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 and Prescriptive Analytics
Predictive Analytics brings together advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, optimization, real-time scoring and machine learning. These tools help organizations discover patterns in data and go beyond knowing what has happened to anticipating what is likely to happen next.
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Prescriptive Analytics provides organizations with recommendations around optimal actions to achieve business objectives such as customer service, profits, and operational efficiency. Prescriptive analytics solutions use optimization technology to solve complex decisions with millions of decision variables, constraints and trade-offs. They provide prescriptive insights whether you want to decide on a configuration, a design, a plan or a schedule.
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Machine Learning is a form of Artificial Intelligence that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine learning model is the output generated when you train your machine learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model.
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