"The skill of data storytelling is removing the noise and focusing people’s attention on the key insights."
Brent Dykes via Beth Stackpole - MIT Sloan - The next chapter in analytics: data storytelling
"When implementing an AI solution, ask yourself, why do I want it? And what is it going to do? If you can’t answer this, then getting an AI tool will not answer the question for you."
Sumit De of TOPdesk - InformationWeek - Business Intelligence before Artificial Intelligence
"Every company is becoming a data company. Heck, every organization is becoming a data organization! The benefits to becoming data driven are clear; obviously, if you can make decisions based on data, and measure the impact of those decisions, it’s a much more effective and efficient approach than the guesswork or gut-feeling that used to drive so much of business."
Barr Moses - Toward Data Science | Medium - Let’s Get Back to Basics
"9 out of 10 CEOs have digital-first strategies, but only 4 out of 10 are ready for digital disruption. Their platform is burning and they've got to move quick."
Bill McDermott - Innovation | Forbes - ServiceNow (Ticker Symbol: NOW) Recently Broke $400 and Is Up 37% in 2020. Meet CEO Bill McDermott, Who Survived A Near-Fatal Accident That Took An Eye (But Not His Vision).
"The goal should be to provide a predictive model of project intelligence that answers the question, 'Will this project finish on time and on budget and to what degree will it miss or beat the plan?’"
Daniel Bevort - Adeaca - How to Approach Artificial Intelligence for Project Management
"Is there more value in the use of a knowledge graph than just a way to uncover non-obvious and indirect relationships hidden in your data, to make better business decisions, as shown in the first part of this series? . . . " [A big "yes" . . . ]
Szymon Klarman - BlackSwan Technologies - Knowledge Graph part 2 – Unleash the power of your data.
"While data-driven AI has led to significant breakthroughs, it also comes with a number of disadvantages. First, models generated by machine learning algorithms often cannot be inspected and understood by a human being, thus lacking explainability. Furthermore, integration of preexisting domain knowledge into learned models – prior to or after learning – is difficult. Finally, correct application of data-driven AI depends on the domain, problem, and organizational context while considering human aspects as well. Conceptual modeling can be the key to applying data-driven AI in a meaningful, correct, and time-efficient way while improving maintainability, usability, and explainability."
Workshop Organizers - CMAI 2020 - 1st Workshop on Conceptual Modeling Meets Artificial Intelligence and Data-Driven Decision Making
"The ability to quickly act on information to solve problems, make decisions, or extend offers has long been the goal of many businesses. However, it was not until recently that technologies were available to address the speed and scalability requirements of real-time analytics."
Joyce Wells (June 2020) - DBTA Magazine - Selecting the Right Tools for Real-Time Analytics at Data Summit Connect 2020
"Businesses around the world have rapidly adapted to the pandemic. There has been little hand-wringing and much more leaning in to the task at hand. For those who think and hope things will basically go back to the way they were: stop. They won’t. It is better to accept the reality that the future isn’t what it used to be and start to think about how to make it work."
Kevin Sneader & Shubham Singhal - McKinsey - From thinking about the next normal to making it work: What to stop, start, and accelerate
Concerning AI, "clearly, the human element and human governance are central. Any sort of rule-based system is not as flexible a sensor; you need to keep humans in the loop."
Alex ‘Sandy’ Pentland via Robert Heeg - ResearchWorld | ESOMAR - Alex ‘Sandy’ Pentland on new data sources and AI
"Recent IDC research demonstrates that 67% of enterprises prioritize the creation of a data management capability to enable them to turn internal data into insight by organizing, maintaining, and refining data sets and data processes. However, 45% of organizations are still at a low level of maturity for data excellence — either Level 1 or Level 2."
Dan Vesset et. al. - IDC - IDC FutureScape: Worldwide Data, Integration, and Analytics 2020 Predictions
"Three elements are critical for entrepreneurship through open data: Open data sources, innovation, and business models."
Diego Corrales-Garay, Eva-María Mora-Valentín & Marta Ortiz-de-Urbina-Criado - Sustainability | MDPI - Entrepreneurship Through Open Data: An Opportunity for Sustainable Development
"As predictive analytics become more widespread in areas beyond education, such as policing and sentencing, finance, healthcare and medicine, and social media, it is necessary to illuminate the data that underpin predictions, not just technically but sociotechnically. The everyday, frequently mundane processes that make data, proxies, and data doubles hold together are subject to deletion, which allows for the linkages between data and people to appear seamless. They are not."
Madisson Whitman - Big Data & Society | SAGE - “We called that a behavior”: The making of institutional data
"Analytics groups in analytically mature organizations that have succeeded in creating production deployments for their algorithms are safer in a recession."
Jeffrey D. Camm, Melissa R. Bowers, & Thomas H. Davenport - MIT Sloan Management Review - The Recession’s Impact on Analytics and Data Science
"This is the crucial but often overlooked step in data preparation: The DW/BI team needs to fully understand source data before further preparing it for downstream consumption. Beyond simple visual examination, you need to profile, visualize, detect outliers, and find null values and other junk data in your data set."
Wayne Yaddow - LightsOnData - Best practices to achieve optimal source data profiling