Organizations need to focus solely on data aspect of their business: Persistent Systems

Data product thinking brings the focus to the act of creating and the production of data products, rather than the more passive roles of governing, stewarding, cataloguing, managing, curating, or shopping for data.

A McKinsey global survey states that nearly 92% organizations are accelerating their investments in Big Data and AI, with nearly 96% organizations already using AI in some form or the other. DaaS (Data as a Service) offers numerous competitive benefits through performance, speed, flexibility and reliability including the practices of “data stewardship” that ensure an organization’s data is accessible, usable, safe, and trusted.

Akshay Chitlangia, Principal Consultant, Growth & Strategy, Persistent Systems Ltd, tells us more. Excerpts from an interview:

DQ: What is the current landscape of DaaS in India?

Akshay Chitlangia: There is a highly divided landscape right now of Data as a Service in India. On the one hand, you have the new-age companies, consider fintech and education, that make a good portion of their business off their customer's data which is provided ahead as a service (Data Monetization). This data tends to get consumed by profile providers (B2B/ B2C) who look at data tangents across multiple sources and create a “target 270 degree” and try to provide this data as a service to the enterprise companies.

The enterprise companies have the burning need to create “360-degree” views of their prospects, customers and even employees to be able to utilize the data provided to them better. Once available they need to provide this to almost all their departments as their utilization of such views evolves.

This combination of increasing variety, volume of data and the growing base of companies leading to more velocity of data is driving very rapid adoption of the cloud. A hybrid approach to data analytics is now considered a "sine qua non” for every CIO. The flexibility that comes with this approach can in turn lead to another round of Data Monetization and a new stream of potential revenue driven by the CIO.

As evidence, marketing materials and websites of almost all SaaS vendors present in India now ensure reference to and/ or a case study on DaaS/360-degree views.

DQ: What are the challenges associated with in building a data-driven organization?

Akshay Chitlangia: Indian companies are growing very fast. They are bombarded with information from vendors,

market researchers and other online sources as well as peers about the greatest “next big thing”. This problem of plenty and complexity of choice often leads to organizations ending up having reasonably unique technology stacks on the inside of the ship which becomes a challenge for partners to support. A very strong preference for emerging start-ups in analytics platforms, AI ML platforms from India increases this complexity.

Another aspect is the time to deliver, which is again often very fast-paced. Unfortunately, building certain structural components which often get ignored such as Master Data Management/ Reference Data Management cannot be squeezed for time as they are a combination of business and process-driven needs implemented in technology.

On the deck, enabling and upskilling employees to work in a data-driven organization is a challenge as well. They need to learn the new(er) tools, as well as understand the data in front of them better to have better Self-Service Analytics. However, deep in the engine room, parts of IT must also evolve into data-IT.

DQ: What is the impact of Data Product Thinking for organizations in India?

Akshay Chitlangia: Data product thinking is a relatively recent phenomenon everywhere. In India, as this concept catches up, organizations in India will need to adjust to having teams/ units focused solely on the data aspect of their business. This group will need to oversee how data is generated, homogenized, unified and consumed across an organization i.e., the overall lifecycle of data related to customers, employees, products/ services, after-sales/ support, etc.

This becomes a shift from just using data platforms and services from an IT perspective of project management to having a holistic view of bringing in concepts from the well-known product development domain into every organization, such as vision/ mission definition, research into the business and underlying technology needs, setting the boundaries of DaaS, planning and control of the DaaS environment, quality management of the DaaS service, use of AI ML, the user experience and skill cliff, and also cost control of the DaaS service v/s the impact it delivers.

In some cases, DaaS may also be charged to departments based on their usage so that it does not remain a cost-only function. Ultimately, if the data lifecycle in an organization will be managed by DaaS, the lifecycle of DaaS itself will need to be managed by using data product thinking.

Thus, data product thinking necessitates acceptance in the organization top-down from the board level of investing in such data-centric functions.

DQ: What is the role of DaaS in improving data security and lineage and how will it help in exhilarating operational efficiency?

Akshay Chitlangia: Centralized, controlled, silo-less and managed data is the underlying outcome of DaaS acceptance in an organization. This means that control to data is granular, usage of data is monitored and governed much better. Questions can be raised towards specific usage or consumption of data if necessary.

India expects to have the Data Protection Bill converted to an Act of Law very shortly. National Digital Health Blueprint by the NDHM is expected to become a requirement for most professional healthcare systems. Such emerging compliance norms can be met better with obfuscation/ masking/ encryption of sensitive PII/ Financial/ PHI data. Data at rest can be encrypted with reliable and regulated key management systems instead of ad hoc discrete attempts using the easiest option available at hand.

Compliance also requires knowing who used what data, when they used it, was it changed by someone or an application, what were the exact changes as well as the user getting to know which version of the data is being accessed, if they can trust it and if it is recent enough to be of use to them. Having a centralized DaaS setup allows such questions to be answered accurately and with minimum effort as against the earlier methods of log traversing in discrete systems and potential suspicion of the answers.

DQ: What is the importance of “Data Stewardship”, and what are the key practices involved?

Akshay Chitlangia: Data Stewardship or Data Stewards is a role encompassing several activities in a data-focused team. Their primary objective should be to create and manage processes to preserve the integrity of the organization's data.

Their work is primarily around understanding where data may be sourced from, how can it be potentially consumed, providing guidance for data quality, documenting and maintaining the document on how the data is expected to be handled. Generally, they are also expected to understand the compliances that apply to each data element and incorporate suitable controls into the processes.