We all know that sharing is caring. But when it comes to data sharing, there’s not a lot of love going around.
A recent Gartner study found that organizations that promote internal data sharing will outperform their peers on most business metrics. And yet, data sharing is still a challenge for many organizations — especially those without a defined data strategy.
Many businesses lack the infrastructure and the culture of trust to facilitate data-sharing at scale, so access to information remains fragmented or restricted. Too much internal competition for data makes it difficult for teams to truly collaborate, perform a comprehensive data analysis, and optimize business processes. As a result, many companies are still struggling to deliver on their business goals and realize the full value of their data assets.
In this post, we’ll uncover:
- What is data sharing
- Why is data sharing important
- What data sharing looks like
- 4 best practices for enterprise data sharing
What is data sharing?
Data sharing is the practice of enabling access to your data within your company or with external stakeholders, too. Data sharing is about removing silos and barriers to access data.
Instead, data sharing encourages openness, transparency, and accessibility to data from anyone who needs it.
When structured and unstructured data is collected, stored, and analyzed effectively, it results in new opportunities and a competitive advantage. This is why sharing data across your organization is critical for innovation and growth.
Data sharing benefits: Why data share?
To understand the benefits of data sharing, it’s helpful to begin by examining why data is not shared in the first place. The primary reasons for non-sharing are data access barriers and organizational silos.
- Data access barriers. Access barriers occur when employees don’t have permission to see certain data, or if they do, it’s difficult for them to find what they’re looking for. These access barriers arise from traditional IT practices that focus on securing important data from unauthorized users. However, this practice can backfire because it limits collaboration between employees. And when different departments need the same information — but can’t get it — it ultimately slows down productivity and efficiency across the entire company.
- Organizational silos. Also referred to as “data silos,” these are internal processes that prevent employees from seeing information that’s relevant to their role but outside of their normal workflows. For example, a sales manager may have no need to see an accounting report while they’re performing sales duties. But if they could access that report during an off-hours meeting or when faced with a high-priority decision, they could leverage that information into better outcomes for the company.
Today, businesses are under increased pressure to differentiate themselves from traditional competitors and new, digitally savvy entrants.
One of the keys to gaining a competitive edge is in your data. The challenge is, how do you access your data and gain insights that can lead to tangible improvements for your company? It’s nearly impossible if everyone is looking at their data in silos.
This is where data sharing comes in.
You can’t be on an island in a silo when you’re working in data. – David Dadoun, Head of Data at Bombardier Recreational Products (“Data is Not a Project”, Data-Driven Leaders episode 6)
Data sharing enables everyone to access the same data, which helps reduce misinformation and ensures you can see the big picture of what’s going on in your business. It reduces inefficiencies, increases collaboration, and opens up new opportunities for business leaders.
According to Gartner, “Data and analytics leaders who shared data externally saw more measurable business outcomes than those who did not share.”
Data sharing is important because it allows organizations to:
- Unlock valuable data assets
- Improve collaboration and productivity among employees
- Reduce redundant efforts and expenditures
- Increase transparency and trust among stakeholders
- Enable more effective use of resources across the enterprise
Essentially, data sharing can provide the business with the right data at the right time, build trust, and ultimately deliver real tangible benefits.
What data sharing looks like
Our first-grade teachers were right: sharing is caring.
Imagine a future where data sharing is at the core of your data-driven culture. What would that look like?
- Openness & transparency: What if all stakeholders had access to the trusted data they need to do their job? What if there was a culture of openness and transparency amongst teams, departments, and lines of business?
- Flexibility: What if you could produce new analytics reports and handle ad-hoc requests easily? What if you didn’t have to request access to another department’s information through IT?
- Unified view: What if you had access to a single source of truth, with data you could trust?
- Speed: What if you could expand the scope of analysis that data consumers can conduct?
- Future technologies: What if you could explore new technologies, like machine learning and artificial intelligence, and have access to advanced analytics and prescriptive data-driven insights and recommendations?
Data sharing best practices
In the Gartner report previously mentioned, it found that data analytics teams who increased data sharing were 1.7 times more effective at demonstrating business value to their stakeholders.
By keeping these four best practices in mind, you can move your data analytics journey forward and deliver measurable business outcomes early and often.
