Buvana is a Data Management Practitioner with 10+ years’ global experience in the insurance and financial industries. Possessing extensive data management experience with analytical, technical and interpersonal skill sets, Buvana has demonstrated a strong ability to manage Big Data initiatives. She has a proven track record of achieving strategic business objectives using data driven solutions while demonstrating an expertise in producing enterprise data strategies. Known by colleagues for being a driven team-player and action oriented visionary with fast capability of learning new techniques, Buvana has successfully implemented enterprise-wide data initiatives. We had the pleasure to hear from her about her journey and her outlook on data for the Master Data Maestros series.
Pleasure to have you as a guest today Buvana.
I want to thank you for the opportunity.
You successfully delivered digital marketing data solution for MetLife’s property and casualty business. What were the project challenges? How did you overcome them? How did it impact the bottom line?
Some key Project challenges were lack of customer 360 to consistently identify our customer and as a result, we were not able to trace their journey through the life cycle of digital marketing. We were never able to tie the campaign outcomes to the actual campaign itself to measure the success and ROI of the campaign.
MDM was the data foundation that helped us establish trusted views of the customer and link their campaign activity to the unified customer views. Our MDM program enabled our business to trace the journey of the customer and measure KPIs across the marketing funnel. These insights helped digital marketing stakeholders to tie the outcomes of the campaigns to the ROI. For example, cost of launching a campaign is say $2M but as a result of our digital campaign , our conversion rate (ie. converting clicks to quote to a sale) has yielded an increase in sales by $16M. Additionally , we have a huge opportunity to preform cross sale and upsale outreach in subsequent campaigns.
Editor’s note: That is a phenomenal achievement, an increase in sales to the tune of $16m through trusted view of customer built by the MDM. Congratulations!
You have worked on Enterprise Data Lake initiatives. It would be lovely to hear about what it involves and how it aligns with your MDM work?
Effective data lake architecture is a pre-requisite to attain maximum value of a MDM program. Pairing of MDM and data lake is instrumental in driving value for data initiatives. Historically , MDM synergizes the capabilities within transactional systems and datawarehouses to extract critical data points to enable the master data. With advent of data lake architecture , MDM has the ability to extract critical data real time, extract critical data from unstructured data sources seamlessly through a scalable data ingestion process. Data lake design offers various storage zones that helps manage application/ transactional data and master data in an optimal fashion.
MDM solution paired with a scalable ingestion process in Data lake and flexible storage methods where transactional, application, unstructured datasets , alternate datasets can coexist is a critical data foundation enabler to support all our business use cases and offers extensive analytical capabilities.
Editor’s note: Agree. MDM can not be a standalone system. It has to be natively integrated with the datalake.
What are the biggest challenges you foresee in the BFSI industry around data quality and master data management?
For MDM and data quality management to be successful, it is critical for an organisation to have a data champion at the Senior leadership level and achieve organisational alignment.
While an MDM program can be quite intimidating due to the complexity, best way to drive success is think big but start small – focus on critical business case that business is focused on, align the data strategy to business use case, establish MDM as a program to support the data strategy and then keep this program as evergreen to support to additional use cases . So best way to achieve success is baby steps and focus on tangible deliverables that has an immediate impact on the business outcomes to drive value.
Editor’s Note: Very well said, start small, think big! Agile data mastering is here to stay.
What would you say are the key benefits on an MDM implementation in the Insurance sector?
Insurance industries business strategies support offense (revenue focused) and defense (operational efficiency, compliance /regulatory) strategies.
MDM data strategy supports both offense strategies – driving sales /profit and defense strategies like GDPR , HIPAA regulations , CCPA etc.
Editor’s note: Happy you brought this up, it resonates with our experience too.
You have done a lot of prior work on sales & marketing analytics data, quality and information governance. Please tell us more about what were the challenges and outcomes.
Our data foundation is critical enabler for enterprise analytics capability . An effective data foundation must be supported with comprehensive information governance to drive certified trusted data to support analytics effort.
The key challenges were lack of data foundation and not having data driven mindset across the enterprise.
A well governed data foundation is critical to drive business insights for an organisation. Often times , we think about technology capability but we should start with business drivers – what outcomes are we targeting for , what success looks to us and how we leverage data to help accomplish our business outcomes.
Also, data literacy is another challenge – stakeholders need to adopt common vocabulary and common semantic understand of the critical data.
Data quality has always been reactive than proactive – we fix the data problems after the fact – this has resulted in data debts because the organisation spends more time fixing bad debt in a reactive manner.
Hence it is imperative that information governance framework is critical for data foundation that aligned with business drivers.
Information governance ensure data quality and promotes data literacy – that is critical to drive business insights using enterprise data assets.
In the recent Magic Quadrant for MDM solutions, Gartner has stated that “MDM continues to shift from reluctant to indispensable spend across a broader range of industries”. What are your views on this?
While I agree to this statement from a research /theoretical standpoint , there is more to this statement in the practical world. The shift reluctant to indispensable spend requires few intermediate stages. I would include reluctant to acceptance spend that will eventually drive indispensable spend . The biggest challenge is shift from reluctant to acceptance spend – accepting that the MDM spend is critical first is key and then the spend to transition to indispensable over time. The spend transition is slow in the practical world.
Analytics are a focus area for most enterprises, big and small. Can you please elaborate some examples you have seen where an MDM implementation fuelled the analytics journey?
I can highlight two critical analytics use case where MDM has been instrumental to the success of these use cases .
- To improve Net promoter score of our customers . MDM enables 360 of our customers and enables operational efficiency of our customer service . This results in better customer experience and opens up huge opportunity to promote our organisation through customer centricity.
- Customer Life Time Value – Again MDM allows marketers understand current product relationships and determine value of the customers . Through MDM , we have opportunity to analyze what is the propensity of our customers to buy other products , thereby increasing their future life time value.
You have a great portfolio of work on analytics, data lineage, data governance, MDM and data quality. In your opinion, how does it all tie together?
For an organisation to be data driven, it is imperative to have MDM program and promote data literacy – bringing together stakeholders and driving towards business outcomes and establishing organisation alignment becomes critical for a successful MDM program.
For credible trusted data domains established through MDM , it is absolutely essential to have data governance framework in place to align people , process and technology . This will ensure organisational alignment, promotes data literacy and helps in establishing enterprise data stewardship model in place for trusted information quality.
While MDM becomes data foundation , data governance drives data literacy and improves data quality by having controls in place through well governed process that manages the data , align people who drive the data quality and technology that enables the data and provides insight on data lineage so that we have full transparency on primary sources of the data.
For a successful analytics initiatives, it is imperative to have consistent , cohesive and trusted data and ensuring we have a good understanding of the data.
A well governed MDM program is a key enabler for analytics initiatives.
Pleasure learning from your experience Buvana, thanks for taking time out and we wish you the best.
Comments, questions, suggestions, feedback – contact us here. Thanks!Tweet
Master Data Maestro – Alessandra Almeida »
« Top 3 Reasons To Opt For Agile Data Mastering