Friday, May 8, 2020
Business Intelligence and Analytics 63
1 Your Business Is Too Complex to Be Digital
https://sloanreview.mit.edu/article/your-business-is-too-complex-to-be-digital/
Business leaders are starting to rethink their strategies to take advantage of digital technologies. They envision omnichannel customer interfaces, ecosystems of tightly connected partners, and novel customer solutions leveraging newly accessible data.
This is smart. Digital technologies are already shifting industry boundaries and competitive landscapes (think of relatively new industry types: information dissemination, entertainment streaming, personal mobility). Ongoing industry disruption means that business leaders absolutely must articulate strategies that are inspired by the capabilities of digital technologies.
An inspired digital strategy, however, is barely enough to get started.
2 Leading Through COVID-19
https://sloanreview.mit.edu/article/leading-through-covid-19/
There are two overarching takeaways from our work. The first is that while an initial crisis may not have been preventable, the secondary crisis of a bungled response is avoidable. The second is that every incident has narratives with victims, villains, and heroes. We are still early enough in the story of COVID-19 that executives and organizations can shape the role they will play. Rising to the part of hero requires intentional choices to put some measure of self-interest aside in order to contribute to a greater good. This is a situation where the stakeholder-centric intentions of the Business Roundtable’s famous 2019 letter redefining corporate purpose are put to the test.
3 COVID-19 and reskilling the workforce
https://www.mckinsey.com/business-functions/organization/our-insights/to-emerge-stronger-from-the-covid-19-crisis-companies-should-start-reskilling-their-workforces-now
Imagine a crisis that forces your company’s employees to change the way they work almost overnight. Despite initial fears that the pressure would be too great, you discover that this new way of working could be a blueprint for the long term. That’s what leaders of many companies around the globe are finding as they respond to the COVID-19 crisis.
4 Alphabet’s Dream of a Smart City in Toronto Is Over
https://www.bloomberg.com/news/articles/2020-05-07/alphabet-s-dream-of-a-smart-city-in-toronto-is-over
Alphabet Inc.’s ambitious dream to create a city of the future on Toronto’s waterfront is over. Millions of dollars and years of lobbying weren’t enough, and the tech giant’s urban planning unit, Sidewalk Labs, officially shuttered the project on Thursday.
The stated reason was the coronavirus pandemic’s effect on real estate prices. Without the ability to profitably sell office space and homes in the development, the project wasn’t viable, Sidewalk Labs Chief Executive Officer Dan Doctoroff said in a blog post.
5 Why You Should Prioritize Data Transformation Above Other Digital Tran
https://datafloq.com/read/why-you-should-prioritize-data-transformation-above-other-digital-transformation-initiatives/8323
Chance are you’re aiming to invest in a BI and analytics program to capitalize on the big data your company has been acquiring over the years. But before you spend millions on opting for expensive BI programs, take a step back and ask yourself three questions:
Do I have data I can trust?
Do I understand my data?
Do I have a data transformation & data quality framework in place?
6 Forecasting Stories 3: Each Time-series Component Sings a Different Song
https://www.kdnuggets.com/2020/05/forecasting-stories-time-series-component-different-song.html
A rich diagnostic tool is decomposing a dataset into its components, i.e. Trend, Seasonality, Cyclical and Irregular/Random
Specially, if a forecast is not behaving as per the set expectations, debugging if one of the components and their percentage contribution has changed, can help. In R or Python, a one liner code such as plot(decompose(time_series)) or decompose(time_series, type=’multiplicative’) does the job. Now, why is it important and how could we interpret the results?
7 5 Concepts You Should Know About Gradient Descent and Cost Function
https://www.kdnuggets.com/2020/05/5-concepts-gradient-descent-cost-function.html
Gradient descent is an iterative optimization algorithm used in machine learning to minimize a loss function. The loss function describes how well the model will perform given the current set of parameters (weights and biases), and gradient descent is used to find the best set of parameters. We use gradient descent to update the parameters of our model. For example, parameters refer to coefficients in Linear Regression and weights in neural networks. In this article, I’ll explain 5 major concepts of gradient descent and cost function, including:
8 Data Transformation for Machine Learning
https://insidebigdata.com/2020/05/07/data-transformation-for-machine-learning/
Industry experts, competitors, and even your customers are talking about machine learning. Machine learning is the process of building and training models to process data. In this capacity, your models are learning from your data to make better predictions. In this way, machine learning allows computer systems to learn from data and make decisions without being explicitly programmed to do so.
9 Demonstrating Leadership or managing response in times of Crisis
https://datafloq.com/read/demonstrating-leadership-managing-response-times-crisis/8310
In the present scenario, the Coronavirus crisis is unfolding over an arc of time with a beginning, middle and end. The actions of executives and their teams now, in the midst of this crisis, will significantly determine their destiny.
For nearly two decades, it is observed that public and private-sector executives are in high-stakes, high-pressure situations. The crises are most often under-led and over managed. The best leaders navigate jagged waters deftly, energizing organizations, saving lives, and inspiring communities. However, it is discovered that many leaders fall into one or more of the following leadership traps:
10 From Apocalypse to Supernova: How the Pandemic Is Changing U.S. Retail
https://knowledge.wharton.upenn.edu/article/apocalypse-supernova-pandemic-changing-u-s-retail/
Total retail sales in March dropped 8.7% from the previous month, a steep decline not seen since 1992, according to the U.S. Department of Commerce. The apparel sector witnessed the most precipitous plunge, with sales of clothing and accessories falling by more than 50%. The numbers for April have not yet been released, but experts expect those figures to be even worse because of widespread closures, stay-at-home orders and crushing job losses that kept many Americans from buying anything beyond essentials.
11 The Post-crisis World: What Changes Are Coming?
https://knowledge.wharton.upenn.edu/article/post-crisis-world-changes-coming/
How will the world look post-COVID-19? The question is as broad as the destruction brought by the virus, but Wharton’s Tarnopol Dean’s Lecture held on April 30 provided some insights through the lens of three sectors — health care, finance and technology. The virtual event, hosted and moderated by Dean Geoffrey Garrett, included a panel of three Wharton alums: Alex Gorsky, chairman and CEO of pharmaceuticals and medical devices company Johnson & Johnson; Marc Rowan, co-founder and senior managing director of private equity firm Apollo Global Management; and Andy Rachleff, founder of Benchmark Capital, a Silicon Valley venture capital firm, and Wealthfront, an online investment management firm. Wharton management professor Lori Rosenkopf facilitated by curating audience questions for the speakers.
