Data is the fuel that drives any modern organization’s growth. Yet, most businesses struggle to appropriately harness and use their data. Rather than taking an all-encompassing view of the data at their disposal, many businesses keep data siloed away in different departments. As teams attempt to navigate bottlenecks and extract value, their efforts usually cost valuable time and resources that are better used elsewhere.
So, what exactly is a data silo?
A data silo is simply a collection of data held by one section of a company but not readily available to other departments in the same organization. Since each department has its own unique goals and processes, teams take an individualist mindset to data ownership which often prevents them from sharing data with each other. Over time, technological challenges, inaccuracies, and misguided priorities tend to reinforce these segregations and create deeper data silos.
When data is not available to the right people at the right time, it bottlenecks workflows, hinders decision-making, and ultimately affects the bottom line.
Crossing the Departmental Divide
We can better understand why data silos are harmful by considering a marketing example. Let’s say we’re trying to gauge the success or failure of our newest marketing campaign. We acquire our audience data from our marketing department and begin a straightforward analysis — only to realize we need sales data as well.
However, we discover no easy way to retrieve our sales data because their department’s software-as-a-service (SaaS) apps haven’t been integrated to “talk” with our marketing tech stack. In short, our departments have isolated the data in silos. We therefore waste precious hours trying to integrate and understand diverse data streams, while our staffing costs increase accordingly.
Unfortunately, coding custom connections between SaaS apps is often outside the business user’s technical skills set. When our IT departments are inevitably tasked to combine diverse information for cross-departmental analysis, siloed data leads to complexity in building data pipelines and custom integration solutions.
For example, inconsistently formatted data or data access issues create an additional burden on overwhelmed IT departments — and another bottleneck for time-sensitive business decisions.
The Cost of Data Redundancy
There’s another common cause of data silos: When multiple teams store similar data, we get duplicate data and discrepancies.
Each department interprets and processes the data according to their requirements, resulting in multiple renditions of the same underlying data.
Standardization and compliance become colossal challenges in the face of inconsistent and redundant siloed data. When organizational data is locked away in silos, it becomes difficult for businesses to know where information is stored and who has access to it. In such a scenario, it becomes tough to adhere to the strict compliance requirements of GDPR, CCPA, and other privacy laws.
By tearing down the data silos in our organization, we create a single source of truth and passalong the resultant cost and processing efficiencies to our businesses.
The Cost of Data Fragmentation
As our data remains in silos, it rapidly becomes outdated and less usable. Due to the difficulty of reassembling fragmented data, teams often ignore the seemingly lost data in a vicious cycle that sustains itself.
Gradually, we’re left with data that’s of no practical use to any department.
As the digital landscape grows, our organizations will need to halt this process before it renders our data worthless.
The Cost of Dark Data
The product of data fragmentation is a lot of lost data, otherwise known as dark data.
Dark data consists of all the unused and abandoned data in our organizations, and its sources are abundant and persistent, according to Gartner. Much of it ends up as redundant, obsolete, and trivial (ROT) data, which holds little value for an organization. Yet, this ROT data occupies valuable space in its storage repositories.
The sheer scale of dark data in most organizations has the potential for huge cost savings from reductions in data storage alone.
Not all dark data is useless, though. Dark data can contain valuable information that has been left unanalyzed – knowingly or unknowingly – and represents lost opportunity. When a department siloes its data, it often makes it inaccessible to other departments — or worse, entirely obscures its existence.
Server logs and geolocation data are excellent examples of this untapped potential, as they both provide clues to customer interaction patterns. These unstructured data repositories grow more useful as machine learning algorithms increase their capacity to fuel agile decision-making with deeper data insights.
The Benefits of Data Connectivity
Data silos harm organizations by inflating storage and staffing costs and bloating workloads with data consolidation workarounds. Without a solution to bridge these data silos, information is difficult to access and leverage while it’s still useful. Lag time between data intake and actionable decision-making buries opportunities in obscure pockets of redundant, fragmented, or dark data —, and ultimately slows your business’ growth.
Intellective’s Employee Experience Pack sits on top of ServiceNow to deliver a personalized and unified employee engagement experience where you can integrate data across software platforms to deliver a world class employee experience with a high adoption rate.
Below are some of the benefits you get from the Employee Experience Pack:
- Replace legacy or expensive intranets in a matter of days or weeks
- A built-in kudos app that makes it easy to recognize employees
- Access information across the entire organization in one place using the EmployeeExperience Pack tools for ServiceNow
- AI driven recommendations to personalize the experience
- Contextual search to make it easy for employee to find what they need in one click
