The Big Analytics: Leaders Collaborative Book Project – For, Of, and By the Data Analytics Leaders and Influencers by Vishal Kumar


6759c33d16cd431-261x361.jpg Author Vishal Kumar
Isbn
File size 7.17MB
Year 2017
Pages 338
Language English
File format PDF
Category information technologies


 

The BIG Analytics Leaders Collaborative Book Project For, of and by Data Analytics Leaders and Influencers Compiled by: Deeksha Joshi & Vishal Kumar Contents SECTION A: STRATEGY FOR THE DATA ANALYTICS LEADERS 1. Rise of Data Capital by Paul Sonderegger 2. Investing in Big Data by Bill Pieroni 3. From Data to Insight: Seven Tips for a Great Data Strategy by Anne Russell 4. Data is Not Always a Substitute for Strategy by Steven Sinofsky 5. Understand How Your Company Creates Value by Cary Burch 6. Act Like an Analyst, Think Like a Strategist by Dr. Justin Barclay 7. The Last Mile of Intuitive Decision Making by Vishal Kumar 8. Three BIG Reasons Companies are Failing with Big Data Analytics by Ian St. Maurice 9. How to Leverage Power of Big Data Analytics in Making Strategic Decisions by Ankush Chopra 10. How to Hire the Best Analytics Leaders: And it’s not the way you think! by Tony Branda 11. Big Decisions Will Supplant Big Data by David Kenny 12. Big Data: The 'Bridge' to Success by Paul Forrest 13. What You Need to Know About Big Data by Dorie Clark 14. Change Your Business One Metric at a Time by Bill Franks 15. The Importance of Analytics and Data Science by James Barrese SECTION B: BEST PRACTICES AND HACKS AROUND DATA ANALYTICS 1. What is a Data Scientist? by Kurt Cagle 2. Sentiment Analysis Meets Active Collaboration by Julie Wittes Schlack 3. Framing Big Data Analytic Requirements by James Taylor 4. Combating the Coming Data Deluge: Creating real-time awareness in the age of the Internet of Things by Ryan Kirk 5. Big Data, Small Models – The 4F Approach by Kajal Mukhopadhyay 6. The Five Letters that Will Change the Data World: BYOBI by Tomasz Tunguz 7. Big Data? No, SMART Data by Tara Hunt 8. Beyond "Big Data": Introducing the EOI framework for analytics teams to drive business impact by Michael Li 9. Can You Seize the Opportunity from Analytics If You are Cheap? by Lora Cecere 10. Why is the Successful Implementation of "Big Data" Applications a Question of Knowing How to Sell? by Jean-Marc Bellot 11. The Big Hole in Big Data by Colin Shaw 13. Big Data: Two Words That are Going to Change Everything by Ali Rabaie 14. What Social Media Analytics and Data Can't Tell You by Beth Kanter 15. Big Data, Great! Now What Do We Do With It? by Ken Kring 16. Decoding Buzzwords: Big Data, Predictive Analytics, Business Intelligence by Cindy Gordon 17. The Case Against Quick Wins in Predictive Analytics Projects by Greta Roberts 18. How Many of Us Think Big Data is Big BS? by Ali Syed 19. So You Can Predict the Future. Big deal. Now Change It by Alex Cosmas 20. Best Practices in Lead Management and Use of Analytics by Marianne Seiler 21. Integrated Information Architecture for Real-time Analytics by Madan Gopal 22. The Practice of Data Science: Investigating Data Scientists, Their Skills and Team Makeup by Bob E. Hayes 23. The Memefication of Insights by Tom De Ruyck 24. The Esoteric Side of Data Analytics by Kiran Garimella SECTION C: FUTURE OF ANALYTICS 1. Full Stack Analytics –The Next Wave of Opportunity in Big Data by Chip Hazard 2. Simplification is the Technology Trend for 2016 by Werner Vogels 3. Future of Analytics by Dr. Anand S Rao 4. Computational Knowledge and the Future of Pure Mathematics by Stephen Wolfram 5. Consumers are on the Verge of Understanding Big Data: Are You? by Douglas Rushkoff 6. The Frightening Perils and Amazing Benefits of Big Data by Vivek Wadhwa 7. Let The Machines Decide by Toby Unwin 8. Is Little Data the Next Big Data? by Jonah Berger 9. Future of Data Analytics and Beyond... by Deeksha Joshi 10. Big Data Analytics, Where Are We Going? by John Ryan 11. A Path Forward for Big Data by Jules Polonetsky 12. Democratization of Data Analytics: Why, Who, and What by Kirk Borne SECTION D: CASE STUDIES & USE CASES 1. Artificial Intelligence Will Make Smart Lawyers Smarter (and dumb ones redundant) by Maximiliano Marzetti 2. Cyber security Solutions Based on Big Data by Steven King 3. Personalization of Big Data Analytics: Personal Genome Sequencing by Peter B. Nichol 4. Internet of Things : Realizing Business Value through Platforms, Data Analytics & Visualization by Pranay Prakash 5. The Private Eye of Open Data on a Random Walk: 8 different routes by Debleena Roy 6. Data Sheds New Light on the On-demand Economy by Alex Chriss 7. Every Company is a Data Company by Christopher Lynch 8. Transforming Customer Relationships with Data by Rob Thomas 9. The Power of Apps to Leverage Big Data and Analytics by Joseph Bradley 10. State of Telecom Business Around Managing Big Data and Analytics by Dr. Hossein Eslambolchi 11. Solution to Key Traffic Problem Required a Fresh Look at Existing Data by Tony Belkin 12. Machines Won't Replace Insurance Agents in 2016, But They Will Do This by Steve Anderson 13. How a Digital Transformation Can Improve Customer Experience with Big Data by Ronald van Loon 14. 