics, the term data analytics is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.[2]

Types of Data Analytics[3]
There are four types of Data Analytics as illustrated in the figure below

Types of Data Analytics
source: Principa

Big Data Analytics - How it Works and Key Technologies[4]
There’s no single technology that encompasses big data analytics. Of course, there’s advanced analytics that can be applied to big data, but in reality several types of technology work together to help you get the most value from your information. Here are the biggest players:

  • Data management. Data needs to be high quality and well-governed before it can be reliably analyzed. With data constantly flowing in and out of an organization, it's important to establish repeatable processes to build and maintain standards for data quality. Once data is reliable, organizations should establish a master data management program that gets the entire enterprise on the same page.
  • Data mining. Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions. With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.
  • Hadoop. This open source software framework can store large amounts of data and run applications on clusters of commodity hardware. It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop's open source framework is free and uses commodity hardware to store large quantities of data.
  • In-memory analytics. By analyzing data from system memory (instead of from your hard disk drive), you can derive immediate insights from your data and act on them quickly. This technology is able to remove data prep and analytical processing latencies to test new scenarios and create models; it's not only an easy way for organizations to stay agile and make better business decisions, it also enables them to run iterative and interactive analytics scenarios.
  • Predictive analytics. Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It's all about providing a best assessment on what will happen in the future, so organizations can feel more confident that they're making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, risk, operations and marketing.
  • Text mining. With text mining technology, you can analyze text data from the web, comment fields, books and other text-based sources to uncover insights you hadn't noticed before. Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.

Data Analytics vs. Data Analysis[5]
The difference between data analysis and data analytics is that data analytics is a broader term of which data analysis forms a subcomponent. Data analysis refers to the process of compiling and analyzing data to support decision making, whereas data analytics also includes the tools and techniques use to do so.

Data Analytics vs. Business Analytics[6]
Business analytics and data analytics are often used interchangeably throughout the industry. There is great debate and much diversity of opinion about the distinctions because there is great similarity between the two functions. Both business analytics and data analytics involve the collection and analysis of data, and both conduct their analysis in order to improve an organization’s effectiveness and decision-making. However, business analysis focuses much more on the functions, processes, operations, and overall architecture of an enterprise. It is the practice of enabling change by identifying business needs and determining solutions to those business problems. Business analysts work across all levels of the organization and can be found focusing on things such as strategic planning, translating project requirements, and developing policy all in an effort to support an organization’s ongoing technology and process maintenance. Data analysis focuses on the process of collecting, cleaning, analyzing, reporting, and presenting data. Analysts in this area break down the data and take the necessary steps involved in converting raw and messy information into clean and usable knowledge. Data analysts focus on the tools and statistical methodology and can often be found working with management, translating data, interpreting results, and testing hypotheses.

See Also

Data Mining
Data Management
Data Warehouse
Customer Data Management (CDM)
Big Data
Business Intelligence
Data Analysis
Data Cleansing
Predictive Analytics


  1. Definition of Data Analytics Techopedia
  2. What is Data Analytics Informatica
  3. What are the Different Types of Data Analytics Thomas Maydon
  4. Big Data Analytics - How it Works and Key Technologies SAS
  5. Data Analytics vs. Data Analysis GetSmarter
  6. What are the differences between data analytics and business analytics?

Further Reading