How do you prioritize data accuracy over quantity?

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Data analytics is a powerful tool for making informed decisions, but it also requires careful attention to data quality and accuracy. Data quality refers to how well the data reflects the reality it represents, while data accuracy refers to how close the data values are to the true values. Poor data quality and accuracy can lead to misleading or erroneous results, wasted resources, and loss of trust. Therefore, it is essential to prioritize data accuracy over quantity, and ensure that the data you collect, process, and analyze is reliable and valid. In this article, we will discuss some strategies and best practices for prioritizing data accuracy over quantity in data analytics.

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SANABIL MIRZA

Executive Imports

1 Define your goals and metrics

Before you start collecting and analyzing data, you need to have a clear idea of what you want to achieve and how you will measure your progress and success. Defining your goals and metrics will help you focus on the data that is relevant and meaningful for your purpose, and avoid unnecessary or redundant data that can clutter your analysis and reduce your efficiency. Your goals and metrics should be SMART: specific, measurable, achievable, relevant, and time-bound. They should also align with your business objectives and stakeholder expectations.

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Jay Hawkinson

Sr Director Data and Analytics at Lamb Weston | Manufacturing Companies AI and Data Expert | Chief Data and Analytics Officer | Digital Value & Transformation | Innovation & Strategy | MBA | PMP
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Though it is easy to say "quality over quantity," understand the implications. Not all analysis needs to be 100% accurate, and trying to get to perfection may delay you getting value from the data today. That isn't to say strategically you don't want high Data Quality, but take into account the maturity of the data, the state of the quality today and the goals of the individual analysis. If the goal and metrics of this analysis requires accuracy, understanding this, being able to state it as a SMART goal, and the challenges to get there will help drive the needed conversations with the leaders and people responsible for accuracy.

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2 Choose your data sources carefully

The quality and accuracy of your data depend largely on the quality and accuracy of your data sources. Therefore, you need to choose your data sources carefully, and evaluate their credibility, reliability, and validity. You should also consider the scope, coverage, and timeliness of your data sources, and whether they can provide the data you need for your goals and metrics. You should avoid using data sources that are outdated, incomplete, inconsistent, or biased, as they can compromise your data quality and accuracy. You should also verify the data sources with multiple sources, if possible, and cross-check the data for errors or discrepancies.

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SANABIL MIRZA

Executive Imports
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My stategy - Use the _Footer_. If you think mentioning something ABOUT the data in between your data representation will divert focus, it is wise to use the footer. Use the footer for: 1?? Source of data external/internal 2?? Data validity 3?? Limitations - in terms of scope and coverage 4?? Assumed Constants that may have an impact such as currency fluctuations, political stability, financial policies etc. 5?? The Method/Stategy used or the theory implied.

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3 Apply data cleaning and validation techniques

Data cleaning and validation are essential steps in ensuring data quality and accuracy. Data cleaning involves identifying and removing or correcting any errors, outliers, missing values, duplicates, or inconsistencies in your data. Data validation involves checking and confirming that your data meets the standards and rules you have defined for your data quality and accuracy. You can use various tools and methods for data cleaning and validation, such as data profiling, data cleansing, data transformation, data verification, and data auditing. You should also document your data cleaning and validation processes and results, and keep track of any changes or modifications you make to your data.

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SANABIL MIRZA

Executive Imports
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An effective way to keep track of changes and modifications when using spreadsheets is to create a separate sheet each time a major change is made. For example, my first step of working on a data is to remove unnecessary columns/rows. Now I wouldn't make these deletions to my original unsorted data. Instead I would make a duplicate of this original data sheet and delete the unnecessary columns/rows in this duplicate sheet. As this practice continues, finally, you have a workbook that contains the entire trail of your modifications.

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4 Use appropriate data analysis methods and tools

Data analysis is the process of exploring, interpreting, and communicating the patterns, trends, and insights from your data. Data analysis methods and tools can vary depending on your goals, metrics, data types, and data sources. However, regardless of the methods and tools you use, you should always ensure that they are appropriate for your data quality and accuracy standards, and that they can handle the complexity and volume of your data. You should also test and validate your data analysis methods and tools, and compare and contrast different methods and tools to find the best fit for your data.

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5 Review and communicate your data analysis results

The final step in prioritizing data accuracy over quantity is to review and communicate your data analysis results. You should review your results carefully and critically, and check for any errors, gaps, or biases that may affect your data quality and accuracy. You should also communicate your results clearly and effectively, and use visualizations, summaries, and narratives to highlight the key findings and implications of your data analysis. You should also provide the sources, methods, and assumptions behind your data analysis, and acknowledge any limitations or uncertainties that may affect your data quality and accuracy. By doing so, you can enhance the credibility, transparency, and value of your data analysis.

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6 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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Bernard Lew Shian Loong

Educator, Economist, and Composer| Situating Niches within Pitches
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The important thing about quality when it comes to data is the diversity of sources. The lower the probability that these sources may be systematically influenced the better. I am thinking along the lines of systematic risks, info-to-noise signals and volumnous data, hopefully crowdsourced as well to approach the ideal of a spontaneous order (think prediction markets and it's ilk). Which will be "accurate" at least with respect to the sentiments of the majority. And from a contrarian perspective, to provide something to "rage against", if "to err on the side of the minority" is what is counted as notable.

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How do you prioritize data accuracy over quantity?

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1 Define your goals and metrics

2 Choose your data sources carefully

3 Apply data cleaning and validation techniques

4 Use appropriate data analysis methods and tools

5 Review and communicate your data analysis results

6 Here’s what else to consider

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