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Modernising Data Supply Chains In The Age Of Algorithmic Intelligence

Modernising Data Supply Chains In The Age Of Algorithmic Intelligence


The ongoing age of Algorithmic Intelligence has huge potential to transform any organisation’s capabilities. Across industries, organisations are working to transform themselves and take a tech-centric approach to solve business problems. The democratisation of computing through cloud, explosion and availability of data and innovations in machine learning has created a perfect storm to enable business differentiation. It is expected that companies that can take advantage of these and apply them in their customer’s experience and business processes are going to succeed exponentially in the coming decade. 

But what does it take to become tech-centric? How do organisations that have inherited legacy infrastructure transform themselves? As customer expectations have dramatically changed in terms of how they want to interact with organisations, with social media and web platforms, modernising infrastructure (both hardware and software) is key to long-term survival.

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AI Infusion Is The Only Route To Intelligent Enterprise

Intelligent Enterprises are organisations that are transforming themselves through re-imagination of business processes and building next-gen applications through AI infusion that empower all stakeholders. In many enterprises, AI is getting industrialised through a factory-based approach to design, build, embed and operationalise intelligence at scale. 

According to industry experts, AI-infused systems are more probabilistic than deterministic (rule-driven) systems of the past and require continuous calibration based on real-world feedback. This requires managing the lifecycle of 100s of models and their datasets and pipelines in an industrialised scenario.

“When this disruption has come in, I think many companies want to become a data-driven driven or AI-driven organisation to compete against these emerging startups. One aspect is to move from hypothesis-driven organisation to augmentation that uses data to create insight and start embedding those insights within business processes,” told Srinivasagopalan Venkataraman, Principal Consultant & Practice Partner – Wipro Analytics.  

“A lot of innovation is coming out of the open-source ecosystem around machine learning and deep learning, and now organisations are thinking beyond data alone,” Venkataraman added.

How To Democratise AI Within The Organisation

While we have witnessed algorithms having developed dramatically over the years and data being collected across organisations, most organisations are yet to become fully AI-driven. Apart from merely looking for insights, there needs to be a revamping of the culture itself across different processes and data groups. This is essential in driving organisations towards data transformation practices that are core to surviving and thriving in constant technology disruption. 

According to experienced practitioners like Srinivasagopalan Venkataraman, Principal Consultant & Practice Partner – Wipro Analytics.  democratising AI within an organisation needs a more comprehensive and agile outlook— one that industrialises and infuses intelligence within processes. This is exactly the place where companies have to rethink their data processes, make them automated, and collect and analyse a constant stream of data through pipelines.

“You want to industrialise the entire data supply chain and make it so robust that you’d be able to churn out AI use cases much faster and stand against competition from emerging disruptors. This includes introducing new features to customers, understanding the pulse of the customers better, and making processes much more frictionless across the enterprise,” explained Srinivasagopalan Venkataraman, Principal Consultant & Practice Partner – Wipro Analytics

When enterprises are constantly looking for various means to transform themselves into an AI-driven enterprise, data transformation plays a central theme. Businesses, therefore, need to have a newer set of capabilities in their data management space to help address this AI infusion across different business processes.

“It’s not about creating models and finishing a particular use case. Most of the AI is probabilistic in nature, and so it’s also about starting systems which are more probabilistic rather than deterministic, meaning that they are not anymore rules-driven. Such systems are going to be driven by an AI model which learns out of data, and as the data changes, the model has to change. Or else it will deviate from what designed for, and you need to course-correct on a continuous basis,” Venkataraman said.

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The Five Vectors Of Change

Wipro— a leading digital transformation player encapsulates the synopsis of everything businesses should undertake in their journey- something they call as the Five Vectors of Change. These are speed, scale, strong, smart and secure. 

  • Speed entails transitioning from monthly software releases to multiple release per day and from manual to automation-driven development. 
  • Scale includes making capacity planning to auto-scaling, and moving away from database limitations to flexible multi model databases. 
  • Strong is to build data applications that are modernized to operate workloads on global-scale, secure and reliable platforms. 
  • Smart means the transition to services that help manage data using machine intelligence, and drive smarter data quality and governance along with intelligent operations.
  • Finally, Secure is all about creating business services that cater to securing data assets, manage access control, comply with regulations across hybrid cloud data environments. 

Venkataraman elaborated, “We see there are five things customers want— they want to build the data applications faster; they want to build data applications that scale; they want to build applications that are very resilient in terms of a load of any kind or a spike in activity; they want to build data applications that augment the data smartly— meaning predominantly leveraging and collecting data like ML-driven when data quality or anomaly reduction in the operations; and finally build data applications that are trustworthy. So, this is something we have seen as the common thread across most of our customers who are trying to achieve data transformation.”

Further the data transformation story, experts say organisations need to use less of manual intervention in data supply chains and become more automated in terms of how they operate them. This entails managing large clusters and predicting when a failure is going to happen in a data supply chain so that enterprises can determine if extra compute is required to throw in additionally, or if a part of the data supply chain has to be re-engineered accordingly. Organisations also have to manage their data pipelines not just for the servers but even for application ops scenarios as well.

“It is critical to understand that with real-time streaming data processing, you need to predict which particular job has the propensity to fail when the data volume is doubled and if that is the case, which are the SLAs that could be missed. There are machine learning models that we deploy with our customers which help figure it all, beyond the infrastructure log and at an application or a business process level,” told Venkataraman.

Closing Thoughts

Traditional enterprises have no choice but to innovate given they are facing stiff competition from technology startups— which may soon take away slices of markets from them. We witnessed such disruption in recent years across sectors like banking and finance where startups leveraging on cutting edge fin-tech disrupted the entire market. As a result of such disrupting startups, experts say enterprises need to become more transformative. For organisations to become transformative in the age of algorithmic intelligence, one critical aspect is to modernise the data supply chains. 

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