15. 16. 17. Doctor in The Smart Refrigerator by Arthur Olesch Wearables at the Workplace by Abhijit Bhaduri The SEAMLESS Customer EXPERIENCE by Justice Honaman Big Data Revolution in the Banking Industry by Marc Torrens 18. Optimizing the Netflix Streaming Experience with Data Science by Nirmal Govind 19. Big Data and Innovation by Daniel Harple 20. Five Things You Should Measure with Digital Marketing Analytics by Judah Phillips ABOUT THE CURATORS INTRODUCTION You are busy preparing a report for your manager. A few cubicles away, you hear a colleague murmuring something to her mate about an analytics project. You hear somebody say the word, “data.” Your interest is raised. You hear more references to the term, “data,” and all things related like “Big Data,” “data science,” “machine learning” and “analytics.” This is the state of today’s highly quantified world. Everybody talks about data and what they can do with it. A few months ago, I was pulled into a conversation at a cocktail party. I was asked by a business owner about how big data could improve his business. Although the conversation remained rather light (he mentioned he used Excel to analyze this data), I told him my standard answer. “You can get a lot of insight into what’s happening in your business. You can understand the current state of affairs. You can make predictions about what will happen in the future. You can also understand what you need to do to improve your business. These insights can help you make better decisions to drive your business forward.” The business owner needed much more information about big data analytics than I could offer in a brief conversation. But our conversation raised my interest. I wondered what other experts in the field of analytics would say about the topic of “Big Analytics.” This was the impetus for this book. The Big Analytics Book Project is an aggregation of articles that best represent thoughts by some of the leading minds in the data analytics industry. My colleagues at AnalyticsWeek and I reached out to some of the top minds in the analytics industry to see what they had to say about the topic. We organized their contributions into a book to share with that curious business owner as well as others who might have the same questions. I would like to thank the contributing authors who have been very generous in providing their time, thoughts and articles for the book. While we gave the authors full autonomy to contribute whatever they wanted, we organized their contributions into sections to, hopefully, make the readers’ journey easier. So, we hope you enjoy the mesmerizing ride of these carefully curated write-ups from some of the great minds in data analytics. Now some basics: Who is the audience for this book? The authors were instructed to contribute material that would be suitable for a diverse audience. Some material, however, required more in-depth examination of the topic. Overall, the book is a great read for Data Analytics enthusiasts who are curious about the subject of Big Data and analytics. How does this book work? The best way to approach the book is to select a category and pick articles in which you find an interest. Each article can stand on its own and does not require insight from other articles. Start your journey by using the Table of Contents above. We have created four categories in this book: A. Strategy for the Data Analytics Leaders In this section, we will discuss the strategy that can help the Data Analytics leaders in building effective data analytics organizations. B. Best Practices and Hacks around Data Analytics In this section, we will discuss the best practices in the domain of Data Analytics especially those that can have a big impact on larger organizations. C. Future of Data Analytics This section addresses about what leaders think will be the future of big analytics and how analytics will continue to help organizations become more productive and transformational. D. Case Studies This section puts theory to practice, including articles that provide examples of how you can achieve success using data analytics. Thank you and acknowledgement I was fortunate to have worked with such a great team of contributors. It was an honor to capture their thoughts and organize them for the readers. I would like to thank all the contributing authors for their submissions. They are the true rock stars who have given us their experience, their name and their trust on the project. They deserve credit for the success of the book. Next, I would like to use the opportunity to thank Vishal Kumar and his team @ AnalyticsWeek. They have been ultra-responsive in helping me use their network and outreach to make this book project a reality. They have been playing a central