Executive Perspective: Future Trends in BI and Analytics
From real-time analytics to data-warehouse-as-a-service, there are plenty of challenges ahead for today's data-driven enterprise. Hexaware’s Vaidyanathan J.R. shares his ideas.
- By James E. Powell
- April 20, 2020
We all need a crystal ball from time to time, and this week we asked Hexaware's senior VP and global head of BI, big data, and analytics, Vaidyanathan J.R., what's ahead. Vaidyanathan describes himself as an analytics evangelist, a change catalyst, strategic innovator, and organization builder with over 25 years of work experience across industries, including information technology services and outsourcing, automotive and manufacturing, financial services, retail, ERP product development and implementation and management consulting. He provides thought leadership and develops strategies to build, grow, and manage profit centers across organizations.
Upside: What technology or methodology must be part of an enterprise's data strategy if it wants to be competitive today? Why?
Vaidyanathan J.R.: Every data strategy today revolves around incorporating real-time analytics capabilities. This is probably the best competitive differentiator to have. Most of the data collected today goes into the archive layer; only a quarter of enterprise data is used for analytics and the rest becomes dark data. Real-time analytics will provide valuable insights so enterprises can take action when a business opportunity is identified (and before it is lost).
To enable this, enterprises need to transform their data warehouse (DW) and the analytics ecosystems through "cloudification" of DW in an agile manner. Enterprises expect their DW landscape to be simple. They want on-demand scalability, high availability, comprehensive security, and consumption-based pricing. Cloud provides all these capabilities, but the DW development and testing on the cloud is still time consuming. Agile data warehousing will accelerate this development process by a factor of two.
What one emerging technology are you most excited about and think has the greatest potential? What's so special about this technology?
We are personally very excited about technologies that enable data-warehouse-as-a-service (DWaaS), that follow an agile methodology to power up the transformation of data and analytics ecosystem of enterprises. This space is now witnessing some game-changing technologies such as decoupling of storage and compute, allowing each of these components to scale independently to handle the unexpected surge in incoming data volumes and reduce costs.
In addition, unified analytics platforms are emerging that allow collaboration between business, data engineering, and data science teams on a fully managed, scalable, and secure cloud infrastructure, reducing total cost of ownership and operational complexity.
What is the single biggest challenge enterprises face today? How do most enterprises respond (and is it working)?
The biggest challenge lies in sticking to conventional mechanisms of data management and data sharing with stakeholders. Enterprises can no longer afford to delay the time to value. The challenge is in eliminating the data silos within the enterprises, developing a single source of truth for all the data, and sharing it effectively with the consumers.
Enterprises do understand the potential of having a robust data strategy for successful digital transformation, but their existing data landscape is complex and they are unable to understand how to start their digital transformation journey.
Enterprises should look to automate the assessment process, have business and IT teams collaborate to develop objectives for their transformation program, and try to untangle the knots of complexities to put forward a target state for simplified data ecosystems. They need to be able to easily add new tech capabilities.
Is there a new technology in data and analytics that is creating more challenges than most people realize? How should enterprises adjust their approach to it?
Any new technology that possesses the potential to transform industries brings a cultural shift that may disrupt the business operations. Real-time analytics, AI, and machine learning can transform businesses, but the key challenge is to make organizations trust the results and insights of these analytical models.
This is where technology solution partners like us can help enterprises by exploring with them the benefits of this transformation, assessing the business requirements, selecting the right use cases and demonstrating their value in a sandbox environment, and then adopting them at scale.
What initiative is your organization spending the most time/resources on today? What internal projects is your enterprise focused on related so that you (not your customers) benefit from your own data or business analytics?
Our organizational endeavor is to become the most trusted digital transformation partner for our customers across industries. To accomplish this, our investments are focused on empowering our employees with analytical proficiencies, thus making them data literate. We can only make our customers data-driven organizations if we have a data literate workforce. Be it cloud transformation, AI/ML model building, or orchestrating data and analytics pipelines, a high level of data literacy makes our project delivery agile, which is the core requirement of any successful digital transformation program.
We have also been enabling every employee with self-service BI capabilities. Business teams within our organization are no longer dependent on IT for getting the required insights from data. We have created a custom BI application where every business user can access, analyze, and visualize data to track their KPIs. We also provide them with recommendations to act on.
Where do you see analytics and data management headed in 2020 and beyond? What's just over the horizon that we haven't heard much about yet?
Data management will continue to be the central theme in the digital business. As enterprises adopt cloud-first and hybrid cloud approaches, cloud deployment and transformation, adoption of nonrelational DBMSs (such as time series and graph databases), and use of AI and ML for not just data quality but holistic augmented data management (metadata management, master data management, data integration, data quality, and database management) will gain momentum.
Data management will evolve with the use of AI and ML. Modern data and analytics architectures should lend themselves to managing the growing depth and breadth of the data landscape in an environment where data explosion is on the increase. Here is where augmented AI and ML capabilities come into play, which can help classify, categorize, and build data catalogues with the required semantic layer, help identify and match data in disparate locations, and securely deliver information for each member in the organization as per his or her needs.
It's all about applying self-learning algorithms that focus on data discovery, data profiling, and data quality improvement. Current data management tools lack self-learning capabilities. Only certain vendors have explored this space. This feature will add immense value to the businesses across the globe. In the next two to three years, we expect to see many vendors offering AI- and ML-enabled data management platforms that will drive digital transformations.
Second, DataOps will fundamentally change the way data is delivered and managed. It will automate data delivery with the appropriate levels of security, quality, and metadata to enhance data usage in a dynamic environment.
Describe your products or solutions and the problems they solve for enterprises.
We offer a comprehensive solution stack to transform and manage the complete data management and analytics ecosystem of enterprises. Our custom-built data management platform, Data Modernization, can automate processes such as data acquisition, data profiling, and data transformation. For gathering BI and analytics application knowledge, we offer BI Modernization platform to perform this information discovery through metadata management.
Amaze for Data & AI makes an enterprise cloud transformation journey agile, reliable, and cost effective. Coming to developing advanced analytics models, our Decision Sciences Lab provides a lab sandbox environment to pilot multiple use cases within two to four weeks.