The last interview with Alessandra was a huge hit with the audience and this time, we have the views from a completely different industry. So glad to have a chance to welcome our dear friend and champion, Ramesh Kalava, Information Management Leader at Citrix. Ramesh graduated from University of Madras with a specialization in Computer Applications. He has a deep experience of 17 years on data and information management with multiple organizations.
Its a pleasure to have you today to talk about yourself and your work. Thank you for allowing us to interview you. Really appreciate your time. Can you please tell our readers a bit about yourself?
I am currently managing Information Management practice at Citrix Systems to develop & implement DI/DQ/MDM solutions and to improve the account data quality for better decision making at Citrix Systems. As a practice leader, I help develop enterprise architectural standards & processes for data in my organization. I also drive implementation of the same for all domains to reduce the complexity & ambiguity.
What is the business problem you are solving currently and what impact does it have on the organization? Can you please elaborate with a few examples on how the master data journey helped fuel these use cases?
Master data is indispensable and the key driver for any organization, because it is the foundation for multiple business user cases including improve data quality, data stewardship, reference data management, hierarchy management, business process management etc. During my 13+ years of journey on MDM, we have solved multiple business problems using various MDM process and technologies. Some key use cases are
- Account de-duplication :
- Account de-duplication is key for marketing operations ( campaigns, market penetrations, lead conversions etc). Creating single source of truth by enriching the single source of truth using third party data vendors enables effective decision making
- Customer 360 / Customer Master:
- Enabling Customer360 for the Supply chain and Sales is key for business operations. Integrating third party data vendors to standardize and enrich is imperative for analytics and dashboards to convert the leads and improve renewal rates. Hierarchical information facilitates effective territory management & executive dashboards.
- Partner Management /Hierarchy : This is another key business use case to solve Territory management for sales reps.
Editor’s note: We agree wholeheartedly. In fact, as a startup, we hit so many new use cases of MDM everytime we talk to a new industry or customer.
What according to you are the key pillars behind a successful master data management project?
Based on my experience, the following pillars are key for any data quality/MDM project
- Define Your Business Problem
- Stakeholders Sponsorship
- Enable Data Governance
- Continuous Monitoring and Prioritize
- ROI Measurement
What are your views on agile data mastering?
Master data projects required high budget to implement and most of the time stake holders don’t see the value immediately. Moving to agile processes allows stake holders to see the immediate value and ROI. This helps to improve the trust and sponsorship commitment. We have implemented multiple data quality / MDM projects using agile processes. Our recent project Customer360 is implemented using Agile fundamentals. This helps to be transparent with stakeholders by providing demos on every sprint.
Editor’s note – Great, keep it simple, use agile tools, deliver smaller sprints.
According to you, what are some key aspects about Customer Data Management that you would advise our readers to keep in mind?
- Customer Master is key for any organization. Defining business problems would help us to focus on data problems like data de-duplication, multiple hierarchies ( Sales, Legal, DUNS, Product, Region and etc.,)
- MDM projects are not IT projects. Establish great collaboration with business stake holders
- Define data champions and owners
- Enable KPIs to measure and monitor
How excited are you about the potential of AI in MDM? Where do you see the maximum impact of applying AI on the MDM discipline?
I am excited as I see that Master data with predictive analytics and enabling AI helps data stewards to take better decisions. It also improves accountability and productivity.
Please name few data practitioners whom you admire and would love to hear from in this interview series.
So nice to get in touch and talk with you Ramesh, many more master data projects for you!
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