role in creating an ecosystem that helps bring Analytics to the masses, being true to their “Analytics to the 99%” mission. This book is a living embodiment of that vision. Finally, I would like to thank my family for their support and words of encouragement that helped me persevere when I had doubts that I could convince the experts to contribute to this book. CALL FOR THEBIGANALYTICS 2017 CONTRIBUTORS #TheBigAnalytics is a living project to bring best practices and thought leadership of data science leaders, influencers and practitioners. As we are launching the maiden run for this book project, we are seeking 2017 contributors. #TheBigAnalytics 2017 will bring lot more channels to showcase and share best practices to the data and analytics enthusiasts. If you or anyone you know could participate in this book project in 2017, we would like to hear from you. Please apply at: http://math.im/tba17 SECTION A: STRATEGY FOR THE DATA ANALYTICS LEADERS Courtesy: Dilbert by Scott Adams Big Analytics will have the biggest impact if business leaders first embrace the ideas in this book. Business leaders are often occupied with current business problems which are not always related to data analytics. They sometimes lack the vision of how data analytics can help them do their jobs. This section is targeted to help make leaders more data driven. The authors have shared their golden nuggets on how tomorrow’s leaders should create and embrace a data driven culture. The best way to approach this section is by reading each article as its own piece. We recommend you read the authors’ biographical information to help you first understand their perspective. Understanding the authors’ background might provide you the right context to help facilitate your understanding of their material. 1. Rise of Data Capital by Paul Sonderegger Paul Sonderegger, Big Data Strategist at Oracle LinkedIn Contact Paul is the big data strategist at Oracle. He is a highly sought-after speaker and author on the data capital. Prior to joining Oracle, Sonderegger was chief strategist at Endeca, a discovery analytics company. Before Endeca, he was a principal analyst at Forrester Research, specializing in search and user-experience design. Sonderegger has a BA from Wake Forest University. -----------Data capital will replace big data as the big topic of boardroom conversation. This change will have a big effect on competitive strategy, and CEOs need to make decisions and prepare to talk to their boards about their plans. To succeed, CEOs need to embrace the idea that data is now a kind of capital—as vital as financial capital to the development of new products, services, and business processes. The implications are far greater than the spread of fact-based decision-making through better analytics. In some cases, data capital substitutes for traditional capital. In fact, the McKinsey Global Institute says that data capital explains most of the valuation premium enjoyed by digitized companies. But, we’re getting ahead of ourselves. First, we need to acknowledge a few basics. Because every activity in commercial, public, and private lives uses and produces information, no organization is insulated from the effects of digitization and datafication. Every company is thus subject to three laws of data capital: 1. Data comes from activity. Data is a record of what happened. But if you’re not party to the activity when it happens, your opportunity to capture that data is lost. Forever. So, digitize and “datafy” key activities your firm already conducts with customers, suppliers, and partners—before rivals edge you out. At the same time, look up and down your industry’s value chain for activities you’re not part of yet. Invent ways to insert yourself in a digital capacity, thereby increasing your share of data that the industry generates. 2. Data tends to make more data. Algorithms that drive pricing, ad targeting, inventory management, and fraud detection all produce data about their own performance that improve their own performance. This data capital flywheel effect creates a competitive advantage that’s very hard to catch, which means that seizing those digital touchpoints is doubly important. 3. Platforms tend to win. Platform competition is standard in information-intensive industries like software and stock trading. The gains that come from exploiting data capital will bring this information intensity to every industry. Healthcare providers, energy producers, and car makers will all compete to be the dominant platform for algorithmic services in their chosen markets. The data capital flywheel effect described above increases exponentially, not linearly. Allocation of Data Capital Here’s where substituting data capital for financial capital really comes in. To play offense and defense simultaneously in this data-fueled free-for-all, chief executives will have to decide where to invest their companies’ data to generate the greatest return. But