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Data Analytics vs. Data Analysis
Quantitative vs. Qualitative Data
What is Behavioral Analytics?
Data Analytics vs. Business Analytics
Data Analytics vs. Data Science
The Difference Between Data Analytics and Statistics
The Difference Between Data Analytics and Data Visualization
Data Analytics Lifecycle
Data Analytics vs Business Intelligence
What is Descriptive Analytics?
What is Data Analytics?
What is Diagnostic Analytics?
Data Analytics Processes
A top-level guide to data lakes
Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
Data Warehouse Architecture
What Is a Data Warehouse?
How to Create and Use Business Intelligence with a Data Warehouse
Best Practices for Accessing Your Data Warehouse
Data Warehouse Best Practices — preparing your data for peak performance
How do Data Warehouses Enhance Data Mining?
Data Warehouses versus Databases: What’s the Difference?
What are the Benefits of a Data Warehouse?
Key Concepts of a Data Warehouse
Data Warehouses versus Data Lakes
Data Warehouses versus Data Marts
Difference Between Big Data and Data Warehouses
How to Move Data in Data Warehouses
What Is Customer Data?
Customer Data Analytics
Customer Data Management
Collecting Customer Data
The Importance of First-Party Customer Data After iOS Updates
Types of Customer Data
What Is a Customer Data Platform?
What is an Identity Graph?
Customer Data Protection
A complete guide to first-party customer data
CDPs vs. DMPs
What is Identity Resolution?
What is Consent Management?
Data Access Control
Data Sharing and Third Parties
What is PII Masking and How Can You Use It?
Data Security Strategies
Data Security Technologies
Data Protection Security Controls
How to Manage Data Retention
How To Handle Your Company’s Sensitive Data
Data Security Best Practices For Companies
What is Persistent Data?
Google Analytics 4 and eCommerce Tracking
What Is Google Analytics 4 and Why Should You Migrate?
GA4 Migration Guide
GA4 vs. Universal Analytics
What are the New Features of Google Analytics 4 (GA4)?
Benefits and Limitations of Google Analytics 4 (GA4)
Understanding Google Analytics 4 Organization Hierarchy
Understanding Data Streams in Google Analytics 4
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Data collection best practices
Data collection is the first and arguably most crucial step that determines the effectiveness of a data analysis. In recent years where big data has exploded, it’s more and more easier for businesses to spin up entire teams that are tasked with making sure that data is being collected properly, and that the work that is going into the lengthy cycles of cleaning, processing and analyzing yield profitable results.
As an example, the ability to collect and analyze customer data has become a vital component of running a successful business in today's digital age. With the rising availability of customer data though, it is critical for businesses to ensure that it is collected in a responsible and ethical manner that promotes trust between businesses and their customers and also shields businesses from any legal risks that could be damaging to their reputation.
In this article we will explore some key best practices for data collection and how following those ultimately leads to better customer experiences and improved business outcomes.
Determine current state of data and define objectives
Collecting data can be a challenging task for businesses, there are a few obstacles that can arise during this step. Those challenges include ensuring data quality, relevancy, managing data volume and most importantly doing all that while maintaining privacy.
A typical start to collecting data would be by assessing the state of the current data, this would include:
- Understanding our data sources: there are various sources that generate data we may want to collect. It’s important to identify the data sources we need as a start. Examples of this include:
- Line of business applications
- Web applications
- Social media
- Identifying types of data that are available: structured, semi structured or unstructured data
- Identifying the format of the data: Data can be in JSON, XML, CSV, Avro, Parquet.
- Determining volume of data: depending on the project at hand, large volumes of data may be needed, this would also require specialized storage and data processing tools to handle those amounts. Data Science projects are a good example of that.
- Determining the need for real-time data: in this step we’ll need to identify the level of latency that is acceptable, this is usually use case or industry specific. In the case of tracking customer behavioral data, that should ideally be in real time.
- Define purpose for data collection: Data can be collected for various different objectives; we may want to improve our business forecasting ability and build use cases for Machine Learning which requires training models using large datasets that may need to be fed with data collected from case studies and surveys. We may also want to collect customer data to understand our customer behavior better and improve our overall purchasing process or we may simply just want to use it for data analysis and BI purposes.
Research data laws and regulations
Failure to comply with data collection laws and regulations can result in serious consequences to businesses such as legal penalties, brand harm, and loss of customer trust. As such, it's essential for them to research and understand the laws and regulations specific to the industry or geographic location in which they operate.
The General Data Protection Regulation (GDPR), which went into effect in the European Union (EU) in 2018, is one of the most significant regulations that businesses must consider while collecting customer data. The GDPR governs how businesses gather and use consumer data, including the requirement to get explicit consent from customers and the protection of client data.
