Data silos, simply defined, are isolated groups of data. These data sets can be isolated to specific systems and/or to specific teams. Among other issues, data silos are an obstacle for organizations trying to make educated business decisions and create a consistent customer experience; and they’re not at all uncommon.
Only 2% of organizations believe they are effective at sharing data across their company, which makes this is a significant problem for the remaining majority.
These breakdowns in data sharing occur as businesses grow and teams expand. As new software solutions are added and new teams emerge, customer data starts to appear in fragments across multiple solutions. But, how concerned should businesses be about this? Are there ways to eliminate these silos? Let’s discuss.
Why are Data Silos a Concern?
Healthy data should inform every business decision and help teams learn from every mistake. When that crucial information is fragmented, it blinds teams from the true meaning behind their datasets. In addition, as teams continue to work from their own sources of truth, spotting these discrepancies becomes even harder. Data silos should be a concern for every business whose goal is to streamline operations, automate processes, and prioritize customers. Here are a few of the major issues that result from siloed data:
- An incomplete view of the business and its customers
- Multiple solutions working against each other, instead of together, wastes money
- Duplicate and incomplete data hinders analytics capacity
- Time is wasted fumbling through multiple systems to manually collect data
- Operational efficiency suffers and results in poor customer experiences
Breaking down the barriers of data silos is a productive way to move past these obstacles and gain true perspective on your business through data. So, how do we do that? In the next section, we’ll highlight three actions you can take to eliminate data silos from your organization.
3 Ways to Eliminate Data Silos
#1 Integrate Systems
One of the most common ways to eliminate data silos is to integrate solutions together. Sharing data through integration not only helps eliminate silos, but it also allows businesses to recognize and fix the issues that occurred because of those silos. Teams can see where their data sets are misaligned and fill in the gaps to build complete customer profiles. Reports generated from the data offer a more accurate model for sales, marketing, and customer service to work from, pulling in customer insights from every touchpoint.
Before you undertake any data integration project, you must clean your data. Integration with invalid, incomplete, and outdated information will add to your data silo woes, not fix them. Removing all the junk data before connecting systems ensures the analytics and insights produced by the integration are accurate.
Another important aspect of your integration (and most especially important if you’re trying to reduce data silos) is defining a single source of truth. Your single source of truth is whichever system your team agrees to designate as the primary source of trusted data. For example, if your team agrees the CRM should be the single source of truth, then any time there’s a discrepancy of information in another system, the CRM will be referenced to find the correct answer. For the plan to work though, everyone must be diligent about entering quality information into the CRM and using best practices to maintain clean, healthy data. You can use a master data management strategy to set guidelines for this.
#2 Centralize Your Data
Another great way to crush data silos is to aggregate your data into a data warehouse or data lake. Migrating all your data to a central repository like these has a few benefits: data is accessible in one location, it consolidates information from disparate sources, it protects sensitive data with powerful security protocols, and it cuts down on time-to-insights for your IT staff.
The question you must answer is whether a data lake or data warehouse is best for your business needs. There are key differences between the two. Data lakes house data in its raw form so users can access it for whatever purposes they need. It’s a pool of information, swimming around with no sense of purpose until you designate one. Data lakes are very quick to update and easy to garner big data analytics from. The potential downsides to data lakes are that the raw data can be difficult for the “average” user to understand. Oftentimes a data scientist must translate the information for specific business purposes. In addition, if the data lake is instituted without clear data quality and governance measures, it can easily mutate from a lake to a swamp, pushing your dreams of data silo management right out the window.
On the flip side, a data warehouse is made up of processed data and is more structured than a data lake. Instead of the information swimming around a metaphorical lake, that data has already been filtered through and categorized for specific purposes within the organization. This makes it easier for the average user to understand what they’re looking at and translate the information into charts, spreadsheets, etc. Data warehouses require less storage capacity because they’re only housing processed, relevant data, which makes them a tempting option for many businesses. The downside of data warehouses is that they’re more complicated to manage and more costly to make changes to.
#3 Unite Managers
Whether you decide to integrate or use data lakes and warehouses to reduce silos, getting buy-in from teams is the most important step to ensuring data silos are eliminated in the long term. To do this, you must start at the top. Manager and team leaders must set the tone for collaboration going forward. It’s important that they communicate why silos are such a danger to the company and how they affect not only data integrity, but the ability of the company to compete in the market.
Encouraging teams to collaborate more openly with their coworkers, follow data management best practices, and build broader data transparency across departments is also on the shoulders of management. It is their responsibility to set expectations for their teams and help them understand the impact that data silos will have on the entire organization. Creating the shift in the company culture will not occur overnight, but if management shows its commitment to these principles, the teams will follow suit.