20 posts categorized "insights for the C-Suite"

08 December 2015

Biased hiring algorithms and Uber is not disruptive.

1. Unconscious bias → Biased algorithms → Less hiring diversity On Science Friday (@SciFri), experts pointed out unintended consequences in algorithms for hiring. But even better was the discussion with the caller from Google, who wrote an algorithm predicting tech employee performance and seemed to be relying on unvalidated, self-reported variables. Talk about reinforcing unconscious bias. He seemed sadly unaware of the irony of the situation.

2. Business theory → Narrow definitions → Subtle distinctions If Uber isn't disruptive, then what is? Clayton Christensen (@claychristensen) has chronicled important concepts about business innovation. But now his definition of ‘disruptive innovation’ tells us Uber isn't disruptive - something about entrants and incumbents, and there are charts. Do these distinctions matter? Plus, ever try to get a cab in SF circa 1999? Yet this new HBR article claims Uber didn't "primarily target nonconsumers — people who found the existing alternatives so expensive or inconvenient that they took public transit or drove themselves instead: Uber was launched in San Francisco (a well-served taxi market)".

3. Meta evidence → Research quality → Lower health cost The fantastic Evidence Live conference posted a call for abstracts. Be sure to follow the @EvidenceLive happenings at Oxford University, June 2016. Speakers include luminaries in the movement for better meta research.

4. Mythbusting → Evidence-based HR → People performance The UK group Science for Work is helping organizations gather evidence for HR mythbusting (@ScienceForWork).

5. Misunderstanding behavior → Misguided mandates → Food label fail Aaron E. Carroll (@aaronecarroll), the Incidental Economist, explains on NYTimes Upshot why U.S. requirements for menu labeling don't change consumer behavior.

*** Tracy Altman will be speaking on writing about data at the HEOR and Market Access workshop March 17-18 in Philadelphia. ***

24 November 2015

Masters of self-deception, rapid systematic reviews, and Gauss v. Legendre.

1. Human fallibility → Debiasing techniques → Better science Don't miss Regina Nuzzo's fantastic analysis in Nature: How scientists trick themselves, and how they can stop. @ReginaNuzzo explains why people are masters of self-deception, and how cognitive biases interfere with rigorous findings. Making things worse are a flawed science publishing process and "performance enhancing" statistical tools. Nuzzo describes promising ways to overcome these challenges, including blind data analysis.

2. Slow systematic reviews → New evidence methods → Controversy Systematic reviews are important for evidence-based medicine, but some say they're unreliable and slow. Two groups attempting to improve this - not without controversy - are Trip (@TripDatabase) and Rapid Reviews.

3. Campus competitions → Real-world analytics → Attracting talent Tech firms are finding ways to attract students to the analytics field. David Weldon writes in Information Management about the Adobe Analytics Challenge, where thousands of US university students compete using data from companies such as Condé Nast and Comcast to solve real-world business problems.

4. Discover regression → Solve important problem → Rock the world Great read on how Gauss discovered statistical regression, but thinking his solution was trivial, didn't share. Legendre published the method later, sparking one of the bigger disputes in the history of science. The Discovery of Statistical Regression - Gauss v. Legendre on Priceonomics.

5. Technical insights → Presentation skill → Advance your ideas Explaining insights to your audience is as crucial as getting the technical details right. Present! is a new book with speaking tips for technology types unfamiliar with the spotlight. By Poornima Vijayashanker (@poornima) and Karen Catlin.

03 November 2015

Watson isn't thinking, business skills for data scientists, and zombie clickbait.

1. Evidence scoring → Cognitive computing → Thinking? Fantastic article comparing Sherlock Holmes to Dr. Watson - and smart analysis to cognitive computing. This must-read by Paul Levy asks if scoring evidence and ranking hypotheses are the same as thinking.

2. Data science understanding → Business relevance → Career success In HBR, Michael Li describes three crucial abilities for data scientists: 1) Articulate the business value of their work (defining success with metrics such as attrition); 2) Give the right level of technical detail (effectively telling the story behind the data); 3) Get visualizations right (tell a clean story with diagrams).

