Across the globe, brands are leveraging ML to drive innovation and better customer experience. From being a niche technology, ML is now seeing increased adoption among organisations in various sectors, says Amit Gupta, CEO & Founder at Rapyder Cloud Solution

Machine learning is one of the most disruptive technologies that we have encountered in our generation. It has the great potential to transform businesses for better. From being a niche technology, ML is now seeing increased adoption among organisations in various sectors.

Across the globe, brands are leveraging ML to drive innovation and better customer experience. Nike, for example, uses ML for personalised product recommendation. Dominos ensures 10 minutes or less pizza delivery time using ML technologies. Another popular example is how BMW Group uses ML to read data from vehicle subsystems and predict the performance of vehicle parts and proactively recommend maintenance.

ML emerged as a key priority area for technology leaders in 2020 itself as they aim to achieve revenue growth while reducing costs. In 2021, enterprises are exploring more matured use cases of the technology as they navigate an environment of flux. Disruptive organisations have been at the forefront of adopting this technology across areas for process automation, customer experience, security and others.

In 2021, here are top five industries that will adopt ML to change forever the way they work.

Healthcare: The global pandemic has underscored the importance of investing on and optimising our healthcare systems. ML is considered to be the most promising technology that allows healthcare providers to churn the massive volumes of data and derive valuable clinical insights. ML offers huge progress in drug discovery, cutting down the long discovery and development pipeline and reducing cost. It can also significantly improve healthcare delivery systems and in turn lift the overall quality of healthcare while keeping cost under control. In the days to come, ML is predicted to have critical application in clinical trials as well. ML is going to have huge impact on almost all branches of healthcare including pharma and biotech, experts emphasize.

Banking & finance: Banking sector has already seen many matured use cases of ML especially in fraud detection and automating processes. ML use cases will be actively explored across areas such as trading, investment modeling, risk prevention and customer sentiment analysis. As digital transactions continue to grow, ML combined with predictive analytics will play a big role in helping financial institutions to improve transaction efficiencies throughout the transaction lifecycle. Banks and financial institutions will also use this technology to customise their products and offerings to stay more relevant in a competitive environment.

Media & entertainment: Companies like Amazon and Netflix have already popularised the data-driven content consumption models in recent years. As the global pandemic further drives up the demand for new consumption models, firms will effectively leverage AI and ML to create value for customers and present the most relevant content to them in real-time. Whether it’s developing better recommendation engines or deliver hyper-targeted services, ML is going to be critical for the media and entertainment industry to address the drastically changing customer expectations. Predictive modelling will be key in responding to customers in real-time, anticipating the future demand and making investments wisely.

Retail and ecommerce: No other industry has better understood the need to be prepared for the unexpected. The global pandemic has disrupted the retail sector in several ways and ML has been looked upon as a key enabler for the sector to effectively address change. Whether it is the traditional brick-and-mortar stores or the ecommerce companies, the sector is on a path to reinvention with technologies such ML. Starting from supply chain and inventory management to personalised product recommendations through chatbots, the retail and ecommerce sector is looking at several ML use cases. It is also being used extensively for predicting user behavior and analysing the trend effectively to be better prepared. Dynamic pricing is emerging as a key ML use case, to help retailers thrive in a competitive market landscape.

Manufacturing & Industry 4.0: With the massive adoption of IoT devices set to further increase in the manufacturing sector, ML will be the most critical technology bridge that analyses the huge volumes of data generated. ML serves as the powerful building block of Industry 4.0 along with automation and data connectivity. While predictive maintenance is the most explored use case so far, manufacturers will look at more matured use cases of ML such as real-time error detection, supply chain visibility, warehousing efficiency and cost reduction, asset tracking among others. As traditional factories transform into smart factories, ML will fuel greater innovation and efficiency in the days to come.

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Some industries will have to do significant machine learning adoption.

Gradually recovering from the effects of COVID-19 pandemic, will be a top priority for practically every firm and industry in 2021. A few organizations may get stale or never recuperate. Others will see the purge as a remarkable opportunity to comprehend and improve their data and analytical assets, operationalize and update their model production process, and promise clients that their machine learning adoption can be trusted. Everybody is hoping to improve over their present AI and ML insights, for example, a bank improving fraud detection, a medical care provider moving to telehealth, a retailer or manufacturer attempting to make your supply chain more proficient.

All through the recent years, there have been a couple of revelations in machine learning and artificial intelligence. Several companies have so far been able to apply those to achieve the fundamental business targets.

With the rising demand of ML and interest in these advances, different ML trends in 2021 are climbing. Basically, in case you’re a tech able or related to innovation in some capacity, it’s overwhelming to see what’s next inside in ML for business. 2021 will see more machine learning adoption in industries that are fundamental to the functioning of society as a whole.

