Government agencies have massive amounts of data about their constituents as well as the businesses that operate within their jurisdictions. As agencies adopt new technologies and improve communication between departments, they will be able to collect and utilize even more data to improve everything from administrative efficiency to public service offerings. Indeed, there are few aspects of modern life that big data won't impact.
Big data, in this sense, generally means using massive amounts of information to form predictive models. In theory, the more we know about a given subject - crime rates, for example - the better we can understand how to plan and react in the future. For instance, if an agency can determine that crime rates spike in a specific location on specific days of the year, they can allocate more resources to that area to prevent future incidents.
This theory, however, applies to everything from population health to crop yields and anything in between. But there are barriers to progress. According to KPMG data and analytics professional Viral Chawda, some of the biggest hurdles governments face in their implementation of big data tactics are antiquated technology, siloed information and lack of transparency.
To manage these challenges, governments need skilled professionals, a challenge in itself. The KPMG Centre for Business Analytics at Imperial College Business School reported that 36 percent of surveyed executives said they lack big data specialists.
"We have hit a point in our technical evolution where our technology is finally capable of achieving what generations have sought. It is a shared thought amongst industry and government leaders: Data is the new oil and the most valuable resource available," says Mike Boyles, Division Manager of Beacon Hill's National Security Division. "The one thing we need to ensure is that we hire and train enough people that can properly interpret the data and get it to the proper resources to analyze and take appropriate actions. In addition to the lucrative compensation structures many data centric roles provide, it is the tip of the spear when it comes to providing what is most beneficial for the customers it serves. In short, these roles are very interesting and as impactful to the mission as you can be."
If you're interested in becoming a big data professional with the federal government, consider these in-demand positions:
1. Chief Data Officer
Chief data officers are responsible for just about everything dealing with an organization's data, from pushing analytics initiatives to establishing policies. According to SearchBusinessAnalytics, the title is a new one at most organizations, which means the position's duties are often nebulous. That could be an exciting opportunity for professionals who want to truly shape the role for future generations. PayScale reported the average CDO salary as $193,607.
The data analyst position is a person responsible for the import, transformation and validation of modeling data, typically with the goal of driving decision making. Data analysts will develop databases and present information in graphs and charts, typically for the CDO. Salaries range between $49,000 and $110,000 depending on the organization and level of responsibility, according to Glassdoor. The average salary is roughly $76,000.
A primary challenge of big data analytics is the siloed nature of information databases. Not only might IT professionals house data in separate locations - they might store it in disparate formats. Data engineers are responsible for building channels that allow for easier access to data as well as the integration of data types within a holistic system. This gives analysts the opportunity to apply the data to predictive models. PayScale reported the median salary as $83,693.
This position is a broad term. Responsibilities can vary greatly from one organization to the next. In general, computer scientists are skilled at developing software, designing and developing databases and engineering communication networks. The duties of a CS at a financial institution will be quite different from those of a CS at a hospital, for instance. These disparities mean average salaries range from $81,000 to $154,000, according to Glassdoor.
Also referred to as quantitative analyst, this position typically helps an organization make smarter investments and manage financial risk. However, job duties can vary greatly, depending on the organization. Some professionals may work solely with the risk management department. Others work with analysts to form financial predictions. Salaries range from $66,000 to $100,000.
These five positions are just the tip of the iceberg. According to research from IBM, there will be an expected 15 percent growth in the number of open data analytics jobs in the U.S. between 2017 and 2020.
To jump-start your career with the federal government, contact the recruiting experts at Beacon Hill Staffing Group today.
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