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Once your business has started collecting and combining all kinds of data, the next elusive step is to extract value from it. Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes. During a 2009 interview, Google's Chief Economist Dr. Hal R.Varian stated, "The ability to take data-to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it-that's going to be a hugely important skill in the next decades." Fast forward to 2016 and many businesses would agree with Varian's astute assessment.

Protests against racial injustice and the COVID-19 pandemic have exposed racial inequalities rife within social and economic systems around the world. Fed up with police brutality and systemic racism against African Americans and other racialised groups, people staged protests against racial injustice in all 50 states across the United States.Apart from these examples, however, there is surprisingly little data or discourse about the impact of the disease on racial and ethnic minorities in the rest of Europe. This silence speaks volumes about Europe's approach to racism.The vast majority of EU member states do not use the concept of race or ethnic origin in data collection, in spite of policies like the European Racial Equality Directive and the Employment Equality Directive which prohibit racial or ethnic discrimination. France outright prohibits it.Without disaggregated data, it is virtually impossible to quantify the extent of discrimination experienced by racial and ethnic groups or the impacts of COVID-19 on their lives.

"Between 2000 and 2015, nearly every African country improved childhood nutrition, especially in reducing stunted growth caused by malnutrition" .... " national averages do not tell the full story. In Kenya, for example, rates of wasting in children under 5 were below 6% on average nationwide in 2015, yet in certain regions plagued by several years of poor rains, crop failure and disease outbreaks, estimated levels of wasting reach as high as 28%."... "Such fine-grained insight brings tremendous responsibility to act. It shows governments, international agencies and donors exactly where to direct resources and support."..."This shows how crucial it is to invest in data. Data gaps undermine our ability to target resources, develop policies and track accountability. Without good data, we're flying blind. If you can't see it, you can't solve it."

On the importance of having data, in order to understand and tackle an issue... The most recent U.S. estimate...concluded approximately 513,000 women and girls at risk of genital mutilation...But that number should be taken with a big grain of salt...the data doesn't account for immigrants from countries where female genital cutting isn't studied or widely practiced...."You also can't assume that people who come to the U.S. are a representative sample of their country of origin," Clark said. That's especially problematic for estimating rates of female genital cutting, since it's not practiced uniformly within countries. It's also possible, he said, that some immigrants abandon the procedure as they assimilate....some advocates point out that although the estimates focus on immigrants, ...female genital cutting isn't new to the U.S. Female circumcision was performed as a treatment for masturbation by American physicians as recently as the mid-20th century...

Journalists know they need to get better with data and statistics, but they have a long way to go Only 25 percent of journalists surveyed said they were "very" well equipped to interpret statistics from sources, and only 11 percent said the same about doing statistical analysis themselves.

For decades, researchers and physicians tended to think about pregnancies as either planned or unplanned. But new data reveals that for a significant group of women, their feelings don't neatly fit into one category or another. As many as one-fifth of women who become pregnant aren't sure whether they want a baby. This fact may reshape how doctors and policymakers think about family planning. For women who are unsure, it doesn't seem enough for physicians to counsel them on pregnancy prevention or prenatal care. "In the past we thought of it as binary, you want to be pregnant or not, so you need contraception or a prenatal vitamin," said Maria Isabel Rodriguez, an obstetrician-gynecologist at Oregon Health and Science University whose research focuses on family planning and contraceptive policy. "But it's more of a continuum." The new data comes from a recent change in the Centers for Disease Control and Prevention's big survey of new mothers, now allowing them to answer a question about their pregnancy desires by saying "I wasn't sure." It shows that some women want to avoid making a decision about becoming pregnant, or have strong but mixed feelings about it.

When creating a machine-learning algorithm, designers have to make many choices: what data to train it on, what specific questions to ask, how to use predictions that the algorithm produces. These choices leave room for discrimination, particularly against people who have been discriminated against in the past. For example, training an algorithm to select potential medical students on a data set that reflects longtime biases against women and people of color may make these groups less likely to be admitted. In computing, the phrase "garbage in, garbage out" describes how poor-quality input leads to poor-quality output. In this case we might say, "White male doctors in, white male doctors out."

the BBC has uncovered a sombre data gap that may be having a profound effect on good governance for some countries in Africa. Only eight out of more than 50 African countries investigated by the BBC have a compulsory system to register deaths, meaning many lack a complete view of mortality trends. This could be having a far-reaching influence on a number of key policy areas - including resource allocation and understanding the impact of Covid-19.

It's human nature to spot patterns in data. But we should be careful about finding causal links where none may existStories about people getting blood clots soon after taking the Oxford/AstraZeneca vaccine have become a source of anxiety among European leaders. After a report on a death and three hospitalisations in Norway, which found serious blood clotting in adults who had received the vaccine, Ireland has temporarily suspended the jab. Some anxiety about a new vaccine is understandable, and any suspected reactions should be investigated. But in the current circumstances we need to think slow as well as fast, and resist drawing causal links between events where none may exist.

We learned this week that the Trump campaign may have tried to dissuade millions of Black voters from voting in 2016 through highly targeted online ads. The investigation, by Channel 4, highlighted a still little-understood online advertising technique, microtargeting. This targets ads at people based on the huge amount of data available about them online. Experts say Big Tech needs to be much more transparent about how microtargeting works, to avoid overblown claims but also counter a potential threat to democracy.

