In agriculture, silos are pretty great. They give us the ability to take things of value—grain, and the like—and put them up and out of harm’s way.
However, the functionality of the silo plays very differently in the data space. Increasingly, big data needs to be shared, not stored. And, as such, data silos are becoming problematic in modern organizations.
To appreciate why, let’s define what data silos are specifically. We’ll examine some examples of how they impact actual organizations. Then, let’s identify some actionable steps to knock them down to size.
What is a data silo?
A form of information silo specific to the data space, data silos refer to any collection of data held by one group in an organization that other groups can’t easily or fully access. These silos can happen naturally over time, as different departments or business units in a company end up using separate apps, systems, and databases over time.
When it isn’t integrated, data ends up isolated and cannot be used effectively across the organization. Isolated data, in turn, can negatively impact data analysis, cross-departmental collaboration, and operational efficiency.
Data silos can also result from specific technical limitations. Most often, they’re a byproduct of cultural entropy, where departments in an organization are left to develop on their own, growing protective over their information over time.
While understandable, this is increasingly unacceptable in modern data-driven environments, where data flow is crucial for analytics, business intelligence, and data-driven decision-making.
The data silo problem: Details on causes and effects
To further appreciate the impact data silos can have on an organization if left unchecked, let’s first specifically outline their most common causes, before digging a bit more into their negative effects.
Departmentalization: Strongly defined departments can have many benefits within an organization. However, left unchecked, this departmentalization can cause siloing of data as members are less inclined to standardize systems and data.
Diverse technologies: Departments may also simply get in the habit of adopting technologies and tools based on their own immediate needs and goals, with little regard to how they may contribute to inconsistent data or duplicate data within the organization.
Cultural barriers: Some company cultures may instead be to blame, especially when sharing information between departments isn’t encouraged and rewarded. This cultural “silo mentality” can easily evolve into restricted or inaccessible data.
Lack of a unified strategy: Organizations also require overarching data management strategies in order to avoid data silos. Without them, departments may fail to prioritize (or even consider) the importance of data sharing and contributing to an integrated data ecosystem.
IT governance: Similar to a lack of strategy, when IT governance is decentralized departments may be left to make their own decisions regarding their technology. While no guarantee, this tends to tip the scales toward incompatible systems and siloed data.
Regulatory compliance: In some organizations, privacy and related regulations may affect departments differently, with siloing resulting from different ways certain types of data are handled.
Rapid growth or mergers: Finally, siloing may result from events largely out of departmental control. Fast-growing companies or those involved in mergers or acquisitions often struggle to integrate data management practices and data platforms, along with organizational structures.
Reduced collaboration: As should be no surprise, as silos begin to hinder the free flow of information, cooperation and knowledge sharing between departments begins to suffer.
Inefficiencies: Less cooperation and knowledge sharing in an organization means more manual processes are required to bridge the gaps. This increases the duplication of efforts, initiatives, and, ultimately, organizational data.
Data integrity issues: As company data begins to contain more duplicate versions of information and datasets being maintained in different siloes, overall data accuracy and consistency becomes increasingly difficult to maintain.
Poor decision-making: More organizations, not just those involved in digital transformation initiatives, rely on data to make better decisions and stay ahead of the competition. Low-integrity data makes it difficult or impossible to maintain a strategic overview of the business, leading to suboptimal business decisions.
Limited insight and innovation: Analytics within an organization subsist on a healthy, robust flow of data. In situations where silos keep this from happening, organizations will begin to miss out on valuable insights that can lead to true innovation.
Increased IT complexity: Maintaining a multitude of poorly integrated systems places an increasing strain on an organization’s IT infrastructure, inevitably increasing costs.
Regulatory risks: Siloing also makes it that much harder to enforce data governance policies, which can easily lead to data privacy issues, regulatory non-compliance, and associated risks.
Customer experience degradation: Customer-facing teams also suffer when they don’t have access to the same and/or accurate data, which can begin impacting the ability of a business to offer consistent customer experiences.
Examples of data silos in different situations
Being that they’re inherently problematic, there are no “use cases” for data silos, per se. But examples of how they exist and the impacts they have in real-world situations can be instructive. Here are three examples in three different industries that help demonstrate the diversity of issues data silos cause.
In the healthcare industry, it’s common for patients to interact with a variety of healthcare providers. These providers may include general practitioners, specialists, hospitals, special clinics, and diagnostic labs. As a variation of the departmentalization noted above, it’s also common for each of these providers to use different electronic health record (EHR) systems to store their patient data.
The data on these systems can quite literally be a matter of life or death—including medical histories, medication lists, allergy information, vaccination records, and lab results. Without integration, this critical information can get trapped in numerous data silos. If nothing else, this results in a fragmented picture of an individual’s overall health and care requirements.
This example of siloing can result in a myriad of consequences. When doctors don’t get access to a patient’s full medical history or recent test results, subsequent misdiagnoses and inappropriate treatment can result as part of inconsistent patient care.
