Explainer Series: Data Analytics

What Is It?

Data analytics describes the process of examining datasets to draw conclusions about the information they contain. Data is aggregated, categorised, extracted, and analysed to identify meaningful behavioural patterns and trends. From there, identified patterns and trends are interpreted and turned into actionable insights.

When Was It Invented?

In antiquity, ancient civilisations developed a primitive form of data analytics; the abacus. Modern data analytics (Analytics 1.0) began appearing in the 1950s in the form of descriptive analytics and activity reporting. It wouldn’t be for another 50 years before the next iteration of data analytics (Analytics 2.0), database information processing services and products would emerge.

How Does It Work?

Before analytics can be applied, organisations aggregate and categorise raw data associated with fields (among many) such as customers, business processes, or market economics. Once prepared, algorithms are programmed and applied to the datasets searching for meaningful data points correlations. Correlations are reported and findings turned into actionable insights used to drive optimal results.

1. Collate and organise data.
2. Apply analytics to data.
3. Interpret data and offer insights.

Numbers & Statistics

By 2020, Forbes estimates 1.7 megabytes of new information will be created every second for every human being on the planet. Over the last two years, we have created more data than in the entire history of humanity. Worldwide, data is experiencing an astronomical growth rate; 40 per cent per annum. McKinsey found companies that put data at the centre of marketing and sales improve marketing return on investment by 15-20 per cent which globally, results in $150-$200 billion of additional value on a marketing spend of $1 trillion.

Pros & Cons

Knowledge is power and in business, an organisation’s database is its power. Applying analytics to datasets allows organisations to make real-time insights, correct observable errors, track competitor movements, make improvements to service and production items, detect fraud, and offer in-depth sales analysis. Conversely, data analytics opponents query the tool’s long-term effectiveness pointing to potential problems in with software capabilities outgrowing computing power, large scale data flow and logistics redesign, input error and database corruption, system complexity, and data privacy.

The Future

Futurists offer a mouth-watering glimpse into our potential future. Trains travelling faster than 1,000 km/h, autonomous vehicles, wearable technology, augmented reality, floating farms, the growth of biofuels, cryptocurrencies and blockchain, computerized medicine, and the explosion of private enterprise space exploration are just a few possibilities. Our technological future requires more than just binary code; it requires well designed systems

Final Thoughts

We live in a time of immediacy; I want it and I want it now. Informed speed is of the essence, the difference between securing or losing patronage. Formerly inanimate objects are being turned into data gathering devices, digital ecosystems developing before our eyes. As digital progresses toward achieving economies of scale, humanity’s desire for information and our infatuation with numbers commands attention. Our increasingly connected world requires rich and informed insights. Data analytics provides such a platform.