Much of today’s data use is focused on how much data we can collect, store, analyze, and derive insights from to help drive our objectives and gain value.
But what happens once this plethora of data is amassed? How can users access the data they need when it is stored across large-scale data storage platforms, both in the cloud and on-premises? Data needs to be accessible and consumable in order to derive insights that inform business decisions.
To break down large data sets into more manageable portions, organizations implement data segmentation. In this blog, we’ll define data segmentation, share why it is important, highlight specific types of data segmentation, and cover its challenges and benefits.
What is Data Segmentation?
Data segmentation is the process of taking large amounts of data and breaking them down into smaller groups based on specific criteria. It is designed specifically to extract relevant data from a larger data set and make it accessible to users who can, in turn, analyze it for targeted purposes.
Gartner defines segmentation as:
“A strategic tool for understanding and targeting audiences. It applies an analytical process to categorize customers into mutually exclusive and collectively exhaustive segments that can then be prioritized according to strategic goals.”
Data segmentation is an especially popular analysis method in marketing, customer service, and sales departments. It provides users within these teams access to relevant information that helps improve communication with existing and prospective customers, better serving their needs.
Types of Data Segmentation
Data segmentation can be carried out in a number of different ways. Some of the most common include:
- Demographic Segmentation: Organizing data based on demographic factors like age, gender, occupation, income, education, etc.
- Geographic Segmentation: Breaking data down by geographic factors like location, region, climate, time zone, population density, etc.
- Behavioral Segmentation: Breaking down user data based on their behavior, including factors such as buying/browsing history, product usage, brand loyalty, etc.
- Temporal Segmentation: Breaking down data based on time-specific factors like day, week, month, season, year, etc.
- Use-Based Segmentation: Dividing data based on product/service usage frequency.
- Value-Based Segmentation: Dividing data based on consumers’ perceived value of the product or service.
Each of these segmentation methods contributes to a more granular understanding of your organization’s key metrics, performance, and overall goal attainment. a unique benefit to the potential data user within an organization.
Why is Data Segmentation Useful?
Think about your last trip to the grocery store. You likely came prepared with a list of items you needed to purchase, and were able to find them using the aisle signage throughout the store. In other words, the segmentation of various grocery items into specific, categorized aisles made your shopping experience easier.
Now imagine this grocery store didn’t have this signage. Instead, it was just one large building filled with groceries, scattered in disorganized piles across the floor. Think you’d be in-and-out as easily as before?
This reflects the usefulness of data segmentation for today’s data-driven teams. Instead of dredging up relevant data from massive data sets, users can access data that is segmented specifically for their required use. A marketing user can access data segmented by geography in order to evaluate a campaign run in a specific region, while a salesperson could use demographically segmented data to make sure they’re reaching out to the right customer profile.
Benefits & Challenges of Data Segmentation
Like any method of data analysis, segmentation comes with a range of benefits and challenges.
Benefits of Data Segmentation
One major benefit of data segmentation is that it helps to streamline and simplify your organization’s data analytics. With data separated into purpose-based categories, users can more quickly access, analyze, and report on the information they need.
Segmentation also helps increase personalization and customization, both for users and customers. Segmented data can be catered to internal teams’ needs, or used for personalized product updates and customer service, increasing product usage and boosting business outcomes.
Lastly, segmentation improves your organization’s cloud data management and data security practices. Similar to the benefits of data products, data segmentation lets the teams that are most familiar with specific types of data maintain the closest relationship to it. These teams can, in turn, manage data more effectively and apply the necessary security and privacy policies to it.
Challenges of Data Segmentation
If a team simply does not have enough data to pull from, it can be difficult to effectively segment it into digestible categories. The same can be said for teams with an excessive amount of data, as segments can pile up, crossover with one another, and become unwieldy.
Similarly, inaccurate or low quality data can be detrimental to segmentation efforts. Without complete or reliable data sets, there’s no telling if segments will be compiled in an accurate, useful manner. Data needs to be of good, accurate quality in order for segmentation to benefit your goals.
Lastly, unchecked segmentation can easily lead to siloed, inaccessible data. If teams segment data for overly specific use cases without visibility from their peers, the insights derived from the segmentation may not benefit as many users as it possibly could.
Ensuring Secure Data Segmentation
How can your team effectively segment your data in a secure and compliant manner?
By applying consistent data access controls and security policies across your data platforms, you can ensure that any segmentation efforts are protected. Leveraging a dynamic data security platform, helps make sure that your data is only accessed by those who have the right to do so – regardless of how it is segmented. These segments can also inform your access control policies, reinforcing which types of users are allowed to see which types of data.
To learn more about breaking down data into useful, secure categories, check out our Data Classification 101 white paper.