Data Analytics

What is 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 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 is 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, but 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 the best assessment of 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 in 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 used 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 analytics encompasses a broad spectrum of techniques and processes aimed at enhancing business performance by converting raw data into meaningful insights. It involves the systematic computational analysis of data or statistics, enabling organizations to make data-driven decisions.

  • Descriptive Analytics: Discussing the use of data to understand past and present trends and performances. This type of analytics helps businesses understand what has happened and what is happening in their operations.
  • Predictive Analytics: Focusing on forecasting future probabilities and trends by using statistical models and machine learning techniques on historical and current data.
  • Prescriptive Analytics: Covering analytics that uses optimization and simulation algorithms to advise on possible outcomes and answer "What should we do?" It aims to prescribe actions based on predictive analytics findings.
  • Data Mining: Exploring the process of discovering patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the Internet, and other data repositories.
  • Big Data: Discussing the technologies and challenges associated with analyzing extremely large datasets. Big data analytics leverages advanced computing techniques to process and analyze big data.
  • Machine Learning: Covering algorithms and statistical models that computer systems use to perform tasks without explicit instructions, relying on patterns and inference instead. Machine learning is a key tool in advanced analytics techniques.
  • Data Visualization: Discussing the graphical representation of information and data. Using visual elements like charts, graphs, and maps, data visualization tools help to understand trends, outliers, and patterns in data.
  • Business Intelligence (BI): Focusing on technologies, applications, strategies, and practices used to collect, integrate, analyze, and present business information. BI and data analytics often work hand-in-hand to support decision-making processes.
  • Statistical Analysis: Exploring the collection, analysis, interpretation, presentation, and organization of data using statistical methods and techniques. This includes hypothesis testing, regression analysis, and variance analysis.
  • Data Governance: Highlighting the management of the availability, usability, integrity, and security of the data used in an organization, including policies, procedures, and technologies used to manage and protect data.
  • Cloud Analytics: Discussing the use of cloud services for performing data analytics. Cloud analytics platforms can offer scalable resources for processing and analyzing large datasets.
  • Ethics in Data Analytics: Covering ethical considerations in data analytics, including data privacy, confidentiality, and the responsible use of data analytics to make decisions that affect individuals and communities.


Further Reading