{"id":21831,"date":"2018-01-17T17:51:13","date_gmt":"2018-01-18T01:51:13","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=wherenext&p=21831"},"modified":"2024-08-07T09:08:38","modified_gmt":"2024-08-07T16:08:38","slug":"smart-city-and-the-chief-analytics-officer","status":"publish","type":"wherenext","link":"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/smart-city-and-the-chief-analytics-officer","title":{"rendered":"Your Smart City Needs a Chief Analytics Officer"},"author":501,"featured_media":22201,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"sync_status":"","episode_type":"","audio_file":"","castos_file_data":"","podmotor_file_id":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":"","_links_to":"","_links_to_target":""},"categories":[401,691],"tags":[281,711],"department":[476782],"wherenext-category":[],"industry":[],"class_list":["post-21831","wherenext","type-wherenext","status-publish","format-standard","has-post-thumbnail","hentry","category-government","category-smart-communities","tag-location-intelligence","tag-smart-cities","department-new-analyst"],"acf":{"short_description":"Good governing requires efficiency. As smart cities generate vast stores of big data, chief analytics officers uncover vital answers quickly.","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"content","content":"Digital transformation provides cities with ever-increasing ways to capture data\u2014from connected streetlights to CCTVs to traffic flow and collision information transmitted from smart roads. But many cities fall short of their digital potential because they focus on collecting and using that data in narrow, traditional ways.\r\n\r\nA truly smart city goes beyond these, improving quality of life and the climate for business by analyzing data in service of specific challenges and using it to make good decisions in real time on a range of critical issues, from public safety to open data to economic investment. Probing deeply into big data and pinpointing solutions to important problems demand a chief analytics officer (CAO), rather than a chief data officer (CDO). Both are essential to a smart city or smart business, but they are quite different.\r\n\r\nSmart City, Efficient City <\/strong>\r\n\r\nSmart cities use location-driven analysis and ground truthing (testing expectations by observing conditions on the ground) to understand where things are happening\u2014even where they are likely<\/em> to happen. This empowers government agencies to address issues more efficiently, even as it improves quality of life\u2014for example, by helping safeguard firefighters or keeping bad landlords from forcing good tenants out of their apartments."},{"acf_fc_layout":"quote","image":21821,"text":"A 2016 Forbes article notes that while job searches for CAOs are growing quickly, the concept of a CAO remains so new that those who fill CAO positions must excel at explaining the function and value of their team.","author_name":"","author_profession_organization":""},{"acf_fc_layout":"content","content":"In this case, I speak with firsthand knowledge. I was the chief analytics officer for New York City; it was\u2014and remains\u2014the only city with a CAO. In fact, the title and nature of the duties are so new that there are relatively few CAOs in the private sector, even though many businesses rely on analytics to increase operational efficiency<\/a> and boost sales.\r\n\r\nThe company or city that understands the strategic capability that CAOs and their team can bring will gain an economic advantage through the ability to resolve pressing issues quickly. By efficiently untangling problems, a company can become more profitable and a city safer and more livable, prosperous, and attractive to residents, tourists, and new businesses. It is not at all surprising that several recent articles indicate that the number of CAOs in the private sector and the number of cities with analytics teams<\/a> are starting to grow.\r\n\r\nThe First Mayoral Department of Analytics<\/strong>\r\n\r\nWhen I had the honor of being chief of the Mayor's Office of Data Analytics in New York City, the group consisted of about a dozen people who all had to learn how to sift through the big data produced by a city of 8.5 million people. For instance, we received an hourly feed from the city's 311 system, which logged 100,000 contacts each day, and another feed from the 911 system, which averaged 30,000 calls a day."},{"acf_fc_layout":"content","content":"We searched for trends that would help lower the risk of firefighters dying on the job, including the incidence of fire associated with different types of building permits. We also uncovered patterns that helped prosecutors convict unscrupulous landlords who were forcing residents out of rent-stabilized apartments. In both cases, we generated algorithms that accurately predicted locations where problems were likely to happen and used a geographic information system (GIS) to communicate those predictions on a map.