What are the five types of data analytics?Posted on: August 3, 2023
Business leaders are increasingly aware of the power and wisdom of data-driven decision-making.
In the right hands, data translates into valuable information that can be used to any business end – from spotting patterns in customer buying behaviour, to highlighting business opportunities, to optimising customer service experiences. In short, it enhances both short-term and long-term, micro and macro, business performance.
What is data analytics?
Data analytics is the process of analysing raw data in order to derive meaningful, actionable insights and knowledge.
It has the potential to inform and impact any number of business decisions – from strategy and product development to market positioning and customer engagement – helping leaders and managers to identify the best course of action in a given situation. The process involves the collection, modelling and analysis of information to extract the required insights, and can be used to investigate any issue for which data exists.
Data analysis generally falls into two main categories:
- quantitative data methods – numerical, measurable information
- qualitative data methods – non-numerical, descriptive information.
As well as facilitating better decisions, data analytics helps businesses in all manner of ways:
- It increases the efficiency of work and allocation of resources – including quickly analysing complex data sets and data sources to support business activities, increasing cross-team collaboration and highlighting areas for improvement
- It supports the personalisation of products and services – including using machine learning algorithms to make recommendations and provide targeted services based on individual customer preferences, demographics and requirements
- It improves the overall quality of products and services – including detecting and correcting errors, making appropriate changes to user experience and conducting automated data cleansing
- It allows businesses to understand their customers in greater depth and track real-time changes – including detecting shifts in customer behaviour in time for businesses to respond and remain competitive.
Big data analytics is also a gigantic, ever-growing market. As defined by IBM, big data uses advanced analytic techniques against very large, diverse data sets (including structured, semi-structured and unstructured data, from different sources) and in different sizes (from terabytes to zettabytes).
What are the five types of data analytics?
Data analytics, of all types, provide distinct and valuable insights – and can be applied to any data-related issue. Businesses employ the skills of data analysts and data scientists to help identify the right type of analytics to fit business needs.
Here is an overview of the five different types of data analytics:
Descriptive analytics focuses on examining what has previously occurred through the lens of both real-time and historical data inputs. The simplest form of analytics, descriptive analytics are regularly used across websites, social media platforms and other tools to perform tasks such as tracking sales leads, revenue generated by different audience demographics and other key performance indicators (KPIs) and metrics, as well as producing financial reports.
Diagnostic analytics focuses on understanding the root cause behind why something happened within a data set (in contrast to descriptive analysis which generally tells us what happened). It’s used heavily within business intelligence dashboards to uncover internal and external factors that affect the sector or industry, helping to provide answers to critical questions. In combination with descriptive analysis, it helps to spot relationships within data architecture and support decision-making ventures. For example, a human resources (HR) team might analyse applicants’ data sets, or a marketing manager might see if campaigns and other activities impacted sales figures.
Predictive analytics focuses on identifying trends, patterns and correlations in past data to predict future outcomes. It’s useful for anticipating changeable buying behaviours, predicting industry trends, and ascertaining the activities of competitors, as well as supporting sales forecasting, lead generation and risk assessment initiatives. It uses past data to build a recommendation-based, predictive model, leveraging machine learning and advanced statistics.
Prescriptive analytics focuses on discovering the best way of achieving a desired outcome, using a combination of descriptive, diagnostic and predictive analysis. It gives leaders oversight over all possible outcomes and resulting actions in order to help maximise business performance and output. Prescriptive analysis proves particularly useful during periods of instability, adversity and uncertainty, and helps with business cycle reports and marketing reports.
Cognitive analytics focuses on combining numerous technologies – from deep learning models to artificial intelligence to machine learning – to analyse information, draw inferences, and produce conclusions. Due to its advanced nature, cognitive analysis is capable of handling huge amounts of undistributed/parallel data in order to derive business insights.
For individuals hoping to harness data analytics for business advantage, Datapine’s 15 essential steps offers a helpful framework to drill down into the best statistical modelling approaches and specific data analysis techniques and requirements.
There are numerous analytics tools available. Common and popular software includes:
- Microsoft Excel
- Apache Spark
- Microsoft Power BI.
Data analytics in real-world practice: healthcare
While data and big data analytics proliferate across any number of global industries, they play a particularly critical role within the healthcare sector. It’s predicted that by 2025, the market for health-related analytics will reach $28 billion. Their role in supporting financial, operational and clinical performance is vital. Above all, the ability of analytics to enable professionals to arrive at better decisions can be – quite literally – a matter of life and death.
By leveraging data analytics, practitioners can detect anomalies in tests and scans, more accurately predict disease outbreak and spread, manage staffing, resource and infrastructure issues, and help evaluate and develop the work of healthcare professionals themselves. It facilitates data sharing between clinics and health specialists, designs effective, personalised treatment plans, can predict health outcomes and risks at population-level, and provides highly accurate, data-driven forecasts to enable providers to respond to rapidly changing environments and situations.
Crucially, by boosting business intelligence, data analytics can help to lower healthcare costs and increase patient safety, welfare, care quality and outcomes.
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