Data analysts who inspect large sets of information often prefer to work with simpler data.
Data granularity, which is the concept of dividing data into sub-fields, is a common practice among data analysts and other similar professionals.
Understanding what data granularity is can help retail companies collect and analyze data more easily.
Definition of Data Granularity
In a data warehouse, data granularity is the level of detail in a model or decision making process. It tells you how detailed your data is: Lower levels of detail equals finer, more detailed, data granularity. Finer, more granulated data allows data analysts to perform more precise data analysis.
For example, time-series data for sales volume can be measured in years, months, weeks, or days (with days as the lowest level of granularity). Performing data analysis on the more granular daily data will result in better insights on sales than yearly data.
The level of data granularity determines what analysis can be performed on the data, and whether results from that analysis lead to appropriate conclusions.
If data is not granular, such as a name or address field being saved as a whole, then it is very difficult for analysts to mine and analyze data because they are in large chunks. Granular data can be easily merged with data from external sources and can be effectively integrated and managed.
What does Data Integrity mean?
Data integrity is the overall completeness, accuracy and consistency of data over its entire lifecycle.
When data has integrity, mechanisms have been put in place to ensure that data-in-use, data-in-transit and data-at-rest cannot be changed by an unauthorized person or program. An important goal of maintaining data integrity is to make sure that when data is recovered after a disruption, it can be trusted.
Levels of data granularity:
- Low Granularity: When data is collected at a coarse level, it provides a broader overview but lacks specificity. For example, in retail sales reporting, low granularity might involve tracking total daily sales across all products.
- Medium Granularity: At this level, data is more detailed than low granularity but not as fine-grained as high granularity. Continuing with the retail example, medium granularity could involve daily sales reports for product categories.
- High Granularity: High granularity means collecting data at a very detailed level. For retail, this could involve tracking sales for individual products on an hourly basis, recording each transaction’s specific details.
Uses of data granularity
Data Granularity in segmentation and targeting
When it comes to segmentation and targeting, granularity determines the level of detail used to separate members of a target audience or customer base. The more granular the segmentation, the narrower the criteria are for each customer segment.
Highly granular segmentation splits an audience into segments or groups with a number of different variables. This may include factors like location, purchase frequency, loyalty point score, age, etc.
Alternatively it can also split an audience into many separate groups along one variable, for example average monthly spend in $50 increments.
The best results usually come from segmentation that is somewhere in the middle of the granularity scale. This gives precise segments, but not too precise to be ineffective when it comes to customizing your marketing efforts. Next to granular segmentation, granular targeting simply aims marketing efforts or promotions towards a specific audience segment. The more granular the segmentation, the more criteria are used to separate customers.
Data granularity in reporting and analysis
Data granularity in reporting and analysis refers to the level of detail or specificity at which data is collected, stored, and analyzed. It involves deciding how finely data is categorized and recorded.
The choice of data granularity has a significant impact on the insights that can be derived from the data and the resources required for analysis.
Data granularity in ordering inventory
Data granularity is common among data professionals who manage retail companies’ inventory levels.
They can determine which items to purchase based on different data levels, such as a product’s line item or purchase order number.
Data granularity in collecting survey results
Data granularity is also useful for facilitating the collection of qualitative data.
If a company issues surveys that ask for open-ended responses to its clients, the clients are likely to produce different answers.
Dividing a survey into several different parts is the best way to introduce some consistency to information that may vary greatly between each respondent.
What are the benefits of data granularity
Enhanced customer insights:
Granular data allows retailers to gain deeper insights into customer behavior. By tracking individual purchase histories, preferences, and interactions, retailers can personalize marketing efforts, provide tailored product recommendations, and improve customer satisfaction, ultimately leading to increased sales and customer loyalty.
Improved operational efficiency:
Granular data can streamline various operational processes. For example, it helps in optimizing supply chain logistics, managing employee performance, and tracking the effectiveness of marketing campaigns.
By making data-driven decisions, retailers can reduce costs, enhance productivity, and allocate resources more efficiently.
Better supplier relationships:
Retailers depend on their suppliers to provide products reliably and at competitive prices.
Granular data on supplier performance, such as delivery times, quality, and pricing negotiations, enables retailers to make informed decisions about supplier relationships. This can lead to better negotiation outcomes, reduced supply chain disruptions, and improved overall supplier partnerships.
Accurate demand forecasting:
Granular data aids in accurate demand forecasting.
By analyzing historical sales data at a detailed level, retailers can identify trends and seasonality patterns for individual products or categories.
This information helps in anticipating demand fluctuations, adjusting inventory levels accordingly, and ensuring that products are available to meet customer needs.
Optimized inventory management:
Detailed data on individual product attributes, such as shelf location, supplier information, and historical sales data, enables retailers to optimize inventory management.
This includes reducing overstock and understock situations, minimizing losses due to expired or damaged goods, and ensuring products are always available when customers demand them.
In summary, data granularity empowers enterprise retail companies to make informed decisions across various aspects of their operations, from customer engagement to inventory management, supply chain optimization, and beyond. It ultimately translates into improved profitability, customer satisfaction, and competitive advantage in the retail industry.
