A 2023 Primer on Data Analytics

Part I: The types

This is the first of an article series on where data analytics stands today and what to look forward to in the coming year

Data analytics is essentially the application of quantitative technologies to data to find trends and solutions. As data volumes grow exponentially, data analytics allows organisations to analyse volumes of data and expedite decision-making processes. However, as tech news conglomerate VentureBeat puts it:

“Within the technical and business realms, however, “data analytics,” especially, has taken on a narrower and more specific meaning. It has come to describe the newer, algorithmic analysis of “big” and often unstructured datasets that go beyond, for example, the financial and entity-based business records that have long informed traditional business intelligence (BI) and analysis.”

The need for it

Data analytics generally tends to be predictive in nature, enabling many new capabilities such as the iterative refinement of algorithms for Machine Learning, driving much of AI development. A recent survey from the International Data corporation (IDC) found that average spending on big data and analytics hit over $215 billion in 2021, with organisations using it usually seeing almost a two-and-a-half times improvement in business outcomes as compared to those who do not.

Firms today are bringing in data managers, setting new policies and using different tools and solutions to collect and store huge amounts of unstructured, semi-structured or structured data flowing in from a plethora of sources within and beyond their organisations.

The goal is to drive value – and that is easier said than done. Large swathes of data are usually compiled in raw form that don’t provide any value without a fair bit of processing. Analytics is thus crucial in drawing insights from all this raw data and thereby, power business decisions.

VentureBeat writes:

“data analytics is usually performed by data analysts (and sometimes data analytics engineers). They look at the entire jigsaw puzzle of data, make sense of it (through cleaning, transforming, modelling) and eventually identify relevant patterns and insights for use by the company. They may also create dashboards and reports that less technically trained business analysts use in their work.”

The Big Four

The major types of data analytics in use today (and how each can be deployed in a model corporate finance department, for example) are as follows:

• Descriptive analytics: This enables organisations to understand their past. By gathering and visualising historical data to answer questions such as what happened and how, giving them a way to measure the effect of their decisions to outcomes at the organisational level.

In the corporate finance or business intelligence unit, it might inform internal quarterly or monthly sales or profit reports across divisions, geographies, product lines etc.

• Diagnostic analytics: VentureBeat writes, “while descriptive analytics provides a baseline of what has happened, diagnostic analytics goes a step further and explains why it happened. It explores historical data points to identify patterns and dependencies among variables that could explain a particular outcome.”

This might dissect the impacts of local economics or taxes, currency exchange etc., based on geographic regions.

• Predictive analytics: This uses the path set forth by descriptive analytics to tell us what  is likely to happen in the future – such as by using historical trends to forecast the business outcome of raising the price of a product. This involves predictive modelling, data mining, statistics and advanced analysis.

Predictive analytics could incorporate forecasted economic and market-demand data by product line and region to predict sales for the next month or quarter.

• Prescriptive analytics: Prescriptive analytics pushes the envelope one step further and uses machine learning to provide organisations’ suitable recommendations for better operations, higher sales and more revenue generation.

Prescriptive analytics could generate recommendations for relative investments in production and advertising budgets based on region or product lines for specified time periods.

[Read further parts for the seven key models of data analytics and the key maturity stages of automation and AI]

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