One of the most frequent mistakes we see in the world of data and analytics is the failure to declare the data grain in fact tables at the start of the design process. The project is in danger of failing if the grain isn’t defined clearly because mistakes can be quickly introduced into the design. The table’s meaning is also established by the declaration of the data grains.
Why is data granularity important?
Data granularity is crucial because it enables data analysts and other experts to examine information in greater detail. Data granularity can be used to more easily segment complex data so that analysts don’t have to deal with it all being in one field. Professionals can combine and separate granular data to meet the needs of their particular circumstances. They can also effectively manage and integrate it by combining it with data from other outside sources.
What is data granularity?
The smallest level of detail that is possible within a data collection is called data granularity. Because there are no subdivisions, data that is present in a single line or field within a database or data warehouse has coarse granularity. A database or data warehouse that contains information across multiple lines or fields has fine granularity because it contains various subdivisions.
Uses of data granularity
Here are some common uses of data granularity:
Ordering inventory
Data professionals who manage company inventory levels frequently use granular data. Various data levels, such as a product line item or purchase order number, may be used to decide which items to purchase. They could even place custom orders by taking the data’s level of detail configuration into consideration.
Managing time-series data
Data that a data analyst obtains by logging consistent measurements over a predetermined time period is known as time-series data. Everything from predicting weather patterns to comprehending the effectiveness of medical devices can benefit from this information. Professionals who want to divide these time-dependent measurements into particular time frames, such as hours, days, weeks, months, or years, can benefit from data granularity.
Collecting survey results
The ability to collect qualitative data more easily is another benefit of data granularity. When a business sends its customers surveys with open-ended questions, the customers are likely to provide a range of responses. The most sensible way to introduce some consistency to information that may differ greatly between each respondent is to divide a survey into several different sections.
Does data granularity present any limitations?
Although collecting and analyzing complex data with greater granularity is a useful method, it does have some drawbacks. For example, higher levels of granularity require more computing resources. Additionally, a database or data warehouse may need to be expanded in terms of memory and storage space.
When a company commits to maintaining a high level of data granularity, data input and storage typically account for a larger portion of operating costs. More methods and objects within an object-oriented programming model could result from this. For situations involving procedural programming and parallel computing, more subroutine calls might be necessary. Most data analysts still choose to achieve a high level of data granularity despite these potential disadvantages because it provides the greatest flexibility when processing data.
Examples of data granularity
Here are some examples of data granularity:
Example 1
Here is an illustration of a marketer who asks for their clients’ names on a survey:
John is a marketer who is attempting to learn more about his intended audience. For each of his clients, he makes a single section of a survey where they can enter their names. When he receives the survey results, he discovers that they are difficult to interpret because the clients’ responses are given in a variety of formats, such as “Garrett Wilder,” “Dr Brown” and “Mrs. Jane Dickens. “.
John wants to send the same survey to a fresh group of clients. He redesigns the survey to include multiple fields for each part of the clients’ names in order to obtain more reliable results. The client’s preferred title, full first name, and full last name are the three fields that must be filled out in the survey. Additionally, he provides a space for each client to enter their middle name if they so choose.
Example 2
Here is an illustration of a data professional using the idea of data granularity in the medical industry:
Melissa is a data expert who assists product designers in maximizing cutting-edge medical technologies. Her team is developing several gadgets, including an insulin tracker for diabetic patients. The team’s initial plan is for the insulin tracker to send hourly updates to the patient’s family about their blood sugar, insulin, blood pressure, and other vital signs. This invention informs relatives of diabetic patients when they should consume sugar or seek other medical help.
Melissa and her team decide that this one-hour period doesn’t give family members enough notice after hearing from medical professionals. Data granularity is a concept that Melissa and her team want to implement so that the device sends information every 15 minutes rather than every hour. Patients’ families benefit from more accurate and up-to-date data and can assist their loved ones more effectively thanks to this finer level of granularity.
Example 3
Heres an example of data granularity in the retail environment:
Michael is an expert in data and works in the retail sector. He wants to make it simpler for store managers to track sales of particular items. It is only possible to track each transaction that happens until he implements a finer level of data granularity.
Given that some transactions involve dozens or even hundreds of items, it is difficult to analyze sales trends as a result. He modifies the database’s operations so that it can now track a particular product in a particular sale. To enable this, the database stores details such as the item code, transaction store reference, transaction till reference, and transaction date and time.
06 Understanding Granularity
FAQ
What is data granularity?
A measure of a data structure’s level of detail is called data granularity. For instance, the level of measurement granularity in time-series data may be based on intervals of years, months, weeks, days, or hours.
What is granularity with example?
The characteristic of an object or activity that determines its granularity is its relative size, scale, level of detail, or depth of penetration. The term refers to the “degree to which a larger entity is divided.” A yard divided into inches, for instance, has finer granularity than a yard divided into feet. “.
Why is data granularity important?
Granular data is detailed data, divided into its lowest level. Because it enables them to break down large amounts of marketing activity so that you can comprehend the smaller components, granularity is important to marketers.
What is high granularity data?
High granularity is what defines data with the greatest precision, so in my scenario Hourly is the highest granularity, followed by Daily, Monthly, and Yearly. Consequently, you will have more data rows the higher the granularity.
