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OmniSci 2021 Predictions: 3 Data Analytics Predictions

vmblog 2021 prediction series 

Industry executives and experts share their predictions for 2021.  Read them in this 13th annual VMblog.com series exclusive.

3 Data Analytics Predictions for 2021

By Todd Mostak, CEO & Co-Founder, OmniSci

2020 has been a year of numbers- infectious rates, voting percentages, unemployment statistics, electoral college totals, vaccine shipments. So it's no surprise that data analytics has played a particularly prominent role in helping both decision makers and the general public alike make sense of the unprecedented events.

With COVID-19 vaccinations front-and-center in the early months of 2021, logistics and location-based analytics will be top of mind. Meanwhile, the growth of IoT, telematics, machine-to-machine communications and other innovations will continue to transform the world of data analytics. Here are three prominent developments in data analytics to expect in 2021:

Geospatial analytics will become a mainstream big data problem, and new tools will emerge to handle location data at scale. The world of analytics has always had a geospatial component, whether people realized it or not. Almost every type of dataset involves location, whether by store, destination, residence, manufacturing facility or some other physical anchor.

However, we're experiencing a complete breakdown in the siloes of what is geospatial and what isn't. The applications of geospatial analytics are no longer relevant to only specific domains in Geographic Information Science (GIS), but now are front in center from a diverse range of critical use cases such as retail customer analytics, 4G and 5G planning, pandemic management and forecasting, autonomous vehicle development, and just-in-time logistics route optimization, to just name a few.

However, these new use cases, mostly driven by location data emitted from sensors (including mobile phones), mean that geospatial analysis is quickly becoming a key part of big data analytics workflows. Unfortunately, legacy geospatial platforms are ill-suited and unoptimized to handle geo at scale, not to mention the breadth of data analytic use cases beyond geo. Hence we'll see an accelerated push by existing big data platforms and data warehouses to implement performant geospatial analytic functionality, as well as the emergence of new tools that are optimized for the unique challenges presented by geospatial data at scale, while also being able to power broader analytic workflows.

The year that AI becomes ubiquitous. 2021 will be the year that AI goes from a lot of talk to a lot of action. We're seeing an increase in new use cases for predictive analytics- an indication of what's to come in industries like operations, finance, customer demand, HR and overall business planning.

Enterprises are starting to leverage predictive data on a regular basis to make smarter decisions about resource application, trend spotting and competitive advantage. In the past, AI was a "promise" that, because it was expensive in terms of both hardware and response times, could only be used in specific instances. Organizations used it for niche use cases where they wanted to showcase what was possible, or on a small subset of data. That's not the case anymore.

Now the expectation is the ability to use AI across the entire business. That means organizations will have the software and the firepower to make all kinds of decisions-and accelerate their time to insight. AI is becoming even more relevant due to increasing hardware acceleration of not only deep learning, but also other common ML algorithms and even broader feature engineering workflows. A good example of this is NVIDIA RAPIDS framework, which provides a variety of GPU-accelerated ML models out of the box, plus accelerates common operations on the data frames that are fed to the algorithms. In the past, AI and machine learning workflows would have five or six steps to them, each of which required different tools and specialties and might require many hours to run. With new data science platforms that both unify and accelerate these key workflows, it's becoming easier and faster than ever to deploy ML within an organization. 

Analytics at the Speed of Curiosity. The world is moving quickly, meaning critical decisions need to be made in near real-time. The COVID-19 vaccine deployment is a perfect example.

Even after months of advanced planning, once the FDA gave the go-ahead for vaccine shipments there were delays, albeit minor ones. Logistics experts had to adjust in real-time to meet these incredibly important schedules that can change on a dime.

With these kinds of requirements around operational and decision making, agility will soon be the norm not the exception. Businesses won't have quarters, months, or even weeks to plan moves. Once a decision is made, the implementation of that decision must be rolled out quickly.  Technologies are there to have the whole solution mapped out in advance, based on data that you have and the trends that can be identified through AI and machine learning.

If organizations have the ability to quickly and interactively interrogate their data, leveraging the natural curiosity and intuition of their subject matter experts, they have a strong foundation for just-in-time decision making. Advanced analytics tools are making it possible, with just a few clicks, to separate the signal from the percentage of the noise in their data, and are providing early adopters a significant advantage in the market against their competition.

Long gone are the days in which a batch report cut daily or weekly was good enough, rather it will become increasingly critical that organizations give their analysts and decision makers the ability to synthesize all data points necessary to chart the right course for the business. We'll see the ascendance of analytics platforms that enable this, while traditional reporting will increasingly become seen as an antiquated reminder of a slower-moving era.

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About the Author

todd mostak 

TODD MOSTAK is the CEO and Co-Founder of OmniSci, a leading data analytics solutions provider. Read Todd Mostak's full executive profile here.

Published Friday, January 15, 2021 7:32 AM by David Marshall
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