Data Quality

Big Data Management: How To Make The Most Of Your Business Intelligence

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Nearly every interaction between consumers and businesses leaves behind a digital footprint chock full of information. 

However, according to a Salesforce study of over 1,600 businesses, a whopping 76 percent of companies fail to grasp the full value of their big data, leaving valuable insights—not to mention tons of potential revenue—on the table.

Below, we’ll examine how to avoid making the same mistake as well as share best practices for effectively managing big data to make more strategic decisions.

What is big data?

Big data refers to extremely large and complex sets of data that are difficult to manage and analyze using traditional data processing tools. It encompasses data that is characterized by the three Vs: Volume, Velocity, and Variety. Let’s break down these characteristics:


Big data involves vast amounts of information. This could be terabytes, petabytes, or even exabytes of data—far more than can be handled with conventional databases and data processing systems.


Big data is generated and collected at high speeds. It’s not just about having a lot of data; it’s about how quickly that data is produced and must be processed. For example, social media posts, sensor data, and financial transactions are all generated rapidly.


Big data comes in various formats, including structured data (like traditional databases), semi-structured data (like XML or JSON files), and unstructured data (such as text, images, and videos). It may also include data from diverse sources, such as social media, sensors, and logs.

How does big data differ from CRM data?

Customer relationship management (CRM) can extract immense value from big data. 

But at its core, CRM data is a subset of data focused on managing and analyzing customer-related information.

CRM data includes details about customers, their interactions with a business, their purchase history, preferences, contact information, and more. To that end, CRM systems are designed to help teams and admins build and improve relationships with customers, optimize sales and marketing efforts, and provide better customer service.

Other key differences between CRM and big data for managers include:


Big data encompasses a broader range of data, which includes customer data, but also includes many other types of data from various sources, both internal and external to an organization. CRM data is specifically focused on customer-related information.

Size and complexity

As mentioned above, big data is typically characterized by its vast volume, velocity, and variety. This makes it more challenging to manage and analyze compared to CRM data, which is usually more structured and smaller in scale.


Big data is often used for broader analytics, such as market analysis, trend identification, fraud detection, and more. CRM data, on the other hand, is primarily used to manage and improve customer relationships and sales.

Tech tools

Big data requires specialized tools for processing, storage, and analysis, whereas CRM data is usually managed using solutions designed specifically for managing relational data. For example, to manage and clean CRM data in less time, many administrators turn to solutions like Validity DemandTools

In short, big data is a broader concept that includes large, complex datasets from various sources. CRM data is a specific subset of data focused on customer information for managing and improving customer relationships.

What’s involved in big data management?

Managing big data involves several key components and tasks. Below, we’ll break them down by type.

Data acquisition

  • Data collection: This involves gathering data from various sources, whether the source is structured, semi-structured, unstructured, or real-time streaming data.
  • Data integration: Combining data from multiple sources into a unified format or data warehouse makes for easier analysis.

Data storage

  • Data warehousing: Data warehouses or data lakes are commonly used as they can handle large volumes and various data types.
  • Distributed storage: Using distributed file systems like Hadoop HDFS or cloud-based storage solutions also assists in handling massive volumes of data.

Data processing

  • Data cleaning: Regularly identifying and correcting errors, inconsistencies, and duplicates is crucial for maintaining data integrity.
  • Data transformation: This involves converting data into a suitable format for analysis, often involving data normalization and enrichment.
  • Batch processing: Helps with analyzing large datasets at scheduled intervals.
  • Real-time processing: Analyzing data as it is generated allows for immediate insights and actions.

Data analysis

  • Data mining: Employ algorithms to discover patterns, correlations, and insights within the data.
  • AI/ML: Leverage artificial intelligence and machine learning techniques for predictive analytics and decision support.
  • Statistical analysis: Use statistical methods to gain insights and make inferences from the data.
  • Visualization: Create visual representations of data to make it more understandable and actionable.

Data security

  • Data encryption: This protects data at rest and in transit to ensure it’s not vulnerable to unauthorized access or breaches.
  • Access control: Implement role-based access control to limit who can view and manipulate the data.
  • Compliance: Ensure that your data management practices align with industry-specific regulations and standards, such as GDPR, HIPAA, or PCI DSS.


  • Big data management systems must be scalable to handle growing data volumes and increased processing demands. Scalability can be achieved through distributed computing frameworks and cloud-based solutions.

Data governance

  • Establish policies and procedures for data quality, data ownership, data lineage, and data lifecycle management.
  • Maintain data catalogs and metadata repositories to track and document data assets.