When researching data laws and regulations, businesses should also consider industry-specific regulations that apply to their sector. For example, healthcare companies must comply with the Health Insurance Portability and Accountability Act (HIPAA), which regulates how patient data can be collected, used, and shared. Financial institutions must comply with the Gramm-Leach-Bliley Act (GLBA), which requires financial institutions to protect customer data and inform customers about how their data is being used.
Collecting data can be both time-consuming and resource intensive without the right tools that let businesses maximize efficiency. Automation can reduce time and resources, eliminate any data entry errors, improve data quality, and increase the speed of data analysis.
- Online forms and Surveys that can be accessed using QR code scanners.
- Data collection APIs such as social media, web and product SaaS APIs.
- Change Data Capture (CDC): captures transactional updates in real time within a database.
- Customer Data Platforms (CDP) for web tracking and collecting customer behavioral data on websites and webapps.
- Use ETL/ELT tools to streamline data collections and processing.
Data relevance and accuracy
Relevancy in data collection means we want the data to be as close as possible to our area of interest, while accuracy ensures it’s consistent with reality. Having both of those makes for a better overall decision making process. We can ensure relevance and accuracy by following these methods:
- Capturing data using appropriate methods: For a predictive analysis experiment as an example, we may want to collect our data from questionnaires, focus groups and case studies.
- Making sure that data is regularly maintained and kept up to date with changes and trends to ensure better quality analysis.
- Organize data in an appropriate storage tool that is secure and makes data management, monitoring and responding to updates easier.
Define accuracy metrics and regularly review performance charts using various data observability tools that let users monitor and understand the overall health and freshness of their data.
Test data collection methods
Regardless of what method we use to collect data, it’s important to go through tests to make sure it’s effective in capturing useful data that can be beneficial to the business. We’ll need to do so at two different stages, before data is collected and after:
- Make sure questions are clear and do not convey different meanings that may be found confusing.
- Make survey questions short and brief so that they are engaging and not tedious.
- Phrase questions in a way that doesn’t trigger negative feelings like discomfort or defensiveness among respondents.
Next we want to test the quality of the collected data, which includes testing that the data is ingested according to a specified schema and that no data corruption has occurred, we can use some of the following methods:
- Data sampling: a statistical approach used in data analysis to select a specific subset of data from a larger dataset. The purpose is obtaining a smaller and easier to manage dataset that can be analyzed to get better insights into the larger dataset.
- Data profiling: the process of analyzing and understanding the characteristics of a dataset through statistical analysis, data visualization and exploration, this includes getting a summary of missing data, null values as well as any other anomalies that may be uncovered.
- Data validation: Data validation is the process of ensuring that data is accurate, complete, and consistent. The goal of data validation is to identify errors and inconsistencies in the data and correct them, or flag them for further review or action.
Being transparent in data collection means being open and honest about how data is collected, what data is collected and how it is used by a business, this can be done by:
- Ensuring consent is being formally obtained according to different compliance laws and regulations of the geographic region or industry. This can be done by including privacy statements in online forms, or by using cookie consent managers when collecting data through the web.
- Being clear about the collection methods being used and the purpose of data collection which could be to perform a study about a specific topic or improve a customer’s experience with more personalized recommendations.
- Documenting the data collection process and using standardized naming conventions.
- Collecting only the data that is needed and not more. This can be advantageous for the data providers as well as businesses using the data as they will incur lower data storage costs.
Data integrity and Security
With the increasing dependency on data to observe current market conditions, create target performance metrics and ultimately make all types of business decisions, it’s more important than ever that the data being collected can be relied on to make informed decisions. Data integrity refers to the accuracy, consistency, and reliability of data throughout its entire lifecycle. It involves ensuring that data is complete, correct, and consistent, and that it has not been tampered with or lost in any way.
- Data validation, this can be done to ensure collected data is accurate before entering the system and can be implemented in the form of data quality checks in different steps throughout the data analysis process.
- Regulate access to data: by implementing tight access controls, following the principle of least privilege which gives least required access to perform a specific task. This could be done by implementing Access Control permissions, Just In time Access and Role Based Access Control (RBAC).
- Data backup and recovery: Having access to secondary copies of the data comes a long way in shielding against system failures, data corruption or data infiltration attacks.
- Audit trails: to be able to monitor data changes, the source and when they occur. Ideally should be a part of the product or an automated process that external access to would not be possible.
- Store data securely: Store data in a secure location, such as a password-protected server or a secure cloud storage platform that supports encryption to prevent unauthorized access.
Following best practices in data collection helps organizations make more informed decisions, gain better insights, and draw more accurate conclusions from their collected data. Additionally, best practices in data collection help protect the privacy and security of different stakeholders, this is especially important in today's world. Therefore, organizations need to prioritize following best practices during the data collection process to reap the benefits of quality data analysis and insights.