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Assess your data needs
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Optimize data architecture
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Automate data processes
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Review and improve data practices
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Here’s what else to consider
Data quality and availability are two key factors that influence the success of data analytics projects. Data quality refers to how accurate, complete, consistent, and relevant the data is for the intended purpose. Data availability refers to how accessible, timely, and secure the data is for the users and analysts. However, achieving both high data quality and high data availability is not always easy, as they may involve trade-offs, challenges, and costs. In this article, we will explore some ways to balance data quality and availability in data analytics.
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- Aayush Kumar [in] expert who knows how to engage and inspire. No sugar-coating, just real experiences and insights.
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- Joe Perez ("Dr. Joe") ✔LinkedIn Top Voice ✔Internat'l Keynote Speaker ✔CTO⠀ ⠀⠀⠀ ✔Best-selling Author⠀✔Senior Systems Analyst ⠀ ⠀⠀⠀ ⠀ ⠀…
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- Mahdi Sheikhi Cloud Engineer | 23x Microsoft Certified Professional | Azure | Power Platform | Data | AI | Developer | MCT |…
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1 Assess your data needs
The first step to balance data quality and availability is to assess your data needs based on your business goals, analytical questions, and expected outcomes. You need to identify what kind of data you need, how much data you need, how often you need it, and how you will use it. This will help you prioritize the most important and relevant data sources, metrics, and dimensions, and avoid collecting or storing unnecessary or redundant data. It will also help you define the data quality criteria and standards that suit your needs, such as accuracy, completeness, consistency, timeliness, and validity.
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- Joe Perez ("Dr. Joe") ✔LinkedIn Top Voice ✔Internat'l Keynote Speaker ✔CTO⠀ ⠀⠀⠀ ✔Best-selling Author⠀✔Senior Systems Analyst ⠀ ⠀⠀⠀ ⠀ ⠀ ⠀⠀⠀ ✔Gartner Peer Community Ambassador of the Year 2023 ⠀ ⠀⠀⠀ ✔2021 Thought Leader of the Year
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Balancing data quality and availability is akin to juggling, where precision and timing are paramount. To master this, start with a thorough assessment of your data needs. Define the data's purpose, volume, frequency, and application. Prioritize essential sources and quality standards, aligning them with your goals. By eliminating unnecessary data and focusing on relevance, you strike the right balance, ensuring that the data you have is not only readily available but also of high quality. This strategic approach empowers your analytics efforts, steering them toward success.
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- Mahdi Sheikhi Cloud Engineer | 23x Microsoft Certified Professional | Azure | Power Platform | Data | AI | Developer | MCT | Developer | Software Engineer
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Interacting with various stakeholders, from data scientists to end users, can provide a comprehensive understanding of data needs. Their insight can illuminate potential problems, gaps, or differences that may not be immediately apparent. For example, a marketing team may prioritize the availability of real-time data for campaign tracking, while a finance team may emphasize data accuracy for quarterly reports.
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- Lisa Grimes Director of Instruction and Research at The University of Tulsa
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When collecting data is extremely important that each data point is well thought out and will contribute to your overall analytical goals. Often, data is collected, but does not actually fit into the overall picture. In addition, be prepared for the data to take you were you didn’t expect to go and keep an open mind. The data is what needs to inform the decisions.
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2 Implement data governance
The second step to balance data quality and availability is to implement data governance, which is a set of policies, processes, roles, and responsibilities that ensure the proper management and oversight of data assets. Data governance helps you establish the rules, standards, and best practices for data collection, storage, processing, sharing, and security. It also helps you assign the ownership, accountability, and stewardship of data to different stakeholders, such as data producers, consumers, and custodians. Data governance helps you ensure that data quality is maintained and monitored throughout the data lifecycle, and that data availability is aligned with the business needs and compliance requirements.
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- Mahdi Sheikhi Cloud Engineer | 23x Microsoft Certified Professional | Azure | Power Platform | Data | AI | Developer | MCT | Developer | Software Engineer
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A strong data governance framework fosters trust among users and stakeholders, when people know there is a structured approach to data management, they are more likely to trust the insights that come from it. Regular audits and reviews should be part of this governance to identify any deviations from established standards and promptly correct them. Additionally, as the data landscape evolves with emerging technologies and regulations, the governance framework must adapt. Training sessions and workshops can be organized to keep all stakeholders informed and aligned with governance policies.
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- Binayak Naag Public policy enthusiast| Tech and data driven governance| Expressed views are personal
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Establishing data governance in a changing business environment points towards consensus based governance rather than role based. The consensus on the basis of common principles would lead to a self governing ecosystem of actors, and will adapt very well to change. Governance hierarchies with rigid data policies are things of the past in today's complex world of data driven living.
