The impact of data fragmentation on businesses
Without a consistent and proactive approach to breaking down information silos, organizations can suffer substantial losses. Below, we highlight the negative impact data fragmentation can have on a business – and how to avoid it.
Data quality
Data quality is often measured by its accuracy, completeness, timeliness, accessibility, and consistency. And when data is fragmented, each of these attributes take a hit. First and foremost, the data isn’t complete, meaning teams are only gaining a fraction of the insight they otherwise could from a holistic view.
Second, it’s increasingly more likely that the data will be inaccurate, due to different owners across teams and systems (which can create different tracking plans, naming conventions, etc.).
Third, other teams can’t easily access the data collected by their colleagues, which hinders everyone’s productivity. Non-technical teams may become over-reliant on engineers and analysts to help them pull data from different systems. Or there can be a lack of synchronicity between teams on how to engage with a customer or improve the user experience (e.g., a product team being unaware that one feature in particular is responsible for an uptick in customer support complaints).
Regulatory compliance
When you don’t have complete visibility into what data is being tracked, where it’s being stored, and who has access to it, you’re at serious risk of being non-compliant with privacy laws and regulations.
This is no small thing: non-compliance can result in massive fines and lasting damage to your brand reputation.
For example, let’s say a user within the EU requests to have their personal information deleted from your databases. You may delete their record from one system, but not realize their information is still stored in a separate data set. This is a violation of the GDPR, even if it was unintentional.
On top of that, there are data security risks. If you’re storing highly sensitive information in a system that anyone in your business can access, you can be opening yourself up to data breaches and theft.
Time and talent
Data fragmentation can be a huge waste of time. Breaking down silos, building ETL pipelines, manually exporting data for reports or campaign creation – it can get time-consuming and repetitive. Of course, there will be things your business will want to build in house. But there are also times when it makes more sense to automate these workflows.
A great example of this is with Univision, a leading Spanish-language media in the U.S. By using Twilio Segment, they were able to connect to 194 sources and destinations in just six months, currently tracking over 400 million events a day. With Twilio Engage, they were then able to unify this data into real-time customer profiles to personalize program recommendations on their streaming platform, ViX – successfully increasing their streaming hours and monthly engagement rates.
Growth insights
The value of data works a lot like compound interest. It has the potential to pay big dividends if you have the right framework in place.
Growth hacking has become a popular phrase to describe agile, cross-functional teams that are focused on fast and cost-effective acquisition and retention strategies. Fueling these growth strategies is often an iterative cycle of experimentation, using insights to keep learning at a rapid rate.
When trying to gain growth insights, there are a few metrics worth tracking – from conversion rates, to churn rates, and annual recurring revenue. But if data is fragmented, not every team will have access to all these metrics, making it difficult to get a clear sense of what is going on. It leads to ill-informed experiments that aren’t taking into account the full user experience, and makes it more difficult to activate data if it’s trapped within silos.
The financial implications of relying on fragmented data
Poor data hygiene costs organizations roughly $12.9 million a year.
Data is what gives businesses the ability to forecast customer lifetime values, understand the most cost-efficient acquisition channels, or even predict certain customer behaviors (like churn, or the best time to ask for a referral). In short: when you’re working off incomplete data, your ability to make strategic decisions (that turn a profit) is severely limited. Or as Melody Chien, a Senior Director Analyst at Gartner said, “Data quality is directly linked to the quality of decision making.”
Data fragmentation remedies: data governance and integration
The natural solution to data fragmentation boils down to proper data governance and integration.
Data governance refers to the policies and best practices an organization creates to properly (and efficiently) manage their data. Data governance helps create internal alignment around what data is being tracked, where it’s being stored, and how it’s being used. It helps pinpoint relevant stakeholders, think through important factors like maintaining security and data privacy, and helps proactively prevent data silos with proper planning.
Data integration is the process of combining data into a single repository. There are several different methods of data integration, like using middleware, APIs, ETL or ELT pipelines, or uniform access integration.
How Segment unifies your fragmented data
Defragmenting your data doesn’t have to be a daunting task. With Segment’s CDP, businesses have been able to fully integrate their tech stack, and ensure data consolidation and cleanliness at scale.
Connections: Integration capabilities to eliminate data silos
Connections offers hundreds of pre-built integrations (along with ability to create custom ones) to ensure an integrated tech stack. With just a few lines of code, businesses can connect all their data sources and destinations, ranging from data warehouses, to business intelligence tools, and marketing automation software.
You can see a full list of all Segment integrations here.
Protocols: Maintaining and enhancing data quality
Protocols offers automated data governance to ensure high-quality data at scale.
Protocols is able to enforce a company’s universal tracking plan to avoid errors like duplicate data entries or inconsistent naming conventions. It’s also able to proactively flag bad data before it reaches downstream destinations. Stakeholders can then review these violations to pinpoint the root cause of the error, and even apply Transformations to correct bad data as it flows through Segment.
