As Trescon gets bigger, the world continues to become smaller. Today, we got a rare chance to speak to Mr. Shreenath Nair from WhereScape. He is a dynamic leader who is adept at designing and leading highly effective go-to-market strategies with implementation and technology partners globally. He has a proven track record of cloud and on-premise database technology sales and marketing across multiple leadership roles.

To know more about his opinion on data and analytics in the ASEAN region, read the full interview below.

1. To begin with, how do you see the landscape of data and analytics developing globally? What specific trends do you see that will shape the future of its adoption?

Data and Analytics have traditionally been a secondary focus that is often owned by a separate team. As a result, business leaders have been underestimating the complexities of data and contributed to diminishing business value. In achieving the set business goals and strategies, accelerating digital business initiatives has become the top priority for business leaders globally. Data and analytics are shifting to a core function that will enable chief data officers to be consistent in producing greater business value. 

The future of data and analytics is largely determined by the relevance of its trends that can help organizations deal with uncertainty, radical change, and the opportunities they will entail in the next 2-3 years. To trigger rapid change in data and analytics, business leaders are leveraging innovations in AI, exploring new methods for integrating diverse data sources, and turning data and analytics into an integral part of the business. In addition, organizations are focusing more on operationalizing the business value to ensure that data and analytics enable the teams to make the right decisions every day and drive the business forward. 

Furthermore, the increased emphasis on Data Fabric as the foundation for data and analytics is an interesting approach.  It can leverage the existing technologies from data warehouses, data lakes, and data hubs with possibilities for new tools and approaches for the future. 

2. What do you think about the general skepticism around newer technologies and how is it affecting the overall understanding of the subject? 

I believe it’s important to balance judgment and data analysis to make an informed decision. Skepticism can be attributed to the inherent fear of failing and the organization’s culture. Culture eats strategy for breakfast. Several data initiatives do not see the light of the day due to cultural challenges, as it’s the biggest barrier to realizing business outcomes. Organizations with a conducive environment for ‘informed skeptics’ who possess strong analytical skills, and listen to others' opinions about analysis adopt the cutting edge of newer technologies. Data illiteracy is rampant among data teams that are fixated on archaic technologies and are drifting away from the ground reality. 

3.During the pandemic, what are the most significant recent developments in your field of business, and what developments should the market be aware of that they aren’t already?

Traditional Data and Analytics processes rely heavily on historical data. COVID-19 has changed the business landscape. Historical data may not be relevant now. Organizations need to figure out how to scale the data and analytics technologies which have been chronically challenging. Business leaders have been going from big to small and wide data to alleviate the problems around scarce data use cases. Wide data has been fostering the synergy between unstructured and structured data sources to enhance decisions and contextual awareness. 

The pandemic has also elevated the need for a more responsible and scalable AI. As a result, there have been a myriad of developments around AI that would allow organizations to enable better learning algorithms, improve time to value, operate with less data and explore adaptive machine learning. 

Traditionally, Data and Analytics dashboards were restricted to data scientists and data analysts. With the advent of the augmented consumer these dashboards will be replaced with dynamically generated automated insights that are delivered to the user’s point of consumption in the near future. Consequently, the data insights will be democratized within organizations and will be readily available to anyone.

4.What is the value proposition for Wherescape and Qubole in ASEAN? How would businesses from the region benefit from it in the long run? 

We see that several businesses in ASEAN are struggling to improve the time to value because of the repetitive manual tasks involved in developing enterprise data warehouses. Writing code that is optimized for the native data platform, creating technical documentation, and developing complicated operations workflows do not permit data teams to deliver value to the business rapidly and quickly. 

WhereScape’s applications build sustainable data warehouses rapidly and cost-effectively. We automate 95 percent of the hand-coding typically required in data infrastructure development and automatically produce high-quality technical documentation with a click of button. With traditional ETL tools, two developers would take 3-6 months to bring a (small-size) data warehouse from initial design through implementation, testing, and production. With WhereScape’s applications, developers can accomplish a task of similar magnitude in 9-10 days. From initial scoping, prototyping, loading, and populating data to ongoing management and optimization, WhereScape software automates the entire data warehouse lifecycle. Essentially, WhereScape is a target agnostic one-stop solution for automating design, development, and deployment of data warehousing projects, and this is our key competitive advantage. 

Similarly, Qubole provides powerful automation that optimizes underlying cloud management for data lakes. Qubole cloud data lake platform continuously optimizes both performance and cost by lowering unnecessary consumptions of compute nodes as well as provisioning low-cost compute nodes. The data platform is a fast, easy-to-use, and completely automated environment for end-to-end data processing — including batch and streaming ETL, ad hoc SQL queries, model training, and deployment — for analytics and machine learning. Qubole Data Platform’s layered architecture includes a common user interface, best-of-breed data processing engines, and an adaptive serverless platform.

5.What in your opinion, are the most pressing challenges that inhibit Data and analytics implementation in Asia? What can be done to overcome them?

Lack of clarity on business objectives across departments are the forerunner in inhibiting data and analytics implementation in Asia. Differences of opinion  within the data teams, either due to nebulous objectives or unrealistic goals, renders the business data analytics practice completely useless, and such projects eventually fail. Data literacy needs to be made a priority in organizations across Asia to improve the success rate of data analytics projects. Another acute challenge is hiring and retaining qualified quant talent. Taking a new data initiative from idea incubation, deployment, and into data analytics operations - it takes a team of data experts rather than a lone wolf. Organizations need to provide a conducive environment for qualified talent to perform and motivate them by offering full transparency into business objectives.

Business leaders often terminate data and analytics projects in Asia due to insufficient ROI. Data teams turn out to be inefficient due to the use of archaic technology that demands them to perform repetitive tasks on a daily basis. Organizations should leverage data automation to reduce the cost and risk associated with such projects to improve ROI and retain talented data professionals. Overall, business leaders should be focused on leveraging their staff to solve business-level problems as opposed to tasks having a potential of automation.