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3 Optimize data architecture
The third step to balance data quality and availability is to optimize your data architecture, which is the design and structure of your data systems, platforms, and tools. Data architecture helps you organize, integrate, and access your data in an efficient and effective way. It also helps you support your data analytics needs, such as data ingestion, transformation, analysis, visualization, and reporting. To optimize your data architecture, you need to consider factors such as scalability, performance, reliability, security, and usability. You also need to choose the appropriate data models, formats, schemas, and standards that fit your data types, sources, and use cases.
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- Saad Abdul Rauf Cloud Data Architect at Small World FS | Transforming Organizations with Data-Driven Solutions
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In addition to the mentioned factors, it's crucial to think about data lifecycle management. This means defining how long you'll keep certain types of data and when it should be archived or deleted. By managing data throughout its lifecycle, you can not only optimize storage costs but also ensure that you're working with the most relevant and up-to-date information. It's like tidying up your data house regularly to keep it efficient and clutter-free.
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4 Automate data processes
The fourth step to balance data quality and availability is to automate your data processes, such as data extraction, loading, cleaning, validation, and enrichment. Automation helps you reduce human errors, save time and resources, and improve consistency and reliability. Automation also helps you handle large volumes and varieties of data, and cope with changing data needs and environments. To automate your data processes, you need to use tools and techniques such as scripts, workflows, pipelines, APIs, and machine learning. You also need to test, monitor, and update your automation solutions regularly to ensure their functionality and quality.
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- Aayush Kumar [in] expert who knows how to engage and inspire. No sugar-coating, just real experiences and insights.
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Balancing data quality and availability is like maintaining a sports car, but one common mistake is neglecting regular engine checks. Sometimes, businesses focus too much on data quantity (availability) and forget to ensure data quality, which can lead to unreliable results. Just as a car needs both fuel and maintenance for optimal performance, your data processes require a careful balance between quantity and quality.
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5 Collaborate with data stakeholders
The fifth step to balance data quality and availability is to collaborate with your data stakeholders, such as data owners, users, analysts, and managers. Collaboration helps you communicate your data needs, expectations, and feedback, and align them with the data goals and strategies. Collaboration also helps you share your data insights, findings, and recommendations, and leverage them for decision making and action. To collaborate with your data stakeholders, you need to use tools and platforms that facilitate data access, exchange, and visualization, such as dashboards, reports, and portals. You also need to establish a culture of trust, transparency, and accountability for data usage and quality.
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- Saad Abdul Rauf Cloud Data Architect at Small World FS | Transforming Organizations with Data-Driven Solutions
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Collaborating with data stakeholders is key to data analytics success. Engage stakeholders early to optimize data quality and availability. Explore practical tips, user-centric approaches, and the shift towards a culture of trust and transparency. Drive actionable insights for better decisions. Elevate your data analytics game with collaborative engagement.
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6 Review and improve data practices
The sixth and final step to balance data quality and availability is to review and improve your data practices periodically. Reviewing helps you evaluate your data quality and availability levels, and identify any gaps, issues, or opportunities for improvement. Reviewing also helps you measure your data analytics performance, impact, and value, and compare them with your benchmarks and targets. To review your data practices, you need to use tools and methods such as audits, assessments, metrics, and feedback. To improve your data practices, you need to implement corrective and preventive actions, such as fixing errors, updating standards, and enhancing skills.
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7 Here’s what else to consider
This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?
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- Binayak Naag Public policy enthusiast| Tech and data driven governance| Expressed views are personal
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One of the most important aspect which needs to be considered in the overall data governance strategy is institutionalization of quality assurance practices associated with data quality.Data quality assurance processes ensures reduction in the errors due to omission and errors due to commission. Errors due to commission is due to non adherence of best practices in data collection, while errors due to commission is associated with issues in maintaining data of data during storage.The best practices such as double data entry as well statistical processes to check data quality to find outliers will ensure reduction in errors of omission. The best practices such as automated tools will ensure meta data management.
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- Saad Abdul Rauf Cloud Data Architect at Small World FS | Transforming Organizations with Data-Driven Solutions
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Another crucial consideration in balancing data quality and availability is the implementation of a data retention policy. This policy helps define how long data should be retained, reducing the risk of accumulating outdated or irrelevant data. By regularly purging unnecessary data, you can improve data quality and make valuable information more accessible to analysts. It's essential to align your retention policy with legal and compliance requirements to ensure data security and compliance.
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