Data Blog

25 November 2020 A Q&A with Jordan Morrow, Head of Data, Design, and Management Skills, Pluralsight In this Q&A, Jordan Morrow, Head of Data, Design, and Management Skills, Pluralsight, shares insights on what can help drive data and analytics success in an organization, and how curiosity, creativity, and critical thinking play key roles in this process. Based on your experience working with numerous organizations over the years, how would you define a data-driven organization? The world as we knew it has fundamentally shifted. The pandemic of 2020 has changed the way we live and work. Will there be a return to a “normal” life? Of course, there will, but what exactly that is, we aren’t sure. One constant throughout the COVID-19 pandemic was the need for individuals and organizations to consume data effectively to make decisions, drive behavior, and so forth. In fact, one of the biggest buzz words or terms throughout the pandemic, as it relates to data, is “data-driven organization”. But what does that even mean? What does it mean to be data-driven? That, in and of itself, could be an entire book, let alone a blog post. But let’s set a definition of data-driven we will use. According to dataversity.netOpens a new window, the term data-driven “describes a business state where data is used to power decision-making and other related activities efficiently, in real-time. For a business, reaching the data-driven state is like the difference between driving an automobile and traveling by horse.”i Basically, this means that data should be considered as an integral part of any business or organization. This pandemic has made it apparent that many organizations were not as “data-driven” as maybe they had hoped to be. Before the shutdown, organizations had been moving along their data journeys, trying to invest correctly and become more savvy using their data, but when the pandemic forced organizations to become digital, remote, and so forth, the ability to use data as effectively as they wanted was not there. One thing the pandemic can do for an organization is illuminate the gaps and holes in a data and analytics strategy. In your view, what is the most important enabler in the data and analytics space? In my work, I have had the opportunity to work with hundreds of organizations, finding out needs, trends, and what can help drive data and analytics success. There are many skills and pieces we could talk about, but, indeed, the one I want to focus on is the one that truly encompasses the human element: data literacy. By definition, data literacy is the ability to read, work with, analyze, and communicate with data. Essentially, data literacy is a person’s ability to effectively and confidently consume data. Did you notice what key term is not a part of data literacy’s definition? Data science! We do not all need to become data scientists, but all of us do need to become more confident in our data literacy skills. This means the ability to effectively read and comprehend the data and information that is presented to us. We need to be able to work with data as part of our lives and our careers. We need to analyze the information to find the insight within. With good insight, we can make decisions and empower people. Finally, we need to develop skills to communicate effectively with data. And that means using less technical jargon and statistical terminology. Now, with data literacy, is this just going to be a onetime thing, or fad? The answer to this question is an emphatic no! Market intelligence firm IDC predicts that the total sum of the world’s data will grow from what was 33 zettabytes of data in 2018, to 175 zettabytes of data by 2025 (a zettabyte has 21 zeros after it). Not only is there an expanse of data, but processing power and tools to utilize data are becoming greater and more advanced. We will need the human element to shape and be a part of this data-driven world. The questions can then be asked: What can I do to become more data literate and how can data literacy help the world on larger decisions, such as how to reopen a world through a pandemic? To become more data literate, personally, do you need to go back to school to drive a background in statistics, coding, and fields along these lines? The answer is no! A person can learn these skills, but just like most things, we want to start with steps towards an end goal. For me, I liken this to one of my favorite things in the world: ultra-marathon running on the trails. One doesn’t go from not being a runner to racing a 50-mile ultra-marathon the next day. The same can be said with data literacy. Don’t just jump in and think “I will study R, Python, or statistics”. We should start with basics. For me, I look to what I have coined the “3 C’s of Data Literacy”: curiosity, creativity, and critical thinking. These are interesting – and rather broad – concepts. Can you please elaborate? Within the world of data and analytics, we get so caught up in the magnificence that is the data itself, the magnitude of it, and the power of the technology. All the while, the human element of curiosity can spark so much power with data and analytics. In this case, we are discussing curiosity as the ability to ask questions, challenge things. By asking more and more questions, we can pull in more and more data to answer those questions. This can lead to insight. We can also utilize the power of creativity to analyze the data differently, and to tell creative stories around the data, bridging the gap between the data/technology and the business. Finally, we need to use our human ability of critical thinking to think about the information and data that is presented to us. We can’t just take all the data and information at face value, but unfortunately that is often what happens. We need to incorporate more questioning with curiosity, dig through things differently with creativity, and critically think to find the nuggets of insight that can change behavior