Strategy Analysis, Design Thinking and a $94.95 million Big Data Case Study
This case study highlights how strategy analysis, which is wider in scope than enterprise analysis, and design thinking…
– an empathetic analysis paradigm with roots in the 1980s – helped solve an enterprise-scale business problem.
Specifically, a major global bank’s market share was under pressure from its competitors and big data analytics proved to be part of a viable strategic response that created $94.95 million in incremental value for the bank in six months.
Strategy Analysis and Design Thinking
The first step to solving a problem is to make sure you properly understand it. To start, market research and other relevant documents were studied, while multiple focus group sessions with a representative sample of the bank’s customers were held to find what customers thought, said, did and felt when it came to their dealings with the financial institution.
With empathy a key part of design thinking, we put ourselves in the customers’ shoes to understand their problems and expectations (Figure 1).
Figure 1: Facilitated design thinking; understanding the problem through the customer’s eyes
By this analysis, it was clear that the bank was not meeting customer expectations (Figure 2).
Figure 2: The difference between the market research and the focus groups highlighted the gap between them
From Figure 2, the requirements to close the gap had become clear. These requirements were prioritized according to their urgency and impact (Figure 3):
Figure 3: Prioritization (extract shown)
Ideation – proposing potential solution categories to the imperatives – was next. Each solution was evaluated against the strength of its risk-adjusted business case and its relative ease of implementation.
Figure 4: From priority requirements to strategic alternatives
This process ultimately distilled the strategic theme of the project – data-driven customer-centricity. To give the vision life, it was essential to integrate the bank (e.g. strategy, HR, finance, branding, marketing, operations and product heads). A successful implementation would require suitable teaming with all of these departments.
Data and Analytics
The need for the cryptic “deep customer insights” in Figure 4 was disaggregated (Table 2 and Figure 5).
Table 2: The categories of “deep customer insights” * Complex, bank-wide data sourcing
Given the data and information requirements, the following actions were taken:
- Negotiating and receiving approval for a diversity of data access requirements
- Integrating data for a single view of customer
- Assessing data quality
- Performing data cleansing for a key data element – a critical success factor
- Data fusion – merging internal and external structured and unstructured data
- Performing big data analytics (Figure 5)
Complex, enterprise-wide stakeholder challenges were gradually overcome to achieve this, testing our soft skills to the limit.
Figure 5: From a keynote at the Teradata Marketing Summit, USA 2014
With the preparation complete, the time had come to present the overall proposal to executive management.
The presentation was successful and we received the go-ahead to prototype. The outcome of the prototype ultimately exceeded business expectations, and the consequent positive word of mouth helped facilitate the all-important work-stream of change management (“change management” is effectively what we do as BAs). This all drove exponential adoption (see the shape of the benefits curve in Figure 6).
Then, for enterprise deployment, batch-generated insights were ingested into a CRM system for distribution to the branches. Bank staff now had access to key customer-level information, enabling them to have more focused conversations with them. The outcome was significant channel differentiation (the bank’s competitors still had traditional training), which helped arrest the pressure on market share and drive the turnaround in the key marketing statistics (shown on the left side in Figure 2).
By improved training (Figure 3), the staff also had better knowledge not only of the bank’s products and services, but also of their fit into each customer’s unique life cycle, thereby improving their ability to offer more meaningful, qualified advice.
This best communicates the outcome for the bank and stakeholders:
Figure 6: From a keynote at McMaster University, Canada 2016
Three Key Lessons
While big data analytics was instrumental to the project’s success, it’s not about technology. Rather, it’s always about what technology can do. Now while there were many learnings (e.g. technology, business, marketing and strategy) at enterprise scale, the most significant learnings were soft:
- Always make everyone look good; success is having had to stand on the shoulders of giants (the work of those before you) – whether success or failure – to achieve it
- Never let difficult stakeholder relationships get in the way of finding common ground with them; find some reason for them to be onside and make it happen
- Persuasion 101: Understand what’s in it for your stakeholders and why you’re best qualified to deliver it to them; let them know it
Strategy analysis and design thinking are powerful tools that can solve an organization’s biggest problems. In this case, these tools were used to solve a strategic problem and to produce significant business results as an outcome. Here are some closing thoughts:
- The fact that only 11% of CEOs create financial value from their data (Rossi 2015) makes this case study significant
- Having clear business goals is important: According to CIO Magazine, “IT [has] learned the importance of tying data initiatives to specific business objectives. The quickest way to derail a data initiative is to make it about the technology rather than business goals.” (Vijayan 2015)
- The VOLUME of the structured and unstructured internal and external data VARIETY was of multi-terabyte scale, while the rate of change of data approached 1,000 tps at peak VELOCITY. Analytics was challenging at this scale, so a High Performance Database Appliance was used to accelerate PROCESSING. Coupled with DECISIONING producing financial business outcomes, we met Gartner’s definition of Big Data (Sicular 2013)
Finally, for those BAs looking to grow their enterprise capabilities, be sure to (re)study Strategy Analysis and techniques like the Balanced Scorecard, Benchmarking and Market Analysis, Business Capability Analysis, Business Cases, Prototyping, Risk Management, Vendor Assessments, and Workshopping. They are all in the BABOK and will serve you well, no matter how senior a role you play in your organization.
Rossi, B. (2015) Business and IT leaders disagree on the future of data. Information Age. http://bit.ly/2t33V63
Sicular, S. (2013) Gartner’s Big Data Definition Consists of Three Parts. Forbes. http://bit.ly/2sIuBrA
Vijayan, J. (2015) As Big Data Hype Increases, CIOs Will Need to Manage Expectations. CIO Magazine. http://bit.ly/1T4HSj2