Data Fragmentation: How to overcome it?
When data becomes fragmented, it means it is stored in separate locations. Processes like data fragmentation may adversely affect your resources, but it’s possible to improve how you handle this type of data. In this article, we discuss what data fragmentation is, the importance of addressing it, what causes it, and how to solve it, along with the benefits of these solutions.
What is Data Fragmentation?
Data fragmentation refers to the dispersion of an organization’s data assets. This is mainly due to the creation of technological silos and the scattering of data. The more data you have from different sources and stored in different spaces, the more likely it is to be scattered. When data is scattered, it is particularly difficult to get a comprehensive view of the available data assets, especially to reconcile them.
To meet the challenges of digital transformation, companies have to gradually evolve their strategy. And because the volume of data that businesses generate is literally exploding, most organizations have opted for private clouds, public clouds, or hybrid clouds. The diversification of information storage naturally has a perverse effect: data siloing. This siloing may prevent companies from having global visibility on information and may lead them to make wrong decisions.
Why is it important?
Data is the most important asset for virtually all organizations. Recently, and with the help of new technologies, businesses have been able to make great strides in collecting, analyzing, organizing, and getting value from their data. Leveraging data strategically is one of the critical drivers of successful digital transformation—which in turn improves productivity, insight, and profits.
Yet, as data grows in different application, storage, geographic, and operational silos as well as in various clouds, teams lose the ability to harness its power and derive full value from it in terms of accurate and meaningful business insights.
This puts businesses at risk of losing competitive advantage. Not only do they fail to monetize their data, but not using it effectively eventually leads to poor customer experience, which directly impacts the bottom line. For these reasons, organizations are working to eliminate mass data fragmentation.
What causes Data Fragmentation?
There is not a single process that leads to data fragmentation. The variability of data operations will often lead to a fragmented data ecosystem:
- Fragmented data stack. As you work with multiple tools (databases, ETL tools, BI tools), each tool will tend to dominate its own piece of the pie. Unless you synchronize them, they will quickly deviate from one another.
- Data silos. Each department and team working with data will have their own interests at heart. For instance, when Marketing is counting new customers, they will look at the user’s first purchase data on the website. When Sales will count new customers, they will look at the first contact they had with a customer. Unless someone unified the two definitions, you can quickly have two conflicting metrics (and double count some customers who talked to sales AND purchased online).
- Engineering practices. It is easier to set up separate development, testing, production, and analytical servers (to appease the different technical teams and their data use cases) than to make sure they are all synced with each other.
How to solve it
There are many ways to solve data fragmentation, depending on what operations you can implement. Creating a process that works for you may involve using some or all of the following strategies :
Organize your data infrastructure
Companies might have multiple programs and systems collecting, storing, and analyzing their data throughout different departments. Your company may have implemented these systems at different times. You may also use programs from different brands, which can make it difficult to share data. Sometimes these systems are a necessary element of business, but they can lead to data fragmentation.
Examine what parts of your data infrastructure you can organize, combine or eliminate. Consider if it’s possible to implement pathways between the systems. Organizing your infrastructure into one system that communicates with its different programs can save time and storage space.
After you’ve examined your data infrastructure, you may find that there are duplicates of information in your servers from the development or testing of different systems and databases. You may also notice that there are multiple copies created from recovering corrupted or deleted data. Maybe these copies were necessary when you created them, but you can delete them after they serve their purpose. When you’re reorganizing your data infrastructure, notice how many data copies you have on your servers and determine which ones are necessary.
Refine cloud usage
The cloud is a term for software and services that run on the Internet, including storage and data management programs. The cloud has created a lot of agility and accessibility for businesses by allowing their employees to access data from more locations. However, some companies may not be using this technology to its full potential, resulting in duplicated data and wasted space. Many companies have multiple clouds, separated by department or purpose, which can further isolate data.
These different cloud accounts can create data silos that separate your data and make it hard to access. However, they can also help you organize your data infrastructure. Consider establishing a single cloud management system. This can help you visualize your data and organize it amongst its various locations for maximum efficiency and minimize duplication.
Challenges related to Data Fragmentation
Fighting against data fragmentation must be a priority for several reasons…
First of all, data fragmentation degrades the project of developing a true data culture in a company.
Secondly, data fragmentation indirectly distorts the knowledge enterprises have about their customers, products, or ecosystem because it limits their field of vision. Moreover, data fragmentation strongly impacts storage costs: keeping large volumes of data that are poorly or not exploited is quite costly!
Finally, data fragmentation exposes companies to another major risk: with the proliferation of data from various sources, fragmented and unstructured data multiplies.
If left unchecked, the management of this data can affect business operations, slow down data processes, or worse, increase the risks associated with sensitive data.