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Data analytics in the government sector

18 minute read
Each government or private entity makes many decisions on a daily basis, Their daily practices are even a series of ongoing decisions about how to allocate money, what to buy or sell, who to contact, and what tasks to accomplish.
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Each government or private entity makes many decisions on a daily basis, Their daily practices are even a series of ongoing decisions about how to allocate money, what to buy or sell, who to contact, and what tasks to accomplish. While sound decisions result in the prosperity and success of the entity, Wrong decisions can have huge repercussions. What is meant by the "right" or "wrong" decision for the government sector? Sound decisions are those based on objective and rational grounds regardless of the limited interests of a particular socio-economic group. A decision is objective and rational when it is based on evidence from scientific and systematic analyses of valid and reliable data. As the value of data-driven information is rediscovered, Data analytics practices have become one of the most common practices for both government and private entities. In rapidly changing environments, Entities are required to make informed decisions supported by data analysis.

The private sector uses data analytics to improve productivity and uncover new job opportunities. The government sector employs data analytics practices to innovate new ways of working and design and deliver higher quality services to members of society. However, The government sector in many countries is still lagging behind and unprepared to take advantage of the valuable opportunities arising from data analytics. In contrast, In data-driven environments represented by big data, the private sector is moving quickly and harnessing data analytics not only to enhance managerial and operational efficiencies, but also to understand customer needs and predict market behavior.

We cannot exclude the government sector because some countries have applied data analytics practices to policies such as traffic monitoring, healthcare services, and other areas with the aim of revealing the nature of the intertwined challenges and coming up with better solutions. An example is public transport policy, where the Transport Authority can collect real-time traffic data from sources such as road sensors, cameras, in-car positioning devices and simple notification messages (SNS). Thanks to new analytical techniques, The Transport Authority can process and analyze that data to provide drivers with real-time traffic information. When data on traffic density, volume and speed are combined with weather conditions and road quality, The Transport Authority can predict the risk of accidents and thus alert drivers to take caution. This type of intelligent transportation system based on big data is one of the key elements of the infrastructure of any smart city.

Despite his efforts in this regard, The government sector has not fully benefited from the benefits of data analytics. If we agree on the importance of data analysis, The success of government sector entities can be determined based on their ability to derive knowledge and insights from these analyses.

Meaning of Data Analytics

The concept of "data analytics" in the usual context refers to the function of "data analysis", The word analysis refers to "the detailed examination and inspection of any intertwined object for the purpose of understanding its nature or determining its essential characteristics". The word "analytics" means "methods of logical analysis". When applying these terms in the field of data science, The scope of data analysis becomes broader, including data analytics, although they are overlapping areas. More precisely, Data scientists interpret data analysis as the process of "screening, refining, transferring, and modeling data" to discover important information.

Speaking of data analysis from an organizational institutional perspective, Its ultimate purpose is to make the most of the results of the analysis to come up with insights and ideas that help make sound decisions. In contrast, Data analytics is related to the effective use of tools and techniques. Although their definitions differ, However, data analysis cannot be separated from analyses in practice. For example, If any entity wishes to realize value from certain data, Structured and unstructured data patterns and relationships between them should be analyzed through the application of relevant analytical tools and techniques. To achieve this, Basic knowledge of mathematics and statistics combined with computer skills is indispensable.

The importance of data analytics in the government sector

Government entities, whether government, state-owned or otherwise, constitute a key pillar of the national economy. The government sector in many countries has witnessed rapid growth over the past years and today has a large share of employment and spending in the digital economy. Considering the increasing demand for various government services, The sector's role is not expected to diminish, at least in the near future. Therefore, The government sector must be managed in a more effective and efficient manner based on transparency and accountability. One approach that the government sector can take to meet this demand is evidence-based decision-making.

