Data quality is a critical aspect of building a successful data strategy, and it goes beyond just the accuracy of the data. A comprehensive data quality check should assess three key levels of data quality to serve the business effectively: data ownership, data accessibility, and data usability.
Conversations with stakeholders provide insights into the types of data required to solve their problems. However, knowing if that data is available and who owns it is equally crucial. Prioritize addressing the most significant problems with the Pareto principle in mind, as 80% of the work will come from 20% of the data. Additionally, assess what data is missing but necessary, as external data may need to be procured and included in the data strategy.
Data professionals and the organization must have straightforward and managed access to the data, irrespective of its location. Quality data should be accessible from a single point of entry, across various data sets, and have appropriate controls in place. Ensuring seamless data accessibility empowers teams to make informed decisions and drive business growth.
For data to be usable, fields must be clearly defined, and the business must adhere to those definitions. If data is not well-documented or if different geographies use disparate processes for the same source system, data usability will suffer. Misaligned or inconsistent business rules within a single table can also hinder data usability. When data usability is compromised, trust in the information generated by the data team diminishes, making it challenging to provide real business value.
Assessing data ownership, accessibility, and usability provides valuable insights into data readiness and gaps that need to be addressed. By ensuring data is accurate, available, and usable, data professionals can generate reliable insights that drive informed decision-making and deliver true business value. A robust data strategy must prioritize data quality to maximize its impact on the organization's success.
Conducting a thorough data quality check is an integral part of any successful data strategy. By paying attention to data ownership, accessibility, and usability, SMBs can harness the power of data to make better decisions, enhance operational efficiency, and drive digital transformation. Remember, data quality is not just about having the right data; it's about making the data right for your business's needs. Prioritize data quality, and your data strategy will become a formidable tool for success in today's data-driven landscape.
Identifying the Business's Biggest Needs
Sifting Through the Programming Languages
Explore how ESG requirements, from sustainability to stakeholder relationships, are crucial in modern business. Embrace digital solutions for data-driven decision-making and proactive social performance enhancement.
Explore how organizations can harness the strengths of both generative AI and traditional analytics to drive innovation and create value. Learn about strategic prioritization, use case analysis, required skill sets, and organizational adaptation for successful integration.
Explore strategies and best practices for maintaining the reliability and integrity of Large Language Models (LLMs) like ChatGPT in production environments. Learn how to effectively test, monitor, and manage LLMs to maximize their benefits while mitigating risks.
