What are the top data processing principles for data reporting?

Test your data throughout. Don’t write all your code and wait till the end to start testing. I may or may not have made this mistake in my early days!

One aspect I find important is automation of data validation, same as an Engineering team would automate any other CI/CD process. Running tests frequently - on change or on a timer - ensures that the Data team can get ahead of any data issues and communicate to stakeholders before any unexpected impact arises. And to make validation even more robust, use tools to help you! Tools like dbt and Great Expectations help make data integrity and freshness testing a breeze

Data validation is the cornerstone of reliable data reporting, ensuring that the information presented is accurate, consistent and free from errors. By rigorously examining data for adherence to predefined criteria such as type, format and completeness, analysts can identify and rectify discrepancies early in the pipeline. This meticulous validation process not only safeguards the integrity of the reported insights but also instills confidence in stakeholders, reinforcing the credibility of the analytical findings.

Data Validation plays a crucial role in an organization in data analytics. As KYC is an important activity in financial industry like wise thé validation of data is. -identifying thé effective and accurate data -helps in identifying issue in the quality of data. -identifies data input issues like missing data -validating thé threshold related to the documented data. -it helps in verifying the alerts which are generated for validation.

For effective data reporting from FMPOV: Accuracy and reliability: Ensure data the correct and sources are trustworthy. Consistency: Maintain uniform data formats and standards. Timeliness: Update and report data in a timely manner - your audiences business problem / decision timelines will dictate that. Relevance: Focus on data that is pertinent to the report's decision insight objectives. Clarity: Present data in an understandable and interpretable way and train the person(s) how to interpret what they are getting from you. Efficiency: Optimize data processing for quick, and accurate results. Security / Privacy: Safeguard sensitive information and comply with privacy laws. Governance: regularly check the report still does its job

Data transformation helps you make more sense about the data structure. It is pivotal in shaping data into a more understandable and analyzable format by restructuring, and organizing it to a more accessible and coherent data type, thereby facilitating better insights and decision-making during analysis and reporting processes.

Cleansing involves identifying and rectifying errors or inconsistencies in the data, ensuring accuracy. Normalization standardizes data formats and units for consistency, enabling meaningful comparisons. Aggregation condenses detailed data into summary metrics, supporting higher-level analysis and visualization. Contextualization involves enriching data with relevant metadata or external information to provide a more comprehensive understanding. These principles collectively facilitate the transformation of raw data into a refined, standardized, and contextualized form suitable for reporting and subsequent analysis.

Data transformation should follow several principles to maintain the accuracy and integrity of the used/transformed data. The transformative process should be optimized to hold the efficiency in processing large datasets. The data transformation process should also be coupled with documentation and consistency across methods.

Data Transformation is pivotal in data analytics, converting raw data into a usable format for analysis. It ensures data consistency, improves accuracy, and enables meaningful insights. Effective transformation is the bedrock for informed decision-making and deriving actionable intelligence.

Data transformation plays a pivotal role in preparing data for analysis. It involves converting raw data into a usable format, ensuring its consistency and compatibility with the intended use. I've witnessed firsthand how data transformation enhances the effectiveness of data analysis. By cleaning, standardizing, and aggregating data, we can extract meaningful patterns and gain valuable insights that would otherwise remain hidden.

I would say it is also helpful to have an organized system for keeping track of the many flat files (namely excels) that exist in your organization, especially those that are business critical or used to enrich data from your main DB. Many of them contain valuable data that may not be able to just be dumped into a database or warehouse due to resource constraints (especially in smaller orgs). This data can easily be stored for free in Google Drive and then easily located and retrieved when needed. It’s also helpful to write a Python script to clean the data and put it into a format suitable for analysis before uploading it to Drive or other cloud storage alongside the original files.

Data Integration is vital in data analytics as it seamlessly combines information from various sources, providing a unified and comprehensive view. This enhances accuracy, facilitates insightful analysis, and drives informed decision-making.

In my experience, data integration has proven to be an invaluable tool for consolidating information from disparate sources into a unified repository. I recall a project where we struggled to gain a holistic view of our customer base due to fragmented data stored in various silos. By integrating data from CRM, sales, and marketing systems, we created a single source of truth, enabling us to identify customer trends, cross-sell opportunities, and enhance customer service. Data integration empowered us to make informed decisions based on a comprehensive understanding of our customer base.

