Companies across the globe are awash with data.

According to a survey conducted by Harvard Business Review, 92% of respondents have increased their investments in big data.

Lying dormant within this data is enormous potential that can be leveraged for business growth with highly actionable, data-driven insights.

In this blog post, we bring to you the data analytics trends that will dominate 2020.

Augmented Analytics

Data professionals spend around 40% of their time gathering and cleansing data.

Augmented analytics democratizes the analytics process.

It helps you speed up data discovery and preparation, as well as reduce dependency on data scientists.

Augmented analytics is projected to drive new purchases of business intelligence, data science, and machine learning platforms.

Data as a Service (DaaS)

DaaS can be seen as a response to the growing quantity and variety of data that is being generated across verticals in the digital environment.

In fact, according to Mordor Intelligence, the DaaS market will expand to approximately 47 billion by 2025, from an estimated USD 26 billion in 2019.

Organizations are increasingly adopting DaaS to enhance strategic perspectives with valuable data insights.

Graph Analytics

Business owners come up with complex questions about the relationship between different data points.

This is where graph analytics comes in. It helps you solve queries more efficiently and provide results in an easy-to-digest visual format.

Graph analytics is becoming one of the most preferred methods of solving complex data relationships and will grow at a rate of 100% in 2020.

Persistent Memory Servers

Database management systems generally utilize in-memory database structures.

However, with increasing data volumes, server workloads require faster processor performance and quick storage capabilities.

With the help of persistent memory, you can drive click-through rates by providing a personalized web experience to customers.

It enables you to swiftly detect and deal with cyber threats for fraud detection by financial institutions.

Conversational Analytics and NLP

Gartner forecasts that by 2021, the adoption of conversational analytics and NLP will increase from 35% to 50%.

One of the difficulties involved in understanding customer needs is the inability of non-specialists to use data analytics tools with ease.

However, an analytics tool enabled with NLP can help you find and interpret data to better understand customer needs.

Conversational analytics and NLP have the potential to transform the manner in which we identify customer needs to provide a highly personalized online experience.

Commercial AI and Machine Learning

Innovations in algorithms and the development environment have primarily been driven by open-source platforms.

These platforms provide enterprise features to help scale project and model management, reuse, transparency, and integration capabilities.

With the increased use of commercial AI and ML, you can benefit from the rapid deployment of models in production and get business value from investments.

The Internet of Things Meets Data Analytics

A survey by Gartner shows that 80% of organizations making use of IoT data analytics have achieved ‘better-than-expected results’.

IoT-data analytics enables you to analyze large volumes of data generated by connected devices.

It helps you increase productivity, optimize and automate business processes, and boost customer engagement.

In fact, IoT-data analytics has already made successful inroads into retail, healthcare, manufacturing, and smart cities.

Use the Power of Data to Drive Business Success!