unlike financial capital, which can be invested in only one opportunity at a time, data capital can fuel multiple analytic or algorithmic investments at once. This embarrassment of potential riches creates new problems. Even when focusing on the highest-priority opportunities to cut costs or increase revenue, there are a dizzying number of possible data sets that could be combined to uncover new insights, and an equally large number of possible ways to put the discovered correlations and connections to use. Two new capabilities cut this opportunity space down to size. First, data marketplaces. Data scientists need a way to shop for data sets like they’re at Lowes picking up supplies. Companies like BlueKai , which Oracle acquired last year, let both data experts and civilians like marketing managers browse hundreds of consumer data sets with more than 30,000 attributes. They can combine any number of sets in innovative ways to quickly gain new perspectives on target customers. New software like Oracle Big Data Discovery brings this kind of easy exploration and discovery inside the firewall, creating internal data marketplaces for previously siloed enterprise data. Second, data liquidity. The marketplaces answer only spot demand from business analysts, but data gets used in many other ways. And as firms store increasingly diverse data in different silos—Hadoop, NoSQL stores, as well as relational databases—the transaction costs of accessing, moving, and manipulating desired data will go up. Firms need ways to bring those costs back down. For example, Big Data SQL lets applications query different repositories full of increasingly diverse data with a single query, as if it were all in a single system. And new data integration products exploiting in-memory and low-cost, scale-out technologies will nearly instantly convert data into new shapes for fast-changing algorithmic pricing, fraud detection, and inventory management. Create “Datavist” Leadership Increasing a company’s return on invested data capital is not a simple matter of technology. It’s a matter of competitive strategy, and the need for CEOs to create a datanative, or “datavist” culture has never been greater. Here’s how to do it: Get conversant. Top-performing CEOs are familiar with the basic concepts that drive key differentiators of their businesses, like branding, product design, logistics, and engineering—without being the firm’s top expert in any of them. The same should go for information technology. CEOs need crash courses in the basic philosophies and trade-offs that drive different approaches to data management, integration, analytics, and apps so they can make informed decisions about digital business strategies. Elevate the chief data officer. The CDO should be on par with the CFO, making sure data capital is as productive as it can be while staying within the bounds of proper use. Your company’s data capital is essentially a deposit your customers, partners, and suppliers place with you. Just like with financial capital, data capital should be under the watchful eye of someone responsible for securing it, shielding it from misuse, and auditing what actually happens to it on a daily basis to ensure policy and regulatory compliance. Highlight your data capital for Wall Street. Technology firms, especially cloud companies like Oracle, have already started to include data capital numbers (such as number of web sessions in their cloud or number of candidate records in their HCM cloud) in their quarterly meetings with analysts. As platform competition heats up in traditional industries, expect retailers to boast about average number of data points collected per customer, usage-based auto insurers to cite aggregate data collected annually, and logistics firms to emphasize the total number of package scans captured. The rise of data capital has already begun. The shift in thinking required to create new strategic positions from shrewd digitization and datafication of value chain activities is a habit that takes a while to build. All CEOs should add this to their to-do lists. Fact: The data volumes are exploding, more data has been created in the past two years than in the entire previous history of the human race. 2. Investing in Big Data by Bill Pieroni Bill Pieroni, Global Chief Operating Officer at Marsh LinkedIn Contact Bill serves as Global Chief Operating Officer of Marsh. He has more than 25 years of experience at Fortune 500 companies, including McKinsey, Accenture, and IBM. He has a bachelors degree in accounting with University and College honors from the University of Illinois. Bill also holds an M.B.A. from Harvard University, where he was named a Baker Scholar. -----------The average adult makes 70 conscious decisions a day, or more than 25,000 a year. Many of those decisions are inconsequential to organizations, but a few of them can create substantial opportunities or problems. While organizations cannot prevent