As you can imagine from the title, in this post we are going to explore the different types of Big Data Analytics, what they consist in, how they can be used in the market, and how specific businesses are currently using them. All this information will give us an insight into which type is best suited to specific businesses.
|Figure 1: Analytics.|
1) Descriptive Analytics
- Aim: To identify the causes that led to success or failure in the past in order to understand how they might affect the future
- Based on: Standard aggregate functions of the database. They require a basic level of mathematics
- Examples: This type of analytics is often used for social analytics and is the result of basic arithmetic operations such as average response time, page views, follower trends, likes etc
- Application: Using tools such as Google Analytics to analyze whether a promotional campaign has worked well or not, with the use of basic parameters such as the number of visits to the page. The results are usually visualized on “Dashboards” that allow the user to see real-time data and send the reports to others.
2) Predictive Analytics
- Aim: To identify the causes that led to success or failure in the past in order to understand how they might affect the future. This can be useful when setting realistic business objectives and can help businesses to plan more effectively
- Based on: They use statistical algorithms and Machine Learning to predict the probability of future results. The data that feeds these algorithms comes from CRMs, ERPs or human resources. These algorithms are capable of identifying relationships between different variables in the dataset. They are also capable of filling gaps in information with the best possible predictions. However, despite being the “best possible”, they are still only predictions
- Examples: One usually uses this type of analytics for Sentiment Analysis. Data enters a machine learning model in the form of plain text and the model is then capable of assigning a value to the text referring to whether the emotion shown is positive, negative or neutral
- Application: Often in the financial sector in order to assign a client with a credit score. Retail companies also use this type of analytics to identify patterns in the purchasing behavior of clients, to make stock predictions or to offer personalized recommendations
3) Prescriptive Analytics
- Aims: Prescriptive analytics don’t just anticipate what is going to happen, and when, but can also tell us why. Further still, it can suggest which decisions we should take in order to make the most of a future business opportunity or to avoid a possible risk, showing the implication of each option on the result.
- Based on: This type of analytics ingests hybrid data; structures (numbers, categories) and unstructured (videos, images, sounds and text). This data may come from an organization’s internal sources, or external ones such as social networks. To the data, it applies statistical mathematical models, machine learning and natural language processing. It also applies rules, norms, best practices and business regulations. These models can continue to collect data in order to continue making predictions and prescriptions. In this way, the predictions become increasingly precise and can suggest better decisions to the business
- Examples: Prescriptive analytics is useful when making decisions relating to the exploration and production of petrol and natural gas. It captures a large quantity of data, can create models and images about the Earth’s structure and describe different characteristics of the process (machine performance, oil flow, temperature, pressure etc). This tools can be used to decide where and when to drill and therefore build wells in a way that minimizes costs and reduces the environmental impact.
- Application: Health service providers can use such analytics to: effectively plan future investments in equipment and infrastructure, by basing plans on economic, demographic and public health data; to obtain better results in patient satisfaction surveys and to avoid patient churn; to identify the most appropriate intervention models according to specific population groups. Pharmaceutical companies can use it to find the most appropriate patients for a clinical trial.