Becoming City-Data-Savvy to Develop a Smart City

Technology is the heart of a smart city. But, where there’s technology, there’s data. And knowing how to manage that data in a smart-city context is absolutely essential.

Cities with technology create a lot of data. With more systems and devices coming online every day, the volume of data produced, collected, and stored is growing rapidly. It’s not just information such as your Facebook posts, Instagram photos, Google searches, and online forms you fill out — it’s also all the data produced by the myriad of processes taking place behind the scenes.

For example, just one self-driving car generates over 4,000 gigabytes of data for each hour of driving. Now multiply that by the millions of autonomous vehicles that will come online in the next few years and it’s clear that just this one type of urban activity will create a colossal amount of data. This colossal amount of data is called data exhaust. Though that’s an appropriate term for vehicles, it applies to all the data that spins off electronic transactions.

Between this data exhaust and the growing number of interactions that people have with all their devices, data growth is headed off the charts. In fact, right now it’s over 2.5 quintillion bytes of data every day. The technical phrase for that scale is, “Dude, that’s a lot!” Here’s another mind-blowing fact.

Considering all the data that’s been produced since people started using computers many decades ago, remarkably, 90 percent of all data ever created has happened in just the past two years. Technologists have come up with an appropriate term for this scale of data: big data. That isn’t exactly an inspired choice, but at least it's accurate.

Every type of organization is now producing, collecting, and storing big data. The clever ones are using it to run their businesses better, to more deeply understand their customers, and to build new products and services. When organizations use data in a way that improves operations, increases their bottom lines, and helps them to outperform their competitors, they’re called data-savvy.

This term indicates that they have recognized the value of data, developed the relevant skills to manage that data, and implemented a strategy to use data as a core instrument of organizational success. Kudos to them.

The popular role of data today has created a marketplace with a broad range of software tools that help with analysis and decision-making. It has also created a high demand for data-related skills and has even helped establish a new branch of study and expertise, called data science.

The private sector has recognized the value of data and data-savviness; rejecting the value of data is hardly conceivable in a for-profit organization. Other sectors of the economy have been slower to fully embrace their data love. Government has been a laggard, but those days are coming to an end. Today, government agencies — and cities, in particular — are jumping head-first into the realm of data science. In a field where everything is scarce, governments have an abundance of data.

Governments create, collect, and store a wide variety of data sets that include ingredients such as crime reports, permits, library lending information, demographics, pavement conditions, geospatial features, tax information, project status, and so much more. With the addition of digital sensors across a city landscape, the amount and variety of data is set to explode in the years ahead.

Using this government data to improve operations, make better decisions, build trust and transparency, and enable innovation solutions has the power to build better and smarter cities.

The smart use of data is a fundamental aspect of a smart city.

Enabling data-driven decision-making in a smart city

When you learn to fly a single-engine plane, part of the training process requires you to rely on the instruments regardless of what your brain might tell you to do. You wear a special cap that prevents you from looking outside. The instructor puts the plane into an usual configuration — let’s say a climb with low power —in order to create the conditions for an emergency situation. The instructor then tells you to use only the instruments to recover the flight orientation to a safe flying configuration.

What happens is that your brain receives signals from the body, such as information about balance, that tell you to take actions that are wrong. But if you rely on what the instruments are saying, you make the correct maneuvers. The first few times you do this exercise, you have to fight your brain. In other words, you have to trust what the instruments are telling you versus what your brain wants you to believe.

This example is analogous to how you must treat data. Good data tells the truth. Even though you might often want to believe something else based on how you believe something should be or on instinct relative to experience, you need to become comfortable with using data to make organizational decisions.

Data will provide important insight, but it won’t necessarily tell you what action to take. That part still largely remains a human function. You will need to consider context, politics, and economics, amongst many other factors.

There’s room for tacit knowledge, intuition, and experience, but they should be used sparingly and likely only in combination with what story the data tells. In fact, you must become hungry for exceptionally good data. The more you have and the richer it is, the higher the likelihood of a more informed data-driven decision.

Data leads to information that then becomes knowledge. This knowledge provides essential insights. It’s not uncommon now for leaders to feel constrained by not being presented with sufficient information to make an informed decision. A smart city cannot exist without the smart use of data.

Managing data in a smart city

It’s hard to think of an organization today that doesn’t use data in some capacity.: But, the existence of data within an organization doesn’t equate to any evidence that it’s being properly managed.

Making a city smarter by using data as the rich, valuable asset it is requires the deliberate use of specialized tools, talent, and processes. Data has a lifecycle, from creation to retirement, and to glean its optimum value, this lifecycle must be managed — a process known as data management.