\r\n\r\nIf your smart city is suddenly confronted with a health or safety or economic crisis and you need to find a solution\u2014to find that proverbial needle hidden somewhere inside big data\u2014you need a CAO and an analytics team to lead the way. Chief analytics officers and their teams are practical, discovery driven, and results oriented. Every project must conclude quickly and deliver clear guidance on what to do next.\r\n\r\nChief Analytics Officers versus Chief Data Officers<\/strong>\r\n\r\nSmart cities, of course, are built on digitized data, which is increasingly derived from the Internet of Things (IoT). As storehouses of digital data grow, many cities and businesses have recognized the need for a chief data officer<\/a>. Chief data officers are essential. They know how to collect and manage data.\r\n\r\nBut having massive amounts of well-organized data is something like having a landscape dotted with silos of corn and grain. They contain enormous potential for fulfilling many needs, but you must know how to use the raw materials\u2014and where to look for what you need. A chief analytics officer turns an organization's data into decisions that solve its problems, increase efficiency, and identify opportunities.\r\n\r\nBy way of illustration, consider this: a chief data officer might plot the known incidents of traffic accidents on a map to publicize the issue of road safety. A chief analytics officer would enlist location analytics<\/a>\u2014examining the day of the week, the time of day, traffic volume, weather, roadway surfaces, and more\u2014to explain why those incidents are occurring and how they might be reduced."},{"acf_fc_layout":"image","image":20001,"image_position":"right","orientation":"horizontal","hyperlink":"http:\/\/www.esri.com\/digital-transformation?adumkts=branding&aduc=advertising&adum=house_ad&utm_Source=advertising&aduca=branding&aduco=dt_learn_more&adut=wherenext_chief_analytics_officer&adupt=awareness"},{"acf_fc_layout":"content","content":"The analytics team always tells a well-documented story accompanied by specific ground truth recommendations\u2014or, as we like to say, the do-these-things-now list.\r\n\r\nUsing Data to Stop Illegal Rental Practices<\/strong>\r\n\r\nWhen unscrupulous landlords in New York City were forcing residents out of rent-regulated apartments to make buildings more valuable to buyers, it created several emergencies.\r\n\r\nThose forced out of their units were hard-pressed to find similarly priced housing. Parents might be compelled to live much farther from their jobs, and children may have no choice but to change schools. Families might be pressured into residing in less safe areas. New Yorkers grew concerned about cruel practices that turned working families into economically stressed or even homeless citizens. If not countered, the problem could undermine the sense of security for tens of thousands of people.\r\n\r\nMayor Bill de Blasio asked us several questions: Where has this been happening? How is it happening? How can we predict where it's <em>likely<\/em> to happen? and How can we stop it? Answers to pressing questions on topics like tenant harassment don't just fall at your feet. And no algorithm existed to help us find ready-made solutions through artificial intelligence.\r\n\r\nWe knew we had to leverage location intelligence and GIS technology to mine our vast data for key indicators and uncover previously hidden trends. And before we offered information that could form the basis of enforcement activity or prosecutions, we had to ground-truth our findings."},{"acf_fc_layout":"quote","image":22191,"text":"Smart cities use location-based analysis and ground truthing to understand where things are happening\u2014even where they are likely to happen.","author_name":"","author_profession_organization":""},{"acf_fc_layout":"content","content":"Location Intelligence Delivers Results<\/strong>\r\n\r\nHousing and landlord data was scattered across several New York City departments. We had to talk to prosecutors, code enforcers, lawyers, residents, housing advocates, and others. But there was no time to have long discussions or wait to set up departmental presentations. Members of an analytics team must know what they're looking for, what questions to ask, and what key traits and patterns to note.\r\n\r\nWe devised new approaches to finding the right data. We began by asking the question, Can we identify a timeline of harassment in city data? We found a tipping point: if a building that was on the market or had been sold recently lost five or more rent-regulated apartments, that was a red flag. Residents of rent-stabilized apartments pay rents much lower than market value. Unscrupulous owners use various methods of squeezing tenants out so that the landlords can rent the units for more money and make the building more valuable to buyers."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"

The MODA Model<\/strong><\/h2>\r\nThe Mayor's Office of Data Analytics (MODA) in New York City was the first such department in the nation. The city also employed the first-ever chief analytics officer (CAO). The analytics group had to establish its own best practices. That process includes answering these questions:\r\n