Data Analytics helps to uncover hidden patterns, correlations and insights which we may be otherwise overlook by businesses.
By Arun Gupta
So what is data analytics all about? To put it in simple terms, it is examination of large amounts of data to uncover hidden patterns, correlations and insights which we may otherwise overlook. With the amount of technology at our disposal nowadays, the analysis of data is much more smoother, faster and efficient.
The history of data analysis dates far before we had computers at our disposal. Businesses used the large amount of numbers in spreadsheets to make forecasts and analyse trends.
Some of essential requirements of data analysis in this day and age are cost reduction, faster as a well as better decision making and cost-benefit analysis for launching new products and services.
As 2018 is drawing to a close, let’s take a look at the top five data analysis trend.
1. Internet of Things (IoT)
It is rapidly becoming backbone of customer value with Amazon Alexa and Google Assistant becoming part of modern-day households — slowly but surely. Marketeers are not eyeing these agents to get in touch with their potential customers. Latest predictions indicate that this market will grow from $170.57 billion in 2017 to $561.04 billion by 2022 at a Compound Annual Growth Rate (CAGR) of 26.9 per cent. Amazon Echo and Google Home devices are increasingly becoming part of most households now. With new innovations coming in most of these devices, using them for analytic and marketing purpose is becoming essential now.
2. Hyper Personalisation
Putting it in simple terms, it refers to creation of target-specific messages to bond and connect with a group of people out of the overall audience. Earlier, companies used to prefer messages meant for a broad spectrum of individuals to increase their outreach, rather than a more focused approach. The ‘Hyper Personalisation’ concept focuses on what individuals want and E-commerce giants will look to focus their brand marketing through this.
According to Google, ‘best’ search phrases have increased by 80 per cent in the past 2 years on mobile devices. People are researching online through apps to make more informed decisions as user engagement with content has also gone down by nearly 60 per cent with overload of information at their disposal.
Top brands like Amazon and Flipkart have moved to a stage of predictive personalization, where AI and machine-learning analyse a bunch of factors to power their recommendation engine.
3. Augmented Reality
It refers to a more enriched version of reality with the use of live direct or indirect views of physical real-world environments augmented with superimposed computer-generated images over a user’s view of the real-world. We all remember how popular Pokemon Go became in a really short span of time. Imagine using that same technology for marketing and business analytics purpose.
It is a increasingly becoming powerful and useful tool. Companies are developing AR apps rapidly with the launch of Apple ARkit and Samsung and Google coming out with the same. Development and growth of Google’s Tango will further boost this segment.
4. Behavioural Analytics
This technique refers to the study of consumer behaviour, trying to decipher ‘what they do’ and ‘how they act’. It aids the businesses in detecting about what their customers desire and how they might be likely to act in the future. Behavioural analytics goes beyond tracking people. Analysing the interactions and dynamics of various processes, machines and equipment — which includes even macroeconomic trends — bring the benefit of new conceptions of operational risks and opportunities, which makes this field a bit more complicated.
Some even refer to it as a branch of science that seeks to understand the behavior of individuals. Behaviour analysts study how biological, pharmacological, and experiential factors influence the behaviour of humans and non-human animals.
5. Graph Analytics
It is a tool that makes the use of graphs to analyse, codify, and visualise links between databases or devices in a given network. It is not a substitute for the classical relational database technology but rather an addition to it. Businesses are utilising the graphs analytics more and more as they face difficulties in their current data analysis set up. These difficulties are often termed a ‘Forbidden Queries’ as they are difficult to solve or resolve. This method of technique is used for detecting crimes, applying influencer analysis in social network communities, or while conducting medical research and bioinformatics.
It helps in detection of financial crimes such as money laundering, Spotting fraud, which applies to fraudulent transactions and applications in banking, benefits fraud in government, applications and claims fraud in insurance and fraudulent activities in telecommunications, preventing crime and performing counter terrorism and even applying influencer analysis in social network communities.
This method of analysis is also helpful in performing grid and network quality of service such as identifying weaknesses in power grids, water grids and transportation networks as well as helping prevent cyber crime attacks on computer network.
The write is CEO of MoMAGIC Technologies.
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