Como você usa a qualidade dos dados para contar uma história com dados?

Data quality encompasses various dimensions, including accuracy, completeness, consistency, timeliness, and reliability. Accurate data reflects the real-world aspects it represents, while complete data contains all necessary attributes without missing values. Consistent data maintains consistency across different sources and formats, ensuring compatibility and reliability in analysis. Timely data is relevant and accurate, reflecting the most recent information available. Reliable data can be trusted to make informed decisions and lead to actionable insights. It is essential for organizations to maintain a high level of data quality, comply with regulations, and maintain value from their data assets.

Data quality, is less a benchmark and more a lifeline, ensuring our narratives are grounded in reality and our strategies aligned with tangible insights. It's about creating a narrative that resonates, informed by facts that are irrefutable and presented in a way that is coherent and compelling. When incorporating third-party data, the plot thickens. Here, vigilance is key, as these external data sets come woven with their own narratives. It’s imperative to dissect their methodologies and harmonize their metrics with ours, ensuring a seamless blend into our story. This due diligence prevents the peril of misaligned insights, preserving the integrity of our strategies.

Quality data is vital for storytelling because it not only builds credibility on the narrative, but brings clarity and helps navigate the conversations to achieve the right outcome. High-quality data supports effective decision-making, and helps mitigate bias, making your story more compelling and trustworthy. Imagine you're creating a data-driven narrative for a pharmaceutical client to make a case for a new drug's effectiveness. The data quality ensures that the clinical trial results you present are accurate, reliable, and free from errors. Quality data helps you communicate the drug's benefits, safety, and impact clearly, which is critical for regulatory approval and healthcare decisions.

I've always found SaaS analytics tools as a great analogy for data quality. Seen thousands of users go into a financial tools where the data is correct but they subvert it to what they know. They attempt to use it to do reconciliation. Coming from their base to gain trust in the data before using system for purpose intended. When they can't then they undermine the value of the system. By structuring your data & making it match monthly billing numbers & statements easily, it ensures that users will trust the data. It meets them on their terms & then they can take that trust forward to unlock new value. Business trust in the data for use case is the most critical component. Stay focused on cases. Don't overbuild. It will never be perfect.

Data quality is important for storytelling because companies takes business decisions based on the reports. If the reports are not accurate, they can cost millions of dollars.

Data quality is vital for data storytelling as it can hinder the reliability and trustworthiness of your reports. For instance, imagine reporting on data that is calculated improperly, not collected in a coherent way, not complete, or not accurate - this can lead to the entire report being questioned, hindering the actionability of your recommendations. Another common pitfall in reporting is using sampled data or third-party data and presenting it as complete - always use footnotes or a separate dashboard or report documentation to explain fully: - how you gathered the data and why this apporach - what sources you used and their limitations (e.g. sampled, third-party, incomplete, etc) - when and for what time period was the data collected

I find that accurate and reliable data is paramount in strengthening our rapport with audiences, reinforcing the authenticity and appeal of our messages. This foundation is crucial; without it, even the most poignant narratives may falter and lose their sway. This highlights the indispensable role of high-quality data in potent communication. However, it's crucial to present this data to clients meaningfully, requiring discernment in both the content shared and its significance. Striking this balance is essential in ensuring that the information is not only insightful but also relevant and actionable.

Business stakeholders would get an overall feel of the quality of the data they are dealing with in data migration or management projects. Taking the current data quality metrics as base we can set benchmarks for us to reach at the end of a tenure or project.

The role of data quality in storytelling cannot be overstated. Storytelling with data is an art that hinges on the accuracy and reliability of the underlying data. High-quality data lends credibility to the narrative, ensuring that the audience perceives, interprets, and responds to the story as intended. Conversely, poor data quality can significantly undermine the storyteller's credibility, potentially leading to confusion and misguided decisions among the audience. In essence, data quality is the backbone of effective data storytelling, pivotal in connecting with the audience and successfully conveying the intended message.

