As the world prepares to recover from the Covid-19 pandemic, businesses will need to increasingly rely on analytics to deal with new consumer behaviour.
According to Gartner analyst Rita Sallam, “In the face of unprecedented market shifts, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to accelerate innovation and forge new paths to a post-Covid-19 world.”
Machine learning and artificial intelligence are finding increasingly significant use cases in data analytics for business. Here are five trends to watch out for in 2021.
1. Augmented analytics within embedded dashboards
Gartner predicts that by 2024, 75% of enterprises will shift towards putting AI and ML into operation. A big reason for this is the way the pandemic has changed consumer behaviour. Regression learning models that rely on historical data might not be valid anymore. In their place, reinforcement and distributed learning models will find more use, thanks to their adaptability.
A large share of businesses have already democratised their data through the use of embedded analytics dashboards. The use of AI to generate augmented analytics to drive business decisions will increase as businesses seek to react faster to shifting conditions. Powering data democratisation efforts with AI will help non-technical users make a greater number of business decisions, without having to rely on IT support to query data.
Companies such as Sisense already offer companies the ability to integrate powerful analytics into custom applications. As AI algorithms become smarter, it’s a given that they’ll help companies use low-latency alerts to help managers react to quantifiable anomalies that indicate changes in their business. Also, AI is expected to play a major role in delivering dynamic data stories and might reduce a user’s role in data exploration.
2. Greater commercialisation of AI and ML
A fact that’s often forgotten in AI conversations is that these technologies are still nascent. Many of the major developments have been driven by open source efforts, but 2021 will see an increasing number of companies commercialise AI through product releases.
This event will truly be a marker of AI going mainstream. While open source has been highly beneficial to AI, scaling these projects for commercial purposes has been difficult. With companies investing more in AI research, expect a greater proliferation of AI technology in project management, data reusability, and transparency products.
Using AI for better data management is a particular focus of big companies right now. A Pathfinder report in 2018 found that a lack of skilled resources in data management was hampering AI development. However, with ML growing increasingly sophisticated, companies are beginning to use AI to manage data, which fuels even faster AI development.
As a result, metadata management becomes streamlined, and architectures become simpler. Moving forward, expect an increasing number of AI-driven solutions to be released commercially instead of on open source platforms.
Vendors such as Informatica are already using AI and ML algorithms to help develop better enterprise data management solutions for their clients. Everything from data extraction to enrichment is optimised by AI, according to the company.
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3. Increased conversational analytics
Voice search and data is increasing by the day. With products such as Amazon‘s Alexa and Google‘s Assistant finding their way into smartphones and growing adoption of “smart speakers” in our homes, natural language processing will increase.
Companies will wake up to the immense benefits of voice analytics and will provide their customers with voice tools. The benefits of enhanced NLP include better social listening, sentiment analysis, and increased personalisation.
Companies such as AX Semantics provide self-service natural language generation software that allows customers to self-automate text commands. Companies such as Porsche, Deloitte and Nivea are among their customers.
4. Automation of data analysis
As augmented analytics make their way into embedded dashboards, low-level data analysis tasks will be automated. An area that is ripe for automation is data collection and synthesis. Currently, data scientists spend large amounts of time cleaning and collecting data. Automating these tasks by specifying standardised protocols will help companies employ their talent in tasks better suited to their abilities.
A side effect of data analysis automation will be the speeding up of analytics and reporting. As a result, we can expect businesses to make decisions faster along with installing infrastructure that allows them to respond and react to changing conditions quickly.
As the worlds of data and analytics come closer together, vendors who provide end-to-end stacks will provide better value to their customers. Combine this with increased data democratisation and it’s easy to see why legacy enterprise software vendors such as SAP offer everything from data management to analytics to storage solutions to their clients.
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5. Integration of IoT and analytics
IoT devices are making their way into not just B2C products but B2B, enterprise and public projects as well, from smart cities to industry 4.0.
Data is being generated at unprecedented rates, and to make sense of it, companies are increasingly turning to AI. With so much signal, this is a key help for arriving at insights.
While the rise of embedded and augmented analytics has already been discussed, it’s critical to point out that the sources of data are more varied than ever before. This makes the use of AI critical, since manual processes cannot process such large volumes efficiently.
An exciting future
As AI technology continues to make giant strides the business world is gearing up to take full advantage of it. We’ve reached a stage where AI is powering further AI development, and the rate of progress will only increase.