and make strong decisions. These things can empower individuals to truly succeed with data and analytics. The question can then progress to: how can data literacy, and data and analytics empower individuals, organizations, and the world to succeed in hard, dark times such as a pandemic or any other challenging situation? Overall, I think these answers are known and, in a way, intuitive. As individuals are inundated with data from all sides of a spectrum, it can be very, very confusing for them if they do not know what to be looking for. It can be confusing if they don’t understand how the data is setup and put together. It can be challenging when data presented one day has to be rescinded the next. Herein data literacy can help individuals to sift through the chaos that can be in front of us. This isn’t just during the pandemic – think in these terms for all decisions and difficult times. As we ask more questions of the data, dig into the fine print, and find the true insight, we can then make better decisions. Unfortunately, the data at times is just taken as what is presented, and we don’t dig in deeper. Use the 3 C’s of data literacy and dig deeper. Read fine print, study the data, find out how and why certain data was pulled and built, and you can find out answers for yourself on what you should be doing, which in turn will enable you to make smarter decisions. For organizations and countries, data literacy will empower all to truly see the data for what it is, what needs to be done, and what they need to do to truly succeed in any situation. I like to think of data literacy as a way to sift through the madness that can be presented to us all day on situations: sports, the pandemic, economies, and so forth. Unfortunately, not all the data presented to us is done with integrity or objectively. This is where individuals, organizations, and countries can utilize the skills of data literacy to properly read, work with, analyze, and then communicate with data. Doing this with the right set of skills and right objectivity can ensure we are all moving forward with the right frame of mind and ability to make the right decision. The world, it is no secret, is now a digital, data-driven world. Jobs and economies are shifting with the advent of more and more data and technology. We speak of upskilling employees, which we need to do, but at times it means the reskilling of individuals. Like other monumental shifts in economies and “ways of doing things”, we can help everyone develop the right skills to succeed into the future. Data literacy is one of those skills. It is up to us to seize our own moment to learn these skills, it is up to organizations to empower their workforces with proper learning plans and tools, and it is up to countries to make data literacy a part of education and curriculum. If we all put forth our effort, we can all be empowered with data literacy skills for the future. Note: The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations.

14 October 2020 By Yu Ping Chan, Senior Programme Officer/Team Leader, Digital Cooperation, Office of the Special Advisor to the Secretary-General As we continue the conversation on how the international community should engage with data, we must also consider the broader context: the need for global steerage on digital technologies. Today, more than ever before, the international community must carefully examine and confront the world of digital technologies. In the last 15 years, the percentage of Internet users globally has grown from 17 percent to 53 percent. By 2025, the number of devices connected to the Internet of Things could exceed 75 billion – nearly 10 times the number of people in the world. And through this growth of digital technologies, unprecedented volumes of data are being generated. COVID-19 has made especially clear our dependence on technology, with millions of people worldwide having to work and learn from home. It has also reminded us that while the digital era has brought society many benefits, there are serious issues that persist and that the pandemic is exacerbating – such as growing digital divides, cyberthreats, and the threat to human rights online. Despite the spread of digital technologies, there is still a significant gap, at the international policy level, in how governments and international organizations are approaching the digital domain. To date, the private sector and major technology companies are largely self-regulating on key issues of hate speech, cyberattacks, privacy and the use of personal data. Moreover, political gridlock is at an all-time high with rising divides over tech regulation, taxation frameworks, and eroding digital trust. Technology is becoming a new front in great-power relations, creating new fractures and divisions. Digital technologies are fundamental to our efforts to achieve our shared global objectives, particularly the Sustainable Development Goals. Technology can accelerate economic and social development in unprecedented ways – safe digital identification can address income inequality by unlocking government and financial services for millions of people; artificial intelligence can improve healthcare access by allowing personalized telemedicine and automated delivery of essential medicines; and 3D-printing can help promote inclusive and sustainable economic growth. Yet, these benefits presume that access to digital technologies is universal and equally shared. Even before COVID-19 struck, however, the digital divide was stark. Almost half the world’s population – over 3.6 billion people – are still offline. The world’s COVID-19 experience shows how connectivity is an imperative—for basic information, health services, education and work—and today, the gap between those with access and those without has widened. Addressing all of this requires global digital cooperation across borders, and between sectors, particularly prioritizing areas where there is shared global consensus