Data Analytics can help you better understand the various parts of your business so you can make more informed and thus better decisions. In this context, understanding the various parts of your business could include changing the business strategy, or improving the business model or processes. It could also mean understanding and managing the risk associated with the business, and in doing so, future proofing the business thereby ensuring its sustainability.
Descriptive Analytics uses data aggregation and data mining to provide insight into the past and answer the question: 'What has happened?' Descriptive analytics includes the collection, cleansing, organisation, analysis, interpretation, and presentation of data in order to summarise and present historical information, find answers to difficult questions, and facilitate knowledge discovery. According to Information Week, more than 80% of data analytics is descriptive.
Examples of descriptive analytics include:
- Data Mining – Taking one or more large data sources and applying a range of statistical techniques to determine patterns and relationships that have a direct impact on sales, marketing, business, or financial performance.
- Text Mining – Extracting unstructured text and classifying and converting it into statistical data so it can be used to find patterns and trends that can be used to drive sales, marketing, business, or financial performance.
- Social Media Mining – Monitoring and obtaining data from social media channels and converting it into statistical data to determine the customer sentiment towards your brand, so you can manage your reputation effectively.
- Infographics – Developing a simple and visually powerful representation of information, data, or knowledge that is quickly digestible and easy to share. We partner with graphic designers to complete the design work.
- Interactive Dashboards – Using business intelligence software to develop a simple, powerful, and historical visual summary of an organisation’s key performance indicators or other data in an easily accessible location.
- Geographic Mapping – Plots static or dynamic geographical location data and other business related metrics and information onto national or international maps to visualise relationships and trends in your customer data.
Predictive Analytics is the next step up in data reduction. Predictive analytics uses statistical models and forecasting techniques to understand the future and answer: 'What could happen?' It utilises a variety of statistical, modelling, data mining, and machine learning techniques to study recent and historical data, thereby allowing analysts to make predictions about the future. Predictive Analytics tries to predict the probability of an outcome given a set amount of input data. Eric Siegel, author of Predictive Analytics, says "Predictive Analytics combats risk, boosts sales, cuts costs, fortifies healthcare, streamlines manufacturing, conquers spam, toughens crime fighting, optimises social networks, and wins elections."
Prescriptive Analytics uses optimisation and simulation algorithms to recommend possible outcomes and answer: 'What should we do?’ Prescriptive Analytics supports decision makers by solving their toughest strategy, planning and scheduling challenges. It seeks to prescribe one or more courses of actions and the likely outcome of each action.
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