data – Resource Library

No matter where you look, the amount of data that the government generates and collects is staggering. When data is properly harnessed, potential benefits are endless.

Financial management in the federal government requires effective data. Tasked with ensuring the financial health of the government and appropriate spending, financial managers must understand complex federal budget processes. Although some aspects of federal financial management function like a business, others are unique to federal budget processes, such as appropriations. To juggle the distinct needs of federal finance operations, financial managers must understand and employ innovative data practices.

In the Data Snapshot series, we will provide snapshots of ways that federal government agencies use data to achieve their missions. In this snapshot, we are showcasing the amazing results of data-focused initiatives at the US Census Bureau.

With the increasing push for IT modernization, artificial intelligence (AI), cloud platforms, and robotic process automation (RPA), we might want to step back and take a look at the aspect of quality in this new reality. Is the data these processes were built upon reliable? Do the results we see demonstrate significant progress? Of course, there will be growing pains, but we can see that mainstream America, and much of the modern world, has embraced these new technologies at work and in life.

Samuel Taylor Coleridge’s Rime of the Ancient Mariner tells the experience of a sailor returning from a long voyage. In the early parts of the poem, the mariner describes how an albatross leads the crew out an ice jam in the Antarctic, but the mariner then kills the bird. The crew vacillates between viewing the killing of the albatross as a good or a bad thing. However, they soon become surrounded by “water, water, everywhere, nor any drop to drink.” Eventually, the mariner encounters death, but survives after accepting his guilt – however as a penance for shooting the bird, the sailor must wander the earth telling his story to passersby.

If you take a quick look at Glassdoor.com and their “50 Best Jobs in America” for 2017, you will notice that Data Scientist ranks at #1, among several other “data/analytics” titles in the list. Although this is evidently a highly desirable job, there appears to be a large talent gap in the field. Recent research by McKinsey Global Institute has projected that by next year, the US could face a shortage of 140,000 to 190,000 professionals with deep analytical skills, i.e., advanced statistical training and knowledge of machine learning. What exactly is data science? While not an entirely new concept, the terms “data science” and “data scientist” have exploded in popularity in the past 5-10 years. At its core, data science is about using scientific methods along with new technology systems to gather insights from massive amounts of heterogeneous data.

In just a couple weeks, I will be on my way to Italy for a two-week vacation! Che bella! When I was deciding where to go with my husband, I experienced a bit of work-related decision-making déjà vu. Every option we considered was very different from the next, but all were possibilities chosen based on the same exact set of data (number of vacation days available, budget, etc.).

I felt like I was back at the office working with a team, arguing about what decision to make, even though we’re usually reviewing the same data. The difficulty can be that different does not necessarily mean wrong in these situations.

Ever hear the phrase, “The first step is the hardest”?

While it may be a common phrase for motivational posters, it also applies to the first step in making data-driven decisions: defining the problem you’re trying to solve.

Without a clearly or accurately defined problem, time and other resources will be wasted, and you’ll be left unable to make an effective decision. Often, nailing down exactly what you’re trying to solve is more difficult than the analysis itself.

I hope everyone had a good Memorial Day Weekend – the unofficial kickoff to wedding season! In that spirit, I’m going to discuss a very important relationship in every data analyst’s life: the marriage of technical skills to their soft-skill counterparts.

Tag: data