{"id":28989,"date":"2022-04-12T06:00:00","date_gmt":"2022-04-12T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=28989"},"modified":"2022-04-13T08:32:04","modified_gmt":"2022-04-13T15:32:04","slug":"insidebigdata-guide-to-government-part-3","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/04\/12\/insidebigdata-guide-to-government-part-3\/","title":{"rendered":"insideBIGDATA Guide to Government \u2013 Part 3"},"content":{"rendered":"\n

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15 things your organization can do to improve your use of big data analytics<\/strong><\/p>\n\n\n\n

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If your agency already has a decade of experience (or more) with big data analytics, your plans for the next year will naturally look a lot different than those for an organization that is just embarking on its first project. However, experts offer fifteen suggestions that apply to most government entities that are involved with data analytics, no matter where you are on your journey.<\/p>\n\n\n\n

1. Learn from the example of other organizations<\/strong><\/p>\n\n\n\n

Public agencies have one big advantage over private companies when it comes to big data analytics: there\u2019s a lot more information available about how similar organizations have succeeded (or not) with big data analytics. For example, the Bloomberg Center for Cities at Harvard University has a project called Data-Smart City Solutions<\/a> that has an online repository of examples of government analytics projects. If you\u2019re wondering where to begin, looking for your next project, or wondering how other regions have handled the problems you are facing, this repository is an excellent resource.<\/p>\n\n\n\n

2. Create clear policies and regulations governing the use of big data<\/strong><\/p>\n\n\n\n

On the other hand, when it comes to privacy and security, public agencies often face much greater scrutiny than their counterparts in the private sector. The World Bank explains, \u201cGovernments need to provide legal and policy guidelines on data ownership, quality and sharing, privacy, civil liberties, and equality. They must make decisions on privacy in conjunction with public opinion, informing citizens about the trade-offs between the privacy and security risks of sharing data, versus the benefits.\u201d And even if you have long had such policies in place, you need to revisit them regularly to make sure that they are still up to date as technology and public opinion evolve.<\/p>\n\n\n\n

3. Shore up your data governance<\/strong><\/p>\n\n\n\n

Not only do public agencies need solid policies, they also need procedures and tools that help ensure those policies are followed. That means having a robust data governance program in place and reviewing it regularly to make sure it is still meeting your needs. In an article on \u201c3 Steps to Accelerating Data Readiness at Federal Agencies<\/a>,\u201d the Federal News Network says, \u201cThe government plays a pivotal role in securing citizen data and many other sensitive data sets, so governance practices and technologies must monitor data use to make sure rules are followed when data is collected, moved, copied, analyzed and shared.\u201d<\/p>\n\n\n\n

4. Invest in big data infrastructure<\/strong><\/p>\n\n\n\n

You also need to make sure that you have adequate hardware to support any data analytics projects. Often, regulations and security concerns make it desirable to house government big data stores on-prem. And in order to support these use cases, you need flexible, scalable servers with plenty of processor cores, memory, storage, and advanced GPUs. McKinsey & Company advises that organizations start small and add more hardware as it becomes necessary. \u201cAdd the elements of the data architecture incrementally, in line with the use case road map,\u201d it says. \u201cNot everything has to be in place from the start. Make sure, however, that the architecture is flexible enough to add new capabilities, as data and analytics needs scale.\u201d<\/p>\n\n\n\n

5. Start with a small, experienced team<\/strong><\/p>\n\n\n\n

As with your infrastructure, you can also start with a very small team. McKinsey recommends, \u201cA team of two or three practitioners, supplemented with specialized external expertise as needed, can create substantial momentum, even in organizations with many thousands of employees.\u201d If the members of this small team have substantial outside experience with similar projects, they may be even more effective than a large team of less-experienced staff.<\/p>\n\n\n\n

6. Clearly define the problem to be solved<\/strong><\/p>\n\n\n\n

The Civic Analytics Network, which is part of the Data-Smart City Solutions project, offers a recommended framework for government agencies embarking on big data analytics projects. Their advice is equally relevant whether you are starting your first project or your hundredth. \u201cThe first step the Civic Analytics Network recommends\u2014before considering what data an organization has available\u2014is establishing a clear understanding of the problem to be addressed by a given analytics project. Determining data readiness or maturity is critical, but before an analytics project can even be scoped, it is important to ensure that the project\u2019s objective is core to the performance or needs of the implementing organization.\u201d<\/p>\n\n\n\n

7. Be willing to experiment with pilot projects<\/strong><\/p>\n\n\n\n

In the early days of big data analytics, many government projects failed because they were overly ambitious. The lesson teams should take from this is not that big data analytics is a mistake, but that they should be willing to start small and experiment to minimize the potential downside of any failures. According to Wiseman, \u201cThe best organizations allow for creativity and have a willingness to take on risk and embrace failure as a part of the process. In fact, many successful analytics efforts today have achieved their results based on failing first with large \u2018moonshot\u2019 projects and later learning and adapting via smaller pilot projects.\u201d<\/p>\n\n\n\n

