Top 7 Data Analytics and Management Trends for 2020

top 7 data analytics trends
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We live in an era of data as it lies at the heart of digital transformation. And datasets are no longer as simple as before. They have increased in volumes, velocity, complexity and above all, are coming from multiple sources. Top tech giants like Google, Netflix, Amazon, and others are crunching massive amounts of data on a daily basis to give you a personalized experience. The importance of this data crunching is evident from the fact that the power of personalization saves Netflix $1B per year.

And the future will only see more data in the digital world.

There will be an explosion of data as it will grow up to 175 ZB by 2025 from just 33 ZB in the previous year. That amounts to a CAGR of 61%. – IDC

IDC further suggests in a whitepaper that this datasphere will come from traditional & cloud data centers, cell towers, PCs, smartphones and the most recent addition, Internet of Things (IoT) devices.

But this deluge of data is not going to be of any use if it is not converted into business value. Digging through this data and uncovering actionable insights is not going to be possible without proper data management and analytics practices in place. So, let’s explore the latest data analytics trends so that your organization can be up to speed in 2020.

Trend 1: Augmented Analytics & data management

Gartner has identified augmented analytics as one of the top 10 technologies that have the capability to solve data & analytics challenges in the next three to five years. But what exactly is Augmented Analytics?

Augmented Analytics basically deals with the automation of data preparation, analytics, and insight recovery so that it can be translated into business value. It aims to remove manual processes and save a lot of time by tapping into the power of Artificial Intelligence and Machine Learning. What organizations will see with Augmented analytics is a complete overhaul of BI processes including data ingestion, insight generation, revealing correlations, and the user experience.

It comes as no surprise that by 2020, augmented analytics will be a leading factor behind new purchases of analytics, BI, and data science and machine learning platforms. – Gartner

Trend 2: Artificial Intelligence empowered data management

When we talk about data analytics trends, AI cannot be left behind. Data analytics and AI go hand in hand in utilizing advanced analytics and algorithms to optimize, automate, and extract business value at points where the human eye won’t be able to see. The integration of AI & analytics has various use cases.

Customer analytics by applying AI algorithms to data collected via chatbots, eCommerce platforms, social media platforms.

Decision automation augments current applications like ERPs and CRMs that are being widely used to manage business processes and combines it with the power of AI & ML. Decision automation uses RPA or robotic process operation to automate changes in these processes.

Edge computing is bringing IoT sensors closer to the data processing centers so as to reduce latency and improve efficiency especially in cases where time is a critical factor.

Trend 3: NLP and Conversational analytics

NLP along with Conversational analytics is what makes digital assistants like Amazon Alexa a reality in today’s world. Gartner predicts that by 2020, NLP & conversational analytics will hold more relevance as 50% of analytical queries will be generated via search, NLP, or voice. NLP make sure that queries are as easy as a Google search and that humans & systems can have more intuitive interactions. Conversational analytics further enhances this concept by allowing questions & queries to be asked via voice search. Both the concepts have a lot of importance in the future and hence, is a crucial mention while talking about data analytics trends in 2020.

Trend 4: Crowdsourced Data Management

The crowdsourcing model of data management replicates what Wikipedia did with the encyclopedia business and gave a complete overhaul of how we consume and share knowledge. It’s the best example of crowdsourcing as it kept the model open to the world for people to share what they wanted to. All this was made possible with minimum intervention & hierarchy and yet we have such a large repository of accurate information.

In Data Management, crowdsourcing has been made possible with the wave of the cloud computing model. Crowdsourcing in the cloud era, or cloud sourcing, is enabling us to connect mass information streams that collaborate in innovative ways. In the world of business, one good example is that vendors are making available templates for business decision-making. The information reserve condensed into these architectural reference models represents a new model for other customers in similar businesses to use and apply the same to their own business scenarios. 

Trend 5: Graph Analytics

Graph processing & databases will see a 100% increase in adoption annually through 2022 to expedite data preparation to make room for more complex & adaptive data science, says Gartner.

Graph processing simply gives you innovative ways to look at and derive insights from that data. It empowers you with the discovery of relationships between data points & entities like the organization, people, and transactions. Once these relationships are developed, graph analytics helps data scientists to create clusters of similar data points. This helps data scientists to develop and train complex models after which a user can directly interact with the graph elements to drive business insights for decision making. Graph technology allows the construction of richer semantic graphs or knowledge bases that can give a boost to augmented analytics models and enrich conversational analytics integrated with your organization.

Trend 6: Blockchain

A 2018 PwC survey of 600 executives revealed that 84% of the organizations were actively involved in Blockchain technology. The reason why you’ve heard Blockchain being associated with “cryptocurrency” is because security is a prime point of the technology. Blockchain can be understood as a distributed ledger or database which exists across the network as each node holds a copy of it. 

The information chunks are encrypted and act as a new “lock” to the sequence of historical records. Consensus protocols are instituted to validate new blocks of records which prevent frauds.

Blockchain strategies should be implemented with a structured approach keeping in mind the following –

  • Companies should take a pragmatic and meticulous approach to assess impact and feasibility at a fundamental level and identify true pain points with specific use cases that blockchain will solve.
  • Executives should capture the business value by designing strategic approaches to blockchain considering the market position,  measures like the capability to shape the ecosystem, lay down standards, and address regulatory barriers.

Trend 7: Persistent Memory Servers

As data expands in volume, velocity & variety conventional database management systems (DBMS) don’t suffice as they make use of in-memory database structures. This can pose problems of memory size restrictions when server workloads demand massive storage requirements along with faster processors. This is one of the key data analytics trends that will solve this problem in the near future.

Persistent memory servers work alongside DRAM to facilitate speedy, high capacity & cost-efficient memory & storage that transform big data workloads & analytics at never-before-seen speeds. It’s an emerging technology and in future, as costs go down, the industry will see more adoption of in-memory DBMS with the growth of persistent memory. 

Looking for Data analytics solutions relevant to modern times?

Organizations often struggle with the growing volume, variety, and velocity of the data, decisions inspired by data, and automating business processes with cutting edge technologies. Because of the quintillion bytes of data that these organizations have to manage, they often battle with a robust information management system that is relevant to their needs.

The Data engineering services at Knoldus precisely help you out at this point of your business journey. Our engineers remove the complexity while accelerating the performance of big/fast data and delivering the business value in the areas of risk mitigation, accelerated time to value, faster product launches, cost optimizations and increased developer velocity.

Get in touch with us to schedule a call with our expert or drop us a line at

Written by 

Niharika has around 3+ years of experience as a Content Professional. She loves writing about technology and the latest trends in the online world although her rich writing experience spans across diverse business domains. Apart from writing, she has also worked on YouTube audience growth strategies. An ambivert by nature, she likes to grab a snack with a warm cup of coffee accompanied by a good book or a close friend.