1. Foster a culture that encourages “data-sharing” instead of “data-ownership.”
At Enterprise Data Governance Online this year, organizers identified the top five most common data governance challenges facing data professionals today. “Engagement and Ownership” was third on the list. In the day’s final session, industry vet Peter Aiken (Ph.D. Professor of Information Systems, VCU and Founder, Anything Awesome) advised dropping “data owner” for good:
I don’t let anybody that I work with use the words “ownership” and “data” at all… Where I always point them is to answer the question “Who owns accounting data?” It’s coming from all parts of the organization. So certainly, there are responsible stewards…. And there are a number of different roles that stewards can play but if you give somebody the actual ownership of the data, it causes nothing but problems in the organization.
By recognizing and celebrating the data stewards at your organization, you’ll promote a culture of data sharing and collaboration.
2. Recognize the organizational biases that affect data sharing.
Most of us are familiar with the concept of personal bias, but organizational bias is also a concern. Some examples of organizational bias that affect data sharing include:
- Leadership promoting one data source over another, based on “gut instinct” or a preconceived notion, instead of data accuracy
- Certain teams or team members being given more or quicker access to different data sources
- Data silos due to poor data management and low data quality
Data democratization is key for enterprise-wide data sharing. The more mature the organization, the fewer data biases exist, and the more data is shared across lines of business, teams, and team members.
3. Prepare your environment for data sharing by establishing data management and data governance strategies.
To truly be data-driven, your organization will need a centralized source of truth from which you can extract actionable insights. This requires a good data management and data governance strategy.
In the past, data management was typically carried out manually (for example, through spreadsheets). Today, there is no need to rely on cumbersome, manual solutions like Excel, so more organizations use a data management tool like a data warehouse or cloud platform.
(If you embark on a data transformation to modernize your data platform – or build a greenfield platform – learn about these five challenges business and IT leaders face when launching data and analytics projects.)
Data governance is critical here. There are many definitions of data governance, but we like this one from our own Jesse Glick (Senior Director of Data Analytics and Data Governance):
I hate to use a definition within a definition, but it’s really about governing your data. It’s making sure your data has checks and balances, from a security, process, and quality standpoint. If you boil it down even further, it’s making sure there’s a structure surrounding your data, where you have the best people looking at the data to make sure that it is accurate, aligned, secure, and safe. (Why Data Governance Matters and How to Get Started)
Data governance can be overwhelming at first glance. But your data governance program doesn’t have to be rigid, overly academic, and inflexible, like the data governance of the past. Instead, a new approach has emerged in recent years that leverages agile thinking. Learn how to implement an agile data governance framework.
4. Leadership should encourage a data-sharing mentality with enterprise-wide data literacy programs.
The most successful data-driven organizations are also the most data literate. After all, if the individual data users in your business don’t understand data and can’t have thoughtful conversations about it, how can they possibly derive any meaningful conclusions from the data at their fingertips?
Here are tips to start your data literacy program:
Identify your fluent “data speakers”
These SMEs can help serve as data “translators” across the organization and will often act as your biggest cheerleaders and advocates.
Conduct data literacy assessments to identify knowledge gaps
This will give you a baseline from which to start your training.
Make it fun and engaging!
David Dadoun recommends using analogies, metaphors, and stories to explain what data teams do, how they do it, where they are going, and what the strategies are.
I put together something that I call The Data Kitchen, and it describes how an individual makes a recipe and makes their meal. I take that whole chain of events from the farmer planting the seed and growing the potato to it appearing on a shelf in a store and then, the individual coming in and bringing it to their house. And I map it back to the flow of data and how somebody creates a record inside their ERP. (“Data is Not a Project”, Data-Driven Leaders episode 6)
Ensure that leadership is leading by example
Leaders should speak the language of data wherever possible—for example, when discussing business outcomes. They should also encourage others to be data literate and champion the benefits of closing the “data literacy gap.”
With a successful data literacy program, business users should understand how to use and access data and be able to ask the right questions about data. Finally, they should have the skills and understanding to act on the data that is available, so that they can drive strategic and tactical decisions from insights.
Data sharing is about much more than just data sharing.
It’s about creating a culture of openness and transparency.
That’s why what sets you up for success when it comes to data sharing is your data-driven culture. If your leadership is not instilling a data-driven culture from the top down, then it’s impossible to truly enable data sharing.
You need the culture first, and then the data sharing will follow.