AiThority Interview with Clarence Rozario, the Global Head of Zoho Analytics Business

Clarence Rozario, the Global Head of Zoho Analytics Business, highlights factors for selecting an analytics platform, and more about data visualization strategies in this Q&A:

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With over 20 years at Zoho Corp, could you share your journey and how you’ve seen the company’s product strategy evolve, particularly in analytics and business intelligence?

In 2009, we launched “Zoho Reports” as a reporting tool. Given the simplicity and profound engineering of the tool, we received an overwhelming response across markets, which was a perfect validation of our goals and strategies.

After years of engineering core analytical capabilities, Zoho Reports swiftly transformed into “Zoho Analytics” – a modern self-service BI & analytics platform. This widened our customer base by enabling them to get deep analytical insights on their data, as well as build custom analytical solutions on top of the platform. It also helped us foray and make inroads into the mid-market and enterprise markets.

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The ingestion of artificial intelligence and self-service data preparation and management capabilities (powered by Zoho DataPrep) into the platform was a game changer for us. We could seed opportunities in the enterprise market and onboard a sizable number of businesses upmarket.

2024 marks a watershed moment in the history of Zoho Analytics. The introduction of GenAI and data science and machine learning capabilities into the platform has transformed Zoho Analytics into a full stack GenAI-powered self-service BI and analytics platform. This strategic move has not only helped us acquire new customers, but has also created revenue expansion opportunities among existing customers.

The metamorphosis of Zoho Analytics to become a GenAI-powered self-service BI & analytics platform was both challenging and exciting. Our ability to work alongside customers has helped us keep our fingers on the pulse of the market. This has helped us understand the latent needs of our customers and proactively built solutions into the platform to help our customers stay ahead of the curve.

In a crowded business intelligence market, what factors should organizations consider when selecting an analytics platform?

Organizations should consider the following factors:

1. Unified Platform: Does the platform offer integrated capabilities addressing all functional blocks of analytics. This include strong data integration, data preparation, analytics, and data science capabilities integrated right within a single consistent experience.

2. 360° Business Insights: Will the platform enable you to get 360° insights across your business by integrating data from different applications and smartly model & blending data across apps, thereby enable  you to get end-to-end insights.

3. AI Powered: Does the platform adopt AI across. It should offer advanced analytical capabilities through prebuilt models, along with the capability to bring your own model. It should also offer integrated GenAI to deliver smart insights through capabilities like copilot, automated insights etc.,

4. Ease of Use: Ease of use is a key enabler of analytical adoption. The platform should be easy to use for any business user to adopt.

5. Low TCO: The cost of owning the software and maintaining it, should be reasonable to the value it offer and not become a overburden.

How is big data evolving across industries today? What key trends are emerging globally that you find most significant?

1. Data Velocity and Diversity: The speed at which data is being collected is exploding. One stat says that the amount of business data that is getting generated is almost doubling every year.

The other dimension is the type of data (data diversity) is also expanding. A business today uses anywhere between 50 to 100 business applications which are generating data. Not just structured data, but unstructured data, real time streams, video etc.,

2. Strong Data Management and Governance: Collecting data is one thing but delivering high quality data which is well governed is a important need. If we do not have high quality data foundation, then whatever is built over it downstream (be it analytics, AI etc,) will not be trustworthy.

3. AI Adoption: AI can be a great enabler in the realms of big data. AI models are more trustworthy with more data. Adopting AI for both advanced analytics and generative AI is really becoming a key enabler in handling big data and delivering impactful business outcomes through data driven decision making.

Zoho Analytics is known for its powerful data visualization capabilities. How do you balance advanced functionality with ease of use for non-technical users?

In the wake of emerging complexities in analytical requirements, the ability to strike a balance between advanced functionalities and ease of use has become a point of discussion in the vendor community.

We’ve always stayed ahead of competition in beefing up the platform with all the sophisticated analytical functions. To maintain the balance between advanced functionality and usability, the entire team at Zoho Analytics works closely with all types of customers to understand their pain points and requirements. This enables us to engineer and build advanced features for specific end users or personas.

With a ‘customer first’ approach, the entire team is attuned to ideate and rollout functions as solutions to address customers’ requirements and not as product features/capabilities. This razor-sharp focus on customer needs has helped us seamlessly harmonize advanced functionalities and ease of use for any BI persona.

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What strategies can organizations employ to balance advanced data visualization capabilities with usability for non-technical users?

1. Self-service analytics: Choose a self-service bi & analytics platform that will enable any non-technical user (business user) to adopt analytics. Self-service analytics services are easy to learn offering both core as well as advanced analytical capabilities, all packed for easy to use adoption.

2. Gen-AI capabilities: Generative AI can really simply the way you interact with data and get into insights fast. It not only offers a simple way of interacting with data, but also offers advanced AI capabilities that can be adopted by business users. Organizations should definitely consider Generative AI as a key enabler for adopting analytics.

3. Setup Strong Data Foundation: As mentioned earlier, when there is a strong, high-quality data foundation, then whatever that is built over it becomes reliable. Business users can trust the insights they get and make impactful business decisions.

Before we wrap up, what advice would you give to IT and business leaders looking to enhance their analytics capabilities and drive better decision-making?

1. Understand Generative AI and Its Possible Impact:  Generative AI is definitely a ground shifting technology. Understand the basics of what Generative AI is and how it could positively impact the usage of analytics and drive better decision-making for you and to your business.

2. Learn the importance of a strong data foundation and governance: Setting up a well high quality data model and data, which is well governed, helps you to build quality analytics and even AI models over it. Learn about it deeper and adopt them in your implementation.

3. Empower teams with a self-service BI platform to enable them to analyze and make data-driven decisions all by themselves.

4. Adopt and Work with a BI vendor who travels with you in your analytics journey. Choosing the right BI tool and vendor can shape the way how you adopt analytics within your organizations and be data driven for impactful decision-making.

Thank you, Clarence, for sharing your insights with us.

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