5 Data Analytics Trends to Watch Out for in 2020
The end of the year throws up the inevitable question – which trends in data analytics shall emerge or be prevalent the next year?
There’s little doubt that data and its analysis made even more gains in 2019 compared to the previous years. Not only did we see some new technologies being introduced, older tech, too, saw rapid advances to make them more mainstream.
We analyzed some of the anticipated trends reported in ‘Analytics Trends 2020’ reports released by Gartner, MicroStrategy, and the like, to find common ground. Our in-house team of experts then added its own predictions to this mix, based on parameters such as client queries and interactions with leads and customers.
Based on all this, we present to you the Express Analytics “Top 5 Data Analytics Trends For 2020”.
The ranking is in no particular order of priority:
1) Natural Language Processing
A sub-discipline of artificial intelligence (AI) that helps machines understand and interpret the language of humans, NLP is a way to bridge the communication gap between man and machine. In 2019, we saw an increase in the use of NLP in text analytics, so also sentiment analytics. This form of analytics groups words into a defined form before extracting meaning from the text content.
With more and more voice search coming into play, in 2020, NLP will be used to make sense of “voice” in interfaces such as digital voice assistants, or smart speakers like Amazon’s Alexa. Gartner, for example, in its 2020 trends report has forecast that by 2021, NLP and conversational analytics will boost analytics and business intelligence adoption from 35% of employees to over 50%, including new classes of users, particularly front-office workers.
The flow of data in enterprises will keep increasing, creating vast pools of unstructured data. To make sense of all this unstructured data, they will require NLP for it gives computers machines the wherewithal to read and obtain meaning from human languages.
2) Embedded Analytics
MicroStrategy’s “Top 10 Enterprise Analytics Trends to Watch in 2020” report says advances in low-code and no-code development mean the adoption of next-gen embedding of analytics on devices and interfaces for faster analytics and real-time decision making.
What are the positives of embedded analytics? It increases the reach of a company’s analytical process internally, and also the ability to analyze in near real-time. Embedded analytics, unlike the traditional model of BI, is proactive. It makes data analytics more consumable, meaning, it does not leave analytics to the motley group of data scientists or BI analytics but makes it available to more employees, allowing them to take business-related decisions, faster.
Two other areas where embedded analytics can make a significant impact is:
- Consistent business decision-making
- Anticipating problems before they occur
3) Blockchain Analytics
Blockchain tech tackles two issues in data and analytics. It offers the lineage of assets and transactions, and also brings in transparency throughout the supply chain and its participants. Blockchain is gradually transforming the way many industries are operating today. Since both, blockchain and analytics deal with data, there’s bound to be an intersection of these two technologies. While data analytics is about quantity, blockchain is about quality. Coupled with data analytics, the use of blockchain analytics in handling dirty data is all set to go up in 2020 across sectors such as banking and insurance.
Use in predictive analytics: Blockchain data reveals insights into the behaviors, trends, etc, and so can be used to predict future outcomes. This coupled with its distributed nature and the vast computational power it offers, analysts in even smaller enterprises can undertake predictive analysis. How? By using the computational power of several thousand computers connected to a blockchain network.
4) Operationalized ML
Customers leave behind copious amounts of data while they go about shopping. Making sense of that data and reacting in real-time are the two things that keep companies one step ahead of their customers (and competition).
Companies need to continuously reinvent themselves vis-à-vis Customer Experience (CX) to understand and serve customers better. Machine learning techniques take the guesswork out of customer engagement. AI, more specifically, machine learning (ML) technology has made it possible not only to test more but to test infinitely faster than traditional methods.
Understanding the potential offered by AI and its sub-disciplines, the commercial application of ML went up in 2019. This growth of ML solutions has created a demand for ready-to-use machine learning models that can be used easily and without expert knowledge.
The new use of commercial AI and ML will help to increase the deployment of models in production, which will drive business value.
5) Data-centric Enterprises
First, there was Big Data, then there was data analytics. In 2019, we saw the first signs of organizations turning into a data-centric enterprise, a trend that is most likely to continue into 2020.
In this way of doing business, data is considered an asset as tangible as a company’s hardware or its headquarter building. It is at the heart of the Enterprise’s operations; in fact, the entire IT and business architecture is built keeping in mind the fact that data is a prime and permanent asset.
A simple, non-technical explanation of data-centricity can be – moving away from an application-centric to a data-centric way of doing business.
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