What is it like to lead a large data quality program in a large banking institution?
In this interview I speak with Purvi Ramchandani, Data Quality Lead at Silicon Valley Bank to learn more about her experiences and progress in delivering an enterprise data quality initiative.
Dylan: Can you briefly talk about Silicon Valley Bank and your current role in the Enterprise Data Quality program there?
Purvi: Silicon Valley Bank (SVB) provides commercial, international and private banking through our locations worldwide.
At SVB, everything we do helps innovators, enterprises and investors move bold ideas forward, fast. Our clients are entrepreneurs and companies of all stages and sizes, and private equity/venture capital investors who fund them. We connect founders, leaders and funders who can help build businesses and bring visionary ideas to life.
The Information Management group at SVB is responsible for managing the following across the organization:
- Enterprise Data Warehouse
- Enterprise Business Intelligence
- Enterprise Data Quality Improvement Services
I am a part of the Enterprise Data Quality Improvement Services group. My role is to lead the enterprise data quality strategy and roadmaps and deliver the successful implementation of cross-functional initiatives.
Our clients are forward thinkers, true believers, optimists and game changers. To support them with creative thinking, proactive processes, and high-quality data, our senior leaders recognized the need to start an enterprise-level data quality program to measure, monitor and improve the quality of data assets.
Data is the life blood of any business and high quality trustworthy data helps senior executives to make more informed strategic decisions. I am leading Enterprise Data Quality Program here at SVB and I am responsible for setting both strategy and vision for the enterprise data quality program.
Since the Data Quality function is a relatively new function at SVB, I champion data quality through education and by bringing awareness around best practices and processes.
I love my role as it provides the opportunity to build out the function and make a difference in the organization with my passion for the topic.
Dylan: What does Data Quality mean to you and why do you believe it is so important?
Purvi: In today’s era of data-driven decision making, data needs to be treated as an organizational asset. In fact, I would say data is a king and data quality gives power to the king. A king without power is of no use, and data without quality cannot serve any purpose.
Data quality is an assessment of data’s fitness for purpose. If the data is not trustworthy, then analytics and reporting that run on the data cannot be trusted.
Due to globally expanding regulations and the need for compliance, data quality has shifted from “Nice to Have” to “Must Address”.
Data Quality is especially important for a financial services company like ours because we also report data externally. Non-compliance may result in increased scrutiny by auditors and federal examiners, fines and penalties to executives, and an increase in reputational risk caused by negative customer experience based on bad data.
Dylan: Can you elaborate on the Enterprise Data quality program at SVB and the next steps you are planning to take this program to the next level?
Purvi: Data Quality is a foundational piece of any Master Data Management, Risk Management or Data Governance efforts.
We started our Data Quality program with a small team and implemented an enterprise data quality tool and framework with “Measure”, “Monitor”, “Improve” and “Publish” sub-functions.
We help business units with rule-based data profiling, scorecards and exception reports. These are actionable reports used by business teams to fix inaccurate data in source systems.
Our approach to data quality is to monitor at the reporting source of truth where data is being used and perform improvements at data entry sources to help reduce or prevent problems from occurring again.
The strategy is to start small, provide incremental benefits and results, then expand. We started with the most critical functions and areas of our business where having good data matters the most.
We now want to apply this proven, repeatable framework to all other functional area of our business.
Currently we are in the process of establishing our enterprise data quality practice to take the enterprise data quality program to the next level in terms of scalability and supporting more business units across the organization, so everyone can ensure the high quality of their data.
We want to provide our senior executives with one Data Quality Index for each functional area as well as an overall Data Quality Index or score to measure organizational data health which is a multi-year journey.
Dylan: What other soft skills do you think are required to be successful in data quality?
Purvi: Data Quality work requires a combination of technical and business acumen. Besides the technical skills, it requires certain soft skills that are essential.
First, it requires passion and courage to do things differently and challenge the status quo. It is a relatively new function and quite different from the other established functions. It requires change in culture and mind set.
Strong presentation skills are required to bring awareness to the organization. Communication will also involve training and orientation to different levels of employees in the organization.
The ability to influence without direct authority is also essential. Data Quality programs are cross-functional efforts and require collaboration from all the teams involved.
Project/Program management experience definitely helps as you frequently draw on prioritization, scoping, communication and change management skills.
Business engagement, strong relationship building, and cross-functional collaboration are also required skills.
Data Quality efforts are not one-time projects but they are ongoing in nature and require commitment to report progress consistently at regular intervals to the stakeholders. Communication skills play a major role in keeping the interest alive amongst all levels of the organization.
Dylan: What were the initial challenges you faced. How did you deal with them?
Purvi: In the beginning, there were lots of misconceptions about data quality.
One of the first misconceptions we faced was the classic 'data quality is an IT problem'. Some people also felt that data quality issues should be fixed by the data quality team.
From my perspective, data quality is a business problem and IT enables the business to improve it through tools and processes. Bad data impacts everyone interacting with the data, therefore it’s everybody’s responsibility to maintain good standards and practices that in turn will increase confidence in data used for reporting and analytics.
To counter the misconceptions we experienced, numerous brown bag and business orientation sessions were held to help increase awareness around data quality and how it should be managed and delivered.
We stressed the importance of IT and Business working together as a team to implement best practices and process improvements at the source.
Another challenge we faced was to establish incremental value and showcase continuous results so that we could get continuous executive and senior leadership support. To achieve this, we focused on prioritization by working on the things that were most critical to the company.
Finally, to establish credibility, we established a monthly metrics publishing process to communicate our continued progress. Communication like this was useful for sustaining the continued support of sponsors and stakeholders.
About Purvi Ramchandani
Purvi Ramchandani is a Certified Information Management Professional Expert (CIMP EX) and Project Management Professional with over 20 years of experience in various information management disciplines.
In the last 10 years, she has been living her passion around Data Quality / Data Governance and focusing on leading enterprise data quality monitoring and improvement initiatives. She is leading Enterprise Data Quality Program at SVB (Silicon Valley Bank). Prior to SVB, she has led data quality programs and data analysis/analytics teams at NetApp and Washington Mutual Bank (Now JPM Chase) respectively.
She led efforts to establish data quality standards and practice. Not only monitoring and fixing data, but she is also passionate about finding gaps in systems and processes and driving process improvements at the source to eliminate the problems from recurring.
She believes that cross-functional collaboration is the key to the success of any data quality program. With her passion for leadership and ability to influence without authority, she has successfully driven many cross-functional business and IT initiatives.