Top 3 Data Integration Methods to Eliminate Data Silos

SaaS tools are meant to increase efficiency, streamline processes, and overall make it easier for companies to compete. They are great equalizers, offering top-notch software technology in packages that even startups and SMBs afford. Without them, the startup revolution we witnessed in the 2010s would probably not be possible.

However, the proliferation of SaaS tools also causes problems for organizations. A Forrester study commissioned by Airtable revealed that companies with more than 20,000 employees “use an average of 367 software apps and systems.” The problems that this sprawl can cause were laid out by a research conducted by Productiv:

• 56 percent of the apps are not managed by IT,
• 97 percent of the IT leaders participating in the study do not have complete visibility into how these apps are used by employees.

Things are not that different on the startup front, either. A typical startup begins its journey with a bunch of SaaS tools and keeps adding to its collection as the business becomes more complex, easily ending up with tens of them in a year. Some of these apps have a limited scope, being used for certain processes and not much else. Therefore, founders need a great many of them to conduct daily operations. Others are abandoned in time as users upgrade to something better. All of these tools act as repositories of data, and unless they talk to each other, the data on one source is not available to other users.

An unlikely culprit

Another factor complicating the situation is the rise of low-code and no-code tools. This huge leap in software development has caused some complications, it seems. Equipped with low-code and no-code platforms, domain experts called citizen developers can create internal tools and solve problems on their own instead of relying on central IT departments. As professionals become familiar with this technology, the pace of software development improves as well.

While companies benefit from the decentralization of app development makes organizations more agile, they have to be wary of the app sprawl it may spur. With no data integration policy in place, every app functions as a data silo and contributes to the fragmented nature of data in companies today. Companies counting on low-code/no-code technology to unlock efficiencies should do this under the guidance of the IT department in order to rein in data silos.

Data silos: How bad are they?

• Data silos undermine the data quality, as isolated clusters of data become outdated, inaccurate, and inconsistent in time. Updating, correcting, and reconciling different data sets is always possible (albeit costly), but good luck finding a volunteer for that.

• Data silos compromise the integrity of an organization’s data by denying people some part of the data produced by others. This leads to duplication of efforts as people unaware of existing data spend extra time and effort to reinvent the wheel, taking away resources that could be used for value-creating activities.

• Teams lacking visibility into each other’s data will find it more difficult to collaborate, which will push them toward pursuing individual goals instead of a shared vision.

• Data silos reduce productivity and cause employee disengagement, as employees spend up to 2.4 hours a day trying to locate the information they need.

• Eventually, a fragmented body of data will hamper data analysis, stifle innovation, and increase the odds that decision-makers will make wrong choices.

How do we tackle data silos?

You should make data searchable and discoverable so that it will be accessible to everyone if you are to eliminate data silos. These two attributes (searchability and discoverability), combined with a data infrastructure that lets data consumers self-serve, can go a long way toward breaking down data silos. There are three concepts that embody these principles:

Data mesh and data fabric (for enterprises)

Data mesh is an analytical approach that regards data silos and data sprawl as a result of top-down data management. It proposes a decentralized data management concept built on three principles:

• 1. Domain-oriented data ownership: With data mesh, the ownership of data is transferred from a central IT department to people in business units who created that data. Data is owned by people who create it and know it better than anyone else, as opposed to a central IT team with little knowledge of operational domains and what users need data for.

• 2. Data-as-a-product: The data mesh framework regards data as a consumable product. People who produce and curate data should keep in mind that it will be consumed by a particular user profile for a particular purpose. This “product thinking” permeates the data creation process and is treated as a priority by data creators. Making data easy to find and consume prevents data silos from forming in the first place.

• 3. Self-serve data infrastructure: Data mesh reduces IT’s role to building and maintaining the data infrastructure. Individuals leverage that infrastructure to find and access the data they need. This arrangement lightens the workload of the central IT department and ensures that business units will be able to carry on with their operations without having to wait for IT involvement.

Data fabric shares the same principles with data mesh but involves a more technology-centered approach to data management. While data mesh suggests a decentralized framework for data access, a data fabric makes data available through APIs or direct connection. This technology-driven method also requires a more centralized data governance under an IT department, unlike the domain-centric approach of data mesh which delegates governance to individual domains.

Data virtualization (for startups and SMBs)

Although startups and SMBs have to deal with data silos at some point, the problem is not as grave as the one enterprises face. These organizations do not need sophisticated approaches like data mesh and data fabric. Considering the scale of the problem and the modest resources they possess, data virtualization offers a good solution.

Data virtualization introduces a semantic layer between data sources and data consumers, providing the latter with a unified view of data. This technique puts an end to the data sprawl because there is no need for copying or moving data during the process. A data consumer using data virtualization pulls in data from different sources, joins with performing ETL, and creates a single view of truth without even knowing where the data was originally located. Thanks to data virtualization, startups, and SMBs do not have to build and maintain pricey data warehouses and employ expensive data teams. This particular technique fits the needs and means of startups and SMBs like a glove.

Peaka’s new version leverages data virtualization to help startups and SMBs break down data silos. Peaka users can bring together data in real time, form a unified view of it, and then send queries and execute analytics. By giving different users and teams visibility into the whole body of data an organization possesses, Peaka promotes collaboration and boosts employee engagement without the need for employing large data teams or needlessly complicated data infrastructure.

Conclusion

Data silos are a nuisance, and a stealthy one at that. The scale of the problem they cause can be hard to gauge, and its ramifications can spread far and wide, undermining everything from daily operations to employee morale to strategic plans.

Thankfully, today, we are better equipped to deal with this problem than we were a few years back. The new concepts like data mesh, data fabric, and data virtualization are more than fancy buzzwords. They are the levers we can use to unlock the full potential of data while eliminating the headache data silos pose.