What are the limitations of data granularity?
Data granularity is a useful way of collecting and analyzing complex data, but it does have some limitations, such as:
Increased storage costs:
One of the most apparent drawbacks of data granularity is the substantial increase in storage requirements. Storing highly detailed data, especially for large retail enterprises with extensive product catalogs and customer databases, can quickly become expensive. This includes both the cost of physical storage devices and the expenses associated with cloud-based storage solutions.
Moreover, as the volume of granular data grows over time, retailers may need to continuously invest in expanding their storage infrastructure.
Higher memory and processing demands:
Granular data requires more memory and processing power to manage and analyze effectively. When retailers collect and store detailed information, such as individual customer interactions or extensive product attributes, it places a heavier burden on their computing resources. This can result in slower query response times, longer data processing durations, and a need for more powerful hardware, which can be costly to acquire and maintain.
Complex data management:
Managing granular data can be more complex and resource-intensive.
Retailers must establish robust data governance practices to ensure data accuracy, security, and compliance. This includes implementing data quality checks, access controls, and data encryption measures.
The complexity of managing highly detailed data can lead to increased administrative overhead and potential data management challenges.
Data privacy and security risks:
Granular data, especially customer-specific details, can present heightened privacy and security risks. Retailers must take additional precautions to protect sensitive information, such as personal customer data. The more detailed the data, the greater the potential consequences if a data breach were to occur. Compliance with data protection regulations, like GDPR or CCPA, becomes more intricate, requiring additional resources for compliance efforts.
Data analysis complexity:
While granular data provides valuable insights, it can also make data analysis more complex.
Extracting meaningful information from detailed datasets may require advanced analytical tools and skilled data analysts. Retailers may need to invest in employee training and specialized software to effectively interpret and derive actionable insights from granular data, adding to operational costs.
Increased data transfer and backup overheads:
As data granularity grows, transferring and backing up data becomes more resource-intensive.
This can lead to longer backup windows, increased network traffic, and potentially longer recovery times in the event of data loss or system failures.
Retailers must invest in robust backup and disaster recovery solutions to handle the heightened data demands.
Diminished performance in real-time applications:
For retail operations that rely on real-time data such as point-of-sale systems, excessive data granularity can lead to performance issues.
The additional processing required to handle granular data may result in delays in critical operations, impacting customer service and operational efficiency.
In conclusion, while data granularity provides valuable insights and detailed information for analysis in the retail industry, it comes with drawbacks related to increased storage costs, resource demands, complexity in data management, and heightened privacy and security risks.
Despite these potential drawbacks, most data analysts still choose a high level of data granularity because it allows for optimal flexibility when processing data.
Examples of data granularity
These examples illustrate how increasing data granularity in the retail sector can lead to more informed decision-making and improved business outcomes:
Customer purchase history:
Sarah is a marketing manager at a retail store. She’s interested in improving customer engagement and loyalty.
Before implementing a higher level of data granularity, the store only records basic information about customer transactions, such as the total purchase amount and payment method.
Sarah decides to enhance the data granularity by tracking individual customer purchase histories. Now, the database records each customer’s name, contact information, items purchased, purchase dates, and even their preferences.
This allows the store to send personalized recommendations and offers, ultimately boosting customer retention.
John is an inventory manager at a large retail chain.
He faces challenges in optimizing stock levels due to limited data granularity. Previously, the inventory system only tracked the quantity of items in stock at each store.
To improve efficiency, John enhances data granularity by tracking the shelf location, supplier details, item condition, and historical sales data for each product.
With this detailed information, he can make more accurate predictions, minimize overstocking, and ensure products are always in the right place on the shelves.
Employee performance analysis:
Emily is an HR manager in a retail company. She wants to improve employee productivity and customer service.
Initially, the company only collects basic data on employee performance, such as hours worked and attendance. Emily decides to increase data granularity by tracking individual employee actions.
Now, the system records details like the number of transactions each employee processes, customer feedback, and time spent assisting customers.
With this granular data, Emily can identify top-performing employees, offer targeted training, and reward outstanding service.
David manages the procurement department for a retail chain.
To streamline the supply chain and reduce costs, he needs more granular data on supplier performance. Initially, the company only tracks basic supplier information like contact details and order quantities.
David enhances data granularity by collecting data on delivery times, product quality, and pricing negotiations. With this detailed information, he can negotiate better terms with suppliers, ensure timely deliveries, and maintain strong, mutually beneficial relationships with key suppliers.
Choosing the right level of data granularity is a balancing act. Retailers need to weigh the benefits of detailed insights against the costs and complexity of managing and analyzing fine-grained data. It’s often a compromise between the depth of insights needed and the available resources.
Interested in learning how Loyal Guru can help you harness the power of your retail data?
Loyal Guru is the leading Customer Data Platform for grocers and retailers, helping them collect and activate their omnichannel customer data in a Single Customer View. As a result, our clients can access accurate predictors of customer behavior and quickly improve their marketing and sales efforts with more personalized experience for their customers.
If you are considering upgrading your Customer Data Platform, please get in touch with our team – we’ll be happy to show you examples or talk through your company’s unique challenges.