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.
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.
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.
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.
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.
Managing big data involves several key components and tasks. Below, we’ll break them down by type.
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.
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.
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.
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.
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.
Establish clear data governance policies, including data ownership, data quality standards, and data access controls.
Choose scalable infrastructure solutions, such as cloud computing or distributed computing frameworks, to accommodate the growing volume of data.
Implement robust data security measures, including encryption, access control, and compliance with data protection regulations.
Regularly monitor data quality and system performance, and perform routine maintenance and updates to keep the data management system efficient and reliable.
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.
By properly and effectively managing big data, you can significantly enhance customer relationships by providing businesses with a wealth of insights. Here’s how:
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.
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:
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.
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.
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.”