and understanding. Cooperation models of the past are no longer sufficient. In these models, governments only speak with governments; academics with academics; civil society with civil society; companies with companies; and then, after parties have agreed within a given community, they present a plan externally and then invite feedback from others. We need to rethink our approach. The challenges of the 21st Century require common understanding, a shared vision of the future, and most importantly, joint action. This is the context in which the Secretary-General launched his Roadmap for Digital Cooperation this past June, following an extensive process of multi-stakeholder consultations that began with the Secretary-General’s High-Level Panel on Digital Cooperation. Emphasizing the importance of multi-stakeholder approaches, the Roadmap calls for concrete action in eight areas: ="199926009">Achieving universal connectivity ="1127099648">Recognizing and promoting digital public goods ="1568745945">Including the most vulnerable in the digital ecosystem ="1060062028">Building digital capacity across all countries ="322277326">Ensuring the protection of human rights in the digital era ="148543865">Supporting global cooperation on Artificial Intelligence ="1161714930">Promoting digital trust and security to advance the SDGs ="75533639">Building a more effective architecture for digital cooperation The Roadmap is the Secretary General’s call to Connect those who are not yet connected, Respect human rights and agency online, and Protect those vulnerable to online harms and threats. The Office of the Under-Secretary-General is now working with key United Nations entities and all stakeholders to take forward the important work called for in Secretary-General’s Roadmap. Closing the digital divide is fundamental to all of this. The Secretary-General has called for digital technology to be a “great enabler and equalizer.” Especially as we seek to build back from this pandemic, we must recognize those who have been left behind by digital technologies – those unconnected, and those excluded. If we are to “build back better”, we must seize this opportunity to build back “digitally” to create a more inclusive and equitable world. The international community is at an inflection point. We cannot reap the full benefits of the digital age without mobilizing the global cooperation needed to mitigate its potential harms. The international community must rise to the challenge and collectively take action to support greater connectivity, inclusion, and the protection of digital human rights, trust, and security. In seeking to do this, we must also listen to the voices of those most profoundly impacted by the actions of the present: the youth. This is why on 23 September 2020, UNICEF and Generation Unlimited, the International Telecommunication Union, the United Nations Development Programme, and the Office of the Special Adviser to the Secretary-General, on Digital Cooperation, convened a High-Level Digital Cooperation event, where Presidents and Prime Ministers, top executives from tech giants and the private sector, as well as technology luminaries, came together to respond to the Secretary-General's call to “Connect, Respect and Protect” us all, with a focus on young people and future generations. A recording of this recent event is available here. And to learn more about the UN’s Digital Cooperation efforts, please follow us on Twitter. We invite all to join us in our collective efforts to further global digital cooperation. “Future generations will judge whether the present generation seized the opportunities presented by the age of digital interdependence. The time to act is now.” - Antonio Guterres Note: The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations.

8 September 2020 A Q&A with Melanie A. Mecca, Chief Executive Officer, DataWise The scope of information that the UN defines, produces and uses is vast and complex. In June, the UN published the Secretary-General’s "Data Strategy for Action by Everyone, Everywhere" – a roadmap for data-driven change in the Organization. In this Q&A, Melanie A. Mecca, CEO of DataWise, explores fundamental best practices for designing and implementing a mature data ecosystem that can advance strategic data and information goals. Throughout the article, the term Enterprise Data Management (EDM) is used to refer to the essential program foundations for success. Should organizations care about data management? Since data is essential to every business, organizations depend on access to the right data, available at the right time, and in a satisfactory condition for fact-based analysis and conclusions. There are many disciplines, processes and practices [1] required to accelerate and sustain maximum value from data assets. The core pillars of EDM, however, can be summarized as: Data Architecture – what you build or buy to capture, store and deliver data, and how the data is organized, shared and provisioned Data Governance – how you define, control, and make collaborative decisions about shared data Data Quality – how you define, evaluate, and improve the condition of data according to dimensions (measurements), such as completeness, accuracy, timeliness, etc. To meet the UN’s full data potential, the Data Strategy roadmap includes data actions and workstreams to build these essential pillars. We’ll return to them in the context of decision-making, but let’s set a marker – no organization that I’ve encountered currently excels in all three areas, because the concept of ‘data as a strategic asset’ is relatively recent. In “Business at the Speed of Thought” Bill Gates stated: “How you gather, manage and use information determines whether you win or lose.” I think we can all agree that the UN must win. However, for many years, data was an afterthought. This is counter-intuitive; data is the POINT – a critical asset. With the advent of data warehousing for better reporting, master