Data Culuture

The paper will address how the data culture within the institution will be transformed to support data democratization initiatives and data governance. A data democratization plan for a hypothetical use case of the pulmonology and respiratory medicine research institution will be explored.

This paper describes how to create a data democratization plan for a hypothetical use case of the pulmonology and respiratory medicine research institution. The best practices for data democratization, the importance of data democratization, and the checklist for planning data democratization will be discussed to enable the group of researchers to access the needed, well-curated datasets. Data democratization has been an issue in the past for the pulmonology and respiratory medicine departments.

The paper will address how the data culture within the institution will be transformed to support data democratization initiatives. Specific policies and procedures that will ensure that everyone has access to the needed data will also be discussed. In addition, existing barriers between data and stakeholders that need to be monitored will be included. The structure that needs to be used to ensure that all stakeholders can access, understand, and use the data available to them will be highlighted.

Data Democratization Planning

Data Democratization is a continuous process of encouraging employees in an organization to work with data to help make data-informed decisions (Strengholt, 2020). It supplies data access to everyone within an organization and breaks down the barriers preventing data access. Employers at the pulmonology and respiratory medicine institution should be empowered to feel comfortable asking data-driven-related questions. This approach encourages employees outside the pulmonology and respiratory medicine department research team to retrieve and use data.

Benefits of Data Democratization: From the data analysis that every employee carries out, they can gather meaningful insights to make informed decisions without needing technical assistance. Users also get exposure to insights on the micro and macro levels of the institution. Employees get a better understanding of the research institute’s goals and strategies. Data democratization will allow self-service analytics within the organization. Non-technical employees will be able to explore data patterns of both structured and unstructured data. It promotes a safe and secure community within the organization.

Checklist For Data Democratization: As the institution establishes data democratization, it should pay attention to where the data will be coming from. Will they be internally generated or purchased from external sources and has there been an evaluation of the data privacy policies? The type of data being collected, whether it is structured or unstructured, should be understood. The data technology stack for storing the data should also be identified in addition to data governance. Clarification on who manages and owns the data should be provided to guide users on how to connect to the data.

Traditional Master Data Management (MDM) Framework:

The traditional way of limiting data to a few key individuals of the pulmonology and respiratory medicine department because they are researchers who are either in IT or are data analysts mostly resulted in the exclusivity of other employees. This approach falls under the data silo model and the IT ownership model. It breeds imbalances and only facilitates a few people with access to data to have the power to make decisions based on data insights. The appropriate tools should also be provided to allow team members to work with data in the departments. A data democratization plan should be viewed as an ongoing procedure that might require a cultural shift within the organization. When data is democratized, it empowers workers to locate and use data of interest

Data Regulations:

Some examples of regulations that might be relevant for data protection and compliance as data democratization is being implemented are the General Data Protection Regulation (GDPR), and the Health Insurance Portability and Accountability Act (HIPAA). Data democratization management forms a critical part of data governance. It involves using practices that aid with data lifecycle management processes. Data governance activities such as data integration, data architecture, data modeling, data storage, and data retention all form part of the data management phase. Some advantages institutions with data management practices established have are the decrease in data silos, and the promotion of data workflow streamlining.

Data Democratization Policies & Procedures

Data Stewardship Policies: Instances of data democratization policies and procedures start with data stewardship. Whilst promoting data democratization in the pulmonology and respiratory medicine departments, data stewardship should be at the forefront of senior leaders. It will direct the obligation for and responsibility of data stewards overseeing and preserving data assets. These data stewards will ensure the data is kept in compliance with the required rules and procedures. They will work with data owners to define the data specifications, and usage guidelines whilst applying policies for data governance (Mirchev et al., 2020).

Data Protection and Compliance Policies: These will point the pulmonology and respiratory medicine institution to the safety and privacy properties of data governance. It will safeguard sensitive information in the research institution from unauthorized personnel. To achieve this, security controls, encryption, and data classification policies that protect data assets would have to be implemented. In addition, data privacy policies and several data regulations that might be relevant to data protection and compliance are important to be explored by the pulmonology and respiratory medicine institution.

Data Governance Procedures

Data governance specifies roles, responsibilities, and procedures for safeguarding liability for and ownership of data assets across the organization (Pike, 2020). The framework in data governance is to help the pulmonology and respiratory medicine department with its data management strategy. This can be achieved by using a holistic method to collect, manage, secure, and store data. Overall, data governance forms one aspect of data management and focuses on who does what, and the processes involved. Data governance shows logical, formal control over the steps and responsibilities. It provides clear instructions for changing procedures and information that will help the pulmonology and respiratory medicine department research team become swift and accessible.

Since steps, processes, and data can be reused, it promotes efficiency, as well as improves conformity with data regulations. Data governance should be implemented alongside data democratization and should be viewed as an important best practice. The absence of data governance within an organization when adopting data democratization can promote information overload, poor data quality, and analysis which could also result in the business’ reputation risk. To prevent these setbacks from happening, data governance training should be established for every employee. This will enlighten them on how to best use data, the need for them to understand data lineage, data privacy, and ways these get converted for business acumen and analytics.

The Data Governance framework helps corporations to know when inaccurate data has entered the system (Abraham et al., 2019). This realization prevents poor data quality and increases the trust of stakeholders in the data. When there are data problems within the pulmonology and respiratory medicine institution, it could increase the risk of non-compliance with government and industry regulatory requirements. With a well-organized data governance framework, all the tactical and operational roles and responsibilities are addressed. There is proper documentation ensured in addition to ease of finding the data within the company in a secure, compliant, and confidential manner.

