.1    The Challenge

Marsha has worked as a business technology consultant at a large investment bank for the last four years. In that time, she has worked on several high- profile engagements and has forged strong working relationships with several key bank employees. John, one of the trading floor managers in the Chicago office, recently attended a big data conference and learned about some new approaches for leveraging predictive analytics tools on the trading floor.

John was convinced that his team would benefit from more sophisticated application of technology and analytics tools. He also worked with Marsha previously to establish and automate various types of trading floor transactions, and he knew Marsha was the ideal person to help with this work. John connected with Marsha and they discussed opportunities to help improve his team's efficiency using some of the newer approaches. After a conference call with Marsha and her consulting manager, John realized there were a number of outstanding issues that needed to be addressed before implementing any new tools. He also realized careful analysis of data would help identify the best way to move forward. John assured Marsha that any information, trading data, resources, and support from his team would be made available as she needed.

.2    Approach for Analyzing the Business Problem

As a seasoned professional, Marsha knows that seemingly simple business problems or opportunities often hide layers of complexity. She has seen the results of poorly conceived data analytics initiatives and knows that various key stakeholders have differing views of potential solutions. She also understands the need for developing a shared understanding of the business problem that needs to be solved as a crucial first step.

Marsha asked that they pause and take some time to develop a shared understanding of the problems to be addressed through application of new technology. Although John was eager to move quickly, he agreed to follow Marsha's recommendation of conducting a discovery workshop to fully understand the problems and prioritize the ones to be solved by use of analytics. Marsha proposed the following approach:

Workshop Stages	Workshop Planning and Activities
Pre-Workshop prep	Identify relevant stakeholders using her experience with the bank and include John's suggestions for additional stakeholders. Conduct initial research to develop relevant knowledge on pricing, market volatility assessment, and how trades are conducted on the trading floors. Coordinate workshop logistics with John and develop the workshop agenda.
Workshop plan	Highlight some of the key areas in investment banking where deep and non-traditional analytics approaches are being successfully used. John expects that it may be a big change for many of the stakeholders to consider new ways to conduct trades. He wants to provide a short explanation on the use of deep analytics. Present some of her high-level research on the topic such as an industry point of view, benchmarking results, and competitive analysis. Use analysis techniques such as root cause analysis, 5 Whys, and business model canvas to identify problems to be addressed. Concretely define the business problems framed as research questions that data can help solve and outline the next steps.
Post-Workshop wrap-up	Share the list of research questions and other workshop results with attendees. Highlight next steps and follow up on the action items that may set the direction of future scoping and elicitation activities.

Pre-Workshop Activities

Based on her experience with the bank, Marsha assembled her initial list of stakeholders which included stakeholders from the Quantitative Research and Analysis team (Quants team) and the office of the Risk Management team (Risk team). John added additional stakeholders from his trading teams, operations, IT, and corporate governance. After agreeing on the workshop participants, agenda, and logistics, Marsha started her research work in preparation for the workshop.

Marsha conducted one-on-one interviews with John and some of the traders to learn about the business of trading desks. She learned how trade call sheets are communicated and how trades are executed on the trading floor, and created process models to capture this information.

She learned trade calls are passed to the traders by the Quants team who have modelled a complex process of predicting option price paths with a combination strategy using Black Scholes and RiskMetrics™ models. She saw that trades are divided into various groups such as speculations, hedges, and arbitrage teams. Marsha also learned how the Risk team sets daily Value at Risk (VaRs) based on traders' book, experience, and prior positions. She followed the process through, noting these parameters and trade plans are fed into the trading terminals. Although aware of the basics of these practices, Marsha realized she would need to understand additional process complexity to help the team discuss alternate sources of information for traders to consume. She decided to leave those discussions for the workshop.

The Discovery Workshop

The workshop started well as Marsha discussed how analytics models can be used in investment banking use cases. However, she felt uneasy with some of the comments. After asking some additional questions, it became apparent that both the Quants team and the Risk team were reluctant to provide their support. The Quants team members felt their predictive models were already cutting-edge and comparable to industry benchmarks. The Risk team were concerned the VaR computation would be impacted by the introduction of new models and significantly impact today’s widely used processes. Marsha was able to redirect the participants to the benefits, including potential bottom line improvements that could occur. She was also able to leverage her research to demonstrate how competitors had addressed similar challenges. Both teams agreed it was important to proceed even if it meant changes to current practices.

With everyone on the same page, Marsha tackled the primary workshop objective which was to identify problems which could be answered through use of data analytics.

Post WorkShop Wrap-up

Marsha was able to consolidate the findings from the workshop and shared these with the group of stakeholders for agreement. By re-framing the business problem successfully, Marsha was able to demonstrate aspects that can be solved through business data analytics, as well as opportunities for both process and application development improvements. Marsha was able to share this outcome with the team and scheduled a follow-up discussion to prioritize these goals with John and other relevant stakeholders.