3. Long clinical trials → Patient expectations → Big placebo effect The placebo effect is wreaking havoc in painkiller trials. Nature News explains that "responses to [placebo] treatments have become stronger over time, making it harder to prove a drug’s advantage." The trend is US-specific, possibly because big, expensive trials "may be enhancing participants’ expectations of their effectiveness".

4. Find patterns → Design feature set → Automate predictions Ahem. MIT researchers aim to take the human element out of big-data analysis, with a system that searches for patterns *and* designs the feature set. In testing, it outperformed 615 of 906 human teams. Thanks to @kdnuggets.

5. Recurrent neural nets → Autogenerated clickbait → Unemployed Buzzfeed writers? A clickbait website has been built entirely by recurrent neural nets. Click-o-Tron has the latest and greatest stories on the web, as hallucinated by an algorithm. Thanks to @leapingllamas.

Bonus! Sitting studies debunked? Corey Doctorow explains it's not the sitting that will kill you - it's the lack of exercise.

20 October 2015

Evidence handbook for nonprofits, telling a value story, and Twitter makes you better.

1. Useful evidence → Nonprofit impact → Social good For their upcoming handbook, the UK's Alliance for Useful Evidence (@A4UEvidence) is seeking "case studies of when, why, and how charities have used research evidence and what the impact was for them." Share your stories here.

2. Data story → Value story → Engaged audience On Evidence Soup, Tracy Altman explains the importance of telling a value story, not a data story - and shares five steps to communicating a powerful message with data.

3. Sports analytics → Baseball preparedness → #Winning Excellent performance Thursday night by baseball's big data-pitcher: Zach Greinke. (But there's also this: Cubs vs. Mets!)

4. Diverse network → More exposure → New ideas "New research suggests that employees with a diverse Twitter network — one that exposes them to people and ideas they don’t already know — tend to generate better ideas." Parise et al. describe their analysis of social networks in the MIT Sloan Management magazine. (Thanks to @mluebbecke, who shared this with a reminder that 'correlation is not causation'. Amen.)

5. War on drugs → Less tax revenue → Cost to society The Democratic debate was a reminder that the U.S. War on Drugs was a very unfortunate waste - and that many prison sentences for nonviolent drug crimes impose unacceptable costs on the convict and society. Consider this evidence from the Cato Institute (@CatoInstitute).

13 October 2015

Decision science, NFL prediction, and recycling numbers don't add up.

1. Data science → Decision science → Institutionalize data-driven decisions Deepinder Dhingra at @MuSigmaInc explains why data science misses half the equation, and that companies instead need decision science to achieve a balanced creation, translation, and consumption of insights. Requisite decision science skills include "quantitative and intellectual horsepower; the right curiosity quotient; ability to think from first principles; and business synthesis."

2. Statistical model → Machine learning → Good prediction Microsoft is quite good at predicting American Idol winners - and football scores. Tim Stenovec writes about the Bing Predicts project's impressive record of correctly forecasting World Cup, NFL, reality TV, and election outcomes. The @Bing team begins with a traditional statistical model and supplements it with query data, text analytics, and machine learning.

3. Environmental concern → Good feelings → Bad recycling ROI From a data-driven perspective, it's difficult to justify the high costs of US recycling programs. John Tierney explains in the New York Times that people's good motives and concerns about environmental damage have driven us to the point of recovering every slip of paper, half-eaten pizza, water bottle, and aluminum can - when the majority of value is derived from those cans and other metals.

4. Prescriptive analytics → Prescribe actions → Grow the business Business intelligence provides tools for describing and visualizing what's happening in the company right now, but BI's value for identifying opportunities is often questioned. More sophisticated predictive analytics can forecast the future. But Nick Swanson of River Logic says the path forward will be through prescriptive analytics: Using methods such as stochastic optimization, analysts can prescribe specific actions for decision makers.

5. Graph data → Data lineage → Confidence & trust Understanding the provenance of a data set is essential, but often tricky: Who collected it, and whose hands has it passed through? Jean Villedieu of @Linkurious explains how a graph database - rather than a traditional data store - can facilitate the tracking of data lineage.

22 September 2015

Writing skills series, encyclopedia of slide layouts, and fantasy sports decision-making

1. Well-crafted writing → Evidence explained → Uptake of ideas On October 14, our founder, Tracy Allison Altman, will talk about Communicating Messages Clearly with Data. This free presentation will include techniques for writing about data, and telling a simple story about complex science.