data management to integrate highly shared data, new technologies emphasizing user empowerment, and the explosion of data volumes in the last decade, today organizations realize that data management is a vital and permanent function. Therefore, much like the management of other core business areas such as Finance and Human Resources, organizations need to manage data, practically speaking, forever. Since most organizations are evolving their technologies, staff education, and culture to manage data well, we can examine three successive broad accomplishments crucial for success – Capability, Maturity, and Deployment. Capability consists of doing the right things. To build capabilities, an organization should first assess the current state of data management processes – identify major gaps in capabilities, starting with persistent problems. For example, a major gap may be the need for consistent business terms for shared data across an organization in a manner that can be easily aggregated (for instance, this capability is frequently required for Master Data Management solutions). Many important capabilities and strengths have already been defined and established - some Member States and organizations have implemented proven, effective data management programs, polices, processes and education over the years. Successful approaches and corresponding work products can be leveraged through the Centers of Excellence envisioned in the UN Data Strategy to benefit the UN family and assist the UN to standardize core data management processes, over time, for all Member States and organizations. Implementing pilot projects can also be an efficient way to establish best practices, and high priority data use case actions are excellent starting points for improving data management processes. If approached strategically, gains will be cumulative, and work products can be baselined and reused for each successive data action. Maturity consists of formalizing and embedding improvements in capabilities, such that they become 'business as usual'. Formalization consists of mandating a well-defined set of capabilities and processes, promulgating them through policies, supporting them with staff resources, and implementing a compliance program to ensure stability, resiliency, and implementation across the enterprise. Maturity practices are commonly employed elsewhere in an organization, such as in planning, resourcing, leading, standardizing, etc. – they should also be applied to data management processes. With increased maturity, the need for compliance processes is recognized (especially for highly shared data), and that is dependent upon robust, operationalized data governance. Maturity practices ensure that the capabilities are followed, even during stressful conditions. An organization can evolve through a ‘minimum viable set’ approach, by determining what capabilities are most critical right now for high priority data actions and then extending the scope over time. Deployment measures the implementation extent of capability and maturity practices across the organization, according to a planned sequence. For instance, the UN COVID-19 Data Hub initiative has developed ready-to-use templates, a standardization mechanism for capturing and reporting COVID-19 data, to accelerate the time to identify insights for national statistical offices. This approach and its corresponding work products can be leveraged for other medical data collection and reporting important for UN Member States and organizations. As capabilities are defined and become mature, they can eventually be implemented across all relevant entities of an organization. What is the relationship between data maturity and improved decision-making within an organization? Better data, better decisions – it’s as simple as that. Let’s consider the worldwide COVID-19 pandemic. The crisis has spotlighted the criticality of timely and accurate data that conforms to agreed-upon standards for an array of metrics—for case counts to the percentage of survivors who experience lasting health challenges. If Member States do not agree on key concepts, such as whether death of a COVID-infected individual is recorded as a heart attack or caused by the virus, a full picture of the pandemic’s spread and effects is not possible, decisions will be less trustworthy, and guidance issued to combat the disease and protect life may be late or insufficiently informed. As the Data Strategy notes, one of the UN’s top priorities is the enablement of expanded analytics capabilities, and user empowerment for self-service statistical analysis, data visualization, and cutting-edge technologies such as artificial intelligence and machine learning. The diagram below is illustrative of the obstacles that organizations must overcome to leverage advanced capabilities and solutions, based on a Forbes 2016 survey of data scientists. Note that 'cleaning and organizing data' accounts for 60% of the time analytics teams spend preparing for their modeling and mining activities. If you add the 19% spent 'collecting data sets,' only 16% of their time is left for mining data, refining algorithm, and building training sets. These time consuming tasks need to be minimized to empower the skilled, creative and timely analysis that an organization counts on. Therefore, in addition to implementing enabling technologies, hiring and training analysts and data scientists, and educating users to ask the right questions and employ self-service analytics tools, the organization must manage its data assets well to accelerate the speed of insight and empower informed decisions. What are three best practices or tips for achieving data maturity? To achieve data maturity, organizations should consider recognizing, establishing, and growing two permanent functions – centralized data management and federated data governance, illustrated in the diagram below: The data management function, recommended to be established as a