.3    Outcomes Achieved

Marsha leveraged her facilitation skills to drive the attendees towards a shared understanding and agreement about next steps. To get there, Marsha used specific business data analytics techniques to achieve incremental agreement, as follows:

• Validating business needs: Marsha decided to use the 5 Whys technique (as referenced in 3.15 Problem Shaping and Reframing) to both validate and develop a shared understanding of the business needs and goals John wanted to achieve. John was instrumental in helping everyone understand how even a small improvement in trading floor efficiencies could result in significant return on investment (ROI) and lead to a competitive advantage for their firm.
  By applying the 5 Whys technique iteratively, Marsha was able to determine that the root cause of some of the recent loses stemmed from specific models currently being used for option pricing. It established that there was a clear business need to improve trading decisions through better analytical approach for predicting price movements. Marsha determined that the next step should involve investigating how existing models work.
  
  
• Validating assumptions: Marsha approached the Quants team and the Risk team to understand the existing prediction models in depth. The Quants team and Risk team stated that those isolated losses were expected and as part of market risk, simply a cost of doing business. Marsha decided to pursue this further and suggested they test the assumptions associated with the current models. The Quants team outlined that their models are based on two primary assumptions. The Black Scholes model assumes that option prices follow a log-normal distribution and prices can be essentially predicted based on current spot price (current market price) and the historical volatility (standard deviation). The RiskMetricsTM model is an auto-regressive time series model. In simple terms it says yesterday's price has more weight than the price that was the day before. Hence, future prices can be predicted by creating a weighted average model. Similarly, the Risk team stated that the daily limits (VaR) are set by determining the probability of loss at a certain confidence interval (for example, 95%) and it assumes either Black-Scholes model or RiskMetrics in determining underlying distribution. These model characteristics indicate that the assumption around losses are valid, and there may be possibilities to better predict these losses using more variety in the data used.
  
  
• Utilizing business knowledge: Marsha noted that these model assumptions are based on very limited historical trading data, and other equally important data was being ignored. For example, the effect of the news cycle was not taken into consideration, type of industry of the underlying stock was not considered, and other macro parameters like GDP, interest rate spreads, and underlying asset fundamentals could also be part of the predictive approach. When Marsha outlined these, the operations reps and the traders stated that they always follow foreign markets, daily news, and sometimes Twitter before triggering the trades.
  
  
• Demonstrating outcome with plausible solutions: Based on previous experience, Marsha realized that without describing a potentially feasible solution option, it would be easy for attendees to get “bogged down in the details” of today's challenges, or worse yet, jump to one of the technology solutions that John had seen at the conference. She started illustrating the business opportunity in more concrete terms and outlined some recommendations to improve trading floor efficiencies:
  • Marsha described a future vision of a new predictive model for trading floor operations by describing benefits that could be derived. She focused on key benefits that would appeal to various stakeholders at the workshop:
    1. Improve confidence of trades by providing more information to traders (improve the availability of relevant information on trading terminals and presentation of heterogenous data).
    2. Limit undue risks to overall book of business (parallel pilot with existing models and/or pilot with arbitrage trades that involve low risk opportunities).
    3. Track the performance of the new model (measure the opportunity cost when trades are not executed based on the new model).
    4. Retain oversight on trades and risk levels (VaR) to maintain regulatory compliance (explore how risk levels can be employed for the new predictive model).
    5. Train traders to utilize the new predictive model results.
  • Marsha also mentioned that the exercise is not only developing a new predictive model for option trading; it would also impact various other operations. For example, it would include application development (1, 2), process optimization (1, 2, 3, 5), integrating enterprise data sources for big data implementation (1), and business intelligence (4) components. Marsha concluded the workshop on a high note by describing the business opportunity in detail and attendees enthusiastically agreed that developing a plan for executing this work was the required next step.

.4    Key Takeaways

• It can be challenging to achieve the desired future state without ensuring stakeholder alignment and understanding. The starting point is not necessarily implementation of new technology but instead developing a shared understanding of the problem that needs to be addressed.
• Clearly articulating the need and business problem requires a significant amount of upfront analysis. For example, in this scenario, the business problem could lead to five considerations, which could include initiatives beyond analytics. Without considering all aspects of a business problem, an analytics solution may be misaligned to the overall business objective.
• When reviewing existing analytics processes or models, verify the principles on which the model was built. This often leads to discovery of limitations in the existing models and leads to better solutions. For example, by examining the model assumptions such as distribution of option prices being log-normal (Black-Scholes model) or higher weight is given to more recent option prices, while predicting future options prices (RiskMetrics) shows that no other parameters are considered in the analytics model.
• Correctly applying effective business analysis techniques such as a workshop leads to collective problem analysis by different stakeholder groups. Through shared understanding and hearing different perspectives, root causes can be uncovered and problems can be effectively prioritized for solving.