In this article, we will explain exactly what data pulling is, why it’s important, and share our simple process to implement data pulling effectively. Read on to learn more.
Data pulling refers to the process of extracting data from various sources, such as databases, web services, or files, for further processing and analysis. It involves retrieving necessary information and making it accessible for use in reporting, analytics, and decision-making.
Example: A marketing analyst at Amazon might perform data pulling by extracting customer purchase data from Amazon's Redshift database. They would retrieve records of all transactions involving the Echo Dot (3rd Gen) in the last quarter to analyze sales trends and customer demographics. This data would then be used to inform marketing strategies and inventory planning.
Data pulling is important for a number of reasons, some of the most common reasons include:
By pulling data from different sources, organizations can integrate and consolidate information, providing a comprehensive view of their operations.
Accurate and timely data pulling ensures that decision-makers have access to the most current information, enhancing the quality of decisions.
Extracting relevant data sets the stage for in-depth analysis, helping organizations uncover trends, patterns, and insights.
Automating data pulling processes reduces the time and effort required to gather data, allowing teams to focus on analysis and strategic initiatives.
Consistent data pulling practices help maintain data integrity and accuracy, which are critical for reliable analytics and reporting.
Use our 10 step data pulling process to efficiently manage data from various sources.
Identify and outline the specific data you need to gather, including the type, scope, and granularity. This ensures that you collect only relevant data, avoiding unnecessary or extraneous information.
Example: If you need to analyze sales performance, define that you require monthly sales figures, segmented by region, for products A, B, and C.
Determine where the data will be sourced from, whether internal databases, external APIs, or third-party providers. This step involves ensuring the data sources are reliable and relevant to the requirements.
Example: For analyzing customer feedback, identify that you will pull data from an internal CRM, Google Reviews, and a third-party survey platform.
Ensure you have the necessary permissions and tools to access the identified data sources. This may involve working with IT teams to secure access or setting up API connections.
Example: For pulling financial data, confirm that you have API keys for connecting to the financial software and read/write permissions to the relevant databases.
Pull the raw data from the identified sources according to the predefined requirements. This step often involves using specific tools or scripts to extract the data in the correct format.
Example: Use SQL queries to extract the last 12 months of sales data for products X, Y, and Z from the sales database.
Clean the extracted data to remove any inconsistencies, errors, or irrelevant information. Validation checks are performed to ensure data accuracy and completeness.
Example: Remove duplicate entries and validate the product pricing information for the last quarter from the product database to ensure accuracy.
Transform the cleaned data into a format or structure suitable for analysis, such as aggregating, normalizing, or categorizing the data. This step prepares the data for deeper analysis.
Example: Aggregate daily sales data into monthly figures for products M, N, and O to prepare for trend analysis.
Store the processed data in a designated location, such as a data warehouse, cloud storage, or local database. Proper storage ensures the data is easily retrievable for future use.
Example: Save the cleaned and transformed sales data for the last fiscal year in the company’s cloud-based data warehouse for future reporting.
Document the entire data pulling process, including methods used, tools, and any issues encountered. Create initial reports to summarize the data findings or prepare the data for analysis.
Example: Document the SQL queries used to extract customer purchase data and generate a summary report showing monthly purchase trends for product G.
Review the data pulling process with stakeholders and gather feedback for improvements. This step ensures the process meets the initial requirements and identifies areas for optimization.
Example: After pulling marketing data, review the process with the marketing team to ensure all relevant social media metrics for campaigns A and B were captured correctly.
Based on the feedback and review, refine the data pulling process to address any gaps or inefficiencies. This ongoing iteration helps in improving the accuracy and efficiency of future data pulls.
Example: After analyzing the feedback, automate the data cleaning process for monthly reports on product defects to reduce manual effort and improve consistency.
DigitalGears Inc. is a mid-sized tech company specializing in smart home devices. Here's how they implemented our 10-step data pulling process to improve their customer retention and sales strategy.
DigitalGears Inc. identified the need to analyze monthly sales data for SmartLock 3000, ThermoGuard X2, and LightSense 500 across different regions. The company also decided to track customer demographics and the impact of seasonal promotions on these product sales to tailor future campaigns.
The company determined that sales data would be sourced from their internal sales database, customer demographics from the CRM system, and promotional impact data from their marketing analytics platform. They ensured these sources were up-to-date and aligned with their data analysis objectives.
DigitalGears Inc. worked with their IT department to secure access to the sales database, CRM system, and marketing analytics platform. They set up the necessary API connections and ensured all team members involved in the process had the required permissions.
The team at DigitalGears Inc. used SQL queries to pull monthly sales figures for SmartLock 3000, ThermoGuard X2, and LightSense 500 from the sales database. They also extracted customer age, gender, and location data from the CRM, along with promotional performance metrics from the marketing platform.
The extracted data was cleaned by removing duplicate entries and correcting inconsistencies in customer demographic information. The team also validated the sales figures by cross-referencing them with monthly sales reports to ensure accuracy and completeness.
DigitalGears Inc. transformed the cleaned data into aggregated monthly sales reports, breaking down sales by region and customer demographic. They also categorized the data based on promotional periods to assess the impact of different campaigns on product sales.
The processed sales, demographic, and promotional data were stored in DigitalGears Inc.’s centralized data warehouse. This allowed the marketing and sales teams to access the data easily for ongoing analysis and decision-making.
The team documented the entire data pulling process, including the tools and methods used, to ensure repeatability. They generated initial insights showing trends in product performance, such as higher sales of SmartLock 3000 in urban areas and increased sales during summer promotions.
DigitalGears Inc. held a review meeting with the sales and marketing teams to discuss the data pulling process and its outcomes. Feedback was gathered on the comprehensiveness of the data and the usability of the generated insights, leading to discussions on further refining the data sources.
Based on the feedback, DigitalGears Inc. automated the data extraction and cleaning processes to improve efficiency. They also refined their data requirements to include additional customer feedback data, enabling a more comprehensive analysis in future data pulls.
We hope you now have a better understanding of what data pulling is and how to implement our simple 10 step process effectively. If you enjoyed this article, you might also like our article on OCR in AI or our article on OCR vs AI.