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DrillDown
the same concept to data portability
and data transparency, respectively. In other words, datasets are most valuable when as many people and applications as possible can see and analyze them.
• IT as a profit center. For many agencies, IT is considered a cost center — a requirement and cost of doing business. Yet technol-
ogy now has a direct role in driving innovation, as exem- plified by the adoption and use of big data. In both the private and public sectors, leaders are beginning to shift their view of technology’s role in the organiza- tion. Forward- thinking leaders
see IT as a profit center and actively fund it to drive rev- enue, insight and innovation. In an agency, approach- ing IT as a profit center can lead to cost optimization that underwrites further insight and innovation, result- ing in increased service to the public.
• Self-service analytics. In agen- cies and enterprises alike, the main beneficiaries of analytics are employ- ees outside the IT department. Yet the IT department is largely responsible for choosing the tools and creating and analyzing the reports. Self-service ana- lytics can open business intelligence and give more people greater access to data and the analytical tools while freeing precious IT resources for more technology-heavy tasks.
2. Adopt a data-driven culture
Another reason agencies might be losing young workers comes down to culture. Many of today’s successful businesses see culture as a strategic
advantage and take a deliberate, top- down approach to establishing and reinforcing their culture across the enterprise. Particularly when it comes to technology, firms project cool, fun and disruptive uses of data as central to their cultures.
How they think of their business, their processes, their people — in short, the very essence of their companies — revolves around
next test is based on the results. That cycle is not new, but today’s approach builds on the culture of agility and allows for much faster, tighter itera- tions in the process. Simply put, orga- nizations can get to the answer more quickly. Becoming more hypothesis- driven requires a culture that facilitates taking risks and managing failure, both of which are fostered by embracing agility.
• Engagement. Open source thrives on the easy and fluid engagement of many to develop and update the best solution. The same concept can apply to creating a culture of innovation at a workplace. Counter to the ill-conceived perspective that the highest-paid per- son in the office is the most innovative person in the office, the reality is that great ideas come from all employees, and embracing openness in its various colors encourages that innovation. Such efforts are often tied to self- service analytics to give the right peo- ple access to the right data and tools at the right time.
Many agencies have begun to adopt a forward-looking data culture to help achieve their missions. Yet fostering that approach is best viewed as an evo- lution, not a departure from traditional approaches to IT.
A great deal of domain knowledge is wrapped up in conventional ways of articulating datasets, applications and processes within an organization. That hard-won knowledge is incred- ibly valuable as agencies create a data culture to attract young workers at a time when many baby boomers are transitioning into retirement.
Paired with the right technologies, that know-how can be shaped and brought forward in forms well suited and familiar to young government workers. That shift will help the U.S. maintain its leadership in defense and across the board. n
Webster Mudge is senior director of technology solutions at Cloudera.
Another reason agencies might be losing young workers comes down to culture.
a culture that embraces data. Unfortunately, the percep- tion of the federal govern- ment’s culture is that of an “old guard” that is plodding and reactive to the value of its information. We know that view is myopic, but many agencies do embrace data in that manner. There is no reason government agen- cies couldn’t estab- lish and promote a similar data-driven culture, however. It starts with a few foundational con-
cepts:
• Agility. This is
powered by the very core of current innovation in data man- agement: agility in terms of computing and storage capability and capacity. IT teams in the private sector have long relied on the agile frameworks of sys- tems such as Apache Hadoop to direct- ly address flexibility and risk mitiga- tion in application development, which allows them to keep an application rel- evant and up-to-date in a constantly changing environment. Agencies, too, must be agile and find ways to continu- ally advance so they can meet changing
expectations.
• Testing. One aspect of the emerg-
ing culture of data is a variation of the standard hypothesis/experiment pro- cess. One has an idea about something, tests that idea and updates it, and the
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