8. Enable self-service<\/strong><\/p>\n\n\n\n

Another mistake of early government big data projects was that they were overly reliant on data scientists and other experts. Today, many of the most successful efforts have come from agencies that deployed self-service platforms that allowed their workers to do their own analysis. Empowering your teams to do big data analytics allows you to tap into the creativity and ingenuity of your workforce and find meaningful ways to use your data that the \u201cexperts\u201d might never have considered. It can also boost morale and build more support for analytics throughout your organization.<\/p>\n\n\n\n

9. Adopt open data policies<\/strong><\/p>\n\n\n\n

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Not only do you want to enable your workers to do big data analytics, you should also enable citizens outside your agency and workers at other organizations to learn from your data. Of course, privacy concerns mean that not all data should be equally accessible, but when it is possible, datasharing usually leads to greater innovation. Data-Smart City Solutions says, \u201cAdopting an open data policy can be a boon to rapidly, transparently, and collaboratively developing comprehensive analytics projects. Open data policies and portals enable city governments to operate with greater transparency to the public and to connect them directly to external researchers, algorithms, and\/or datasets that can support more effective analytics project development.\u201d<\/p>\n\n\n\n

10. Look for ways to work with private organizations<\/strong><\/p>\n\n\n\n

Along those same lines, your agency should also actively seek ways to collaborate on big data analytics projects with the private sector. When you combine the resources of both types of organizations, public-private partnerships can often accomplish things that individual organizations cannot do on their own. For example, the Global Partnership for Sustainable Development Data<\/a> has experienced success in bringing together public and private entities to achieve goals related to sustainability.<\/p>\n\n\n\n

11. Become a data evangelist<\/strong><\/p>\n\n\n\n

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If you lead a data analytics project in your agency\u2014or even if you are just a team member\u2014one of the most important things you can do is talk about your project and inform others about the benefits you are achieving. Wiseman says, \u201cAn important way that a data leader advances public value is by serving as a data evangelist or advocate for data-driven government.\u201d Some people in your organization or some of the citizens you serve may initially be hesitant to embrace data analytics. Speaking out about your efforts can help build support for your projects and multiply the potential benefits.<\/p>\n\n\n\n

12. Encourage data literacy<\/strong><\/p>\n\n\n\n

As you go about your evangelism efforts, encourage your co-workers to learn more big data and perhaps even become involved in some small way. In its ebook 2021 Top Priorities for Data and Analytics Leaders: A Public Perspective<\/a>, Gartner tells leaders to focus on data literacy. \u201cData literacy is not about turning everyone into a data scientist,\u201d the firm explains. \u201cIt is about developing a common understanding of what goals and outcomes are important across the agency, so that all stakeholders contribute to the whole (rather than deflect and misdirect with competing or alternative outcomes).\u201d<\/p>\n\n\n\n

13. Investigate AI technologies<\/strong><\/p>\n\n\n\n

For 2022 and beyond, many experts are encouraging government data leaders to expand their use of artificial intelligence (AI) and machine learning (ML) in their data analytics. Software with AI and ML capabilities is becoming much more affordable and user-friendly, and with the right hardware in place, these technologies can speed the time to insights and help agencies achieve more accurate results with their analysis.<\/p>\n\n\n\n

14. Use change management techniques to overcome resistance<\/strong><\/p>\n\n\n\n

Within public agencies, the biggest hurdle to big data analytics projects is often internal resistance. McKinsey acknowledges, \u201cIn the public sector, years of institutional knowledge are highly regarded and critical to delivering on the mission. That can make it challenging to persuade employees that data-driven decisions are sometimes sounder than those based on experience.\u201d To overcome that resistance, Deloitte recommends, \u201cTackle organizational change management proactively.\u201d It\u2019s always easier to address these problems if you anticipate and prepare for them than if you simply react when it happens.<\/p>\n\n\n\n

15. Measure your progress<\/strong><\/p>\n\n\n\n

Another important tip for building support inside (and outside) your organization is to have clearly defined key performance indicators (KPIs) and track your success in quantifiable terms. McKinsey says, \u201cHaving an established baseline for comparing results and clearly communicating progress are important in building support for long-term change and a continuous-improvement program.\u201d<\/p>\n\n\n\n

Quantifiable results also make it possible to share your results with other agencies. That in turn, can encourage them to tackle new projects and share their results that you might later learn from. With greater use of big data analytics, government agencies can maximize the benefits of big data analytics projects and ultimately become more successful in fulfilling their missions.<\/p>\n\n\n\n

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Over the past few weeks we\u2019ve explored these topics:<\/p>\n\n\n\n