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Tuesday, May 28, 2024

Automated Analytics Drive Top-Line Growth and Bottom-Line Savings

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Khushbu Raval
Khushbu Raval
Khushbu is a Senior Correspondent and a content strategist with a special foray into DataTech and MarTech. She has been a keen researcher in the tech domain and is responsible for strategizing the social media scripts to optimize the collateral creation process.

With over 20 years of experience, Digvijay Lamba, CTO at Alteryx, leads with vision, driving innovation to meet data pipeline and cloud adoption’s evolving needs while fostering a collaborative, data-driven culture.

Few in the technology realm blend experience and insight like Digvijay Lamba, Chief Technology Officer at Alteryx. Over two decades, he’s relentlessly pursued how cutting-edge tech unleashes powerful business impact.

Lamba tackled complex data and AI challenges as founder and technical leader, building innovative products and architectures to unlock data’s potential. His visionary leadership as founder and CEO of Lore IO, a pioneering no-code analytics company, empowered organizations to extract valuable insights effortlessly.

In 2021, Lamba joined Alteryx following its acquisition of Lore IO. As CTO, he now crafts the technology vision and strategy across Alteryx’s product roadmap, ensuring it aligns with customer needs. He leads a high-performing team that fosters innovation within the platform, focusing on enhanced connectivity, cloud-centricity, and accessibility for both technical and non-technical users.

Under his guidance, Alteryx is poised for a transformative journey. This interview delves into his insights on the evolving tech landscape, Alteryx’s strategic direction, and how technology unlocks success in the modern era.

Excerpts from the interview; 

Alteryx’s platform plays a crucial role in modern data pipelines. How do you envision the future of data integration and automation within Alteryx, particularly considering real-time and streaming data sources?

Digital business outcomes don’t come from a single data set or source. They come from multiple business sources, often aggregated into a multi-cloud data warehouse, data lakehouse, or other on-premises and hybrid storage environments. From transactional data to user behavior data, sensor data, log files, and social media; organizations are trying to discover business-changing insights from an increasing volume of various real-time sources. According to Ventana Research, 56% of organizations cite using more than 10 data sources. However, many use complex architectures and tools to process this data, requiring specific skill sets.

The demand for data analytics to deliver meaningful real-time insights has never been more urgent. Technology is a great enabler. However, the ability to mine company data for the trends and insights needed to make challenging decisions in real time requires a data tech stack built for the domain experts who know the business and that every expert can use to deliver better outcomes for themselves. With its deep technology integrations, Alteryx is known for its easy-to-use, accessible interface that allows you to better utilize best-in-class data architectures deployed in your enterprise – including cloud and data lakehouse technologies from AWS, Azure, Google, Databricks, Snowflake, and more to empower real-time collaboration that enables critical data and analytics to be accessed by anyone, anywhere.

Furthermore, this approach dramatically accelerates the development of a collaborative data-driven culture within the enterprise, as anyone can creatively solve analytical problems in real-time and at the source while benefiting from powerful storage and compute resources.

Only by empowering the entire workforce to take full advantage of accessible automated data preparation and AI-powered analytics will you unlock the full potential of the data pipelines available and ensure they deliver ROI by driving business value across the entire organization.

As cloud adoption accelerates, how is Alteryx ensuring its platform remains cloud-agnostic and caters to diverse hybrid and multi-cloud?

Aggregating data across disparate sources and preparing clean, quality data for analysis has long been challenging for organizations. The AI boom is making this issue even more relevant, as the data variety and quality directly impact the quality of the resulting models and insights. With access to over 180 data source connectors, analysts and data engineers alike can quickly automate the process of extracting, loading, and transforming data into robust ETL/ELT data pipelines at any scale, ensuring they are ready to feed and train models at the core of technologies such as generative AI.

Continued pressures around digital transformation and big data challenges drive more organizations to adopt cloud-based analytics. Research shows that by 2025, nearly 70% of enterprises plan to put most of their analytics solutions in the cloud. By offering a range of cloud-native and cloud-connected products as part of the Alteryx Analytics Cloud Platform, such as the Alteryx Designer, Auto Insights, Machine Learning, Location Intelligence, and App Builder, we continue to support any architecture and cloud strategy, including hybrid and multi-cloud environments. Additionally, with our Private Data Handling technology, customers can deploy trusted analytics in the cloud environment of their choice to connect to and transform data wherever it lives securely. This strategic approach demonstrates Alteryx’s commitment to providing flexible solutions that adapt to diverse digital landscapes, ensuring scalability and versatility for different organizational needs​​​​.

The concept of a democratized data culture is gaining traction. How is Alteryx empowering broader access to data analysis and insight generation across organizations?

No matter how much data you have, it’s useless unless the right people can access it. Achieving long-term success requires everyone to easily access and mine company data for trends and insights to make challenging decisions.

Our innovative and powerful purpose-built approach to accessible analytics enables every persona to leverage their tool of choice to collaborate in real-time to access data anywhere and develop analytical insights – from chat prompts to no-code workflows and code-friendly Python and SQL notebooks – all of which can be live translated by generative IA, into a single analytical solution that enables collaboration across the business at scale. The result? More automation that drives top-line gains from better decision-making by domain experts and bottom-line returns from efficient self-service analytics across the enterprise. 