Tests may be duplicated, or treatment delayed, impacting time-sensitive decision-making as providers wait on needed information from siloed sources. Even the comparatively benign concept of poor patient experience takes a longer-term toll, creating frustration and decreasing trust in the healthcare system.
Retail and ecommerce
Pivoting to another industry–one slightly less dramatic but important in its own right–data silos can also hamper the retail and ecommerce industries. This is especially true on the other end of industry-wide changes enacted in response to the COVID-19 pandemic, where more retail companies juggle the operation of both physical stores and digital storefronts.
As a result, businesses must collect customer data through various means, including in-store point-of-purchase systems, customer relationship management (CRM) systems, online sales systems, and customer support channels. However, similar to our healthcare example, that data is only as valuable to business owners as its ability to paint a complete and informative picture of their customers, current and potential.
Left to silo in this way, owners pay the price for this incomplete data, making it difficult to efficiently manage inventory, and ultimately leading to costly overstocking or stockout situations. When sales staff and online recommendation engines can’t leverage full purchasing histories, cross and up-selling opportunities may be lost.
Customer service, now more important than ever, suffers as those shopping both online and in-store may receive off-kilter marketing messages and product recommendations, wasting advertising budgets while potentially alienating customers. All this, as siloed sales data also prevents business owners from analyzing the overall behavior and preferences of their customers, impacting sales decisions and strategic campaigns.
The inverse of the business owner who’s trying to use data to draw customers in, sales teams across industries do the opposite—constantly on the offensive, they’re working to make new connections and nurture new leads. It’s interesting how data silos in some of the same places that retailers face lead to very different complications.
Sales teams also rely on customer data, but they do so in order to target their sales efforts, strategically expand their professional networks, and, hopefully, critically, close deals. For these teams, valued aspects of customer data might also include purchase histories, in addition to audience demographics, buying preferences, and contact information.
While a factor in retail, CRMs and the data they contain are often the lifeblood of modern sales teams. Because the CRM will capture the history of interactions sales has, or has not, had with a potential customer. However, in a silo situation where a given CRM can’t integrate with those other valuable sources of data, the sales process suffers.
Sales professionals are often the best at spotting an opportunity to cross or upsell, but only when they have complete and up-to-date access to the data of the person seated across from them. The trust and rapport that’s such a vital part of the sales process is difficult to build when customer history, past issues, and potential frustrations are unknown.
The sales process itself can begin to break down due to data silos, as pitches can’t be personalized, RFPs get mishandled, and members of the team are unaware of each other’s efforts.
Breaking the silo cycle
Chances are equally good that you now appreciate the dangers of data silos and are inspired to prevent any from occurring. However, much like the modern generation of farmers, most data silos of note will already exist long before you get the lay of the respective land.
Take heart, though—the process needed to bring data silos down is arguably more straightforward than keeping one from ever occurring. Broadly, here are the seven basic steps we’d recommend building upon:
1. Align leadership behind you: Perhaps most importantly, start by securing executive sponsorship and leadership support for your silo-busting. Also, look to implement change management practices to address resistance and lay the foundation for your initiative.
2. Develop an integrated data strategy: Then, establish a comprehensive data management strategy that aligns with the overall objectives of your organization. At a minimum, this strategy should encompass data collection, storage, access, and sharing across the organization.
3. Implement technology solutions: With your strategy established, look to adopt data integration tools that can consolidate data from different sources and departments into a unified format. Also, consider implementing cloud-based solutions that inherently facilitate collaboration and data sharing.
4. Prioritize data governance and standards: Empower individuals to work together in establishing and owning clear data governance policies, designed to ensure data quality, security, and compliance. Go a step further and encourage data literacy, ensuring common data standards and definitions are embraced throughout the organization.
5. Provide training and education: Cultivate adoption of the changes taking place by providing regular training to employees, making sure new systems are introduced and that the benefits of data sharing are easily understood. Work to center this ongoing education around how breaking down data silos benefits their roles specifically.
6. Monitor, then adapt: Measure the effectiveness of your data integration efforts continuously. Identify areas of potential improvement and adapt your strategies as necessary. Establish feedback mechanisms to improve processes over time, and further facilitate buy-in across the organization.
7. Promote collaboration-as-culture: Ultimately, the ability to sustainably prevent data silos from occurring hinges on the ability to weave data sharing into a company’s culture. Hold regular interdepartmental meetings to discuss data needs and usage. Make sure to encourage and reward silo-busting behavior and mentalities, to continue to bolster collaboration company-wide.
Leveraging the right tools for anti-silo initiatives
In closing, data engineers and leaders in the data space should also focus on how data tools can help prevent data silos from forming.
But particular attention should be paid to data orchestration platforms, as the right platform acts as a hub, facilitating seamless integration of disparate data sources, enabling easier monitoring and governance while providing ample opportunities to automate key aspects of data accessibility and transparency.
Remember, keeping the impact of data silos to a minimum is a marathon, not a sprint. Which is all the more reason to subscribe to our substack, as your organization will thank you.