Last week, Olivia Hebner and Laura Hoesly attended the Government Analytics Breakfast Forum: Using Analytics to Combat the Opioid Crisis sponsored by Johns Hopkins University and REI Systems. The speakers included Allison Oelschlaeger (Centers for Medicare and Medicaid Services), Dr. Mona Siddiqui (Health and Human Services), and Dr. Jim Kyung-Soo Liew (Johns Hopkins University). During the forum, speakers and audience members discussed data sources available to the federal government, challenges the government faces when analyzing the data, steps already taken, and possible methods for analyzing the data going forward.

Here at Summit, we guide Federal agencies, financial institutions, and litigators as they decode their most complex analytical challenges. But we know our clients aren't the only ones who want to hear what economics and statistics can teach us about our daily lives.

That's why we're launching a new podcast and video series: Complexity Simplified. Through interviews, panels, and question-and-answer series with our staff of economists, econometricians, and data scientists, we'll break down topics in statistics, economics, and data analytics.

Last week, I attended the Data Intelligence Conference hosted by Capital One in McLean, Virginia. The three-day conference focused on a community of practitioners in the field of machine learning and artificial intelligence modeling. The talks at the conference centered on predictive modeling methods, real-life case studies involving machine learning and predictive analytics, data governance and security issues, and other data cleaning and visualization methods.

On September 2016, I attended the first annual White House Open Data Innovation Summit. This event brought together individuals from the government, nonprofit, and private sectors to talk about the future of open government data. 

More and more organizations are releasing their administrative data to the public, whether because of Federal regulations or calls for increased transparency. There's a lot to consider before releasing this kind of information, and many groups struggle when deciding how to share their data most efficiently. Thankfully, many groups are already packaging their data in new and creative ways. Read on for several examples.

In recent years, various groups have asked government agencies to release their administrative data. Before thinking about the considerations an organization should weigh before releasing its administrative data, it's important to discuss the benefits of making this information available.

This week, Summit attended the 2016 Annual Conference of the Association of Public Data Users (APDU) in Alexandria, VA. APDU works to raise awareness of available public data, support the use of public data, and facilitate communication among the producers, disseminators, and users of public data.

Summit was one of several sponsors and exhibitors at the APDU conference, including the Economic Research Service (ERS), the Bureau of Economic Analysis (BEA), and the Laura and John Arnold Foundation (LJAF).

When thinking about how to appropriately release administrative data to the public, there are a number of things to consider. Today at the Association of Public Data Users' (APDU's) 2016 Annual Conference: "Using Public Data to Inform Better Decision Making," I am speaking on a panel about how to build demand for open data. As organizations look to make their administrative data publicly available, there are some decisions they need to take into consideration. Below are four key questions organizations should ask themselves before disclosing administrative data to the public. 

Sampling is all about doing things smarter—not harder. A recent Wall Street Journal article (“How Uncle Sam Conducts Surveys: Complex sampling gives U.S. representative data on big issues at less cost than simple random sampling”) summarizes some frequently deployed complex survey designs that economize on data collection costs. 

For sports fans, this is a great time to be alive. We’re entering an age where data analytics is revolutionizing the way we see sports. A perfect example is basketball, where the NBA now collects spatial data on player and shot locations on the court. This data can be used to create shot charts, which show where shots are taken and how successfully. A single shot chart can tell a story about a player’s shooting tendencies, strengths, and weaknesses.