Data lifecycle management

  • Define how data is created, stored, used, and retired over time. This includes data archiving and purging policies.

Data monitoring and maintenance

  • Regularly monitor data quality, system performance, and other data-related issues.
  • Perform regular maintenance, updates, and optimization of data management systems.

Disaster recovery and backup

  • Implement robust backup and disaster recovery strategies to ensure data availability in case of system failures or data loss.

Cost management

  • Optimizing infrastructure and resources to manage the costs associated with storing and processing large datasets.

9 best practices for managing big data

Now that you’re aware of all the steps involved, it’s important to realize that effective management is essential for organizations looking to leverage big data for strategic decision-making. Below are some recommended best practices.

1. Conduct thorough data profiling

Data profiling is the analysis of data to gain insights into its structure, quality, and characteristics. This helps users understand the data before processing or analyzing it.

Conduct thorough data profiling to identify data anomalies, outliers, missing values, and data distributions. This can help in making informed decisions about data preprocessing and analysis.

2. Dedupe your data

Deduplication (or deduping) is the process of identifying and removing duplicate records or entries from a dataset. Duplicate data can skew analysis and lead to crippling inaccuracies.

Implement deduplication techniques to ensure data quality and accuracy. This may involve using algorithms to identify duplicates and then deciding whether to merge or remove them. (This process can be made faster and more efficient with a data management platform that has specific dedupe functionality—like Validity DemandTools

3. Maintain good data hygiene

As you can imagine, poor data quality is the #1 killer of business intelligence—which makes regular data cleansing essential. Data cleaning involves the correction of errors, inconsistencies, and inaccuracies in the data. This is essential for maintaining clean data.

Establish a data cleaning process that includes data validation, standardization, and enrichment to ensure that the data is accurate and reliable for analysis.

4. Hire data scientists

Data scientists are professionals who specialize in extracting valuable insights from data using various analytical and statistical techniques.

Invest in hiring skilled data scientists who can design and execute data analysis projects, create predictive models, and generate actionable insights from big data.

5. Establish clear data governance policies

Establish clear data governance policies, including data ownership, data quality standards, and data access controls.

6. Choose scalable infrastructure

Choose scalable infrastructure solutions, such as cloud computing or distributed computing frameworks, to accommodate the growing volume of data.

7. Implement strong security measures 

Implement robust data security measures, including encryption, access control, and compliance with data protection regulations.

8. Regularly monitor and maintain

Regularly monitor data quality and system performance, and perform routine maintenance and updates to keep the data management system efficient and reliable.

9. Manage costs

Continuously optimize infrastructure and resources to manage the costs associated with big data storage and processing.

By following these best practices, organizations can effectively manage their big data assets and leverage them for valuable insights without getting bogged down by the sheer magnitude of their dataset.

How big data management improves customer relationships 

By properly and effectively managing big data, you can significantly enhance customer relationships by providing businesses with a wealth of insights. Here’s how:

1. It helps you understand your customers better

Big data allows businesses to gain a deeper and more nuanced understanding of their customers by analyzing a vast amount of data from various sources. This deeper understanding can lead to more personalized interactions and tailored marketing strategies.

2. It helps you keep a finger on the pulse of your business

When combined with advanced predictive modeling techniques and other tech tools, the insights found in big datasets further empower teams to make data-driven decisions in various aspects of the business. For example:

  • Forecasting––By analyzing historical sales data, web traffic, and other relevant information, businesses can use predictive modeling to forecast future demand, enabling better inventory management and production planning.
  • Competitive analysis––Big data can be used to monitor and analyze competitors’ activities and customer sentiment, providing insights for a competitive advantage.
  • Churn analysis––By analyzing customer behavior and identifying churn indicators, companies can proactively retain at-risk customers through targeted retention strategies.

3. It helps you benchmark your performance

Analysis of big data also enables businesses to benchmark their performance against industry standards and competitors, allowing teams to identify areas where they excel and areas that need improvement.

4. It leads to better decision-making

The insights derived from big data analysis can lead to more informed and strategic decision-making in CRM. Businesses can use these insights to refine their marketing strategies, optimize customer service processes, and tailor their products and services to better meet customer needs.

Make light work of big data management

There’s no getting around it—businesses that get data management right experience better customer relationships, create more effective sales and marketing campaigns, and uncover valuable insights that increase revenue and growth. Fortunately, there are tons of resources and tools at your disposal, whether you’re looking to automate your Salesforce data quality (or other workflows) or turning strategic insights into eye-catching, digestible reports for key stakeholders. 

For more tips on how to avoid the pitfalls of poor data management, check out our cheat sheet, “Surviving the Avalanche of Data.”