centralized organization, serves as the backbone of anchoring capabilities and persistent work products (i.e., strategies, policies, processes, standards, and templates for the EDM program), which the organization needs to define, implement, and expand. It’s recommended that the UN treat the data action initiatives envisioned in the Data Strategy as incubators for policies, processes, standards and products (for example: a data catalogue, a glossary of shared terms, and an evolving set of sound data management processes), through Centers of Excellence and a dedicated core organization. The data management organization can assist the Strategy Oversight function in measuring progress and ensuring that accomplishments in the data action initiatives are baselined and leveraged across the UN. We can consider this organization to be the ‘collective data management memory’ of the organization. The data governance function is, in essence, mutual decision-making about shared data. Shared data can be defined as: (a) within the scope of ‘enterprise data’ defined by the Data Strategy; (b) produced by one or more organizations or business areas; and (c) consumed by multiple stakeholders. Reference data and master data – examples of ‘highly shared data’ – are especially important, as those data sets need to be timely, accurate, and highly available to multiple applications and user groups. Key responsibilities of governance groups include: Data definition – achieving agreements on key concepts (business terms) is a primary responsibility of governance groups and representatives. The more important a shared concept is to the organization, the more engaged governance participants need to be in creating a consensus definition. Data improvement – governance engagement in improving data quality is vital. Governance representatives are needed to determine what level of quality is desired, what level of quality is acceptable and what quality rules should be applied to improve the data. Issues escalation – when different stakeholders cannot agree on a decision due to conflicting requirements, governance groups need to determine when issues should be escalated to a more senior decision body. Access control – the parties who control a data source have accountability for determining how access will be granted and to whom; the parties who maintain a data source (‘data custodians’ or ‘technical data stewards’) are accountable for how access will be managed and executed. Approvals - governance participants need to review and provide their organization’s input for core work products – strategies, policies, processes, standards, and templates for the EDM program. This is critical, as the Data Strategy aims to increase enterprise-wide understanding of shared data assets and build consistent management practices. Here are three tips that can assist an organization in rapidly and resolutely progressing towards data maturity: To evaluate the current state on the Capability-Maturity-Deployment wheel, a comprehensive assessment against a framework for best practices is highly recommended, to measure where an organization is performing well and what gaps are evident. For analytics, such an assessment can pinpoint the capability deficiencies that result in almost 80% of a data scientist’s time spent on laborious manual data preparation tasks. The selected best practices framework should be industry and technology neutral and focus on clearly stated functional practices, with consensus decisions made by key stakeholder representatives, and it should include a path to gauge further improvement in the processes that contribute to timely, accessible, high quality data. Through this effort, the organization can also quickly discover and mine exemplary work products and save considerable time and redundant work efforts by not reinventing the wheel. Identification of high priority data initiatives requires scoping and definition of critical data sets (also called ‘domains’ or ‘subject areas’). By defining, documenting, publishing, and educating relevant staff about these data sets and the overall data scope [2] encompassed by the initiative, the organization can more quickly determine what data is shared, what data needs to be reconciled, what data is redundant, what data can be integrated, what sources are more complete, what systems can be retired, etc. The importance of providing education for all staff levels according to their job role is paramount. [3] Several levels of education and training are useful, at a minimum: All staff who use a computer in their work should be educated in data awareness, key concepts supporting the Data Strategy’s vision. Staff responsible for defining, organizing, or aggregating data need education in data analysis skills, such as defining data, data quality, metadata, etc., as well as specific training in selected platforms. Staff responsible for developing statistics to support critical decisions should be educated in data literacy approaches and concepts that help to extract meaningful information from data, as well as training in the selected analytics tools. As processes are implemented, role training for process actors should be offered. For instance, as data governance is established, new data stewards need to know their responsibilities, the types of tasks that require their engagement, how to escalate issues, etc. Senior staff need to learn how to lead evolution to a data-aware culture, how to navigate major decisions about shared data, how to effectively interact with governance groups, and how to champion data actions and data improvement initiatives. Organizations are advised to develop an enterprise-wide education plan by staff role type, including a rollout schedule, and identify organizational units interested in piloting the educational offerings. Computer-based training is the most effective method to deliver education at scale, supplemented by instructor-led training, focused workshops and