Banner Data Standards & Guideline For University Data Standards: Fill & Download for Free

Probably the wrong question, as everyone will tell you their own piece of software is somehow compliant. GDPR opens many variables in the MarTech space and it will take a few large fines for people to agree on their interpretation. I am yet to find a company that admits their software won’t comply by May 25th, and yet it is obvious most solutions are unable to propagate user rights or properly obtain consent.I think you should instead provide your own “clean data” standard to make sure that everyone abides by a safe interpretation of the law. In my opinion this boils down to accepting a few basic principles:Whatever your chosen system collects, stores, processes or shares about your audience will often require prior, express consent - regardless of anyone’s ability to use such properties or encrypted non-PII data points to get back to a specific individual. This matters little, as GDPR covers pseudonymous data, and that is the case in most instances. PII is simply not a valid threshold.Any cookies that allow you to track clicks and requests across different environments will require express, prior consent (the cookie banner will not do anymore). The only way to avoid this is keeping cookie-supported data points isolated, used solely in aggregate for a limited period of time.There should be a mechanism for the withdrawal of consent, as well as a means to exercise user rights (portability, in “machine-readable format” can prove painful).Data should never end up in a shared pool, or stored in the US without explicit consent (an even higher bar for it).Of course there is much more, but these can act as a safe filter, or so I strongly believe. Not to be considered legal advice.

Here are...7 ways data scientists use statistics1. Design and interpret experiments to inform product decisionsObservation: Advertisement variant A has a 5% higher click-through rate than variant B.Data Scientists can help determine whether or not that difference is significant enough to warrant increased attention, focus, and investment.They can help you understand experimental results, this is especially useful when you're measuring many metrics, running experiments that affect each other, or have some Simpson's Paradox happening in your results.Let's say you're a national retailer and you're trying to test the effect of a new marketing campaigns. Data Scientists can help you decide which stores you should assign to the experimental group to get a good balance between the experimental and control groups, what sample size you should assign to the experimental group to get clear results, and how to run the study spending as little money as possible.Statistics Used: Experimental Design, Frequentist Statistics (Hypothesis Tests and Confidence Intervals)2. Build models that predict signal, not noiseObservation: Sales in December increased by 5%.Data Scientists can tell you potential reasons why sales have increased by 5%. Data scientists can help you understand what drives sales, what sales could look like next month, and potential trends to pay attention to.See What is an intuitive explanation of over-fitting, particularly with a small sample set? What are you essentially doing by over-fitting? How does the over-promise of a high R², low standard error occur? to understand why its important to only fit on signal.Statistics Used: Regression, Classification, Time Series Analysis, Causal Analysis3. Turn big data into the big pictureObservation: Some customers only buy healthy food, while others only buy when there's a sale.Anyone can observe that the business has 100,000 customers buying 10,000 items at your grocery store.Data Scientists can help you label each customer, group them with similar customers, and understand their buying habits. This allows you to see how business developments can affect certain groups of the population, instead of looking at everyone as a whole or looking at everyone individually.Dunnhumby breaks down grocery shoppers into groups including Shoppers On A Budget, Finest, Family Focused, Watching the Waistline, and Splurge and Save [1]Statistics Used: Clustering, Dimensionality Reduction, Latent Variable Analysis4. Understand user engagement, retention, conversion, and leadsObservation: A lot of people are signing up for our site and never coming back.Why do your customers buy items from your site? How do you keep your clients coming back? Why are users dropping out of your funnel? When will they come out next? What kinds of emails from your company are most successfully engaging users? What are some leading indicators of engagement, activity, or success? What are some good sales leads?Statistics Used: Regression, Causal Effects Analysis, Latent Variable analysis, Survey Design5. Give your users what they wantGiven a matrix of users (customers, clients, users), and their interactions (clicks, purchases, ratings) with your companies items (ads, goods, movies), can you suggest what items your users will want next?Statistics Used: Predictive Modeling, Latent Variable Analysis, Dimensionality Reduction, Collaborative Filtering, Clustering6. Estimate intelligentlyObservation: We have a banner with 100 impressions and 0 clicks.Is 0% a good estimate of the click-through-rate?Data Scientists can incorporate data, global data, and prior knowledge to get a desirable estimate, tell you the properties of that estimate, and summarize what the estimate means.If you're interested in a better approach to estimating the click-through rate, check out What are the advantages of Bayesian methods over frequentist methods in web data?Statistics Used: Bayesian Data Analysis7. Tell the story with the dataThe Data Scientist's role in the company is the serve as the ambassador between the data and the company. Communication is key, and the Data Scientist must be able to explain their insights in a way that the company can get aboard, without sacrificing the fidelity of the data.The Data Scientist does not simply summarize the numbers, but explains why the numbers are important and what actionable insights one can get from these.The Data Scientist is the storyteller of the company, communicating the meaning of the data and why it is important to the company.The success of the previous six points can be measured and quantified, but this one cannot. I'd say this role is the most important.Statistics Used: Presenting and Communicating Data, Data VisualizationFollow my blog at Storytelling with StatisticsChinese Translation: 数据科学家的7种统计学使用场景 | 大数据观察TL; DR - With statistics, data scientists derive insights to encourage decisions that improve product or business, distilling the data into actionable insights that promote the vision of the company.For a similar answer I wrote on this topic, check out How much of statistics/math do you need to work on as a data scientist?[1] - dunnhumby & hack/reduce: A Product Launch Challenge

The Windows Phone 7 was a pretty decent piece of hardware for its time.Boasting a 4-inch display, Internet Explorer access and up to 16 gigabytes of storage, it’s obviously pathetic by today’s standards, but back in 2010, the phone was a solid threat to the iPhone’s market share.However, several Microsoft employees who worked on the project may have seriously overestimated how successful the Windows Phone 7 was going to be.On September 10th, 2010, a group of Microsoft employees celebrated their achievements too soon in a very grand and very dramatic gesture.Do you want to know what that gesture was?They held a funeral for the iPhone.Complete with a hearse, banners and a performance of the zombie dance from Thriller. A giant iPhone was carried by employees in place of a corpse, and a second ‘coffin’ was made for the BlackBerry, another competitor in the smartphone market at the time.[1][1][1][1]Banners at the parade claimed the Windows Phone 7 would ‘bury the competiton’, and I cannot emphasise enough how inaccurate this claim was.As you probably know, the iPhone has seen nothing but success and profit in the last decade. Meanwhile, the Windows Phone 7 fell flat on its face, both in sales revenue and general consumer reception.A tech analyst website described the phone’s impact as ‘barely a blip on the radar’[2][2][2][2]So I think it’s safe to say that Microsoft got too confident, too soon when it came to this phone. But judging by the literal mock funeral, it was more than just a disappointment; it was a humiliation.Instead of just delivering an underwhelming product, Microsoft’s gloating made the Windows Phone 7 a crushing blow to their credibility in the smartphone industry.That’s the difference between arrogance and confidence. Being proud and optimistic about your work is one thing; but prematurely celebrating a victory this big is absolute arrogance.And clearly, that arrogance was misplaced.Stay vicious.Footnotes[1] Microsoft's funeral for the iPhone (photos)[1] Microsoft's funeral for the iPhone (photos)[1] Microsoft's funeral for the iPhone (photos)[1] Microsoft's funeral for the iPhone (photos)[2] Web Data Shows Windows Phone 7 Barely a Blip on the Radar[2] Web Data Shows Windows Phone 7 Barely a Blip on the Radar[2] Web Data Shows Windows Phone 7 Barely a Blip on the Radar[2] Web Data Shows Windows Phone 7 Barely a Blip on the Radar