2.6 Tasks
2.6.6 A Case Study for Guide Organization-Level Strategy for Business Data Analytics
Guide to Business Data Analytics
Singapore has one of the oldest and most well-developed retirement programs in Asia, consisting of defined contribution plans in individual accounts. Mai Tan works as one of several product owners at Retire Inc., a young company that recently launched a new product, RetireSafe for the citizens of Singapore. RetireSafe allows qualified individuals to determine how prepared they are for retirement. RetireSafe verifies citizenship based on tax identification data and demographic details, allowing it to interact with government databases for publicly available data. It also allows individuals to enter employment details, current net worth, financial assets, health status, post retirement plans, and desired future income streams.
With this information identified, the product generates assessments and scenarios depicting what kind of retirement the individual can expect through an interactive, intuitive dashboard. Sensitivity analysis features are incorporated to allow individuals to modify parameters and see the impact of those changes. In this way, the product provides information to help citizens better understand and maximize the expected results for their individual accounts.
.1 An Overwhelming Success
RetireSafe was developed by Mai's team and has been available for approximately twelve months. It is both the company's largest investment in product development and is on track to becomes its highest generating product. RetireSafe has been warmly embraced by Singaporean citizens who have flooded the company with requests for new features and additional functionality. The product team adds each request to a product backlog where it is prioritized for upcoming releases. Mai uses several metrics to track performance and adoption of various product features. She also uses this information to decide which features to remove to reduce ongoing maintenance costs. Afterall, as a young organization, it's a constant challenge to ensure positive returns that can be used to fund building additional features.
To add greater depth in understanding adoption and customer satisfaction, Mai decided to track product usage and satisfaction indicators through a net promoter score (NPS) as a single high-level metric. She used several drill-down metrics, detailed information tracking feature usage, and time individuals spent constructing various types of scenarios to provide more clarity. This type of data helped the team assess and prioritize requested new features.
The product team has built and rolled out a steady stream of new features but is overwhelmed by requests. What was originally envisioned as an annual product release cycle has become a quarterly release from a team that has doubled in size since the initial launch of this product. Their work has become increasingly more difficult in the last few weeks as other Retire Inc. departments have requested access to the information being collected through this product. Other teams want to use this data to build a repository of prospects, cross sell existing financial products, and design new or enhanced products. As busy as the product team is, Mai realizes her work is no longer just about rolling out new enhancements to a popular product.
.2 The Challenge
To continue its growth and success, Retire Inc. needs to holistically view how data is managed and governed on behalf of all interested stakeholders. Requests for data tracked by Mai's product team has identified issues that need to be resolved at an organization level. These include:
Mai relied on several business analysis techniques to build her understanding of what needs to be done, including: benchmarking, business capability analysis, organizational modelling, and SWOT analysis. Through her benchmarking analysis and discussions with other organizations that have greater data maturity, she learned about the importance of data architecture and data governance. It also helped her develop a clear picture of what future practices could look like. Using organizational modelling techniques, Mai developed a good understanding of where changes could be made in Retire Inc.'s current data practices and she recognized the shortfall in skills that are currently missing - those that are needed to effectively evolve data analytic practices. She also started to have appreciation for what the desired changes would cost and wondered how she could possibly secure the required budget to propose a centralized data analytics function. Mai's business capability analysis helped her identify areas of opportunities and components that could be leveraged as Retire Inc. continues this journey. She discovered that all the existing data expertise within Retire Inc. lies within her product team. Her SWOT analysis helped strengthen her analysis and provided vital information to include in her assessment.
This assessment also solidified the important responsibility that Retire Inc. had to protect customer data. Mai realized that RetireSafe data included personally identifiable and sensitive information such as age, income and net worth which needed to be securely managed within internal departments. Formulating internal guidelines for use of this information, anonymizing it while still providing value, meeting General Data Protection Regulation (GDPR) regulations, and meeting other government or financial regulations would require time and expertise.