Ultimately, our easy-to-use, self-service approach to analytics makes it easier for everyone -regardless of analytic skill – to access data with governance and at scale, to collaborate with experts easily when needed, and to put the power of data-driven decision-making into the hands of every employee. This initiative is pivotal in cultivating a data-driven culture where informed decision-making becomes widespread. Furthermore, our role in democratizing data extends beyond just making tools accessible. It also involves helping to educate and upskill the workforce. Initiatives like the Alteryx SparkED and Alteryx Maveryx Community offer learning paths and certification programs that critically equip individuals across different organizational levels with the skills to leverage data analytics. These educational aspects are key to fostering a truly data-driven culture within organizations and ensuring that a broader range of individuals can grow and learn how to use data daily​​.

What are your biggest technology priorities for Alteryx in the next 5 years? What emerging trends are you most excited about, and how will they shape the platform’s future?

As the quest to digitize business intensifies, organizations seek to improve efficiencies and drive automation and analytics across their workforce. Faced with an increasingly siloed data ecosystem, they are looking for faster and more intuitive ways to orchestrate data within the business while enforcing good governance. The challenge is turning these increased data volumes and varieties into business opportunities by preparing for this increasingly complex, data-driven future. 

On that note, our innovation and technology areas of focus remain an easy-to-use analytics experience that is tech stack agnostic while leveraging generative AI to open new use cases and empower more people. We believe that the potential analytics value creation from generative AI is huge in terms of its capacity to unlock automation in areas where it was previously difficult – whether because applying AI to these problems was expensive or because the experts did not have the right tools to build solutions for themselves easily.

Generative AI is perfect for decision-makers with no data science skills to advance their data skills while delivering smarter data-driven decisions. Its use of natural language prompts makes analytics easier and more approachable. Enabled by a generative AI-augmented self-service analytics platform, analytics can be placed into the hands of all employees across all systems and for all decisions. By lowering the cost of applying AI to a problem, an increasing breadth of problems become solvable at the hands of creative experts. Everyone can automate data tasks so that human operators can spend more time on strategic, innately human value-added activities.

While technology is set to shape how future enterprises operate and perform, preparing for this increasingly complex, data-driven future requires focusing on skill transformation within the workforce. Beyond technology, people remain key to the success of digital transformation. Embracing upskilling in data analytics and machine learning across an organization remains the catalyst for driving the path toward sustained business growth and success. Therefore, the organizations that will flourish will be the ones that have nurtured and equipped their domain experts with essential critical thinking, domain knowledge, data literacy, and analytical skills to navigate this era of AI-driven intelligence.

With the rise of data lakes and distributed data architectures, how is Alteryx adapting its platform to handle diverse data storage and processing environments?

As more people become involved in data in some way, whether establishing the pipelines, cleaning the datasets, building the models, or consuming the insights to make decisions, governing how data is used becomes critical. As many organizations know, cloud-based data lakes, warehouses, and lakehouses offer infinite scalability and better governance to support your data storage and processing needs. Whether on-premise, private cloud, public cloud, multi-cloud, or hybrid, users should be able to transform their data without understanding the complexity of the often multi-source and multi-warehouse environments that exist in their organization. They should be able to seamlessly build a data workflow that spans multiple data sources and processing systems. Our analytics cloud and data-source connection capabilities have simplified how organizations access, analyze, and deliver insights from this data. Our customers are more efficient and deliver high-value insights from data lakes and distributed data architectures at unparalleled speed by empowering business users to build complex workflows that work seamlessly with their organization’s data architecture. Users can utilize the enterprise-grade, easy-to-use, self-service platform to speed up analytic processes with AI/ML-based suggestions that guide users through their data transformations, and our platform makes sure these execute closest to where the data lives and pushdown processing and storage to IT’s preferred cloud and data warehouses.

However, adopting a whole-business approach to generating data-driven insights is vital to leverage the decision intelligence from effective in-cloud analytics. We support multiple personas, unlimited use cases, and future feature expansion by infusing AI innovation into the Alteryx Analytics Cloud Platform. While this innovative and powerful multi-modal approach enables every persona to leverage their tool of choice from chat prompts to no-code workflows and code-friendly Python and SQL notebooks, it also allows IT to easily govern who has access, how the data flows, and where the processing happens. This allows enterprise-wide use of data insights on any choice of data stack.

As a leader in the tech industry, what are you doing to promote diversity and inclusion in data science and analytics?

Diversity in data analytics is important for businesses that want to succeed in today’s distinct and digitally inclined world. The technology industry is growing fast, and keeping that in mind, it is increasingly important that all businesses have a diverse workforce.

Diversity has been an increasingly key pillar of the company’s culture for several years and is a critical factor in driving global growth and business success. Alteryx introduced “Alter.Us,” the company’s diversity, equity, and inclusion council, in 2019. Alter.us provides resources to grow and promote a comprehensive range of employees across its leadership and associates, including race, gender, religion, country of origin, education, cognitive thinking, experience, and background.

One of the reasons I joined Alteryx was because their ‘analytics for all’ mantra echoed my professional beliefs. Retrieving and understanding data is very powerful, no matter your role or industry. Alteryx does an excellent job supporting that principle through SparkED, its no-cost analytics education program

Over 170,000 learners from 1,180 institutions across more than 50 countries are part of the Alteryx SparkED program. In addition, Alteryx learning programs have been integrated into Return-to-Work programs globally, with the software provided for free.

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