The Data Analytics Opportunity

John Eccles, 01 December 2021

“It is a capital mistake to theorize before one has data.” — Sherlock Holmes


The Age of Data

“Information is the oil of the 21st century, and analytics is the combustion engine.” — Peter Sondergaard, Senior Vice President and Global Head of Research at Gartner, Inc.

The economy of the US and other modern economies has been fuelled by the digital sector in the first two decades of the 21st century. Much of that growth has come from cloud computing which boosted business efficiency and enabled new ways of doing business.

But data is growing much faster than computing. Data has become the new enabler of business. Data is the basis of the breakthrough technologies of artificial intelligence (AI) and machine learning.

To thrive in this new age of data, every organization needs a complete view of its data. It needs real-time insights and the ability to take real-time action.

Data Analytics

Data analytics is the process of analysing data to answer questions, extract insights, and identify trends. It is done using computer skills, mathematics and statistics, descriptive techniques and predictive models.

Applying data analytics tools and methodologies in a business setting is typically referred to as business analytics. In this case, insights from data are used to recommend strategies or action or to guide decision making in the business context.

Types of Data Analytics

When we look at the four types of data analytics, we get a better view of the role of data analytics and how it can be deployed in business:

· Descriptive analytics

· Diagnostic analytics

· Predictive analytics

· Prescriptive analytics



The opportunities to utilise Data Analytics for organisational gain are limitless. Here are some important categories:

1. Inform decision-making

All types of data analytics can be used to guide business decisions. Descriptive analytics can provide a clear view of the current situation and trends over time. Diagnostic analytics can help understand why the organisation is where it is. Predictive analytics can suggest what could happen in response to possible changes, and prescriptive analytics can suggest how the organisation should act.

Take, for example, decisions about the product range or pricing.

· Harvesting of data from various sources, combined with data analytics can indicate when demand changes or price expectations move significantly.

· Changes to pricing or product offerings could be modelled to determine how those changes would affect customer demand.

· Changes to product offerings can be evaluated to explore different product features or prices.

· After collecting early sales data on the changed products or pricing, data analytics tools can be utilised to determine the success (or not) of the changes and to visualise the results to inform decisions regarding roll-out.

2. Improve the customer experience

Customers expect a relevant, seamless, real-time experience across all touch-points and for organisations to know them however they engage. Data analytics applied to customer data (customer analytics) makes that possible. With customer analytics we aim to create a single view of each customer or customer group so we can make decisions about how best to:

· acquire new customers;

· retain existing customers;

· identify high-value customers and

· proactively interact with customers.

Customer data may be available from many different sources, both internal and external. Information may be stored in internal systems such as CRM and ERP or may come from the e-commerce site and social media. By merging data from multiple sources and applying data analytics to all the data, customer profiles may be generated to provide insights into customer behaviour and a more personalised experience.

For example, a retail clothing business could analyse its sales data together with data from its social media pages to create a targeted social media campaign to promote products the customers are already interested in. They could run a predictive model on e-commerce transaction data to determine products to recommend at checkout to increase sales.

3. Streamline operations

Organisations can improve operational efficiency through data analytics. Here are some possibilities:

· Analysis of data from the supply chain could reveal the origin of production delays or bottlenecks and predict where future problems may arise.

· Data analytics could be used to optimise inventory, taking into account factors such as seasonality, holidays, and trends.

· The efficiency of field operations may be improved by data analytics to optimise deployment of fieldworkers to meet business needs and changing customer demands.

4. Mitigate risk and handle setbacks

Organisations face multiple risks including customer and employee theft, uncollected receivables, employee safety, legal liability and data breaches.

Deterrence of fraud requires mechanisms (including data analytics) that allow organisations to detect potentially fraudulent activity quickly, anticipate future activity and identify perpetrators.

Data analytics can help an organisation both understand the risks and take preventive measures. For example, a retail chain could model the risk for theft for each store. From this they could determine the amount of security necessary at the stores.

Data analytics can also be utilised to limit losses after a setback occurs. A business might over-estimate demand for a product and be left with excess stock. Data analytics could determine the optimal price at which to clear inventory.


There are many opportunities for data analytics to be used in virtually every organisation to make better decisions, improve customer experience, improve efficiency or reduce risk.