City Intelligence: Navigating Digital Transformation to become a Smart City
The city of the future will not be defined by success or growth while resources are abundant, but instead will be defined by the way intelligent, risk-mitigating decisions are made to maximize delivery of critical city services.
To make intelligent decisions about complex City systems and services, you need data. And not just census data or statistics. You need the inner workings of the city’s operations. The metadata and messy, non-normalized data from all of your various applications, services, and systems.
Hopefully, as you spend time making sense of operations data, you’ll start to make some sense of your city.
Data-driven decisions are at the core of every Smart City’s shift towards a transparent and modern civic future. Through technological innovation, cities are becoming more effective, but as they produce more data through operations, the burden of analyzing and making data-informed decisions becomes more complex and resource-intensive.
As cities begin their journey toward becoming Smart Cities, first the city must learn how to plan and execute data-informed decisions. Without a data-informed decision-making process, the cities brain—the data warehouse—sends mixed signals to the body. It might tell one arm of the city to do one thing, and another arm to head in the complete opposite direction. Without guiding principles in applying data insights, the city can’t achieve the promise of maximizing efficiency and reducing risk in delivering services by leveraging data insights.
There needs to be an actionable feedback loop for stakeholders to make holistic, informed decisions.
What does it take to become a Smart City?
A Smart City requires not only a concerted effort in technology to collect, aggregate, and integrate data across every level of operations—it also requires a shift in the decision-making paradigm for city initiatives.
To think with data, you must think with measured outcomes in mind.
Measured Outcomes - quantifiable metrics that you can use to determine the success or failure of a given initiative.
That means quantifying expected outcomes before new projects are taken up, measuring outcomes during the project delivery, and reviewing analytics at project completion.
For Facet, this aligns closely to our Value-Driven Philosophy: decisions must be made before new work is taken up to quantify outcomes, and compare the cost of initial investments and maintenance with the return on investment (ROI) of the business—or in this case—city.
Measured Outcomes Drive Intelligent Innovation
At Facet, we empower organizations with a data-driven process to propose, qualify, and quantify City Initiatives based on the Objectives & Key Results (OKRs) and/or Key Performance Indicators (KPIs) that will determine project success. Organizations change quickly with an agile data methodology for new initiatives:
- Propose OKR/KPI metrics to quantify outcomes.
- Propose % change in metrics to qualify project success.
- Attach a requirement and budgeting for Data Analytics Report to each City Initiative.
Practicing forethought about measurable outcomes unifies teams in planning and delivery. Bind together operations teams with focused outcomes around data, and secure cohesive data stories for city leadership and constituents.
Building Momentum for City Intelligence
Making sure that your investment into a City Intelligence platform means you must also think about the organizational engineering required.
- How will city employees, constituents, and stakeholders interface with the City Intelligence platform?
- How do we consistently educate people to interpret data to drive actionable, well-founded conclusions?
- How do we consistently inform constituents about limitations with data interpretation so that data cannot be weaponized?
While we can’t tell you everything required to make your adoption of a City Intelligence platform a success, we can provide a few tools that will help align your interdepartmental and intradepartmental communications and planning around measured outcomes.
Navigating Budgetary Negotiation
Requiring each new City Initiative to contain budgeting for analytical reporting allows IT departments to easily bill other departments for analytics costs, reducing the complexity of lump-sum cost-sharing across all departments.
Attaching analytical reporting requirements to the project budget also puts the burden of proof for success on an initiative’s sponsors and stakeholders—forcing the City Initiative stakeholders to create a formula for project success before investments are made into a new initiative. This kind of accountability ensures stakeholders think data-first. With a forward-thinking model, City Initiative planners can more appropriately consider data metrics to prove results in the execution of each project.
At least, with these formulas in place, the reporting on such outcomes is executed with forethought, and not reactionary.
Avoid Reactionary Reporting
Smart Cities may take inventory of their city operations from time-to-time and retroactively review data and performance across systems. For some, this is the initial focus of the city: analyze available data and review findings. But casting a wide net does not usually offer actionable insights besides identifying gaps in data.
Gaps in data are the missing data metrics required to answer critical City Intelligence questions.
A city-wide inventory of data metrics can help you to identify what data you have, but mapping out the relationships of data, and understanding the flow of data through the city adds another layer of complexity few are equipped to navigate on their own.
Our key takeaway: avoid reactionary reporting after projects are complete, and equip your constituents, city staff, and leadership with proactive data insights.
Inquire & Share Knowledge Transparently
Data-Focused Town Halls
Create city-wide data-focused town halls where individuals can get together and ask questions of data engineers and analysts. Data experts make assumptions in producing reports, and often times constituents and leadership will need help interpreting that data with those assumptions. Ensure access to appropriate data interpretation education by providing access to the authors of the reports.
City Departmental Forum
Invite collaboration between departments by sharing questions and answers in a transparent way. A ticketing system used by the city’s data engineering team should easily allow for approved questions and answers to be circulated more widely across departments. Examples of these questions and answers will help each department refine their knowledge of data interpretation, and their personal understanding of data possibilities.