{"id":1756,"date":"2024-09-09T08:12:55","date_gmt":"2024-09-09T13:12:55","guid":{"rendered":"https:\/\/blog.motorolasolutions.com\/en_us\/?p=1756"},"modified":"2024-09-09T08:12:55","modified_gmt":"2024-09-09T13:12:55","slug":"not-all-data-is-created-equal","status":"publish","type":"post","link":"https:\/\/blog-test.motorolasolutions.com\/en_us\/not-all-data-is-created-equal\/","title":{"rendered":"Not All Data is Created Equal"},"content":{"rendered":"<p><span style=\"font-weight: 400\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1757 alignright\" title=\"Not All Data is Created Equal\" src=\"https:\/\/blog.motorolasolutions.com\/en_us\/wp-content\/uploads\/sites\/3\/2018\/11\/Not-All-Data.jpg\" alt=\"Not All Data is Created Equal\" width=\"436\" height=\"520\" \/>We live in a society driven by data. Recent surges in data collection technologies and the widespread adoption of sensor-based hardware have put more \u201cdata at the fingertips\u201d than ever before. As a result, organizations \u2013 including public safety and government agencies \u2013 are becoming more and more data-driven. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Today, intelligent cameras, sensors, agency personnel and even the general public are potential sources of public safety data. This extensive supply of data is the fuel driving artificial intelligence (AI) and machine learning. However, not all data is created equal. And while getting data isn\u2019t a challenge \u2013 getting the right kind is. <\/span><\/p>\n<p><span style=\"font-weight: 400\">The current belief is that the efficacy of AI is largely limited by the amount of available data to train algorithms on. If there is enough data, new AI systems can be introduced or existing systems can be improved. This mindset has led to the view of data as the \u201cnew oil\u201d of the digital economy. <\/span><span style=\"font-weight: 400\">In truth, poor data quality is the biggest barrier to widespread, profitable AI and machine learning. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Like technologies that have come before, the true value of AI and machine learning lies in the quality of data provided \u2013 clean, tagged, reliable and consistent. No matter how good an AI model is, if the data going into the model is poor, the model will be unable to provide accurate, actionable insights \u2013 which is key in today\u2019s mission-critical public safety environment. For machine learning-powered applications in particular, the raw data is often not in a form that is suitable for learning. Features identified and extracted from the raw data tend to be domain-specific and would benefit from additional expertise provided by subject matter experts. In essence \u2013 for AI and machine learning \u2013 \u201cgarbage in, garbage out.\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400\">Unfortunately, less than <\/span><a href=\"https:\/\/hbr.org\/2017\/09\/only-3-of-companies-data-meets-basic-quality-standards\"><span style=\"font-weight: 400\">three percent<\/span><\/a><span style=\"font-weight: 400\"> of data meets basic quality standards, with 47 percent of new records containing a critical error. While organizations believe they are sitting on a gold mine of data, many fail to realize that they are, in reality, making critical business decisions using bad data \u2013 containing missing fields, wrong formatting, duplicates or irrelevant information. Within the public service industry, <\/span><a href=\"https:\/\/voicesfrompublicservice.accenture.com\/unitedstates\/five-technology-trends-for-public-safety\"><span style=\"font-weight: 400\">78 percent<\/span><\/a><span style=\"font-weight: 400\"> of agencies are basing critical decisions on data that can\u2019t always be verified \u2013 a costly practice in an economy where the estimated annual cost of bad data is a whopping <\/span><a href=\"https:\/\/hbr.org\/2016\/09\/bad-data-costs-the-u-s-3-trillion-per-year\"><span style=\"font-weight: 400\">$3.6 trillion<\/span><\/a><span style=\"font-weight: 400\">.<\/span><\/p>\n<h4><strong>So what can public safety organizations do? <\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Defining and assessing data quality is a difficult task, particularly as data is captured in one context and used in totally different environments across the public safety workflow. More specifically, data quality assessment is domain-specific, less objective and generally requires significant human involvement. <\/span><\/p>\n<p><span style=\"font-weight: 400\">For public safety \u201cmission-critical data\u201d there is a unique set of requirements that dictate adherence to federal Criminal Justice Information Services (CJIS), state and local standards for security and privacy of law enforcement-provided data. In addition, there is an abundance of new data sources whose potential has not been yet fully leveraged. Smart city sensors, wearable devices, car sensors revealing driving patterns, environmental sensors, and user-generated content in social media are only a part of a new rapidly evolving data ecosystem waiting to be explored. With all of these data sources, one of the biggest challenges for public safety is lack of data discoverability and \u2013 more specifically \u2013 the lack of a common structure and semantic model for data that bears the same information type and comes from similar sources.<\/span><\/p>\n<p><span style=\"font-weight: 400\">In order to ensure critical, timely and accurate information can be provided and produced by AI and machine learning, the data must be correct \u2013 properly labeled and cleaned, with sufficient accuracy \u2013 cover the entire range of inputs, and be examined for bias<\/span><span style=\"font-weight: 400\">. <\/span><span style=\"font-weight: 400\">Data that meets these standards is required to reduce bias in predictive models. Data quality and understanding is also a pivotal part of \u201cExplainable AI\u201d, which enables our users to understand, appropriately trust and effectively manage the emerging generation of artificially intelligent partners \u2013 a key driver for public safety.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Improved data collection methods and a solid process for data management is important. In order to collect good data, companies need to incentivize users. Products should be designed in a way that makes it easy for users to contribute their data, which in turn feeds back into improvements of the underlying models that power their applications. Good usability and user experience will further encourage individuals to contribute valuable information. In addition, public safety agencies need to look at their data \u2013 scrutinizing data patterns for anomalies. AI and machine learning technologies should be monitored on an ongoing basis to understand, detect and resolve potential errors or biases.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Ultimately, ensuring the collection and input of high-quality data is a foundational step towards fully embracing and engaging with AI and machine learning. \u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"We live in a society driven by data. Recent surges in data collection technologies and the widespread adoption of sensor-based hardware have put more \u201cdata at the fingertips\u201d than ever before. As a result, organizations \u2013 including public safety and government agencies \u2013 are becoming more and more data-driven. Today, intelligent cameras, sensors, agency personnel&#8230; <a class=\"btn-featured test\" href=\"https:\/\/blog-test.motorolasolutions.com\/en_us\/not-all-data-is-created-equal\/\" data-uet=\"{&#39;event_category&#39;:&#39;blog featured post interaction&#39;,&#39;event_action&#39;:&#39;blog read more button click&#39;,&#39;event_label&#39;:&#39;Not All Data is Created Equal&#39;,&#39;event_page_area&#39;:&#39;body&#39;,&#39;restriction_status&#39;:&#39;public&#39;,&#39;link_text&#39;:&#39;Not All Data is Created Equal&#39;,&#39;link_url&#39;:&#39;https:\/\/blog-test.motorolasolutions.com\/en_us\/not-all-data-is-created-equal\/&#39;,&#39;tealium_event&#39;:&#39;link_click&#39;,&#39;page_type&#39;:&#39;&#39;}\" >Read More<\/a>","protected":false},"author":44,"featured_media":1796,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[7,8],"tags":[1570],"class_list":["post-1756","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-law-enforcement","category-fire-and-emergency-medical-services","tag-artificial-intelligence"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - 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