In the past, Reliable data was limited and data collection and analysis required a great deal of time and money. This has made it difficult to make evidence-based decisions. Today, Thanks to the development of new environments capable of generating a large amount of data in real time, whether via social media, Internet search, Internet of Things or other sources, Government entities are now able to collect data at an unparalleled speed. Moreover, New data analytics technologies are becoming available to enable the government sector to gain insights that help it make better decisions.

These changing environments certainly offer unprecedented opportunities for the government sector. If it fails to take advantage of these changes, Its competitiveness will be weakened because it adopts a "business as usual" approach with little development. Under the circumstances, It is no longer a matter of choice, Rather, it is a necessity and a duty for the government sector to ensure data analytics and anchor it in operations, policies and management to achieve the desired goals.

Data analytics is not only able to enable better information-based decision-making; the government sector faces challenges arising from the expectations of individuals looking for new data-driven business opportunities. In other words, The private sector believes that public data can provide the market with an opportunity to create value and demand a higher level of transparent government. While how public data is used remains at the discretion of the private sector, At the same time, the government sector must leverage data analytics to determine the types of data that should be published for community members to see.

Key challenges facing data analytics in the government sector

Despite the importance of data analytics, The government sector in many countries of the world is still not yet ready to extract value from public data from the government sector. In contrast, The sector faces many obstacles that prevent it from making potential gains from data analytics. Here are some of the foundations that ensure the effective use of data analytics that the government sector sometimes ignores.

Understand the fundamentals of data analytics

There are many new tools that the government sector can use to analyze data. For example, SAS and R software, which today have become common tools in statistical analysis and data modeling. In addition to Tableau and Python, which are widely used in the field of data visualization. Although data analytics can be performed by intra-industry data scientists or external experts, government sector employees are required to possess basic knowledge and skills that include statistics as an integral part of it. The ideas and insights required to enhance decision-making cannot be extracted by simply observing the results of the analysis; knowledge of how data is collected, analyzed, interpreted and visualized must be acquired.

Building digital infrastructure for data collection, management and opening

Data analytics begins with the first step, which is data collection, as the government sector performs several tasks, and this is what makes it have a variety of sources of data. During the data collection process, The government sector may sometimes lose sight of calibrating and standardizing data so that it has a common format and structure. In the absence of data standardization, It is difficult to consolidate data and extract valuable information from it. The data collected must be digitized and stored at the regulatory and national level. To do this, The government sector, as in many private sector entities, needs to appoint a data officer and form a dedicated team to manage data quality. The data collected should be publicly available to enhance the transparency of government sector processes and procedures and create new business opportunities.

Cultivate a culture of data integration and sharing

The government sector should recognize that data is an important and essential element for designing better policies and implementing them more effectively. In order to take advantage of the role of data, The data collected must be consolidated through refinement, organization and transformation. However, the data is often fragmented and spread across and across different entities. This prevents their effective use. One of the reasons for the fragmentation of data is the prevailing idea that data and information are at the disposal of their owners and are seen as part of their property and should not be exchanged and shared. The organizational leadership and the data officer must make the necessary effort to get rid of this old idea and establish a new culture based on data sharing.

Capacity Building for Data Analytics

New environments built on big data are revolutionizing the size, diversity, and speed of data. Therefore, New tools have been developed to help entities implement data analytics more easily and quickly. One of the prerequisites that enable the government sector to successfully integrate new analytics technologies into its organizations is the constant investment in data analytics capabilities among employees. This involves not only learning about rapidly changing analytics tools; it involves gaining a clear vision that aligns data analytics with organizational functions, basic statistical information and data ethics.

The way data is handled, managed and analyzed is essential for the government and private sectors to manage day-to-day operations more efficiently and make information-based decisions that better serve community members and customers. Due to the nature of the challenges to be faced, which are multifaceted in the government sector compared to the private sector, Data analytics is one of the paths that every government entity should take. There are a large number of examples and practices that illustrate how the government sector can benefit from the use of data analytics. For government sector entities that are still hesitant and unwilling to employ data analytics in the decision-making process, It is not too late and it can bring benefits from data analytics now and start from the basics.


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