Public sector data analytics: Leverage data to make better decisions
Public sector organizations generate and report on ever-increasing quantities of data — much of it is limited to compliance. However, reshaping the way organizations think about that data opens up whole new worlds of possibilities for using it in more meaningful ways.
- What if your data could reveal new insights beyond routine, static reporting?
- Why should one analytics project be prioritized over another to achieve strategic goals?
- How can you ensure your people, processes, and technology are able to collaborate and fully capitalize on advanced analytics?
You might not have answers to all of these questions, and that’s okay. Embracing data analytics involves a culture shift that’s often supported by centralizing data analysis activities , examining your readiness for more advanced analytics, and committing to building public trust and transparency.
Are you ready to harness the full value of your data?
Explore the potential of your data to answer questions you never thought to ask
Transitioning your organization from reporting to decision-making requires a new focus on hypothetical questioning and the “art of the possible.” The value of data collected from your current systems, and the increasing availability of third-party data, opens up a broad range of questions. While most data systems are designed to support descriptive and diagnostic analysis through reporting on what happened and why, leaders should begin to leverage their data to ask more proactive questions regarding what could happen and what should we do in response.
Proactive analytics can be tailored to your unique challenges, like prioritizing public safety, optimizing your operational performance, and the ongoing maintenance of your public infrastructure. Understanding where to begin can be a challenge, but a good place would be reflecting on your current reporting realities. For example, let’s say you receive a monthly report on parking violations for the area’s major entertainment district. This information might provide a historical count of violations by day or week, along with the time of day. This type of scenario is ideal for transitioning from basic reporting to proactive analytics by considering how these historical data points can provide greater insights into commonly recurring problems. Using these data patterns, it might be possible to identify where and when an increase in parking violations might occur whenever larger events have taken place in the area. This will enable you to be more proactive in addressing these challenges by better preparing for the influx of parking needs, leading to more efficient and effective resource planning decisions.
Overcoming preconceived notions about the role of data within your organization, and using critical questioning of historical data to infer the implications on future events, will set the tone for data optimization across the organization to drive strategic decision-making.
Centralize your data analytics infrastructure
Maintaining a sprawling, disconnected matrix of analysts spread thinly across your organization reduces the efficiency and effectiveness of your data analytics capabilities. Improved synergy and collaboration will better align with your organizational strategic goals, while reducing redundancies.
Developing an Analytics Center of Excellence (ACOE) can enable a foundation of self-service analytics while also promoting a culture of innovation.
Developing an Analytics Center of Excellence (ACOE) can enable a foundation of self-service analytics while also promoting a culture of innovation.
An ACOE is a cross-functional team of stakeholders and analysts from across the organization with a single, shared objective: ensuring data analytics activities are strategically prioritized and well-coordinated.
The activities of the ACOE should include, but are not limited to:
- Identifying needs for data analytics tools and technologies.
- Defining and promoting alignment with data quality standards.
- Assisting in compliance audit procedures.
- Identifying and prioritizing data analytics projects.
One of the defining qualities of an effective ACOE is its ability to create and maintain a prioritized data analysis project portfolio by aligning analytics projects with strategic goals, ensuring that projects undertaken will have maximum benefit.
Furthermore, the cross-functional nature of this group can enable more intentional selection of analytics projects by providing a comprehensive sounding board of stakeholders from across the organization. A common issue within a decentralized analytics environment is the duplication of data sources and analyses that can increase the risk of making decisions based on incorrect data. An ACOE seeks to streamline the individual, or ad hoc, analyses into a portfolio of reviewed and accepted projects; the process of review generates greater questioning about the intended goal of an analysis and determines if it creates value, and if not, offers alternatives to the current approach to improve its efficacy and alignment with strategic priorities.
Examine your organizational capacity for impact
Successful analytics projects require integrating the right skill sets, streamlined processes, and effective technologies. The increasing availability of more advanced analytics systems is expanding how business users can uncover new insights in complex data via easy-to-digest, “at-a-glance” visualizations.
Successful analytics projects require integrating the right skill sets, streamlined processes, and effective technologies.
This requires thoughtful reflection about your internal capabilities:
- People: Do your staff have the right skill sets to quickly provide you with actionable insight? Supporting the development and organization of analytics staff and skills will ensure your people can optimize your data.
- Process: Does your organization consistently use best practice analytics processes? Eliminating wasteful internal processes will improve the efficiency of your analytics projects.
- Technology: Do you have the right suite of analytics tools and enabling technologies? Selecting the appropriate applications that enable machine learning and advanced analytics capabilities, and maintaining a supportive infrastructure for these technologies, is critical to fully realizing the value of your data. Ideally, you’ll make these considerations after defining your strategic goals and clearly articulating the needs of your organization.
Integrating artificial intelligence (AI) capabilities within widely used enterprise asset management systems will lead to more effective scenario analyses and the generation of predictive algorithms. The value of these changes in technology can only be realized if your organizational capabilities are aligned to efficiently respond to these new insights.
Invite stakeholder engagement to ensure transparency and build trust
As smart cities become a reality — nearly two-thirds of cities have already invested in developing a smart city infrastructure — it’s critically important to be intentional about data security, transparency, and integrity when communicating to your most important stakeholder: the general public.
Moving into proactive decision-making might mean using data that reflects citizen activities, or at the very least making decisions based on citizen data that might impact daily life. Involving your community stakeholders upfront will ensure concerns are addressed, and transparency and insight on how data might improve their everyday routines can be integrated into the operational changes that will be made. While the general public is likely your most visible stakeholder, it’s also critical to build trust and seek engagement with internal stakeholders, partner agencies, and specific divisions that will be impacted by your initiatives.
Consider managing this transition in the same way you solicit insight in developing a new program or initiative by committing to an iterative development of ideas, with an emphasis on using stakeholder feedback to guide the process. Providing an inclusive process when establishing effective policies, procedures, and standards that govern data management, as well as technology standards and architecture, will help mitigate risk while enabling a valuable data analytics capability within your organization that meets the expectations of your community stakeholders.
Turn your data into action
Data isn’t about the data. It’s about what you do with it — the what, the why, and the how. To paraphrase Author Geoffrey Moore, without data analytics, organizations are blind and deaf, wandering out onto the web like deer on a freeway.
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