India’s future depends on data engineering, not data models | Jobs Vox


“There is no dearth of data in India,” said IBM Country Leader – Data, AI and Automation (India and SE). Siddhesh Naikin an exclusive interview with Analytics India Magazine,

Citing the Government Cancer Hospital in Mumbai, Naik said the country probably has huge data compared to some of the world’s medical record giants. The diverse DNA data available here could in all likelihood boost oncology research. However, the biggest challenge here is how to channelize all this data.

In this regard, the government is already taking small steps. For example, Minister of State Rajeev Chandrasekhar recently said that the government had drafted a policy to share anonymized data sets collected under the National Data Governance Framework with Indian startups and researchers. The policy will standardize all undefined data, data management, and data processing silos — how to store, manage access, control, and protect — such that they will constitute an administrative component of the policy and its first pillar.

“The government will push out all the data to industries in the form of APIs in a secure manner (while ensuring all privacy requirements are met) so that large enterprises and startups can build their offerings on top of APIs,” Naik said. IBM will focus on building a strong technology foundation. For example, India is currently going through a wave where there is a lot of enthusiasm in developing data science models. However, he believes that this can be sustained only if data engineering is worked on.

The move towards multi-cloud and hybrid environments has also prompted enterprises to work towards bringing data at an arm’s length to all departments within organizations. This has made the democratization of data – making as little data available as possible within an organizational hierarchy chain – an essential asset for enterprises.

the future is ai governance

Citing the example of a painting giant, Naik said that it is working with them to democratize data, where they are helping them to govern the right – what can be exposed and what cannot – as well For adequate data analysis to be available. So, for example, marketing teams can use it to tailor campaigns. Because, when it comes to paint, what works in one area may not necessarily work in another. Therefore, data democratization is about personalizing data, hyper-personalization/hyper-localization at all levels of the organization. In this way, IBM is working on bringing down the center of gravity and making data consumable at a much deeper level.

But its implementation continues well beyond the AI ​​part. The entire execution workflow process that enables this has to be sorted. “The beauty about it is that we are moving at an unprecedented pace where there is no resistance to getting there; It is clear to everyone that we will get there and have started taking small steps.”

IBM is addressing the challenge of data democracy with technologies such as Data Fabric. Data fabric, as defined by NetApp, is “an architecture and set of data services that provide consistent capabilities across a choice of endpoints spanning a hybrid multi-cloud environment. It is a powerful architecture that integrates cloud, on-premises and standardizes data management practices and practicalities in edge devices. Thus, it is a virtual fabric that connects all data endpoints. Here, you use technologies like automation, federated governance, integration, security, and it To be able to access all the data without physically moving it has the potential to create an analytics backbone or dashboard that cuts across all silos.

Another example of how with the advent of 5G, IBM’s partnership with telecom giant Airtel enables data consumption at the edge. IBM Cloud Satellite allows cloud capabilities to be used without physically moving data. For example, when it comes to automobile manufacturing and production lines, any amount of latency can disrupt the entire production line. But, with 5G, efficient use of transient data becomes possible – as the control plane sits in the cloud and the actual data is at the edge.

Biggest Challenges in AI and Data Analytics

India has taken a giant leap in the last two years in fostering a startup ecosystem leveraging AI and data. This can be evidenced by NASSCOM’s 2022 report, which suggested that the adoption of AI would add $500 billion to India’s GDP by 2025. However, India’s AI maturity score of 2.45 reveals the untapped potential of using AI in this technological age.

In this regard, one of the major barriers to widespread AI adoption is that 44% of enterprises have either insufficient or siled data, which prevents them from scaling AI solutions.

There are several challenges around this:

  • Thorough knowledge of capturing the right kind of data, storing data, protecting data as well as managing data privacy and governance aspects.
  • Making data accessible to all, especially when organizations integrate with multiple partners.
  • Breaking down the silos formed by data stored in multiple clouds to access data across the organization’s own on-premises footprint as well as across different departments within it.


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