Public sector organizations generate and report on ever-increasing quantities of data — much of it 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?
- What if you could interact with your data visually and in real-time, having it show you the information you need in the format you need it?
- How can you maximize the value of advanced analytics and AI?
You might not have answers to all these questions, and that’s okay. Embracing data analytics often involves a culture shift into increasing data literacy, validating your readiness for more advanced analytics, and building trust and transparency with the information.
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 by reporting what happened and why, leaders should consider leveraging their data to ask more proactive questions regarding what could happen and what they should 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 traffic, 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 analytics infrastructure
Maintaining a sprawling, disconnected matrix of analysts spread thinly across your organization reduces the efficiency and effectiveness of your analytics capabilities. Improved synergy and collaboration will better align with your strategic goals, while reducing redundancies.
Developing an Analytics Center of Excellence (ACE) can enable a foundation of self-service analytics while also promoting a culture of innovation.
An ACE is a cross-functional collaboration of technical and business, stakeholders and analysts from across the organization with a single, shared objective: ensuring analytics activities are effective, prioritized and coordinated.
The basic activities of the ACE should include:
- Identifying needs for information and analytics and understanding the audiences for it.
- Evaluating and prioritizing those projects.
- Researching the data sources needed to feed that reporting.
- Getting agreement on the presentation and functionality of the end product.
- Collecting and actioning user feedback.
One of the defining qualities of an effective ACE 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 “one voice” set of stakeholders from across the organization. A common issue within a decentralized analytics environment is the duplication of data sources and analyses that can produce differing results and increase the risk of making incorrect decisions. An ACE seeks to streamline the series of individual silos into a portfolio of reviewed and accepted projects; the process of review generates greater questioning and discussion about both the goals and meaning of an analysis. It can also determine value and, if needed, offer alternatives to the current approach to better align 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.
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 can lead to more effective scenario analyses and the generation of predictive algorithms. However, the value of these changes in technology can only be realized if applied correctly and if your organizational capabilities are aligned to effectively 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 records and categorizes activities, or at the very least, makes decisions based on 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 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. It’s about providing the right information to the right people at the right time in the right format.