2. Use the Slide Chooser → Tell your story → Inspire action The Extreme Presentation method is a step-by-step approach for designing presentations of complex or controversial information. It's based on empirical research, and has been tested with big companies. The companion book is the Encyclopedia of Slide Layouts by Paul Radich and Andrew Abela.

3. Fear of losses → Baseball decisions → Daily fantasty sports results In baseball daily fantasy sports, as in life, we are more motivated to minimize losses than to maximize gains. Dr. Renee Miller explains how cognitive biases expedite decision-making and influence outcomes.

4. Rethink strategy → Lower blood pressure targets → Reduce death risk The U.S. National Institutes of Health released early findings from a big study of blood pressure management in people over 50. The Sprint trial seems to tell us that keeping systolic pressure below 120 can reduce cardiovascular disease and risk of death by as much as one third.

5. Large data sets → LASSO method → Valid predictions Today's large data sets create fantastic opportunities to make useful predictions - but traditional methods of variable selection are unwieldy and unreliable. Daniel Samarov writes on the Experfy blog about LASSO, a modern way to select variables for predictive models. This sparse regression, using Least Absolute Shrinkage and Selection Operator, is becoming a mainstay for analyzing data with lots of variables.

19 August 2015

Science of criminal sentencing, pharma formulary decisions, and real astrology?

1. Criminal patterns → Risk assessment → Science of sentencing The Marshall Project describes the new science of sentencing, where courts use statistically derived risk assessments to inform their decisions about which prisoners should be released on parole, and how bail should be set. (Thanks to Gregory Piatetsky, @kdnuggets.)

2. Clinical & cost effectiveness → Evidence base → Pharma formulary Express Scripts, a large U.S. pharmacy benefits manager, has released its 2016 formulary outlining which drugs will be covered, and which will not. @BioPharmaDive explains the decision process: An independent group of physicians reviews the evidence on clinical and cost effectiveness of each candidate. "Me-too" products aren't making the cut.

3. March birthday → Atrial fibrillation → Real astrology? The Journal of the American Medical Informatics Association has findings from a retrospective population study that systematically explored (with a phenome-wide method) the connection between birth month and disease risk for 1,688 conditions. Authors claim that for 55 diseases, "seasonally dependent early developmental mechanisms may play a role in increasing lifetime risk."

4. Data-driven → Fewer middle managers → Nimble decision processes Data-driven management processes need careful driving, says Ed Burns. Benefits include transparent and objective decisions, and more nimble ones when analytics can eliminate middle managers. However, some efforts have backfired. More in this podcast by @EdBurnsTT, What are your tips for putting in place data-driven management strategies?

5. Aggregated economic data → Positive trends → Data-driven optimism Economist Max Roser is an optimist. Jeff Rothfeder writes in @stratandbiz about Roser's analysis of disparate data covering "everything from African development to violent death rates", and his conclusions that evidence unambiguously shows a world evolving for the better.

28 July 2015

10 Years After Ioannidis, speedy decision habits, and the peril of whether or not.

1. Much has happened in the 10 years since Why Most Published Research Findings Are False, the much-discussed PLOS essay by John P. A. Ioannidis offering evidence that "false findings may be the majority or even the vast majority of published research claims...." Why are so many findings never replicated? Ioannidis listed study power and bias, the number of studies, and the ratio of true to no relationships among those probed in that scientific field. Also, "the convenient, yet ill-founded strategy of claiming conclusive research findings solely on... formal statistical significance, typically for a p-value less than 0.05." Now numerous initiatives address the false-findings problem with innovative publishing models, prohibition of p-values, or study design standards. Ioannidis followed up with 2014's How to Make More Published Research True, noting improvements in credibility and efficiency in specific fields via "large-scale collaborative research; replication culture; registration; sharing; reproducibility practices; better statistical methods;... reporting and dissemination of research, and training of the scientific workforce."