There is a black hole in our knowledge of women and girls around the world. They are often missing from official statistics, and areas of their lives are ignored completely. So campaigners say - but what needs to be done?

Interesting look at data around private healthcare and marketisation. Each year, for well over a decade, more people have faced higher health insurance deductibles. The theory goes like this: The more of your own money that you have to spend on health care, the more careful you will be - buying only necessary care, purging waste from the system. But that theory doesn't fully mesh with reality: High deductibles aren't working as intended. A body of research - including randomized studies - shows that people do in fact cut back on care when they have to spend more for it. The problem is that they don't cut only wasteful care. They also forgo the necessary kind. This, too, is well documented, including with randomized studies. People don't know what care they need, which is why they consult doctors.

An introduction to a set of deep dive articles an important issue....When we tell people that we work at the intersection of statistics and human rights, the reaction is often surprise. Everyone knows that lawyers and journalists think about human rights problems … but statisticians? Yet, documenting and proving human rights abuses frequently involves the need for quantification. In the case of war crimes and genocide, guilt or innocence can hinge on questions of whether violence was systematic and widespread or one group was targeted at a differential rate compared to others. Similar issues can arise in assessing violations of civil, social, and economic rights. Sometimes the questions can be answered through simple tabulations, but often, more-complex methods of data collection and analysis are required.

On Friday, the Australian Taxation Office, as it does every April, released the latest batch of annual taxation statistics. And as ever, the data was used in rather contorted ways to suggest the budget needed to reduce the level of taxation paid by the wealthiest and to make it seem like the richest were the ones doing it tough. The other old chestnut that got a run in the Australian is that more school teachers actually use negative gearing than company executives. Again, is it really a shock that "while 72,000 investors were listed as company executives, 99,000 people claiming rental losses on their tax returns were either teachers, nurses or midwives"? Given there are about 300,000 more people working as teachers, nurses or midwives than there are company executives, does anyone really think that because 27,000 more of them might use negative gearing is proof of anything? The crucial thing is not the total number, but the proportion of teachers and nurses (and any other profession) who use negative gearing.

The Supreme Court does not compute. Or at least some of its members would rather not. The justices, the most powerful jurists in the land, seem to have a reluctance - even an allergy - to taking math and statistics seriously. For decades, the court has struggled with quantitative evidence of all kinds in a wide variety of cases. Sometimes justices ignore this evidence. Sometimes they misinterpret it. And sometimes they cast it aside in order to hold on to more traditional legal arguments. (And, yes, sometimes they also listen to the numbers.) Yet the world itself is becoming more computationally driven, and some of those computations will need to be adjudicated before long. Some major artificial intelligence case will likely come across the court's desk in the next decade, for example. By voicing an unwillingness to engage with data-driven empiricism, justices - and thus the court - are at risk of making decisions without fully grappling with the evidence. This problem was on full display earlier this month, when the Supreme Court heard arguments in Gill v. Whitford, a case that will determine the future of partisan gerrymandering - and the contours of American democracy along with it. As my colleague Galen Druke has reported, the case hinges on math: Is there a way to measure a map's partisan bias and to create a standard for when a gerrymandered map infringes on voters' rights?

Until the first Abbott-Hockey budget in 2014, a statement of budget measures that disproportionately affect women was published at budget time. At times given different names, the first was delivered with the Hawke government's 1984 budget. In its foreword, then Prime Minister Hawke promised that "within the overall economic objectives of the government" important budget decisions would from then on be made "with full knowledge of their impact on women". These women's budget statements shed light on the impact of decisions that might have been thought to have little to do with gender, such as the Hawke government's reduction of tariffs on imports of clothing, textiles and footwear. The statement pointed out that two-thirds of the workers in these industries were women and that without special support for retraining (which was given) they would be disproportionately disadvantaged. Increasingly, and especially during the Rudd and Gillard governments, the statements made visible the economic impact of women's greater responsibility for unpaid care work.

The census has been used for hundreds of years to determine how many U.S. House members each state will have, and it currently helps determine how hundreds of billions of dollars in federal spending is divvied up. "The risk that really troubles me is that there's a big undercount and then there's a big lack of representation," said John Thompson, who was director of the U.S. Census Bureau until he resigned last year (the bureau is still without a director).

Excellent analysis from the FT (you'll need to login to view via the link) that uses knowledge of the Mean and Median to show that some companies have reported incorrect (fabricated?) pay-gap information! One in 20 UK companies that have submitted gender pay gap data to the government have reported numbers that are statistically improbable and therefore almost certainly inaccurate, a Financial Times analysis has found. Sixteen companies, each with more than 250 employees, reported that they paid their male and female staff exactly the same, that is they had a zero average gender pay gap measured by both the mean and median. Experts on pay said that it was highly anomalous for companies of that size to have median and mean pay gaps that were identical because the two statistics measure different things. The mean gap measures the difference between the average male and female salary while the median gap is calculated using the midpoint salary for each gender.

What's the most efficient way to communicate a large amount to a reader? We ran an experiment to find out. The results show that we must give up with senseless "football fields" comparisons and focus on finding out if a number matters or not.

Climate deniers have been peddling the myth 'no warming since [insert date]' for over a decade. It's a popular myth among those who benefit from maintaining the status quo because if the problem doesn't exist, obviously there's no need for action to solve it. And it's an incredibly easy argument that can be made at any time, using the telltale technique of climate denial known as cherry picking.