Data Integration: How B2B Businesses Can Break Down Data Silos To Accelerate Growth

Data silos are a challenge for virtually every business today. And yet, those silos persist, limiting a team’s ability to revolutionize business operations with data.?

Entrepreneur estimates businesses deal with more than 2,000 silos of information, each gatekeeping key customer data, and roadblocking revenue operations like ICP creation and segmentation.

The reality is, these challenges aren't going away by themselves. Unlocking data’s full potential requires that businesses address silos across their tech stack head-on.?

Today, data-rich businesses need a better approach to data silos for superior operational efficiency, more insightful decision-making, and ultimately, a stronger bottom line.

Understanding the current data landscape

Data silos aren't merely the result of a few reluctant teams hoarding data. Large companies, on average, use more than 360 software tools across teams according to Forrester Consulting, with each tool using, producing, and storing its own set of data.

A report by Treasure Data suggests one of the biggest challenges data-led teams deal with is how difficult it is to access siloed data.?

Forrester’s joint study with Airtable found employees are losing as many as 12 hours a week thanks to siloed data. Moreover, about 79% of respondents cited teams across their organization are siloed, taking away key visibility needed for cross-functional campaigns.?

The challenge is clear: data silos are an issue embedded in the DNA of how businesses manage and perceive data. Consider a typical mid-size B2B company—the sales team might be using Salesforce as its CRM, while the marketing department uses HubSpot. Simultaneously, the operations team uses an ERP system, and customer support is managing tickets on Zendesk.?

Without any connection or communication between these applications, they become individual data silos, isolating critical information and blocking marketing insights, and negatively impacting revenue.

And it doesn’t end there. Moving and interpreting data is resource intensive. Currently, it’s common for organizations to use a data-requesting system where teams must submit a ticket and wait to get data. Once the data is received, they then need to assess the validity and usability of that data—leading to wasted time and human resource costs that accumulate quickly.?

Identifying the data silo challenges

Addressing the issue of data silos involves more than simply integrating several data systems with APIs and ETL tools. Instead, it's a multi-faceted challenge that requires a detailed understanding of your organization's data landscape.

It takes considering three overarching themes:?

1. A variety of technical systems and platforms

While there are more data management tools and platforms than ever, counterintuitively, it’s becoming more difficult to unify, manage, and maintain data for go-to-market insights and best-in-class customer experiences. However, solving these issues starts with tech at the core.?

Diverse systems and platforms: Most businesses use a wide array of software applications tailored to specific departmental needs, like CRMs, DMPs, CDPs, ERPs, marketing automation platforms, or even customer support systems. These systems may not be inherently designed to communicate with each other.

Different data formats: Systems today house data in a myriad of formats, each serving specific needs and use cases. This diversity, while beneficial in isolated systems, becomes a hurdle when trying to bring data together cohesively.?

• JSON (JavaScript Object Notation): Commonly used for web applications due to its lightweight nature and easy readability. It's often the go-to for RESTful APIs, which many modern web services utilize. While JSON excels in transporting data between a server and a web application, it's not always the preferred storage format for large-scale databases.
• XML (Extensible Markup Language): Unlike JSON, XML is more verbose and allows for complex document structures. It's been around longer and has robust support for data interchange, especially in environments like SOAP-based web services. However, parsing XML can be more resource-intensive than JSON.
• Relational Databases: Systems like MySQL, PostgreSQL, or SQL Server use structured query language (SQL) to store and retrieve data. The data here is usually in tables, which are joined and queried in various ways.
• NoSQL Databases: Databases like MongoDB, Cassandra, or Couchbase store data in non-relational formats. The exact format can vary - document-based, column-oriented, graph, or key-value pairs.

When integrating data across these diverse formats, one often needs tools or middleware that translate and map data from one format to another. Additionally, issues like schema conflicts, where two systems define a data item differently, need to be resolved.?

It’s the reason why the integration process isn't just about connecting systems—it's about translating, transforming, and ensuring data consistency across them.

Inconsistent data access and retrieval: While some platforms offer seamless data extraction, others might be restrictive or require specialized methods to access the data. For instance, one system might readily provide data through APIs, while another might necessitate manual exports or proprietary connectors. This inconsistency makes the data integration process more difficult.?

2. Cultural and organizational barriers

Data silos aren’t just a technical issue—they’re a human issue, which is why the cultural and human resource aspect of any data integration initiative cannot be overlooked.?

Departmental division: Different departments or teams may be protective of their data, viewing it as "their domain." This leads to reluctance in sharing or centralizing data.

Lack of data literacy and training: Employees might not understand the benefits of integrated data or may lack the training to work within an integrated system.?

Resistance to change: Established processes and workflows lead to inertia. Even if a new system promises efficiency, there's often resistance due to the learning curve involved.

3. Governance and compliance concerns

Data security and privacy: With increasing regulations like GDPR and CCPA, there's a heightened focus on data protection. Integrating data sources might be viewed as a risk if not done securely.

