Importance of Data Analytics
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This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
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Importance of Data Analytics
- 1. Importance of Data Analytics
- 2. Importance of Data Analytics …for Product Management
- 3. About Me Twitter: @etugopm LinkedIn: /etugonwokah Etugo Nwokah VP Product,
- 4. Data analytics should be a first-class citizen Importance of Data Analytics Data analytics team should be a key stakeholder Everyone should ‘own’ the data
- 5. 3 Use Cases
- 6. The good… Use Case 1
- 7. Use Case 2 …the bad
- 8. Use Case 3 And the really, really ugly.
- 9. Introduction to Data Analytics
- 10. My first exposure to Data Analytics for Product Management 2004 2017
- 11. Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.
- 12. Data Analytics is not: Big Data Artificial Intelligence (“AI”) or Machine Learning Data Science
- 13. Data Science is a concept that unifies statistics, data analysis and their related methods in order to understand and analyze phenomena with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science.
- 14. Big Data is defined as high-volume, high- velocity and high-variety information assets that demand innovative forms of information processing for enhanced insight and decision making. 1012
- 15. Data Science has several sub-domains MachineLearning Visualization DataMining ClusterAnalysis Classification
- 16. Research and extract information from various sources to describe the main features of a data set in support of business operations and decision making. Data Analyst/Journalist Data Scientist Cleanse data, apply various techniques to large data sets and create visualizations to understand to answer more abstract business questions, provide more predictive insights and anticipatory behaviors.
- 17. Data analytics should be a first-class citizen .. especially in software companies
- 18. 23 6 19
- 19. What is NOT a data-driven organization
- 20. 5 characteristics of data-driven organization
- 22. Leadership Top-down commitment “Day 1 Company: High Quality and High Velocity Decision Making” Metrics define your culture
- 23. Liquidity Breaking down silos of data Structural - applications are optimized for their main function, not to encourage data sharing Political - Knowledge is power Growth - Company evolution results in different approaches to data Vendor - Incentivized to store data exclusively
- 24. Usage How data is used to make decisions Company Scorecard Avoidance of Proxies
- 25. Access Who has access and how are they using it Meetings and Cadence Administrative Management
- 26. Protection How data is stored and shared in a secure manner Investment in data security Training Data across various platforms Regulatory
- 27. Data analytics team should be a key stakeholder … not just on-demand
- 28. Make friends with your “Data Team”
- 29. Organizational Structure - Example + Dir of Product CPO Sr Director of UI/UX Dir of Analytics CEO Leadership Team
- 30. Ensure someone is representing data on your agile/scrum team PMPM UI/U X UI/U X EngEng DataData QAQA Build metrics into user stories to measure success Build tags into features Identify R&D opportunties
- 31. Data Analytics Data Analytics Data Science Data Science Data Engineeri ng Data Engineeri ng Product Manager Ensuring data needs are supported is an important responsibility Limit user overhead to use data R&D and strategic input to PM questions PM input into the metrics to care about
- 32. Everyone should ‘own’ the data .. especially product managers
- 34. What is this the problem? How big is the problem? How do we know if we’ve solved it?Julie Zhou, VP Product Design Approach to building new features Example use of analytics
- 35. // Referral Acquisition Activation Retention # of app downloads #of registrations % of users registered # of physician reviews # of searches performed app reviews Net Promoter Score Referral Emails # of unique return users per day # of sign-ins per month per cohort % of conversion to desired behavior Example Metrics The funnel is a critically important framework
- 36. Some data analytics tools
- 37. How we used it: Across web and apps, understand effectiveness of customer campaigns and cursory user patterns. Description: Powerful analytics tool describes what users are doing and how they got to your site/app, but not why they are behaving a certain way. // Acquisition
- 38. How we used it: Determined primary user focus on homepage Description: Heat map, scroll maps and click tracking to understand user focus and what draws their attention // Acquisition
- 39. How we used it: Across web and apps developed deeper understanding of how users engaging per page and through the conversion funnel Description: Behavioral analytics and engagement tool // Activation Retentio n
- 40. How we used it: Built a set of custom metrics in our admin tool that looked similar to the kiss metrics UI interface but more dynamic Description: set of APIs that allow you to stream, compute, and visualize events // Activation Retentio n
- 41. // Activation Retentio n Acquisition How we used it: Funnel analysis, cohort analysis, click behavior Description: A click-stream technology that auto-tags all of the activity on your product to enable analytics and insights
- 42. How we used it: To produce ROI reports and custom reports for our large employer customers Description: Data visualization tool focused on business analytics
- 43. Wrap-up
- 44. Help your organization demonstrate the 5 data-driven characteristics Learn the latest data analytics tools and methods in the industry Find the data analytics team in your organization and make friends If you don’t have a data team, be the Lorax for the data Etugo’s tips:
- 45. Appendix
- 48. Part-time Product Management courses in New York
Notes de l'éditeur
- Thanks for coming!
