It is a well-known fact that collaboration leads to a better work environment and culture within a company. Collaborating around data isn’t any different. When done right, this process transforms data management as we know it and becomes inevitable for data-driven decision-making.
At the most basic level, the aim of data collaboration is to allow multiple users to share access to their data. This gives everyone involved a more holistic view of the project and enables deeper insights – in real time.
Not only do teams benefit from having instant access to a broader range of data, but IT departments also benefit from reduced data fragmentation and cheaper management expenses, as well as a simpler deployment architecture.
So, how should the data collaboration process look like… ideally?
How Should Real-Time Data Collaboration Look Like?
1. Establish a virtuous input-output cycle.
The data analytics cycle should be a repeatable process.
A piece of data prompts an action, which in turn initiates a process that results in the generation of new data, which prompts another action.
That is what Matthew Miller, product management senior director at Tableau, recommends in order to make the most of data a company collects.
2. Data workflows should reflect how people already work.
The best data-driven processes support and enhance the tasks and responsibilities of those who interact with them and simplify the common challenges they face on a regular basis.
“Human patterns of collaboration illuminate how to design data systems for harmony,” said David Gibbons, senior director for analytics at Salesforce.
Gibbons also believes that “the shape of the data” helps organizations identify which teams are going to connect and interact, allowing them to complete their work more effectively. “A flexible data platform that lets you embed analytics wherever they are needed in the middle of those collaborations will maximize success and increase data harmony in the process,” Gibbons says.
3. Make data easy to question and validate.
Data is most often used to confirm assumptions that already exist.
“Most executives have a gut sense of how they’re doing, and when they see the analytics, they aren’t often all that surprised,” Miller says. “So, if they see a number and wonder where it came from, you need to be able to track it back to the source.”
The most effective method to do this is to ensure someone gets to examine every piece of data and analysis exchanged. Companies can do this by certifying data sources or, in other words, by effectively putting a mark of approval to show the data is up-to-date and trustworthy.
The benefits of real-time data collaboration are numerous.
From rapid integration and development to advanced data visualization, data collaboration produces instant results by removing heavy lifting.
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