Data quality and integrity: The fear that integrating data might lead to loss or corruption can be a barrier. Ensuring data accuracy and consistency across integrated platforms is crucial.

Regulatory compliance: Especially in industries like healthcare and finance, where data handling is tightly regulated, there might be concerns about how data integration affects compliance.

Addressing these challenges requires a combination of technological solutions, organizational change management, and robust data governance protocols.?

Solving for a variety of technical systems and platforms

Data is notoriously difficult to move. And it doesn’t help that the applications in a vast digital ecosystem weren't created with the intent of seamless intercommunication, leading to costly data gaps and operational inefficiencies.

Data integration is the game-changer here—acting as a bridge that links these diverse platforms. By applying tailored connectors and API integrations, a seamless flow of data between systems becomes achievable. This interconnectedness ensures every department works from one data-informed business perspective, maximizing revenue potential and minimizing data lapses.

When information moves between systems with the help of automation, as well as clear governance protocols, and a data-first culture, it's reshaped into a format the receiving system understands. As a result, businesses can trust that their data remains accurate, consistent, and usable, no matter where it's sourced from or where it's headed.

Eliminating cultural and organizational barriers

The issue of data silos isn't just about mismatched systems; it's deeply rooted in people and processes. The way individuals and teams embrace data literacy and manage data is often the biggest hurdle data leaders need to clear. A study by IDC and Collibra highlight these findings.

“This is reflective of many other IDC studies in which we have identified a lack of adoption of data intelligence as being a significant inhibitor to realizing the true value of data intelligence.”

Picture a team that has spent years crafting its database. To them, this database is more than just numbers; it's a treasure trove of insights and hard work. They might view their data—as well as how they manage it—as exclusive, an asset that sets them apart. So, when the company proposes an integrated data system, it's not surprising that they might hesitate and resist change. They fear losing control over "their" information.

But what if the team simply doesn't grasp the big picture? Maybe they don't see how merging their data leads to broader, more actionable insights. For example, combining marketing data with sales figures could reveal which campaigns drive the most profitable sales.?

This is where introducing a culture of data literacy is highly relevant. Training sessions, showcasing real-world use cases and the benefits of integrated data convert skeptics into believers.?

It’s important to remember that apprehension to change is natural. After all, it's more than just a new data system being proposed; it's a change in each team’s daily routine. Leadership has to provide ample top-down support during this transition, emphasizing long-term gains over the short-term learning phase. The more data leaders guide by example in valuing the significance of data, the more manageable the transition to integrated data becomes.?

Applying sound governance and compliance measures

The significance of data governance and security can't be overstated. New laws, like Europe's GDPR and California's CCPA, highlight the need to protect personal information.?

The multitude of benefits—though more difficult to quantify—speak to the importance of investing in scalable governance practices. These include:?

• Cost savings: A reduction in redundant data storage and processing costs and decreases errors that require costly remediation.
• Operational efficiency: Streamlined data workflows and reduced time spent searching for or validating data as well as faster decision-making with access to readily available and trusted data.
• Risk mitigation: Lowered risk of non-compliance fines and sanctions (e.g., GDPR, CCPA) as well as reduced exposure to data breaches or misuse.
• Enhanced data quality: Improved accuracy, consistency, and reliability of data across the organization.
• Increased revenue opportunities: Data-driven decision-making leading to better market strategies. An improved customer experience due to better data insights, potentially leading to increased sales or retention.
• Stakeholder confidence: Strengthened trust from investors, partners, and customers due to transparent and effective data handling.
• Future readiness: Easier integration of new technologies or systems with a well-governed data infrastructure. The agility to respond to future regulatory changes or industry shifts.

Introducing data governance as a way to eliminate silos from different departments can prove tricky. Think of a SaaS company that works with a financial firm as one of its clients. The integration now has an added layer of industry-specific compliance issues. The financial data, combined with CRM data, needs to be treated with extreme care, ensuring that no financial forecasts or sensitive transactions are accidentally disclosed in a marketing communication.

Data governance is the gatekeeper that ensures data management practices adhere to internal policies while upholding external regulations. In our SaaS company example, before integrating CRM with a marketing platform, a data governance strategy would dictate who has permission to execute the integration, how the data is mapped and validated, and how to handle any discrepancies that arise.?

To begin, assemble a data governance team. It’s crucial to document and establish best-practice standards and procedures. Introducing data governance tools is an additional building block that helps organizations break down silos and keep them that way.

Thankfully, with the rising popularity of cloud-based governance services, it’s becoming more accessible to organizations across industries.

Eliminating data silos requires tech, culture, and governance

Doing away with costly data silos isn’t just a technical task—culture and process play into it just as much. Effective tech stacks serve as the backbone, streamlining operations and enhancing data capabilities. Strong governance protocols ensure data security, quality, and compliance. Establishing a non-siloed culture maintains your efforts long-term.

Harmonizing technology, culture, and governance sets businesses up to fully harness the power of their data, drive insights, and stay ahead in a hyper-competitive market.?