Data Culture & Data Democratization Initiatives

Healthy Data Culture: With data democratization being an issue at the pulmonology and respiratory medicine institution, establishing a healthy data culture within the institution will go a long way to transform and support data democratization initiatives. One of the benefits of data culture is that a healthy data culture within the research institution will speed up the application of analytics. It would also elaborate its power whilst driving the business away from an uncertain outcome. For pulmonology and respiratory medicine institutions to achieve productive analytics initiatives, they need to insert analytics expertise into their primary business and stay compliant with the data laws and policies (Diaz et al., 2018).

Ways to Promote Healthy Data Culture: Several factors can promote a healthy data culture at the pulmonology and respiratory medicine institution. They should not approach data analysis as an experiment where the institution gathers data for data’s sake. But assemble data to analyze them and deploy the results for data-driven decisions. The research institution should also factor in the privacy of the participants whose data was collected and encourage employee training in adhering to the data privacy policies. Also, the promotion of a healthy data culture in the organization requires the allegiance of senior and executive leaders and must be more than sporadic high-level announcements (Hubbard et al., 2020).

This adherence of executive leaders could be achieved by helping them to appreciate the distinction between traditional analytics and advanced analytics. Most business intelligence and reporting tools are utilized in traditional analytics whereas advanced analytics applies predictive and prescriptive tools for machine learning. Once the sponsors and top leaders grasp the distinction, they will be in a better position to define problems for the analytics team to solve. And invest resources in building their team skills to get footing using the latest tools and techniques (Diaz et al., 2018).

Best Practices for Data Democratization

Data democratization best practices that could be implemented to promote the pulmonology and respiratory medicine institution’s strategy start by getting a grasp of the complete data ecosystem. This is necessary because as the business continues to grow, the volume of data, its variety, and velocity also increases with all the challenges supplementary with managing it (Kadadi et al., Oct 2014). Data gets siloed in systems and becomes accessible by only appropriate team members which might create a narrow vision of the data space for users. A better understanding of the data ecosystem and the fragmented systems that hinder it is important to designing a cohesive data space. This will provide the employees with a general overview of the data assets, together with the metadata.

A second data democratization best practice is making the data integration and analysis tools available to everyone in the organization. The IT department should not be the only team acting as gatekeepers of data. This approach to the data management process is very slow, frictional, and highly IT-reliant. However, companies that want to benefit from data democratization would need to put more effort into data integration and analysis tools that supply similar usability and performance to every employee irrespective of their technical background. Implementing these integration tools is critical for data democratization and analytics within the company. The context of these supplies allow everyone to understand the need to feel more certain about the significance and trustworthiness of data.

The third data democratization best practice requires cooperation to release data trapped within legacy systems for responding to queries that were not designed by people who initially sampled this information. This will give way for the fresh data being curated to be made accessible for analysis and reporting. Since legacy systems are not flexible enough, it could hinder the data democratization efforts within the business. An approach to overcome these issues would need for the legacy data to be integrated into modern infrastructures. Although that could be cost-intensive, if the organization invests in data integration tools that could offer instant API connectivity, it will provide interoperability to both popular databases and cloud-based systems.

The final data democratization best practice that will be discussed involves the empowerment of users with self-service analytics by companies. To gain more from data democratization, organizations should encourage their employees to retrieve data and make analyses out of them to gather insights for reporting as part of their everyday operations. To achieve this solution, institutions would need to find business intelligence tools and technologies with a better management platform. This will facilitate access, analysis, and reporting of data in a highly consumable way by the employees within the institution. These data integration solutions will allow non-technical employees to easily manipulate and analyze datasets.

Barriers Existing Between Data Democratization & Stakeholders

There are several challenges faced by corporations in implementing data governance when establishing data democratization. The first limitation is an organization’s ability to understand the business value of data governance. The second limitation is people’s view that IT owns the data (Al-Badi et al., 2018). The availability of limited resources forms the third limitation, and the presence of siloed data in the company is another limitation. In addition, poor data quality and lack of trust in the data as well as lack of data control is a huge challenge for many enterprises.

With the vast increase in data generation within organizations, it has been challenging to demonstrate the importance of having quality data sourced to businesses that are slow at adopting digital transformation across the entire institution. Earlier, most IT departments within several organizations including the pulmonology and respiratory medicine institutions were handed data governance. Most people thought establishing rules to restrict access to data was important without prioritizing data governance. Since ownership of the datasets is not properly clarified for data governance, it makes it difficult for all employees to embrace digital transformation.

The implications of a poorly designed and managed data science technology stack on data science democratization initiatives and planning might pose a threat to the healthy data culture the pulmonology and respiratory medicine institutions want to establish. It can result in a lack of senior managers’ buy-in. If key stakeholders are not involved in leading the data science revolution, they might not see the importance of it within the organization. Another implication of a poorly managed data science technology stack is when data science is not democratized broadly to include everyone. A poorly designed data science technology stack will not consider looking for insights that deepen their clientele’s experience.

Conclusion

Ultimately, data democratization procedures cause data to be available to everyone in an organization without the need for intense IT engagement. Once the data democratization policy is established, it permits all stakeholders to have access to data. They get to analyze and understand the data promptly to gather insights to make data-driven decisions. Sometimes, despite the desire for corporations to provide every staff member with easy access to data, there could be many infrastructures, culture, and governance-related enhancements that would be required to be put in place to make data available to employees. These factors summarize the structure that needs to be used to ensure that all stakeholders can access, understand, and use the data available to them.

Author: Adwoa Osei-Yeboah

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References

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