Data silos are often a reality for modern companies. Businesses rely on big data to drive analysis and revenue goals – and that data flows in from more channels than ever before. Managing all that information falls to individual teams working on disparate software and collecting a variety of metrics which isolates data into silos as a natural consequence.
Though this may seem like an acceptable approach for handling multiple large data sets, it causes a great deal of problems in the long run. Isolated data leads to inefficiencies that inhibit the ability of teams and management to make educated, real-time decisions. Performance suffers and potential revenue is lost when information is confined to an Excel file.
Many organizations realize that data collaboration is paramount to generating thoughtful, usable insights. Yet implementing a strategy to support data collaboration is often difficult. Confusion identifying roadblocks and uncertainty in how to remove these roadblocks delay or halt the implementation process.
Ad-Juster’s recent white paper identified data silos as one of the primary hindrances to data collaboration in the workplace. In this blog post, we’ll identify some of the root causes behind data silos and offer steps on how to eliminate them from your business.
Identifying the Root Causes of Data Silos
Identifying the underlying causes of your data silo problem is an essential step towards promoting a more collaborative environment in your organization. Much like a weed that grows back unless pulled out by the root, so too will data silos if foundational issues aren’t addressed. It’s important to remember that, in most situations, multiple factors contribute to data silos.
- Structural: Organizations are increasingly divided into separate teams, with a clear hierarchy and division of responsibilities – for example, marketing information and insights are funneled to the company’s CMO, while supply chain data and insights go directly to the CPO. This set-up does allow for highly focused performance and increased accountability, but it also creates separation and misalignment. Departments are often left to implement their own processes and software, which contributes to difficulties integrating data sets and confusion over which ones contain the most current information. Reconciling this is time consuming and costly, and the margin for error is high. Left alone, these types of structural silos grow strong roots and business processes must accommodate by working around them.
- Technological: To improve operations, businesses rely on a vast suite of software now more than ever. With the rise of cloud computing and Software-as-a-Service (SaaS) companies, the quantity of available tools for organizations has increased while the expense and technical knowledge required to operate such software has decreased. Naturally, individual teams have quickly adopted a wide variety of programs to improve their productivity. However, at the company level, this leads to two issues that contribute to data silos. The first is product incompatibility: each additional system implemented by an organization collects a great deal of data, but because not all products are designed to work together there is a misalignment of data. The more 3rd-and-4th party vendors added to the equation, the more obfuscated data becomes and the harder it is to reconcile. The second issue stems from vendor lock-in (a tactic we’ve seen Google use as part of their GDPR compliance). SaaS vendors may try to keep customers using their platform exclusively (by limiting the ability to port data outside of the platform, for example) which creates a data silo for the customer.
- Cultural: Many organizations fall into a pervasive type of “silo mentality”, wherein departments do not share data, priorities, or processes with other stakeholders due to a sense of proprietorship over their data and a lack of awareness of how other departments use it. In a fast-paced environment where making data-driven decisions is critical to success, even groups with the same goals may feel suspicious when others want to access their data, particularly when the scope for potential misuse (intentional or accidental) is so broad. This reluctance to share control of and access to information has a detrimental effect on operations. When data is analyzed without proper context there’s a high chance of misattributing success and failure. Secondary effects of “silo mentality,” like suspicion and distrust, also have a negative effect on employee morale and overall company culture.
Eliminating Data Silos
Data silos often become a part of the structure of a company, but they aren’t integral to it. Demolishing them will take thoughtful analysis of how your business runs and doing so will ultimately make it easier to identify issues within your organization. But where to start? Here are some helpful steps for breaking down the silos in your workplace.
- Identify key roles in your organization and their data needs. When starting the process of deconstructing data silos, take some time to find out how each team in your organization collects and uses their data. It’s important to understand what systems are currently in use, who is using them, and why. Determine what information each department needs from the others and how it is currently communicated. Incompatible systems are often a symptom of data silos and a great deal of time may need to be spent just decoding data to make it translatable to another team.
- Find opportunities to consolidate technology. Collaborate interdepartmentally to find opportunities to consolidate technology and strategize how best to share information. Plan your solution according to your company’s specific workflow needs. Consider the flexibility and scalability of possible solutions and how data storage, preparation, and analysis will impact your team. Then decide which system(s) could be merged or discarded entirely and be replaced with a more efficient solution. Ideally, you should utilize applications that offer a common view of data across departments, the ability to easily export data, and allow users to sort and analyze information to suit their own needs.
- Implement clear policies for data transparency. Businesses can take proactive steps toward eliminating silos by making sure that the company policy regarding data transparency is clear and agreed upon by all. A data policy should focus on promoting a collective sense of “data stewardship” within your organization. This encourages a level of self-maintenance wherein inefficiencies are quickly found and flagged so corrective measures can be taken.
- Stop “silo mentality.” As discussed above, silo mentality is characterized by an overly-protective, closed-off nature arising from a culture of general suspicion and lack of open communication. Putting an end to this requires tackling the issue from both a structural and cultural perspective. From a structural standpoint, it is critical to put a formal communication system in place. This can take the form of an in-person meeting, phone conference, or teleconference, and, most importantly, it should focus on collective goals and idea analysis. The second way to stop silo mentality is to create a culture that does not allow silos to flourish. Measures should be taken to help employees connect over non-work related events to build trust and rapport. Operations run smoother and conversations are easier to have when employees trust each other and share a common belief in the company’s goals.
- Maintain your new operations. After implementing a new system, make sure to spend adequate time maintaining and adapting it to changes in underlying data. Data silos build up over the life of a company and eliminating them takes time and effort. Develop a plan and stick to it while maintaining enough flexibility to get over unseen hurdles. Not every solution is the right one. Refining the process takes time, but it’s important to not give up.
Data silos are a wide-scale problem that causes data misalignment, hinders communication, and leads to incorrect analysis. They are often built into the structure of a company and can be difficult to break down. But you don’t have to accept data silos as a consequence of handling big data. With a concentrated effort, an open ear, and this list, you will be equipped to demolish the data silos in your company.
FlowIQ: Role-based Data Analytics
The right data to the right people, organized the way you work. Ad-Juster FlowIQ delivers a powerful role-specific platform that supports data collaboration and data prioritization to successfully deliver on all digital direct-sold campaigns. Our experience and knowledge allows us to design and build tools that enable media professionals to successfully handle the industry’s biggest challenges.