2. Speedy decision habits -> Fastest in market -> Winning. Dave Girouard, CEO of personal finance startup Upstart & ex-Google apps head, believes speedy decision-making is essential to competing: For product dev, and other organizational functions. He explains how people can develop speed as a healthy habit. Relatively little is "written about how to develop the institutional and employee muscle necessary to make speed a serious competitive advantage." Key tip: Deciding *when* a decision will be made from the start is a profound, powerful change that speeds everything up.

3. Busy, a new book by Tony Crabbe (@tonycrabbe), considers why people feel overwhelmed and dissatisfied - and suggests steps for improving their personal & work lives. Psychological and business research are translated into practical tools and skills. The book covers a range of perspectives; one worth noting is "The Perils of Whether or Not" (page 31): Crabbe cites classic decision research demonstrating the benefits of choosing from multiple options, vs. continuously (and busily) grinding through one alternative at a time. BUSY: How to Thrive in a World of Too Much, Grand Central Publishing, $28.

4. Better lucky than smart? Eric McNulty reminds us of a costly, and all-too-common, decision making flaw: Outcome bias, when we evaluate the quality of a decision based on its final result. His strategy+business article explains we should be objectively assessing whether an outcome was achieved by chance or through a sound process - but it's easy to fall into the trap of positively judging only those efforts with happy endings (@stratandbiz).

5. Fish vs. Frog: It's about values, not just data. Great reminder from Denis Cuff @DenisCuff of @insidebayarea that the data won't always tell you where to place value. One SF Bay Area environmental effort to save a fish might be endangering a frog species.

14 July 2015

Data-driven organizations, machine learning for C-Suite, and healthcare success story.

1. Great stuff on data-driven decision making in a new O'Reilly book by Carl Anderson (@LeapingLlamas), Creating the Data-Driven Organization. Very impressive overview of the many things that need to happen, and best practices for making them happen. Runs the gamut from getting & analyzing the data, to creating the right culture, to the psychology of decision-making. Ugly Research is delighted to be referenced (pages 187-188 and Figure 9-7).

2. Healthcare success story. "Data-driven decision making has improved patient outcomes in Intermountain's cardiovascular medicine, endocrinology, surgery, obstetrics and care processes — while saving millions of dollars in procurement and in its the supply chain."

3. 1) description, 2) prediction, 3) prescription. What the C-Suite needs to understand about applied machine learning. McKinsey's executive guide to machine learning 1.0, 2.0, and 3.0.

4. Place = Opportunity. Where kids grow up has a big impact on what they earn as adults; new evidence on patterns of upward mobility. Recap by @UrbanInstitute's Margery Austin Turner (@maturner).

5. Open innovation improves the odds of biotech product survival. Analysis by Deloitte's Ralph Marcello shows the value of working together, sharing R&D data.

15 October 2014

The lean way to present evidence to decision makers.

There's a lot of advice out there about how to design presentations. But most of that fails to prepare you for delivering complex evidence to senior-level decision-makers. Here's what I do. Hope this helps.

First, ask yourself this: How might your evidence help someone better understand the steps required to reach an important goal?

  1. To develop an answer, put together what I call lean evidence, embracing lean management concepts. As explained by the Lean Enterprise Institute, "The core idea is to maximize customer value while minimizing waste." Keep this in mind when planning a presentation, writing your report, or sending that email: Focus on what's valuable, and reduce waste by stripping out nonessentials. Show how value flows over all the details to what's important to your audience.
  2. Skip the storytelling. Begin with "Boom! Here's my answer." You're not Steve Jobs, and this isn't a TED talk. You're delivering lean evidence to a busy executive, so think of all that buildup as waste. Stay true to lean, and get rid of it. Jeanne Tari, VP at Power Speaking, makes a similar point, saying the way to present to executives is to "bottom line it first, then have a dialogue".
  3. Go easy on the pretty pictures. Everybody loves eye candy. But the data visualization is not the point: It just helps you make your point.
  4. Connect dots that matter. Keep the focus on your insights, and how they can help the decision maker improve outcomes. (If you find that you're simply reporting results without connecting at least two important things together, then go back and re-evaluate.)
  5. Avoid the dreaded SMEs disease. Provide enough detail about your methods to establish credibility as a subject matter expert. Then stop. Pay yourself $5 for every word you delete. Andrew a/k/a @theFundingGuru gives this advice, and I agree.

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