bad decisions from being made, firms can minimize the risk by investing in data and analytics capabilities. Data and analytics isn’t a new concept. It has been formed over the last century with the aid of two key macroeconomic trends. The first was the migration of the workforce from labor-intensive to knowledge-intensive roles and industries. The second was the introduction of decision-support systems into organizations in the 1960s. As an increased number of knowledge workers began to interact with more powerful technologies and accompanying data stores, analytics began to take a more critical role within organizational decision-making and execution. However, firms initially had some difficulties incorporating data and analytics into their operations. They gathered a limited number of variables and stored them in multiple data stores with different formats and structures. Additionally, filtering the data to validate what is relevant and impactful, or the signal, from the noise became difficult as the amount of data increased exponentially. Based on a study conducted by IDC, an IT consultancy, the amount of data available globally grew 27-fold to approximately 2.8 trillion gigabytes from 2005 to 2012. The study also noted that roughly 25% of this data is useful, but only 3% of it has been tagged for leverage and only 0.5% of it is currently analyzed. Most leading organizations see a need to enhance internal capabilities to collect, store, access, and analyze these exponentially large, complex datasets, increasingly known as Big Data. However, leaders need to allocate greater investments to Big Data capabilities in order to fully realize the value potential. These investments need to be made across the five segments of the data and analytics value chain. Collection & Readiness: Large, complex datasets need to be collected and managed effectively. Organizations generate data within independent silos. In order to maximize data leverage, organizations should maintain data standards to ensure data accuracy, consistency, and transferability. Processing: Data must be processed in real time. Gaining a few days on competitors can be the key to survival. Therefore, organizations should evaluate their architecture, algorithms, and even programming language to substantially increase processing speed. Visualization: Processed data needs to be presented in a manner that can be readily understood. Humans struggle with processing large amounts of numerical and textual data. Organizations should use visualization tools to enhance human pattern recognition, insight, and actions. Interpretation: Visualized data has to be interpreted correctly and communicated to knowledge consumers. Organizations should screen for biases that can distort insights, while guarding themselves against “gut-feeling” decision-makers as well as data extremists because both ends can lead a firm to act sub-optimally. Refinement: Knowledge consumers must provide feedback and guidance to knowledge producers. Organizations should facilitate a feedback loop across diverse stakeholder groups, which can support continual analysis, learning, and issue identification in order to attain informational scale and scope. Organizations have significant hurdles to overcome in order to capture the value potential of Big Data. These hurdles span the continuum of investment capacity, skill availability, legacy infrastructure, and operating models. However, organizations that are able to effectively leverage data and insights to drive differentiated value propositions and outcomes will dominate their industries. Ultimately, these organizations will be industry leaders rather than just industry participants. Quote: "Data really powers everything that we do." – Jeff Weiner, chief executive of LinkedIn. 3. From Data to Insight: Seven Tips for a Great Data Strategy by Anne Russell Anne Russell, Managing Partner at World Data Insights LinkedIn Contact Anne is the founder of World Data Insights. She has 12+ years of research experience of how data produces narratives of mission relevance; designing, developing, and implementing techniques to characterize, ingest, and process data and identify patterns of interest; She has a Masters from John Hopkins University and Bachelors from Antioch College and Univ of Maryland -----------So there’s a lot of data out there. Now what? How is that going to make a difference to you? That depends on what you want it to do, how many resources you have to devote, and how much effort you want to put into maintaining your data driven approach over the long term. Make no mistake. There are a lot of different options for solutions out there, some more human intensive than others. But only some will transform the data that is important to you and turn it into relevant and insightful information you can use. To get there, you should start with a great data strategy. Any good data strategy is going to involve understanding key factors that