- I am made up of a patchwork of different experiences: Product Executive with a background in Industrial Engineering, Consultant and also Painter/Artist
- •In 1999, a young company based in Los Gatos, California with less than 20 employees and on the edge of going bust. They had a couple experienced co-founders but the problem was that they were stuck at about 300,000 customers. They were essentially providing the same general pay-per-rental movie experience as other big box movie rental stores.There were, as always, some early adopters but generally people didn’t seem very willing to change. The team knew that the service wasn’t better enough to get people to change. Even worse, DVD sales were starting to lag, and a Hollywood backlash further muddied the situation. Then there were challenges with fulfillment logistics, difficulty maintaining DVD quality, and trying to figure out how to do all this in a way that covered costs and generated some cash. There was a product manager on the team knew they needed to do something different. One of many tests they tried was to move to a subscription service. Get people to sign up for a month and offer them unlimited movies. Would that be perceived as “better enough” to get them to change their media consumption behavior? The good news was that yes, actually, this really did appeal to people. A flat monthly fee and all the videos they could consume sounded pretty awesome. The bad news is that the team created some real problems for themselves. No surprise that Netflix customers wanted to rent mostly newly released feature films, yet these were much more expensive for Netflix to stock, and they would need to stock so many copies of these, that they’d very likely run out of money fast. So the product challenge became how were they going to make sure Netflix customers could watch a set of movies they would love, yet wouldn’t bankrupt the company? They knew they needed to somehow get customers to want a blend of expensive and less expensive titles. Necessity being the mother of invention, this is where the queue, the ratings system, and the recommendation engine all came from. Those were the technology-powered innovations that enabled the new, much more desirable business model. Between working with the co-founders on the strategy, validating concepts with the users, assessing the analytics, driving features and functionality with the team, and working with finance on the new business model, marketing on acquisition, and the warehouse on fulfillment, you can imagine the workload Kate faced on a daily basis.
- Every time you go shopping, you share intimate details about your consumption patterns with retailers. A columnist for the New York Times wrote about this by taking to a Target statistician Andrew Pole -- before Target freaked out and cut off all communications. about the clues to a customer's impending bundle of joy. Target assigns every customer a Guest ID number, tied to their credit card, name, or email address that becomes a bucket that stores a history of everything they've bought and any demographic information Target has collected from them or bought from other sources. [Pole] ran test after test, analyzing the data, and before long some useful patterns emerged. Lotions, for example. Lots of people buy lotion, but one of Pole’s colleagues noticed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester. Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date. My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?” The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again. On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.” https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/2/#65e63b047e85
- Among the bad publicity Uber has got lately from sexual harrassment in the workplace to stealing IP, the one that caught my eye was related to how they used data analytics. Now it's a story in the New York Times about how Uber dodged authorities worldwide where law enforcement and other officials were running sting operations when Uber wasn't authorized to operate. Using data from the app and other unnamed sources, the company would identify who was working for the government, cancel any rides they ordered, and put up a faked screen of where vehicles were at the time.