What I found helpful is thinking of data visualisation a similar way a web or app product team thinks about shipping features or pages - user-first. Something we tried and found useful is creating a mockup of a dashboard or new chart and presenting that to the stakeholder first - from my experience, it helps clarify their need, priorities and also helps the data team set expectations and create a picture of what success looks like (because we know it's so easy to just keep endlessly adding "that one more breakdown")

I find that map charts are an effective way to visualize state or country-wide data. With map charts from Excel and Tableau, I can display data by region, which helps me avoid clutter and distortion that other types of charts may have. Using color and shading to create a heat map can quickly highlight areas with high or low values, making it easy for me to understand and present the distribution of data. They provide a clear and concise way to represent complex information.

Human brains process images thousands of times faster than text. Data visualization is the key to arm business users with data needed to improve decision making. Data visualization is definitely more than just creating charts. Dashboard creator should think how business users will use this reporting tool and design it with high interactivity and readability aspect. Chosing the right chart type, adding descriptive titles, strategic use of color and contrast (to highlight crucial insights), grouping Dashboards elements (slicers and filters pane, KPIs section, major charts section) are one of the most important rules to create Dashboard which will tell story of data in a clear and understandable way.

Data visualization in data reporting follows key principles such as clarity and simplicity for easy understanding, accuracy to reflect true data, and relevance in choosing the correct data. Its goal is to the engage with audience, often using interactive elements and contextualization for deeper insight. To do that I use Tableaucor QlikSense.

Effective data visualization swiftly communicates rich context. A well-crafted visualization succinctly narrates a story, saving considerable time compared to more extended verbal explanations.

One of good examples of data interpretation is a calculation of sentiment score based on text data with NLP methods. This method examines statements, quantifies words and phrases, and employs natural language processing for customer sentiment analysis. It considers context, emotion, and analyzes content in various languages to generate positive, negative, or neutral sentiment scores.

Data Interpretation is crucial in data analytics as it transforms raw data into meaningful insights, enabling informed decision-making. It empowers businesses to uncover trends, patterns, and actionable conclusions for strategic planning. For instance, interpreting sales trends from monthly data guides effective inventory management, optimizing resources, and maximizing profits.

Throughout my work experience, I've found data interpretation to be an essential skill for transforming raw data into actionable insights. I recall a project where we analysed customer feedback to identify areas for improvement. By carefully interpreting the data, we uncovered hidden patterns and trends that revealed customer pain points and unmet needs. This interpretation enabled us to prioritize product enhancements and improve customer satisfaction. Data interpretation proved to be a valuable tool for translating data into meaningful actions that drive business value.

For the main datasets in and org that are used for frequently business reports, it is important to keep track of where the data is produced (for example, the POS, webscraper, other systems) who created the final dataset being viewed (it may have gone through several rounds of enrichment, aggregation, or transformation by an analyst), and who the point of contact is for each step in case something breaks or there are questions. Sometimes the person presenting the final report may not know or understand how the data got to its final state, so it is important that the path to the origin of the data is clearly marked.

Adherence to essential processing principles is very important. Comprehensive metadata inclusion, embodied in detailed data dictionaries, lays the foundation for understanding data origin and structure. Implementing version control and transformation logs ensures traceability and facilitates reproducibility, while the documentation of consistent units, formats, and data lineage enhances interpretability. Data quality metrics and ownership delineation contribute to reliability, and a clear publication schedule enables users to anticipate updates.

Data Reporting Expectations I know it might sound like common sense but through my experience it is so important to state an agree what the reporting expectations are along with acceptance criteria. The requirements must map to delivering a business value and ideally connect to the business strategy. I have seen many examples where data reporting requirements were requested without the report owner understanding where the data was coming from along with how the data report would be used by end users to drive change in the business.

Think of data processing for reporting like baking a cake. Accuracy ensures you use the right ingredients in the right amounts. Completeness means you don't leave out any crucial ingredients. Consistency is like following the same recipe each time for reliable results. Timeliness is serving the cake at its best, not letting it sit too long. Relevance is focusing on the essential ingredients for the recipe, not adding unnecessary ones. Following these principles ensures a tasty and reliable report that experts can trust.

An example i have seen, once i was working on data analytics and it took me hours to fix the data errors. If you have spelling mistakes in the data you cannot make analysis. There can be two type of possible errors (alpha & numeric) in your data. Your analytics are based on both things. If even one thing is not correct you cannot make your analysis correct. Data accuracy is most important part of your data analysis. Always guide your data operators to use alphabet and numeric forms correctly so it will be easy when it comes to the analysis.