will impact implementation. Some of it will be obvious and based on what you should expect in any data should at minimum be some sense of requirements, how the pieces will work together, how data flows will be managed and visualized, and how different users will interact with data over time. But if the process is really good, it will also explore the burgeoning potential of new data driven approaches and help you determine which are right for your approach. To get to great, consider the following: 1. Brainstorming on Current and Future Goals You would be surprised by how many people forget to figure out what they really want their approach to achieve. Many hold minimal discussions with organizations about capturing requirements from the immediate mission and move straight to designing implementation schema without fully exploring what is possible. But a great strategy will involve asking the right questions to get you beyond what you know and into the realm of “Hmm… I didn’t think about that.” It will get you thinking about how to use data now and how you could use it years from now. It’s only once you understand the potential goals and possibilities that you can narrow down your options into something realistic that will meet expectations and build for the future. 2. Understanding your End-to-End Business Processes Yeah. This one is pretty obvious. But for a data driven solution, some strategists will just look at processes related strictly to data flows. To design a great approach, a strategist will look beyond immediate data flows and seek to understand how your organization’s broader goals, and current and future process could potentially be impacted by data, how users work with data now, and how they might work with data in the future. Indeed, a really good exchange will get into the details of process and reveal the possibilities of where the right data approach can provide multiple levels of insight, and help your key personnel save time and resources over the long term. 3. Doing a Data Inventory One of the great opportunities data driven approaches provide is the ability for organizations to look at the data they currently have and explore how it can be augmented by the ever growing availability of new data sets and streams. Some of that new data might emerge from the implementation of new tools or techniques that enable you to mine untapped sources within your organization. Others might be well structured feeds provided by vendors, structured data coming from the ever expanding web of inter-connected things (the Internet of Things), semi- or unstructured data coming from indexed sources on the internet, or even data emerging from alternate, non-obvious sources that exist but have not been fully integrated into any grid (aka, the “wild”). A good data inventory will get the basics of what you have, but a great data inventory will also help you understand what else is out there that is relevant, and what will be available soon. 4. Knowing which Tools and Techniques to Use With hundreds of different tools and techniques available, knowing what the strengths and challenges of different approaches are and what will work for you is critical. It’s not always obvious which tool is right for you. Some techniques may be more than what you need. Some, not enough. Indeed, when it comes down to your architecture and design, your strategy will need to explain how these components will work together, identify potential issues that can emerge, and provide workarounds that will help your approach succeed. Because a good strategy will be designed based on how the tools should work, but a great strategy will reflect the experience of what actually does. 5. Legal and Policy Considerations It’s a given that data governance and policy for most industries is still largely unregulated. There are plenty of good strategies that have and can be developed with little regard to outside influences, but that’s changing. In the era of increasing data breaches, cyber-attacks, calls for regulation, and lawsuits, it is inevitable that outside influences will affect your approach. A good strategy could be developed with minimal consideration of external factors, particularly in industries where little to no regulation exists. But a great strategy is developed with foresight into which external factors are most likely to affect your approach, the associated level of risk of each, and flexible contingency plans that can help maximize benefits and minimize negative impacts. 