- Source: Techopedia
- https://datascientistinsights.com/2013/09/09/data-analytics-vs-data-science-two-separate-but-interconnected-disciplines/
- Source: https://903ink.onlinebusiness.american.edu/blog/comparing-analytics-data-science/ Predictive, Prescriptive Interconnected but different disciplines. The lines between analytics and data science can definitely be very blurry. https://en.wikipedia.org/wiki/Data_science
- Source: http://www.sciencedirect.com/science/article/pii/S0268401214001066 One terabyte is considered to be big data. One terabyte stores as much data as would fit on 1500 CDs or 220 DVDs, enough to store around 16 million Facebook photographs. Beaver, Kumar, Li, Sobel, and Vajgel (2010) report that Facebook processes up to one million photographs per second. One petabyte equals 1024 terabytes. Earlier estimates suggest that Facebook stored 260 billion photos using storage space of over 20 petabytes. Variety refers to the structural heterogeneity in a dataset. Technological advances allow firms to use various types of structured, semi-structured, and unstructured data. Velocity refers to the rate at which data are generated and the speed at which it should be analyzed and acted upon.
- Statistics and modeling; training your model to recognize and anticipate patterns in the data.
- Real-time vs non-real time; R&D vs Operational
- McKinsey Global Institute found that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain those customers, and 19 times as likely to be profitable as a result. http://www.ey.com/Publication/vwLUAssets/EY-global-becoming-an-analytics-driven-organization/%24FILE/ey-global-becoming-an-analytics-driven-organization.pdf - E&Y found that surveying thousands of
- https://www.entrepreneur.com/article/237326 Metrics define your corporate culture. More than anyone I’ve ever met, Bezos knew that things don’t improve unless they’re measured. A data-driven culture is meaningless without the support of the CEO and executive team -- and their own willingness to challenge assumptions that they hold dear.
- While I was at Amazon, my team proved (65 pages of proof, in fact!) the impact of website load times on sales, and identified the metrics that mattered. Without hesitation, Bezos and Amazon reorganized around a new, even more aggressive method of measuring website performance, changing hundreds of jobs to obsess about these very metrics.
- https://hbr.org/2016/12/breaking-down-data-silos Since the popular emergence of data science as a field, its practitioners have asserted that 80% of the work involved is acquiring and preparing data. Structural. Software applications are written at one point in time, for a particular group in the company. In a world of limited resources, applications are optimized for their main function. The incentives of individual teams are unlikely to encourage data sharing as a primary requirement. This focus on function, for instance, may result in recent sales being stored in different systems from historical sales, thus presenting an immediate barrier to boosting sales through personal product recommendation. Political. Knowledge is power, and groups within an organization become suspicious of others wanting to use their data. And often with some justification, as the scope for misuse, even accidental, is broad. Data isn’t a neutral entity — you must interpret it with knowledge of its history and context. This sense of proprietorship can act against the interests of the organization as a whole. Growth. Any long-lived company has grown through multiple generations of leaders, philosophies, and acquisitions, resulting in multiple incompatible systems. Even if there are no political issues in integrating data, it is costly to reconcile and integrate sets of data that embody different approaches to important business concepts. Vendor lock-in. Software vendors are among the first to know that access to data is power, and their strategies can frustrate the desire of users to export the data contain in applications. This is particularly dangerous with software-as-a-service applications, where the vendor wants to keep you within their cloud platform. Vendors have also worked hard to create entire job functions and career paths centered around their software. Any hint of move from that world could threaten the livelihood of a trained and certified software professional.
- Folks like Larry Page, Elizabeth Warren
- Apple typically does development
- Avoid the tension with data science/engineering. Ideally this should be the same team.
- The product manager is like the Lorax, speaks for the trees. The PM speaks for the customers, the users, others in the organization and the most importantly with data!
- Thanks for coming!