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In a recent BCG study they found that 50% of data that companies store is dark data. As such, executives will have difficulty making effective data-driven decisions. Customer teams cannot deliver outstanding customer experiences. Without data-driven insights, the entire organization misses opportunities for innovation and growth.
Let’s explore why data silos are so problematic and how a more open, scalable alternative can empower a transformational, data-driven business culture.
Published July 31, 2023
Data silos are partially or wholly inaccessible data sets that result from a combination of technical and cultural forces. Proprietary databases and legacy systems may lack modern APIs to streamline data access.
However, corporate culture is the primary driver of enterprise data fragmentation. Domains build data storage to meet their business needs without considering the broader company.
Companies cannot thrive in a dynamic business ecosystem with a fragmented data infrastructure. Consider these six reasons why data silos make companies uncompetitive.
Company data becomes patchy, inconsistent, and duplicative when different departments build data silos independently. Certain data may be readily available, while another data set takes extra effort to reach, and a third is completely invisible beyond its business domain.
Even when data teams can tap into data silos, data structures will vary from one department to the next. Each department may apply standards for definitions, formats, and quality to its systems. However, that internal consistency does not translate across organizational structures.
Data silos magnify the duplicate data problem. For example, departments collect identical customer information without a shared source to draw from. This duplication raises questions about data quality and currency.
Letting business units build data platforms independently undermines data governance.
Complying with data privacy regulations is particularly problematic. Without a single source of truth to reference, each business unit must collect personally identifiable information (PII). This duplication makes it more difficult to erase or correct PII in compliance with Europe’s General Data Privacy Regulation (GDPR).
Data silos also increase the risk that security breaches will compromise protected data. Storing the same data in multiple places increases an organization’s attack surface. The risks increase when each business unit applies different security policies to data repositories.
Information silos lock data within different departments and create inefficiencies in data management.
Using partially-siloed data requires additional time and resources from over-burdened data teams. Data engineers must develop custom ETL pipelines to integrate data from different systems. Inconsistent data structures between sources require extended testing cycles before pipelines can provision data to end users.
That assumes, of course, that anybody knows the data exists. Many silos are inaccessible to the point of invisibility. Siloed organizations spend more on data projects to acquire data that already exists elsewhere in the company.
Slow-moving business cultures constrained by data silos are at a competitive disadvantage relative to their agile, data-driven peers.
Analytics are more complex and time-consuming. Exploration and discovery require additional checks to confirm the accuracy, currency, and context of data buried in silos. Then, end users must wait for pipeline development before they can begin their analysis.
Collaboration between departments is more challenging without consistent data sharing. Something as straightforward as “revenue” can mean different things to teams operating in separate silos. Stakeholders must agree on things as basic as definitions before they can begin any collaboration.
Protracted analysis and weak collaboration extend time to insight, making the company less prepared to leverage opportunities in its data.
Effective decision-making requires consensus based on rapid access to shared insights. Data silos interrupt this process. Analyses based on inconsistent and incomplete data lead to poor insights. Disagreements between teams using different data delays consensus — or worse, force business leaders to choose between opposing recommendations based on flawed data.
As a result, data silos slow a company’s management processes and produce less effective business decisions.
Data silos are both a symptom and a cause of dysfunctional company cultures. When kingdom-building dominates management thinking, business domains look out for themselves. They develop systems to meet their needs and do not share information willingly.
Not only is a data silo problem inevitable, but it also generates a feedback loop that reinforces dysfunction by undermining collaboration and consensus-building.
Data silos impact every aspect of the business. They blind decision-makers to insights and promote inefficiencies in daily operations. Let’s consider how poor information sharing impacts the customer experience.
Customers expect a consistent experience, whether dealing with a company’s sales team, e-commerce sites, or customer service reps.
Without the holistic view that shared data provides, however, each channel’s profile of those customers will be incomplete or inaccurate in its own way.
For example, an address change supplied to one channel should apply to all channels. The company appears disorganized when the customer must correct it again.
Combining site visits, demographics, purchase history, and other customer data can yield insights into a customer’s purchasing intent. These insights feed interactions during the customer journey that can lead to upselling and cross-selling opportunities.
Data silos block these insights, reducing the revenue potential of each sale and sending unfulfilled customers to competitors.
Customer communications, marketing campaigns, pre-sales touchpoints, customer service calls, and other interactions must reinforce a unified image to build brand loyalty. Marketing teams need a holistic view of the customer to do this right.
Otherwise, disjointed experiences create the impression that nobody at the company talks with each other. An inconsistent experience confuses customers and erodes their trust.
Service and support teams require a complete picture of the customer to resolve issues promptly. When silos obstruct data sharing, each service call starts with a blank slate.
Support reps must start from scratch to understand the customer’s issue and any previous interactions. Issue resolution takes longer at the cost of growing customer dissatisfaction.
Technology will break down data silos, but only when the business culture shakes off the silo mentality. Companies must commit to becoming data-driven. Domain and corporate management must agree upon a collaborative data management strategy.
A data lake liberates data from silos to create a scalable shared data source for the entire company. Overcoming the limitation of legacy systems and organizational boundaries supports transformational cultural changes, including:
Data lakes decouple storage and compute to enable affordable scalability. Data teams can optimally balance the cost and performance of storage architectures without impacting users’ experiences. Simultaneously, they can scale compute resources on-demand without over-investing in IT resources.
Breaking down silos unifies enterprise data and enforces governance policies within a single system. Data lineage tracking and access control policies keep the organization compliant with privacy regulations while letting authorized users access the data they need.
A data lake becomes a single source of truth for everyone’s reference. When departments work together, they start with a common understanding of the underlying data.
The data itself is more consistent and complete than before, allowing customer service, web teams, and the salesforce to see one customer from three perspectives.
Data lakes break down barriers between current and historical data. Users can capture the most up-to-date insights by analyzing real-time data streaming into the lake. Long-term trend analysis can draw on the lake’s immense store of historical data.
More importantly, users who need current and historical data no longer must combine data from different silos. A well-designed lake provides both with consistent structures and formats.
Data lakes make information accessible to more employees. User-friendly interfaces support a self-service model that eliminates gatekeepers and skills barriers.
Employees can access data without submitting requests to data teams. Instead, they use their existing Excel spreadsheets, CRM systems, or business intelligence tools to include accurate data in their workflows and daily decision-making.
Directly accessing data for discovery and exploration lets analysts find the best data for their reports. Data scientists will streamline big data initiatives.
Across the organization, employees will generate new insights that foster innovation and make decision-making more effective.
Starburst is a modern data analytics platform that fast-tracks the dissolution of data silos. We create a virtual data layer that integrates legacy data sources with your data lake to create a single point of access to every data set in your company.
Starburst democratizes data access through the SQL tools your end users already understand. At the same time, our built-in and third-party governance capabilities let you control access and enforce compliance policies.
By making your data accessible, secure, and actionable, Starburst promotes your data-driven culture based on shared information and collaboration.
Up to $500 in usage credits included
Up to $500 in usage credits included