| | APRIL 20199CIOReviewinformation in audiovisual formats. This goes a long way in making data accessible to the non-technical user. Managers who frequently use data to make better decisions are likely to understand the value of sophisticated algorithms.Opportunity 2: Embedded Analytics and Business Process TransformationEmbedding predictive models in business processes and tools helps large user groups in decision-making. AI can help solve the harder prediction and classification problems involved. It can also help generate predictive insights at an industrial scale.· Why: Delivering personalized value to customers requires decentralized decision making. This diffusion requires surfacing relevant insights seamlessly at each step of the process. · How: Executives often complain that AI-based solutions are hard to integrate with existing business processes. They are right. AI is most potent when you allow it to transform operations. The root cause is often a poorly defined problem statement. Sometimes, it may be necessary to redefine misclassified BI and AI efforts as Business Process Transformation programs. · What: Business process transformation involves looking at each step of the process to ask which actions can be executed better, faster or simply automated. Often this eliminates some steps or changes the sequence of execution. In some cases, the new process can look significantly different from the current version.Enablers - People, Products, the elusive 3rd P Past efforts to realize such opportunities have resulted in a healthy mix of successes to celebrate and opportunities to learn from failures. People:Data Scientists and Systems Designers bring complementary skills to the table. Scientists know how to design experiments, examine empirical evidence and generate insights from data. Systems designers and developers understand process and data flow, entity relationships and state transitions. More importantly, they also have different ways of thinking and soft skills. Scientists are known for their intellectual curiosity, logical reasoning and the kind of rigor that is evidenced by methods like recursion. These are entirely different from the systems and design thinking mindset of using empathy to understand user requirements and innovation to overcome technical challenges. From recruiting to project staffing, it is critical to ensure a good mix of skills from these disciplines. Most important of all is the collaboration that helps people with these diverse backgrounds to work together effectively. The acronym C.I.R.C.L.E is a simple way to remember these ingredients of success.Products: Both proprietary products and the open source ecosystem offer an ever-increasing array of options. Mature users of technology can sidestep false dichotomies (e.g., build vs. buy) and strengthen foundations to then integrate a variety of micro-services. Put another way, when you have the right people, this problem solves itself.The elusive 3rd P is Process. It's hard to define a sequence of steps to follow when the goal is to transform other processes. We can, however, build a repository of guidelines and best practices. As a first step towards researching this, eight best practices were shown to a group of about 100 CXOs, BI professionals,and Data Scientists. They were asked to identify the ones they thought were relevant. Here are the best practices and their perceived relevance. Relevance (N=76)1 To know customers look at transactions; to understand them, interactions.71%2 Including execution teams in planning dialogue makes insights actionable.71%3 Active involvement of expert users during development is critical!55%4 Revisit unrealized opportunities with new data.54%5 Big ideas create incentives to break silos and integrate disparate systems.39%6 Change management is complex; requires formal and informal processes.38%7 If the goal is big, hairy and audacious, find a c-suite sponsor.43%8 Leverage synergies across product, technology and analytical teams.58%Best Prac cesThe C.I.R.C.L.E. of Success
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