What is data analytics?

data analytics

Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialised systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organisations to make more-informed business decisions.

As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it’s similar in nature to business analytics, another umbrella term for approaches to analysing data with the difference that the latter is oriented to business uses, while data analytics has a broader focus.

Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimise marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals with the ultimate goal of boosting business performance. Depending on the particular application, the data that’s analysed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources.

Types of data analytics applications

At a high level, data analytics methodologies include exploratory data analysis (EDA) which aims to find patterns and relationships in data and confirmatory data analysis (CDA) which applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial.

Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive as it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view.

At the application level, BI and reporting provide business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers working in IT or for a centralised BI team. Now, organisations increasingly use self-service BI tools that let execs, business analysts and operational workers run their own ad hoc queries and build reports themselves.

More advanced types of data analytics include data mining which involves sorting through large data sets to identify trends, patterns and relationships; predictive analytics, which seeks to predict customer behavior, equipment failures and other future events; and machine learning, an artificial intelligence technique that uses automated algorithms to churn through data sets more quickly than data scientists can do via conventional analytical modelling. Big data analytics applies data mining, predictive analytics and machine learning tools to sets of big data that often contain unstructured and semi-structured data. Text mining provides a means of analysing documents, emails and other text-based content.  

Data analytics initiatives support a wide variety of business uses. For example, banks and credit card companies analyse withdrawal and spending patterns to prevent fraud and identity theft. E-commerce companies and marketing services providers do clickstream analysis to identify website visitors who are more likely to buy a particular product or service based on navigation and page-viewing patterns. Mobile network operators examine customer data to forecast churn so they can take steps to prevent defections to business rivals; to boost customer relationship management efforts, they and other companies also engage in CRM analytics to segment customers for marketing campaigns and equip call centre workers with up-to-date information about callers. Healthcare organisations mine patient data to evaluate the effectiveness of treatments for cancer and other diseases.

At PointStar, we provide BI services that help organisations optimise their capabilities to leverage various types of data and make it into something the business can use to make better and more informed decisions. We work closely with clients to assess their goals and challenges to ensure we deliver the most appropriate solution to meet their needs and provide a competitive advantage. From forecasting, strategy, optimisation and performance analysis to trend analysis, customer analysis, budget planning and financial reporting, we offer expert-level BI services. We use only proven open technologies to help businesses reduce costs and allow for easy integration with other platforms and applications.

https://www.point-star.com/solutions/big-data-analytics/

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