Strategy plan

Today’s objective You have now:

• Assessed the analytic readiness of your org • Defined a vision for future capability What is the path to get there? Building analytic capability involves evolution on several dimensions • People and culture • Processes • Technology Five Elements of Analytics Transformation Analytics Transformation Roadmap StatSlice 2013 White Paper A roadmap is needed to map the path for this evolution GOALPeople/ Culture Processes Technology Year 1 Year 2 Year 3 [Milestones] [Milestones] [Milestones] [Milestones] [Milestones] [Milestones] [Milestones] [Milestones] [Milestones] Roadmap Post - Session Assignment • Following this session, you will individually complete a 4 - page strategic and tactical plan with details of how your organization will execute the roadmap Evolution of people and culture Building analytic capability - people “ So now you’ve got the data available in some data warehouse configuration, and then the question is, how do I access it? How do I input it in decisions? How do I utilize that data effectively? That’s where people are now.

They say, ‘Let’s go hire a data scientist or some statisticians. Let’s go hire some data engineers.’ And they find out everybody else is trying to hire the same people.” Hal Varian (Chief Economist, Google) Importance of analytical talent Internal expertise is needed to drive competitive advantage, i.e. less benefit if analytics is fully outsourced Source: MIT Sloan Management Review “The Talent Dividend” Levels of analytic maturity S. Ransbotham , D. Kiron, and P.K. Prentice, “Beyond the Hype: The Hard Work Behind Analytics Success,” MIT Sloan Management Review , March 2016 Analytically mature companies apply basic through advanced methodologies Source: MIT Sloan Management Review “The Talent Dividend” Building capability requires complementary strategies for recruiting and training Source: MIT Sloan Management Review “The Talent Dividend” Role types within analytics http://blog.hackerrank.com/the - biggest - misconception - about - data - scientists/ Role types within analytics: specific skills Business Analyst:

Business analysts’ strengths lie in their business acumen . They can communicate well with both the data scientist and C - suite to help drive data - driven decisions faster. They typically work across sales and marketing teams to make data - driven decisions. Data Scientist:

Data science is largely rooted in statistics, data modeling, analytics and algorithms. Data mining (the most in - demand skill on LinkedIn) is a subset of data science as the means to the end of extracting value from data using techniques, like pattern recognition, algorithm design and clustering, to better predict future behavior. Data Engineer:

While data scientists dig into the research and visualization of data, data engineers ensure the data is powered and flows correctly through the pipeline. They’re typically software engineers who can engineer a strong foundation for data scientists or analysts to think critically about the data.

http://blog.hackerrank.com/the - biggest - misconception - about - data - scientists/ Modern Data Scientist Data Scientist Profile http://www.kdnuggets.com/2014/09/hiring - data - scientist - what - to - look - for.html Data Science Teams How does Data Science differ from Statistics? Evolution of analytical culture STAGE 1: OVERRELIANCE ON MANAGERIAL JUDGMENT SUCH AS INTUITION AND INSTINCTS STAGE 2: SILOED USE OF ANALYTICS IN A FEW DEPARTMENTS STAGE 3: EXPANDING USE OF ANALYTICS IN SEVERAL DEPARTMENTS, NOTED BY AN INCREASING AMOUNT OF COLLABORATION STAGE 4: SCALING DECISION MAKING THROUGHOUT ALL RANKS OF THE ORGANIZATION IN AN INTEGRATED, HOLISTIC APPROACH STAGE 5: CONTINUOUS IMPROVEMENT BUILT ON AN EVOLVING CULTURE The Evolution of Decision - Making: How Leading Organizations Are Adopting A Data - Driven Culture (Harvard Business Review) Analytical culture in mature organizations The Evolution of Decision - Making: How Leading Organizations Are Adopting A Data - Driven Culture (Harvard Business Review) For discussion • How should talent acquisition and cultural change advance in parallel?

• Is it possible to skip any stages in cultural change in the journey towards maturity? Evolution of analytical processes Building Capability - Processes How does analytics guide decision - making across the organization?