Throughout this analysis, Mai used her knowledge of key stakeholders to share her findings, determine what Retire Inc. leaders really wanted, and identified potential sources of funding to support the future implementation of data analytics expertise. Although not an exhaustive list, Mai was able to summarize the pros and cons for her two basic options as follows:
.4 Recommendation
Based on her analysis, Mai realized it wasn't a binary option. She recommended establishing data standards within the RetireSafe team, specifically around data sharing and light-weight data governance practices. As the organization gained better understanding of their data needs, Mai recommended they transition to a centralized analytics function sourcing some members from the RetireSafe team and augmenting them with key personal to account for deficiencies in key skillsets. In the meantime, she recommended carefully adding data experts to the RetireSafe team through short term contracts to help mature data architecture practices.
.5 Rationale
Both centralizing and decentralizing in themselves would be helpful but the advantages or disadvantages are finely balanced. There were not enough benefits for either option to merit one over the other. Other criteria proved to be instrumental in Mai's recommendation. Particularly the need to mature data management practices, align with budget considerations and the ability to move fast.
Retire Inc. is a new company with an "in demand" product that is demanding additional organizational funding to keep up with the backlog of customer requested enhancements. Based on conversations with Retire Inc. leaders, Mai realized it would be difficult to secure the funding required to build an effective centralized function at this time. Additionally, the time to hire new staff, effectively onboard them, and then start to standardize data management practices would take too long before the centralized analytics function would be able to provide data to other departments.
Mai felt decentralizing the analytics function with the current lack of data maturity was risky and could result in poor use of data, corrupted data, data silos being created throughout Retire Inc, or proliferation of unsecured sensitive data. This could lead to poor decisions, stalled progress, or worse yet, irresponsible use of customer data. She assessed the risk to be high and it would set back the organization for many years - she could not recommend a fully decentralized structure.
The final deciding point for the recommendation was the fact that although the RetireSafe backlog was long, it would reduce significantly over time. She estimated that the current team was sufficient to manage this workload and reasoned that over time, there would be less and less requests for additional enhancements.
.6 Key Takeaways
With this information identified, the product generates assessments and scenarios depicting what kind of retirement the individual can expect through an interactive, intuitive dashboard. Sensitivity analysis features are incorporated to allow individuals to modify parameters and see the impact of those changes. In this way, the product provides information to help citizens better understand and maximize the expected results for their individual accounts.
.1 An Overwhelming Success
RetireSafe was developed by Mai's team and has been available for approximately twelve months. It is both the company's largest investment in product development and is on track to becomes its highest generating product. RetireSafe has been warmly embraced by Singaporean citizens who have flooded the company with requests for new features and additional functionality. The product team adds each request to a product backlog where it is prioritized for upcoming releases. Mai uses several metrics to track performance and adoption of various product features. She also uses this information to decide which features to remove to reduce ongoing maintenance costs. Afterall, as a young organization, it's a constant challenge to ensure positive returns that can be used to fund building additional features.
To add greater depth in understanding adoption and customer satisfaction, Mai decided to track product usage and satisfaction indicators through a net promoter score (NPS) as a single high-level metric. She used several drill-down metrics, detailed information tracking feature usage, and time individuals spent constructing various types of scenarios to provide more clarity. This type of data helped the team assess and prioritize requested new features.
The product team has built and rolled out a steady stream of new features but is overwhelmed by requests. What was originally envisioned as an annual product release cycle has become a quarterly release from a team that has doubled in size since the initial launch of this product. Their work has become increasingly more difficult in the last few weeks as other Retire Inc. departments have requested access to the information being collected through this product. Other teams want to use this data to build a repository of prospects, cross sell existing financial products, and design new or enhanced products. As busy as the product team is, Mai realizes her work is no longer just about rolling out new enhancements to a popular product.
.2 The Challenge
To continue its growth and success, Retire Inc. needs to holistically view how data is managed and governed on behalf of all interested stakeholders. Requests for data tracked by Mai's product team has identified issues that need to be resolved at an organization level. These include:
- Who decides which departments can have access to the data?
- How much data can be shared?
- What data should be restricted?
- How will data be secured as its shared?
- What other standards or policies need to be in place for data usage?
- Who in the organization enforces these data management procedures?
- How will Retire Inc. avoid inconsistent data silos cropping up?
- advocate for a single focused analytics team (centralized function), or
- recommend analytics expertise be developed throughout Retire Inc. (decentralized function).