6. How your Approach will Work in Different Locations We’re going mobile and we’re going global. And we are increasingly finding ourselves deploying approaches to collect and analyze data in multiple, cross-cultural operational environments. It’s no longer enough to develop good strategies that look at approaches with a single “global” or “national” standard. With mobility and accessibility to data from multiple devices as the new normal, the strategies we develop for our data approaches must enable us to adapt to different, increasingly localized environments. That means we have to understand the differences of infrastructural capacity, geophysical climates, bandwidth, accessibility, and user usage norms that can exist at the local level. It can also mean understanding what is local from a local’s perspective and how that will affect the implementation of your approach, whether that locale is a small farming village in Sri Lanka, a manufacturing zone in a Baltic State, an elephant preserves in Kenya, or a technology firm in a small town in North Carolina. 7. How your Target Market will Interact with your Data It used to be that we could design interactive experiences based on assumptions of how users have interacted with data in the past. But data driven approaches are just starting to enable the exploration of what data can do and how users can interact with it. There are very few, if any, use cases of direct relevance to most organizations or industries from which to look for inspiration. As a result, developing interactive experiences for use within a great data driven strategy means more than just research into user preferences and knowing what’s already been done within your industry. It also requires knowing what has been done in other industries, and in other related and unrelated sectors. Inspiration can come from anywhere, and a great strategist will recognize when it is relevant to your approach. This is all to say, it’s worth it to invest resources in developing a data strategy before you start implementing and testing any approach, whether that strategy is good or great. Investing a smaller sum at the onset of any data driven approach will help to assure that the system you ultimately implement works the way you want it to, avoiding costly modifications in the future. But if you’re going to invest in a data strategy, it’s worth it to spend a bit more and get a great one. Why? Because a good data strategy will help ensure that the approach you choose will work within your current business processes and grow with you in the short-, medium-, and long-term. But a great strategy? That’s what will let you see what is and is not possible, and ultimately help your data tell the stories that really matter to your bottom line. Tip: Think long term by thinking short term 4. Data is Not Always a Substitute for Strategy by Steven Sinofsky Steven Sinofsky, Board Partner a16z LinkedIn Contact Steven is a Board Partner at the venture capital firm Andreessen Horowitz where he serves on boards of investments. He is an Executive in Residence at Harvard Business School, and advisor to Box. He is passionate about working with entrepreneurs building the next generation of software-driven products and services. -----------Access to rich usage data is something that is a defining element of modern product development. From cars, to services, to communications the presence of data showing how, why, when products are used is informing how products are built and evolve. To those developing products, data is an essential ingredient to the process. But sometimes, choices made that are informed by data cause a bit of an uproar when there isn’t uniform agreement. The front page of the Financial Times juxtaposed two data-driven stories that show just how tricky the role of data can be. For Veronica Mars fans (myself included), this past week was an incredible success as a Kickstarter project raised millions to fund a full length movie. Talk about data speaking loud and clear. This same week Google announced the end of life of Google Reader, and as you can see from the headline there was some controversy (it is noted with irony that the headline points out that the twitter sphere is in a meltdown). For all the 50,000 or more folks happy with the VM movie, it seems at least that many were unhappy about Google reader Controversy The role of data in product development is not without controversy. In today’s world with abundant information from product development teams and analysis of that data, there is ample room to debate and dissect choices. A few common arguments around the use of data include: Representation. No data can represent all people using (or who will use) a product. So who was represented in the data?

Author Vishal Kumar Isbn File size 7.17MB Year 2017 Pages 338 Language English File format PDF Category Information Technologies Book Description: FacebookTwitterGoogle+TumblrDiggMySpaceShare Leaders Collaborative Book Project – For, Of, and By the Data Analytics Leaders and Influencers.     Download (7.17MB) Big Data: Techniques and Technologies in Geoinformatics Real-Time Big Data Analytics: Emerging Architecture Cognitive Computing And Big Data Analytics Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data Driven Models Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance Load more posts

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