Value initially demonstrated through ad - hoc projects Analytics conducted as planned, scheduled projects Analytics conducted as iterative, closed - loop processes Analytics focused on operations and optimizing existing processes Analytics applied to strategic questions and to explore new ideas Treating Predictive/Prescriptive models as high - value organizational assets For maximum value, the models must be:

• Deployed across the organization • Embedded into business processes • Continuously monitored and optimized over time Setting up an iterative, closed - loop analytic process Business Manager Data ScientistData Engineer http://www.slideshare.net/TheMarketingDistillery/building - an - analytics - culture - a - best - practices - guide - 20502011Source: “Building an Analytics Culture”, SAS Conclusions Paper Business Analyst Ensuring collaboration across roles http://www.slideshare.net/TheMarketingDistillery/building - an - analytics - culture - a - best - practices - guide - 20502011Source: “Building an Analytics Culture”, SAS Conclusions Paper• Analytic processes are highly iterative • Roles do not work sequentially – they must collaborate closely • Skills, processes and culture must support effective collaboration Requirements for team effectiveness • Clarity of roles and responsibilities in the analytic process • Clarity of end - to - end steps in process through construction of analytic workflows • Effective communication across roles through a common business - oriented vocabulary RACI framework for role clarity http://racichart.org/the - raci - model/ RACI exercise for analytic process Assign R,A,C or I designations to each role in each step of the process Domain Expert Makes decision Evaluates process and ROI Data exploration Data visualization Report generation Exploratory Analysis Descriptive Segmentation Predictive modelingModel validation Model deployment Model monitoring Data preparation Clarity of end - to - end process steps • Analytic process typically involves detailed sub - steps driven by specific team members • If knowledge is not shared with others, high risk of process disruption if team member leaves • “Best practice” considered to be documentation However: documentation alone does not guarantee smooth transitions or reproducibility Analytically mature organizations set up analytic workflows for each process Benefits of building analytic workflows • Transparency on the details of every step in process to all stakeholders • Repeatability and efficiency of overall process • Reproducibility Examples of platforms for creating reproducible workflows • Enable live connections to source databases • Embed and integrate different tools and programming languages • Enable process automation Evolution of technology Big Data in Startups • Use big data for product and service innovation • Work on tools, not just applications • Give responsibility to data scientists • Contribute to the commons - open source • Impatient!

• Take advantage of free stuff • Experimentation on large scale • Foster close collaboration Big Data in Large Companies • The existing technology infrastructure meets the organizational needs and are mission critical.

• Changes to systems require processes, budgets, project management, pilots, departmental deployments, full security audits, etc.

• Cautious about having young startups handle critical parts of their infrastructure.

• Reluctant to move their data to the cloud, at least the public one.

• Big Data success is not about implementing one piece of technology, e.g. Hadoop. It requires integration of technologies, people and processes . Analytics Technology Ecosystem Wang, Y., et al., Big data analytics: Understanding its capabilities and potential bene fi ts for healthcare organizations, Technol. Forecast. Soc. Change (2016), http://dx.doi.org/10.1016/j.techfore.2015.12.019 Analytics Stack and Capabilities Data Scientists and analysts Developers and engineersBusiness users and consumers Technologies for Big Data Technology Definition Hadoop Open - source software for processing big data across multiple parallel servers MapReduce Architectural framework on which Hadoop is based Scripting languages Programming languages that work well with big data (e.g. Python, Pig, Hive) Machine learning Software for rapidly finding the model that best fits a data set Visual analytics Display of analytical results in visual or graphic format Natural language processing Software for analyzing text - frequencies, meanings, etc.

In - memory analytics Processing big data in computer memory for greater speed From Thomas H. Davenport, Big Data @ Work , 2014.Take 5 minutes and map the technologies to the layers in the stack. Big Data Landscape http://mattturck.com/2016/02/01/big - data - landscape/Trend is for innovation to move from left to right Interactivity and Visualization Capabilities From static …… ….. to dynamic and interactive