When using third-party sources, consider whether you can benchmark the data from one source with one or two other sources - this can enable you to provide your stakeholders with a better understanding of the hindrances of the third-party sources. Whenever possible, include documentation and explainations how third-party sources have collected their data or calculated their metrics - this will enable your audience to self-inform and understand your presentation better, at their own pace.

The assessment of data quality involves a comprehensive assessment of various attributes and characteristics of the data. This encompasses examining the accuracy, completeness, consistency, timeliness, and relevance of the data to its purpose. Data profiling techniques can be employed to analyze the distribution, patterns, and anomalies within the data. Data validation processes verify the accuracy and reliability of the data, while data cleansing techniques address any errors or inconsistencies. Furthermore, data quality assessment frameworks, such as the Total Data Quality Management (TDQM) model, provide structured approaches to evaluate data quality against predefined criteria.

You can assess data quality based on the number of quality rules that passed/failed, time taken to resolve, frequency of recurring issues, financial impact of issues etc...

One of the most known ways of assessing data quality is to profile the data. For the same any DQ tool can be used or simply SQL can be incorporated

Improving data quality needs to come from solving the root cause of the problem. If you don't solve the cause, it will keep happening. The cause could be a vendor sending bad data or an employee entering data incorrectly. If you take a cleansing approach and you load data weekly over the data you cleansed, then you've giving yourself a weekly operational process of cleansing data. No one wants that job. It might be needed on a temporary basis, but solve the root cause so you don't have to have that operational process forever. Cleansing is typically done prior to a conversion so you can start with good data, but if you still don't know how the data got into that state, your shiny new database will quickly go bad as well.

This involves implementing data governance strategies, such as data stewardship and metadata management, to establish accountability and accountability of data quality initiatives. Data standardization and normalization can be employed to maintain consistency and uniformity across different data sources. Data quality tools and software automate processes such as data profiling, validation, and cleansing, facilitating the improvement efforts. Continuous monitoring and feedback loops allow organizations to identify and address data quality issues promptly, ensuring that data remains accurate, complete, and reliable over the course of time.

Data quality can be improved by calling out exceptional data and marking the same to data stewards for action in the respective systems.

Improve data quality by managing the lifecycle-> Identify: Allow users to report data quality issues Prioritize: Identified issues should be added to the backlog for prioritization Perform Root Cause Analysis: Perform root cause analysis through lineage or impact analysis Fix: Fix the quality issues at the source system. Control: Proactively look for data quality issues by writing data quality rules or performing anomaly detection. Implement policies & procedures to reduce the occurrence of quality issues.

Utilizing high-quality data to craft a compelling story is a powerful tool in data communication. First and foremost, ensure the data selected is relevant, reliable, and representative of the story you wish to tell. Choosing the right format is equally important – it should be clear, concise, and engaging. Providing accurate context is critical to make the data actionable and relatable. Leveraging tools like charts, graphs, maps, and infographics to visualize the data brings the story to life, capturing your audience's attention and emotion effectively. This approach not only informs but also inspires your audience, making your data storytelling both impactful and memorable

In addition to assessing and enhancing data quality, consider the wider context in which data is collected, analyzed, and interpreted. Be aware of biases and limitations in the data, as well as ethical considerations regarding data privacy and security. Continuously monitor and evaluate data quality to ensure that it remains high throughout the course of time. Utilize training and development for data users to enhance data literacy and promote a data-driven culture within your organization. By addressing these additional considerations, you can increase the effectiveness of storytelling with data.

IMHO Data Quality is not a feature nor a capability - it is the approach of governing your data from ingestions to delivery (to apps, databases, reporting, APIs, users) and with that every time data points are being 'touched' they change and require an on going circle of data quality. How'd you feel if your sales rep misspells your name in the first interaction? And this is the 'lowest' (albeit not simplest) approach to data quality!