internal knowledge sharing presentations. The UN Data Strategy’s publication has launched a major data transformation initiative, which has the power to transform the Organization, increase its agility, and sharpen its ability to predict and respond to worldwide challenges. Since a high-functioning UN is vital for the stability and safety of all nations, this is an exciting development for everyone on the planet. Note: The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations. [1] For example, the Data Management Maturity (DMM) Model has 25 Process Areas and 414 functional practices, the Data Management Body of Knowledge (DMBoK) has 16 knowledge areas and numerous constituent topics within each. [2] Data scope, in this context, is the answer to the questions “What data do I need to accomplish these results (or implement these features)?” [3] The following article provides further information EDM Education Part 1 - Why, What and Who

4 August 2020 By Lambert Hogenhout, Chief of Data Analytics and Innovation, OICT Databases have been around a long time, since the 1960s. Times when the world was less complicated and less inter-connected, at least in a data sense. Traditional databases focused on "things" with properties, like employees with birthdates and addresses and salaries. Even though they are called relational databases, the relationships were secondary. Today, however, with ubiquitous digitization and data collection, the relationships outnumber the entities. Social platforms like Facebook and LinkedIn rely primarily on the connections between entities. It is no wonder that a new type of database was invented that makes those relationships the focus: graph databases, based on the mathematical concept of a “graph”. Entities and relationships have equal weight in graph databases. Luckily, mathematicians had been obsessed with graphs for over 100 years and many theories, proofs and models existed that could readily be turned into algorithms to explore graphs and solve queries. The Emerging Tech Lab (ETL) of the Office of Information and Communications Technology (OICT) has embarked on several projects that explore how to store knowledge in graphs. Use-case: SDGs One use-case for such “knowledge graphs” could be the Sustainable Development Goals (SDGs). The SDGs are strongly inter-related: If you want to make progress on SDG 1 (Poverty), ensuring quality education is critical, so you are touching on SDG 4 (Education). In doing so, you may decide to ensure that education is available to women and girls as well, thereby supporting SDG 5 (Gender Equality). One use-case for such “knowledge graphs” could be the Sustainable Development Goals (SDGs). The SDGs are strongly inter-related: If you want to make progress on SDG 1 (Poverty), ensuring quality education is critical, so you are touching on SDG 4 (Education). In doing so, you may decide to ensure that education is available to women and girls as well, thereby supporting SDG 5 (Gender Equality). The image above depicts a few of the connections between the three SDGs mentioned; however, reality is much more complex –there are innumerable direct and indirect connections between the SDGs. Capturing these connections in a knowledge graph would make a very useful resource for anyone studying the SDGs and their inter-linkages. That is exactly what we in ETL are aiming for. We organized a workshop earlier this year together with Accenture Labs to explore the idea of Knowledge Graphs for Social Good and form a community of people to help us towards this goal. We are continuing this project throughout 2020. Humanitarian projects Another example of an application of knowledge graphs is in humanitarian affairs. For its Country-based Pooled Funds, the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) needs to align more than 100 proposals for humanitarian projects per year with its strategic priorities to ensure funds are used where they are needed most. This process of alignment is currently done by OCHA staff, who read through the proposals in detail. ETL had already built a prototype using natural language processing (NLP) to find keywords in the proposals related to the strategic steers. But there are obvious limitations. For example, the phrase “education of women” may not appear in the text of a proposal, but the words “school” and “female students” may, which makes it a relevant project. ETL came up with the idea to use knowledge graphs to model strategic priorities. By having semantic knowledge around these concepts, we have a stronger basis to compare against the priorities. We then partnered with the Slalom AI Center of Purpose, which very kindly helped us kick-start the implementation of this idea in an intense two-week effort that resulted in an awesome set of tools. We were much impressed by their work and their support. ETL continues to develop the knowledge graphs and algorithms for this project. The images below show some preliminary outputs. This is only a basis on which so much more can be built to increase efficiency, provide transparency and support decision making at OCHA. Conclusion In the 1960s, databases started out by simply storing the letters of words. An apple became . But the computer had no idea that it was a fruit. In the past few years, NLP has become good enough that machines can routinely analyze a sentence like “Lambert eats an apple” and conclude that “Lambert” may be a person’s name, “eating” a verb, and “apple” a noun. However, the computer still has no concept of what an apple is (and that it may cease to exist when eaten!). A semantic layer of knowledge is the next level of adding value to our data and it will help anyone involved in information management, analytics, AI or user interfaces. Search engines, chatbots, translation and automated document analysis will be taken to a whole new level by semantic knowledge. This is only the beginning. Note: The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations.

