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Staying Ahead of the Curve with Real-Time Intelligent Applications

Deb Dutta - General Manager, Asia Pacific & Japan, DataStax
Deb Dutta - General Manager, Asia Pacific & Japan, DataStax

Based on “The State of the Data Race 2022”, a survey of over 500 IT leaders and practitioners, DataStax found that a successful data strategy today results in significant business value in two key areas. The first is higher revenue growth and the second is increased developer productivity. 

Love it or loathe it, artificial intelligence (AI) is fast becoming integral to differentiating between modernisers and laggards in competitive business environments. Hardly surprising, considering that the current economic climate is marked by customers expecting more from the organisations they interact with. At the same time, companies are striving to be more responsive, efficient and profitable. 

While AI and machine learning (ML) can bridge the gaps between companies and their customers, successful AI/ML projects rest on data. Specifically, businesses need to structure their organisations around a data-driven culture. 

There is a case to be made that the organisation cannot harness the full potential of AI/ML without being data-driven. In their highly regarded 2020 book titled ‘Competing in the Age of AI:  Strategy and Leadership When Algorithms and Networks Run the World’, Marco Iansiti and Karim Lakhani make the case that future-proofed organisations are those that position themselves to become so-called “AI factories”. At their core, these organisations unlock new growth opportunities by connecting user engagement, data collection, algorithm design, prediction and improvement to uncover new opportunities for growth. 

Indeed, what we’re seeing today lends weight to that observation. Incorporating AI/ML into analytics tools equips organisations to develop models and algorithms faster, even as data troves proliferate, and provide valuable customer insights, greater personalisation, and make more of an impact on customers. 

While AI and machine learning (ML) can bridge the gaps between companies and their customers, successful AI/ML projects rest on data.

Deb Dutta – General Manager, Asia Pacific & Japan, DataStax

Unlocking the value of data 

Companies that lead the way in terms of successful AI projects are those that build innovative applications and have the scalability to continually grow by pooling together ever-growing volumes of data in real time to convert them into meaningful, actionable insights. 

These qualities enable these organisations to improve analytics and increase competitive advantage via better customer experiences and improved agility across the business. 

What results is an organisation capable of finding new growth avenues, as it is able to collect data at scale and develop algorithms that improve – not diminish – as datasets grow. At the same time, organisations also gain more rich data ecosystems that fuel better decision-making. However, achieving the kind of success akin to the likes of Amazon, Apple, Netflix and Google requires legacy firms to pivot towards modernisation. 

Leveraging open-source 

Based on “The State of the Data Race 2022”, a survey of over 500 IT leaders and practitioners, DataStax found that a successful data strategy today results in significant business value in two key areas. The first is higher revenue growth and the second is increased developer productivity. 

For instance, 71 percent of respondents agreed that revenue growth was directly tied to real-time data. Meanwhile, 78 percent of respondents said real-time data is now a ‘must-have’ and 66 percent said their developers were more productive thanks to the available access to  real-time data. 

For too long now there has been a misguided notion that proprietary technologies and software are crucial for a business. However, as Iansiti and Lakhani advocate, there needs to be a shift towards “an emphasis on shared community based development and adoption of the decentralized power and innovation of open source constructs”. The rationale for this is clear; if data is the lifeblood of the organisation, then the competitive edge they enjoy comes from the data they possess generated from the business they conduct with their customers and other stakeholders  – not the software that’s developed in-house. 

DataStax’s survey findings further legitimises this idea. Organisations from across AI/ML maturity levels noted that open-source was at the very least somewhat important. More crucially, 84 percent of organisations who had deployed AI/ML across the enterprise rated this as very important, with more laggards not yet grasping the value open source brings to AI/ML success. 

Separating the wheat from the chaff 

For many laggards, AI leaders seem to be on another planet. They have assembled technology infrastructures expressly to deal with instantaneous changes and respond based  

on real-time feedback. These enterprises lean into real time to champion personalisation and tailoring experiences for customers.  

They leverage real time to mitigate risks and nimbly navigate disruptive forces. The State of the Data Race survey points to the fact that real-time data is a core strategy for AI leaders, and correlates with AI maturity. Eighty-one percent of companies who have broadly deployed AI/ML attest to this, compared to just 48 percent with limited deployment and a mere 32 percent of those whose AI/Ml deployments are in their infancy. 

Furthermore, the advanced group was four times likelier than organizations in early deployment to leverage real-time data as a strategic focus across the organisation, and twice as likely when compared to those with limited deployment. Nearly all of today’s AI/ML leaders also see themselves developing real-time apps – either exclusively or for the most part – within three years. 

For laggards, the time to move is now. Fortunately, there are platforms that can ease that transition and put them down the path of success. The key is to mirror the attributes of AI factories and leverage open-source software that empowers real-time decision-making and feed accurate data into AI/ML models.  

At the end of the day, leveraging AI/ML should prompt decision-makers to evaluate if they have the data to make better decisions with speed and at scale. Not only is the AI factory model an enabler for operational improvements and delivering the services customers love, it also equips decision-makers with predictive analysis to invest in future growth with confidence.  

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