Data management typically includes these activities:

• Having the ability to collect, create, update, and remove data across disparate systems
• Possessing the capability to retain data in various formats across different types of storage systems
• Ensuring the high availability of data to authorized users
• Maintaining disaster recovery options consistent with organizational needs
• Supporting data's utilization across different types of systems and solutions
• Managing data privacy and security
• Being able to archive and destroy data according to policy and compliance requirements

These minimum data activities must be addressed in your data strategy.

You can easily test whether data is being well managed in a smart city. Consider the following basic questions:

• Does every data set have an owner?
• Can authorized people access the right data when they need it?
• If a disaster — such as a cyberattack, a fire that destroys systems, or an accidental loss or deletion of files — occurs, is service restored quickly and without a headache?
• Can data move securely between people and systems in order to best leverage its value?
• Is talent readily available to produce reports, identify insights, and perform research with data?

If the answer to these questions is generally yes, you’re in a better position than most. On the other hand, if any of these questions can’t be answered with high confidence, there’s a good chance you don’t have a data management strategy, or the existing strategy needs to be reworked.

Many larger cities have already embraced data management, but many still need to elevate this competency to the mature level it deserves. Smaller cities, while recognizing its value, struggle with this topic because of challenges with insufficient budgets to afford data scientists and specialized tools.

Some good advice is for all city agencies to create a data strategy that rightsizes it against needs and the available budget. For example, for a large city, hire a chief data officer (CDO), and for the smaller ones, find staff that are interested in the topic who can carry out data roles as part of their other responsibilities.

Developing a data strategy for a smart city

Cities must have a data strategy if they want to have operational excellence, increased quality of life, and better performance results. The purpose of a smart city strategy is to have a plan designed to achieve some desired outcomes.

Recognizing that data is your friend and that it can provide enormous value in every aspect of building and operating a smart city means that you have to create a deliberate set of actions to achieve results.

A data strategy is an agreed-on plan that all appropriate stakeholders sign off on.

A mistake that many organizations make after developing a strategy is to blindly follow it, even as circumstances change. The right way to deal with a strategy is to regularly confirm with stakeholders that the desired outcomes are still relevant and, if appropriate, modify the actions periodically. After all, nothing stays the same. Organizational agility is a valued 21st century characteristic.

The worst type of strategy is one that’s created and never acted on. Creating a strategic plan isn’t the goal — achieving your outcomes is. Many excellent strategic plans are sitting on the shelves of executives, simply gathering dust.

A data strategy must include, at minimum:

• A description of the roles and responsibilities that various leaders and staff play in the management of data
• The capabilities desired from the supporting systems
• Any policy, legal, or regulatory requirements
• An articulation of how data value will be derived

Creating a strategy for a smart city usually follows a sequence similar to this one:

1. Agree on the vision for the smart city.

   Document and agree on the desired results (the vision) of the plan. It’s defining what you want the future to look like. Often, this is the hardest step. You might be surprised to discover the degree to which stakeholders aren’t on the same page when this exercise first begins. However, after all the arm wrestling and debating end, it’s gratifying when everyone does finally agree on the vision.

2. Perform a gap analysis.

   A what? A gap analysis is the result of identifying the difference between where the organization’s current performance is and where you want it to be. For example, you might look at business metrics and determine where you are versus where you want to be. Only by completing a gap analysis can you take the next step and identify and define your objectives.

3. Identify the objectives for the smart city.

   To reach your desired outcomes, often called goals, means that you need to have actions to get there. These are the plan’s objectives. They should be SMART: specific, measurable, attainable, realistic, and time-bound.

4. Define how the plan and the outcomes will be measured.

   Okay, here’s another truism: What gets measured gets managed. Without metrics, how do you know whether you’re winning? Define those targets. Don’t overlook this essential part of the strategy.

5. Get the right people to sign off on the smart city strategy.

   This step is important. Without the right people putting their signatures on the plan, you’ll experience issues later on. It’s much harder for a leader to argue that they didn’t support or agree with a plan if there’s evidence that they have endorsed it. Making the final sign-off less difficult can be achieved by engaging those leaders throughout the strategy creation process.

6. Execute the strategy and evolve as necessary.

   Yup, do the work. During this essential phase you’ll be obtaining funding, identifying project resources, running projects, and training or recruiting the right talent to manage the outcome.

Though this set of steps is applicable to creating a data strategy, it can be applied to any strategy. Use it every time you identify a goal and need to come up with a plan.

Want to ensure your smart city is successful? Avoid these ten mistakes.