8 July 2020 By Wafa Aboul Hosn, Chief of Economic Statistics, and Juraj Riecan, Director of Statistics, Information Society and Technology Cluster, Economic and Social Commission for Western Asia Data and statistics are no longer the purview of researchers and academics. This has become most evident with the COVID-19 pandemic, as people worldwide are following daily statistics on confirmed cases, deaths, and PCR (polymerase chain reaction) testing, and governments put in place policies to contain the spread of the disease among their population. Vital and health statistics are administrative data sources that feed into official statistical systems and can be critical to effective public policymaking. Specifically, official statistics produced by national statistical offices (NSO) and the national statistical systems (NSS) , serve as robust, high-quality, and impartial bases for evidence-based policies (EBP). Using statistical evidence, policymakers can target responses to social and economic issues at the right time and place, thereby improving government service delivery.[1] In 2013, the Economic and Social Commission for Western Asia (ESCWA) developed a conceptual framework for enhancing the effective use of statistics in EBP in the Arab region.[2] The framework presented the benefits and priority issues of EBP in the context of the development agenda, tackling shortfalls in statistical processes and documenting and sharing good practices among countries. Statistics for EBP in Arab countries Based on the framework, countries met statistical data needs for EBP: in the preparation of national development strategies and plans in Jordan and Iraq; on the relationship between users and producers; and on the assessment of development goals in Morocco. Additionally, Gulf countries, through the Gulf Cooperation Council Statistical Center (GCC-Stat), developed joint statistical programs that serve policymaking in their respective countries. More recently, Jordan, Egypt, and Palestine shared best practices, equipment, and Arabic applications for the 2020 round of their GIS-based population and housing census, which provided basis for policies on refugees, poverty reduction and urban planning. This co-investment and knowledge sharing can be experimented with in other countries that use a common language. To continue providing statistical evidence to urgently inform on COVID-19 responses, NSSs in most Arab Countries undertook rapid assessments of the impact of COVID on lives and livelihoods. Three main challenges for the implementation of statistics for EBP Due to instability and conflict, Syria, Iraq, Libya, Sudan, and Yemen each confronts big challenges to the essential functions of its statistical system as statistical production has been disrupted during years of crisis. This is a critical challenge as data is needed most for informing the rebuilding of societies and economies. Another challenge for the region on the use of statistics for EBP is ensuring a greater level of independence for national statistical systems to release sensitive official figures on growth, poverty, inflation, and unemployment to generate stronger public trust.[3] ESCWA’s framework needs to be constantly updated and adapted to integrate more good practices; to address modernization, agility and responsiveness of NSSs; and to continue to be relevant for the 2030 Agenda for Sustainable Development and current global challenges. Amid economic, financial and health crises, support and funding of NSOs in developing and poor countries is mostly needed to improve their systems and infrastructure to address emerging needs. Data links to UN mandate In a historical milestone for the international statistical community, the United Nations General Assembly adopted a resolution on 29 January 2014 concerning the Fundamental Principles of Official Statistics. The resolution reaffirmed the importance of the use of good statistics as evidence for decision-making. Official data produced by NSSs from all Member States feed into the data supply chain of the UN network at global and regional data repositories, including ESCWA’s Data Portal, as shown in the diagram below.[4] The data provides the basis for UN global and regional reports on sustainable development, on social and economic developments, financing, trade, population, migration and others. Note: The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations. [1] Cairney. P. 2016. The Politics of Evidence-Based Policy Making PDF Oxford Research Encyclopedia of Politics UNESCWA Effective Use of [2] Statistics in Evidence-Based Policymaking Conceptual Framework PDF. E/ESCWA/SD/2013/TP.1 [3] Committee for the Coordination of Statistical Activities 2020. How COVID-19 is changing the world: a statistical perspective PDF [4] United Nations Fundamental Principles of Official Statistics Implementation guidelines 2015. Global data flows bring data (A) to UNSD) and the UN specialized agencies. These serve feeding data and updating the content of the centralized UN database https://unstat.un.org/ either directly (B) or through specialized agencies (C). Specialized agencies also update the sectoral databases (D). Data are published either by UN Headquarters (J) or specialized agencies (K). The regional data flows are directed mainly to the UN regional commissions (E), with a minor part of data collected by regional offices of specialized agencies. Regional Commissions disseminated these data (I) through the regional data hubs and use them for production of regional reports, studies and policy analysis. Regional Commissions also provide data to global statistical databases (F), and complement their own data collection from UNSD databases (G) and specialized agencies (H).