Mai relied on several business analysis techniques to build her understanding of what needs to be done, including: benchmarking, business capability analysis, organizational modelling, and SWOT analysis. Through her benchmarking analysis and discussions with other organizations that have greater data maturity, she learned about the importance of data architecture and data governance. It also helped her develop a clear picture of what future practices could look like. Using organizational modelling techniques, Mai developed a good understanding of where changes could be made in Retire Inc.'s current data practices and she recognized the shortfall in skills that are currently missing - those that are needed to effectively evolve data analytic practices. She also started to have appreciation for what the desired changes would cost and wondered how she could possibly secure the required budget to propose a centralized data analytics function. Mai's business capability analysis helped her identify areas of opportunities and components that could be leveraged as Retire Inc. continues this journey. She discovered that all the existing data expertise within Retire Inc. lies within her product team. Her SWOT analysis helped strengthen her analysis and provided vital information to include in her assessment.
This assessment also solidified the important responsibility that Retire Inc. had to protect customer data. Mai realized that RetireSafe data included personally identifiable and sensitive information such as age, income and net worth which needed to be securely managed within internal departments. Formulating internal guidelines for use of this information, anonymizing it while still providing value, meeting General Data Protection Regulation (GDPR) regulations, and meeting other government or financial regulations would require time and expertise.
Throughout this analysis, Mai used her knowledge of key stakeholders to share her findings, determine what Retire Inc. leaders really wanted, and identified potential sources of funding to support the future implementation of data analytics expertise. Although not an exhaustive list, Mai was able to summarize the pros and cons for her two basic options as follows:
| Solution Options | Pros | Cons |
| Single focused analytics team (centralized) |
|
|
| Analytics expertise throughout Retire Inc. (decentralized) |
|
|
.4 Recommendation
Based on her analysis, Mai realized it wasn't a binary option. She recommended establishing data standards within the RetireSafe team, specifically around data sharing and light-weight data governance practices. As the organization gained better understanding of their data needs, Mai recommended they transition to a centralized analytics function sourcing some members from the RetireSafe team and augmenting them with key personal to account for deficiencies in key skillsets. In the meantime, she recommended carefully adding data experts to the RetireSafe team through short term contracts to help mature data architecture practices.
.5 Rationale
Both centralizing and decentralizing in themselves would be helpful but the advantages or disadvantages are finely balanced. There were not enough benefits for either option to merit one over the other. Other criteria proved to be instrumental in Mai's recommendation. Particularly the need to mature data management practices, align with budget considerations and the ability to move fast.
Retire Inc. is a new company with an "in demand" product that is demanding additional organizational funding to keep up with the backlog of customer requested enhancements. Based on conversations with Retire Inc. leaders, Mai realized it would be difficult to secure the funding required to build an effective centralized function at this time. Additionally, the time to hire new staff, effectively onboard them, and then start to standardize data management practices would take too long before the centralized analytics function would be able to provide data to other departments.
Mai felt decentralizing the analytics function with the current lack of data maturity was risky and could result in poor use of data, corrupted data, data silos being created throughout Retire Inc, or proliferation of unsecured sensitive data. This could lead to poor decisions, stalled progress, or worse yet, irresponsible use of customer data. She assessed the risk to be high and it would set back the organization for many years - she could not recommend a fully decentralized structure.
The final deciding point for the recommendation was the fact that although the RetireSafe backlog was long, it would reduce significantly over time. She estimated that the current team was sufficient to manage this workload and reasoned that over time, there would be less and less requests for additional enhancements.
.6 Key Takeaways
- It often takes one successful analytics driven project to open the floodgates for data requests. Organizations often transition between centralized, decentralized, and hybrid models as needs dictate and data maturity evolves.
- Recommendations do not have to be an "either-or" decision. In this case, a hybrid recommendation of initially allowing the RetireSafe product team to make foundational data management decisions could prove to be the most cost-effective way to build organizational-level data management strategies. Transitioning to a centralized data management function in the future would then allow much broader use of data in a disciplined, organized, and secure way throughout Retire Inc.
- Decisions often go far beyond the "pros and cons" analysis as many other factors come into play.
- How best to structure the business data analytics function at an organizational level can be a complex challenge and context is everything.
- Important considerations including analytics maturity, existing workload, available budget, and political considerations help drive decisions to centralize, decentralize or a hybrid approach to building organizational level data capability.
