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In a vast sea of time series data, it can be difficult to determine which sequences of events constitute anomalies or sequences of interest. Many time series datasets hold millions or billions of events which are individually not very interesting, even if it were possible for anyone to look closely at each one. However, when patterns or anomalies can be detected in sequences of events, more concentracted action can be taken on a much smaller subset of data.
According to a recent Reuters study, 30% of 450 technology executives stated that their groups had no women in leadership positions. Only 25% of the IT jobs in the US were filled by women and considering the fact that 56% of women leave IT in the highlight of their career, it’s no surprise that there’s so few women leading the tech industry. There has been momentous push to highlight gender inequality within tech, yet the question still remains: Why are there so few women in tech leadership roles?
In 2015, there were over 2.5 million Americans addicted to opioids; 33,000 of them died of overdose. Nationwide, that rate is over ten deaths per 100,000 people, and the rate is over 30 in some states, such as West Virginia and New Hampshire. At the height of the crack epidemic, in comparison, crack was “only” causing about four deaths per 100,000 people. The vast majority of opioid abusers are addicted to legal prescription pain killers such as Vicodin or Percocet, and many of those eventually become addicted to illicit drugs like heroin. As analytics experts, we outline ways that technology can help the Federal Government reverse this trend and curtail this destructive epidemic.
Research shows that people tend to be overly risk averse when weighing the potential success or failure of a decision. This tendency is compounded when we consider the vast number of decisions being made across an organization. For various reasons, both individuals and groups are often cautious at the expense of their long-term success. From an analytics practitioner’s point of view, this misalignment presents opportunities to improve outcomes through the strategic use of data.
After working with a client’s data for over three weeks with no real progress, you finally hit upon a real breakthrough. You’ve been searching for insights that will help identify which customers are most likely to turn into regular purchasers; the ultimate goal is to focus the company’s marketing efforts on this group in order to earn more revenue per advertising dollar. Studying customer purchase history has been unfruitful. Suddenly, you find that customer geography seems to be a better predictor of future purchases. You have a few more weeks to explore that connection, so you should be able to find some real value for the customer, right?
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