Data models can distill powerful insight from raw data. Yet, this insight is only valuable when it is acted on, and it’s only acted on if it’s understood. Visualization plays a vital role in revealing key aspects of data within its overall context, enabling understanding for technical and non-technical decision makers alike.
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[fa icon="calendar'] April 21, 2017
[fa icon="calendar'] April 14, 2017
Predictive models typically estimate the likelihood of future events, such as whether it will rain tomorrow or which customers are most likely to “churn” by cancelling their phone contract. In the case of the weather, we do not expect to change it; we just want to know how to adapt. However, the goal for most use cases is to be more proactive; we want to understand what action to take to change the outcome in a favorable way. In these cases prescriptive, not just predictive, analytics is required. The return on investment comes directly from knowing the impact of alternative treatments. By knowing the impact of each treatment, resources can be targeted where they will be most effective and withheld where they will have negligible effect or worse, have a negative effect. This great objective of data science, to intelligently drive day-to-day business decisions based on data, is the purview of uplift modeling. This blog will explain what uplift modeling is and why it can be much better than directly modeling the outcome.
[fa icon="calendar'] April 7, 2017
Analytics is a disruptive technology and many organizations fail the first time they attempt a major analytics initiative. Extracting value from data to drive intelligent business decisions requires a cultural shift within a business to institutionalize analytics-based decision management. Elder Research has found that having an “analytics culture” is one of the strongest indicators of future analytics success and developing an effective analytics strategy is an active process that plays a key role in building a successful, data-driven culture. Using analytics to achieve a sustainable competitive advantage and generate significant return on analytics investment begins with a well-conceived analytics strategy.
[fa icon="calendar'] April 1, 2017
Understanding and effectively communicating a concept often requires first building a simple mental model. Consider, for example, how we teach the physical laws to students: it helps to walk with algebra before you can run with calculus. This kind of model trades correctness (shaving off "unnecessary" detail) for an increased ability to grasp the larger picture.
[fa icon="calendar'] March 31, 2017
Recent articles have touted the boundless value locked in sensor data, if businesses would only strike the Machine Learning match to set it all ablaze. Any organization not thinking seriously about Sensor Analytics is at risk of being burnt by the disruption to structure, competition, and workflows that this nascent technology brings. New entrants will need to ask themselves “What industry are we in?”
[fa icon="calendar'] March 24, 2017
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 concentrated action can be taken on a much smaller subset of data.
[fa icon="calendar'] March 17, 2017
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?
[fa icon="calendar'] March 10, 2017
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.
[fa icon="calendar'] February 24, 2017
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.
[fa icon="calendar'] February 10, 2017
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?