3 Steps to Break Down Data Silos and Connect Disparate Data
Data is the most powerful four-letter word in business today. That’s because data has the potential to transform every facet of the organization. But for all its hype, the full promise of data rarely comes to fruition because of a more sinister four-letter word: “silo”.
Enterprises try to glean insights from data that’s trapped in the silos that exist across business units, organizational functions, and systems. Unfortunately, silos have made it hard to manage and analyze enterprise-wide data, making it difficult to identify the type of valuable opportunities that spark growth.
Data silos tend to arise naturally in organizations because each department has different goals, priorities, responsibilities, and systems. Every department works their own way, which only exacerbates the problem.
More often than not, the overwhelming volume and velocity of data creates chaos instead of opportunity. Just think about the inherent pace of change in business today.
Now think about the challenge of capturing that information, accurately and consistently, across every department. How laborious and intensive a task it is to manage and maintain datasets spread across multiple applications and tools. But it’s happening every day.
Sales typically has their preferred CRM or system to manage data, while marketing and finance have other systems with customer data. Still, companies are investing billions of dollars on enterprise applications with the goal of getting a 360-degree view of the customer. But when data resides in multiple, disparate databases, it’s often inconsistent and poorly organized. Instead of getting a complete and accurate view of a customer or prospect, companies are enacting a strategy that is often informed by a string of misinformation. This siloed nature of enterprise-wide data management fouls up operations and decision making every time.
To truly maximize the full potential of data, organizations must break down these silos and connect disparate databases to create a single, complete, and connected view of their business relationships. This is not an easy process but one that is imperative to extracting real value out of data.
Many companies use multiple systems to capture and manage their data, without having a common data source.
Chances are your organizational structure is similar to the depiction above – departmental silos with distinct applications operating independently of each other. It’s okay if this looks familiar. You’re not alone. The fact is data silos will likely always exist, but it is the “siloed approach” to managing data that must be changed. Data management policy, tools, and process need to be considered and implemented across systems (silos) rather than developed as one-offs. Chipping away at these pesky silo walls is no easy task, but the following three steps should help get your organization on the right track.
1. Appoint a Data Leader
Every enterprise needs to consider employing a single data leader that ties the disparate departments together so they can take the information to that next level. Ultimately, there should be someone who interacts with each of the organizational leaders, from marketing to finance, who can help distill the information and turn intelligence into insight. Within large enterprises, it may fall under the responsibility of the Chief Data Officer. Smaller companies may not have this leader in place, but there should be somebody who can identify the data and analytics that can sift compelling insights from the noise and who understands how it can be used to drive value across the enterprise. This person should be responsible for decisions governing steps 2 and 3.
2. Institute Common Data Standards
It’s important for everyone to be on the same page with respect to how data is defined and collected. There must be common standards leveraged across data assets, whether they are standards the company creates or established industry standards like SIC for industry, ISO for countries, or a D-U-N-S® Number for hierarchies. Adoption of standard data sets, models, schemas, and codes significantly decreases time to value and complexity within your data supply chain.
Without the benefit of these defined standards, known as having a common master data structure or customer ID, data that is of value to one department may not be recognized by another. What’s more, inconsistent naming conventions can lead to duplicated records. As a result, the information and data output of these systems may render rich details but cannot accurately be used. Speaking the same language will ensure richer insights are produced and shared.
3. Make It Universal
The days of having to preserve everything in one physical location/database are gone. Companies are leveraging common data standards and API technology to take data and pull it together on demand to create the required insight. This approach has transformed the way data is shared, eliminating in many cases the need for “pre-processing and/or conversion.”
This will allow data to flow from one system to another, giving businesses the freedom to collaborate and share information that can inform future insights.
Data is proving to be a valuable resource in your mission to grow the business. But turning that information into valuable insight requires quality, dependable, and accurate data. By breaking down the silos, you’ll be able to identify untapped opportunities that can be shared across the enterprise. And the answer is not just about having the right technology. It’s about the combination of technology, people, and process that defines your competitiveness and the ability to transform your business.