To handle this rush of information, business intelligence (BI) tools that specialise in data models like descriptive and predictive analytics have become an invaluable resource.

Adopting these types of advanced data analytics models can help to drive business growth. Descriptive and predictive analytics are of particular use in this area and we’re going to explore both types to show how they can be leveraged.

What is descriptive analytics?

Descriptive analytics is a data-driven approach that provides insight into the current state of your brand. It summarises existing and historical data e.g. social media usage from customers into an easy-to-understand format. This data may be presented as tables, charts or graphs.

The main aim of descriptive data analytics is to learn from the past and it acts as the starting point for preparing information for deeper analysis at a later stage.

How does descriptive analytics work?

Descriptive analytics uses two approaches - data aggregation and data discovery to unearth historical information. Data aggregation involves collecting and organising information into manageable sets, while the mining process using those sets to identify patterns that are presented in a simple format.

Pros of descriptive analytics

• Provide a historical context for your business information and understand how products and customers relate to each other
• Identify performance gaps and start to build a base for how to improve your processes
• Report on return on investment (ROI) through showing any key metrics that have been achieved

Cons of descriptive analytics

• The most basic form of advanced data analytics will only provide a limited viewpoint
• Further work must be done to get to the heart of how gathered data can be put to good use
• Domain experts input will be needed to understand variables not included in the data set.

What are predictive analytics?

Predictive analytics is a forward-thinking kind of data model that focuses on predicting what will happen in the future. Through analysing past data patterns and historical information, predictive analytics can help to shape your growth strategy and optimise different areas of your business.

An example of predictive analytics is if an IT security company needed to identify possible security breaches. By examining previous breaches, a predictive analytics model would be able to forecast solutions and make security protocols more effective.

How does predictive analytics work?

As predictive analytics is based on probabilities, there are several techniques used in the process. This ranges from data mining to machine learning algorithms to forecast possible outcomes and the likelihood of multiple events.

A practical example is machine learning algorithms using existing data sets to fill in data gaps with educated guesses.

Pros of predictive analytics

• It’s a highly scalable data model solution that can be understood by everyone in a business
• Predictive analytics is compatible with a self-service structure and machine learning can be used to develop new insights
• It’s a versatile data analytics model that can be applied across multiple industries such as eCommerce, healthcare and finance
• Decision-making can be automated to improve internal processes and reduce the need for human involvement

Cons of predictive analytics

• The probability-based approach means the methodology will never be 100% accurate
• Customer behaviour is always changing, and the model may not always be able to provide fast updates

Closing thoughts

Now that you know the processes behind descriptive and predictive analytics it’s important to ask the question as to what the most suitable approach is. The answer is both.

Descriptive analytics helps to form the basis of successful predictive analytics and they go hand in hand to future-proof businesses.

At Panintelligence, we’re experts in advanced data analytics, our software can help to take your organisation to the next level. Book in for a free demo today to find out more.