It’s 2022, and there’s more data available than we could ever imagine or use. It’s inexhaustible.
However, there is one tricky thing about it all, as well. Data as is, in the form of numbers or plain stats, is just like raw material – valuable but not something that can be used.
Plain insights can’t help transform any organization. However, gathering the right people and giving them the right tools and access to the right data will allow organizations to do amazing things. To “collaborate” around data – that’s the goal.
Keep reading to find out what data collaboration is and what it should look like.
What is Data Collaboration?
Data collaboration is the process of sharing, exploring, analyzing, and discussing information with others. It involves two or more people working together to solve a problem or make an informed decision. The team creates a shared understanding of the issue at stake by bringing multiple perspectives, experiences, knowledge bases – including subject matter expertise – to the collaboration.
Your organization is probably already using data collaborations to some extent – here are the most common examples:
- Weekly update meeting
- Periodic reporting (e.g., monthly status reports)
- Project status reviews
- Monthly/quarterly/yearly strategy review, etc.
How to Achieve Data Collaboration?
Data collaboration can happen in a lot of different ways. For example, you could do it via conference calls, video conferences, or surveys.
Also, it is quite helpful to use tools to facilitate data collaboration – for example:
- A collaborative space (e.g., Basecamp)
- OneNote
- Slack
- Google Docs
The goal is to create a shared workspace where everyone is on the same page, and everyone can contribute and drive better decision-making.
What Does Data Collaboration Look Like?
Data collaboration looks different in every organization because it will be tailored to a company’s specific problem.
Here is what data collaboration should look like and the four steps needed to achieve it.
1. Share and leverage the knowledge of others.
One of the obstacles to effective collaboration around data is that employees tend to grade themselves against each other. Companies can overcome these obstacles by making their data collaboration initiatives as inclusive as possible. It is usually best suited to adopt the bottom-up approach of making data platforms more efficient, effective, and trustworthy.
2. Take advantage of a diverse set of skills and knowledge.
Providing employees with seemingly endless data sources and analytical tools will not transform them all into equally effective knowledge workers. While that technique may benefit educated data scientists, it isn’t the most efficient or well-coordinated way to communicate. Instead, allow your employees to describe the challenges they need data to address and let the specialists focus on how they can assist them.
3. Create spaces where employees can ask questions.
Make sure to create spaces that encourage your employees to ask questions. Whatever platform you decide to use, make sure that it is flexible. For example, it is recommended that your employees can access the platform when they work from home and regardless of the device they use.
4. Make it simple to verify and question data.
When a decision-maker sees a number and wonders where it came from, your employees need to be able to trace that number back to the source easily. This can be accomplished by certifying data sources—essentially, putting a stamp of approval on the data to demonstrate that it is current and reliable. There should also be cultural support and incentives for giving prompt responses and explanations so that decisions aren’t blocked, and insights aren’t dismissed by users who don’t have time to wait.
Data collaboration? Let’s make it easier, simpler, and faster. Only with a proper understanding of data can one make impactful business decisions. Book your TRUECHART demo today.
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