What is the best approach to balancing data quality, quantity and timeliness in data-driven decisions?

For any activity, setting the goals has to be the top most priority and more so for Data Quality. 1. In Today’s world, there is lot of data, if one doesn’t define clear goals then we would be lost. 2. It is necessary to focus on the outcomes while defining the criteria. This will help us to stay focused on the goal

Decision-making is often the goal of a data project. When organizations are faced with a situation that requires a decision (capital investment, headcount growth, strategic initiative), data teams and experts are called in to scope out the approach and structure the data set. Minimum quantity is important here (i.e., no more than what is necessary). By properly defining the goal - which is to collect and analyze enough data to support decision-making - data quality, integrity, and speed of analysis can and should follow naturally.

Its Very important to make a good job. If you dont have a data goal, your project maybe finished to late and you cant make again a good inpression

In Engineering Usability Testing, achieving an optimal balance between data quality, quantity, and timeliness is crucial. Prioritize data quality through rigorous validation processes and regular audits. Emphasize the relevance of data over sheer volume to ensure it aligns with usability testing goals. For timeliness, adopt real-time or near-real-time data collection methods to provide decision-makers with the most current information. Striking this balance involves integrating high-quality, relevant data collected in a timely manner, forming a foundation for effective usability testing in engineering.

Here are some tips that can help you manage these factors effectively: 1. Define clear objectives. 2. Establish data quality guidelines. 3. Use a representative sample. 4. Invest in data analysis tools. 5. Use data visualization tools.

The best and most reliable results often dont come from a single method of data collection. Use triangulation for validity - three different methods, data sets, investigators or theories in your research to obtain more reliable results.

For large companies analyzing financial data, general ledgers are often the primary source of information, and BI tools are usually employed to collect data. If the project team does this in a vacuum, however, there is a high degree of risk that the output will fail to meet the project goal. Therefore, the project team should use interviews and surveys to complement the automated collection process.

Reviewing model output (i.e., stepping back) is vital. It needs to be done frequently, collectively, and continuously to ensure that the project remains on track and is not prone to error that can come from outliers or bad data. Data visualization is a great resource here and can help the team refine its final presentation at the same time. In this context, data visualization often means charts and graphs, but even simple tables can quickly uncover a potential issue.

Analysis without action signifies an issue with the overall data project, either stemming from its inception (i.e., during initiation or planning) or during the final stages of the project (i.e., when presenting results or closing the project). Effective communication at the project's outset, which includes capturing the right level of management sponsorship, can ensure the inclusion of actionable steps which should be continuously shared during project updates. This will keep the project team focused and motivated, and serve as a precedent for future data projects.

Drawing from personal experience, I've found 'lessons learned' to be incredibly valuable. Conducting a comprehensive review of a data project upon its closure and carefully documenting this process allows for the preservation of successful strategies while enabling necessary improvements. This includes refining data collection processes, enhancing communication plans, and implementing system upgrades.