Big data analytics has revolutionized businesses of all sizes and industries over the past decade, allowing companies to organize huge stores of data, extract actionable insights, and push toward their loftiest business goals. If you’re a new entrepreneur or you’re moving into a professional niche that involves working with big data, you may be confused by some of the concepts and terms tied to the topic. Here’s an overview of what big data analytics is, the different forms it can take, and some examples of how it can be used in a business context.
What is big data analytics?
Big data analytics is the process of scrutinizing big data (large, varied data sets) with a view to uncovering patterns and correlations such as emerging market trends, consumer behaviour, and other findings that might be useful for a given business. Whether it’s conducted in-house or with the help of a big data consultancy, big data analytics always aims to give company leadership a more granular view of the business’s performance and operations and typically involves statistical algorithms, predictive models, and other functions powered by complex analytical systems. While basic data analytics can usually be handled in-house using standardized, user-friendly tools, big data consultants and professionals offer the expertise and know-how required for more innovative kinds of information processing. This enables them to tackle information assets that are either high-volume, high-variety, or high-velocity.
Types of big data analytics
There are four main types of big data analytics, each one used to deal with a different type of data: Descriptive analytics This type of big data analytics is widely adopted in marketing contexts and is effective at finding patterns within particular audience segments. Using descriptive analytics, a big data analytics company can help businesses better understand events and patterns that have happened in the past, see these elements in a higher level of detail, and make better decisions for the future. Common examples of descriptive analytics include clustering, basket analysis, and summary statistics. Diagnostic analysis Diagnostic analysis is used to find the root causes of a given problem, and give decision-makers key information they need to ensure a swift recovery. This form of big data analytics is widely used in e-commerce, as it can often provide an effective way to diagnose usage trends and churn indicators, helping managers make the right calls to maintain loyalty among their customer base. Some common examples of diagnostic analysis include data mining, drill-down, and churn reason analysis. Predictive analytics Predictive analytics, as the name suggests, is a form of big data analytics focussed on forming predictions about the future, such as consumer trends, sales trajectories, and incidents created by market forces. As predictive analytics can be applied to almost any area of business, it’s the most widely adopted and widely understood form of big data analytics. Predictive analytics draws on large sets of historical data to extract trends and predict how trends will continue into the future, offering major benefits to both the business and its consumers. Some common examples of predictive analytics include churn risk, next-best offers, and renewal risk analysis. Prescriptive analytics Prescriptive analysis is an uncommon, but hugely valuable form of big data analytics, which works in conjunction with predictive analytics to investigate the best course of action based on the results of the predictive analysis. Following predictive analysis, prescriptive analysis determines the optimal next move, helping businesses to manipulate future events and mitigate risks. While other forms of big data work can exist independently, prescriptive analytics combines data findings with various policies and processes set by the organization itself. One common example of prescriptive analytics is next-best offer analysis for customer retention.