Banking

As the world starts its recuperation from the pandemic in 2021, sensational swings will happen across the macro-economy. A significant topic will be the impacts of fiscal stimulus and the reverberations that will be felt by families and bigger organizations. Banks and other financial institutions will be searching for both generous opportunities and huge threats, and the persistent suppression of interest rates will be a significant challenge as compressed spreads will burden profitability.

Utilizing obsolete models of machine learning will make banks quickly lose profit, market share of the overall industry and, now and again, reputation. Hence, the skill to quickly update models in sectors, for example, fraud, underwriting, customer management, etc., will be crucial.

Healthcare services

The worldwide pandemic has underscored the significance of investing in and streamlining our healthcare systems. ML for business is viewed as the most encouraging technology that permits healthcare suppliers to beat the enormous volumes of data and infer important clinical insights. ML and AI offer remarkable advancement in drug discovery, chopping down the long discovery and development pipeline and lessening cost. It can likewise altogether improve healthcare delivery systems and thus lift the overall quality of medical care while controlling cost. One of the ML trends in 2021 is that it can be used in clinical trials also. Machine learning will immensely affect almost all parts of medical care including pharma and biotech, experts underline.

Retail

The retail business is in crisis, yet there is a lot of strength and opportunity, and the retail landscape will keep on seeing sensational trends in consumer behavior. A few unsure variables will keep on challenging the business in 2021: jobs, the economy, and the logistics of facilitating pandemic restrictions in individual districts. Retailers will be compelled to do machine learning adoption for their business decisions, particularly to comprehend the always changing, underlying data. MLOps will be a key ML trend in 2021 in retail to operationalize the model update process, identify changes in economic and consumer data, and comprehend the significance of those changes.

Manufacturing

With the monstrous adoption of IoT devices set to additionally grow in the manufacturing industry, machine learning will be the most crucial technology that analyzes the enormous volumes of data produced. ML for business fills in as the incredible building block of Industry 4.0 alongside automation and data connectivity. While predictive maintenance is the most common use case up until this point, manufacturers will see more developed use cases of ML like supply chain visibility, cost reduction, real-time error detection, warehousing efficiency, and asset tracking among others. As traditional manufacturing plants shift to smart factories, ML will fuel more noteworthy advancement and productivity in the days to come.

Transportation

If you think self-driving vehicles are the results of a distant future,  smart cars have effectively penetrated into the markets. Back in 2015, the execution of AI-driven systems in cars and vehicles were simply 8%, yet by 2025, the rates are expected to leap to 109%. Connected vehicles are the in-thing in the automobile business at the present time, where predictive mechanisms precisely tell drivers the likely breaking down of spare parts, routes and driving directions, emergency crisis and disaster prevention protocols and much more. Gartner anticipated that connected cars with embedded wireless connectivity and networks would be the benchmarks for vehicles by 2021. This is likewise gradually transforming into a reality with the prototypes of autonomous cars hitting the streets.

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Stirista, a data-driven digital marketing solutions provider known for its identity resolution and authoritative data assets, announced the acquisition of Venture Development Center (VDC), a data marketplace with unique data assets and clients such as Accenture, Asurion, TransUnion, Warner Media and more. VDC is led by Matthew W. Staudt, who has held the CEO role at VDC since 2018, and who will join Stirista as Chief Strategy Officer.

Currently, Stirista combines the power of authoritative identity data with the execution of omnichannel marketing.

Through its data and customer-centric approach, Stirista is helping Fortune 500 and mid-market brands including Great Clips, Oracle and Verizon, among others, increase brand loyalty and acquire new customers. Stirista’s privacy-compliant data insights helps clients interact with customers and prospects via digital, email and social channels.

The acquisition of VDC will help Stirista, which recorded extraordinary revenue growth in 2020, continue to expand its identity solutions which are the basis for its clients’ audience segmentation, CTV, email marketing, and programmatic programs. Additionally, VDC data contributors will have the opportunity to maximize and monetize their data assets thru Stirista’s Data Marketplace Exchange. This is Stirista’s third acquisition in the past 10 months.

“We are excited to welcome the VDC team to Stirista as their unique data sources, consultative approach and strategic thinking have been critical to helping their clients leverage big data,” said Stirista CEO Ajay Gupta. “This is another important milestone for Stirista as we continue to expand our solutions to help brands provide more value to their customers and prospects. Matt Staudt has been a friend and mentor for the past decade, and I look forward to having him on our leadership team.”

Founded in 1996, VDC is a noted innovator of data sourcing, data management and data monetization strategy with a who’s who roster of clients across multiple industries. Founded by the late Dr. Charles “Charlie” Stryker, a data marketing pioneer, VDC’s breakthrough uses of alternative data have helped its clients drive revenue and elevate existing business practices.

“Today’s leading marketers rely on privacy-compliant options for sourcing identity and customer data,” said Staudt. “I am thrilled that the VDC mission and team will be able to continue to bridge the data gap for our new and existing clients with the Stirista identity and technology as its spine.”

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