While predictive analytics, descriptive analytics continue to disrupt industries, companies will soon adopt X- analytics practices

Organizations of each technical field and non-natives are producing more data than ever before. In today's data-driven world, if the data is analyzed using right tools, the generated insights can help fact-based decision-making. This means analytics is something that every manager, business leader or in fact anyone who works in data-based industry should be aware of. And by industry, analytics is not limited to IT operations but is also leveraged in healthcare, sports, and other relevant sectors. Not only that, they must also familiarize themselves with upcoming data analytics tools and trends, e.g. X analytics.

In the Gartner Top 10 Trends in Data and Analytics for 2020, the analyst firm mentions about 'X analytics' which is primed to gain more traction in coming years. Gartner defines this as an umbrella term, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc. Soon, global leaders will be employing X analytics to solve toughest challenges of the world, including climate change, disease prevention and wildlife protection. Gartner also mentions that when combined with AI and other techniques such as graph analytics (another top trend), X analytics will play a key role in identifying, predicting and planning for natural disasters and other business crises and opportunities in the future.

Existing Data Analytics Tools

Last year, the pandemic acted as enabler and catalyst for organizations to utilize data analytics capabilities to a plethora of reasons. For business brands trying to learn more about their customers and find ways to be more efficient in offering personalized programmes, analytics processes like sentiment analytics, predictive analytics came to rescue to help brands understand the changing demands and expectations. In other sectors too, analytics enabled forecasting demand, identifying potential supply-chain disruptions, targeting support services to at-risk workers, and determining the effectiveness of crisis intervention strategies and more. Even epidemiologists and healthcare officials used analytics to understand the spread of coronavirus, identify emerging hotspots, vulnerable populations and trace infection waves, among many others. Location analytics helped with contact tracing and contextualize specific figures pertaining to sales, logistics and supply chain, and measure location-wise success rates of marketing campaigns.

New data analytics patterns also came into light. E.g. some enterprises pivoted towards descriptive analytics over predictive analytics, as the former offers better data insights about the present and recent past. It was also a good year for cloud analytics, as more and more organizations switched to cloud amid pandemic emergencies. Diagnostic analytics become more popular in retail and healthcare industries, as it provided in-depth insights into a particular problem countered by the stakeholders. Further, video analytics went mainstream since it offers real-time update about the subjects during mass surveillance programs, which were instituted to prevent coronavirus infections, track shipment at logistics center and more.

The Future: X analytics

No wonder that the market demand and use cases for analytics will rise in coming years, while new analysis tools will emerge. X analytics that encompasses varied formats of data types, will allow organizations to extract value from all data types, compare the old dataset against the new ones to understand how behavior has changed, what patterns have remained, and how to capitalize on these shifts. When used in conjunction with other analytics methodologies like predictive analytics, descriptive analytics, X analytics will reap enormous benefits. By mining insights from all data types, X analytics will augment the capability to extract maximal value based information from all touchpoints. However, before that, data professionals must develop models that will enable all data types to talk to each other and come together to provide the end-to-end analysis.