Data makes the world go round. And it’s spinning faster every day. Businesses with the momentum to lead the race are getting the most from their data. And a key driver in their strategic success is data collaboration.
At the heart of this next-gen alliance lies the dichotomy of data and people. Yet, data collaboration roadblocks often fall on the people side, according to Gartner. A lack of a structured process for collaboration between data creators and data users is the major cause of more than 65% of data science projects not being operationalised.
Though data collaboration may seem like a complex dynamic to navigate, BI managers and other data champions can make rapid headway. Since a recent Stanford study suggests that even the idea of collaboration or a collaborative environment can make employees more dedicated, resilient and productive, there’s hope for all data teams! So, read on!
How can a business generate quality data?
Data used incorrectly still provides answers, but the wrong answers. Since data must be clean to be accurate and effective, it requires teamwork to achieve the quality data your business requires. And it’s good teamwork that ensures data consistency.
The solution – data teams should find ways to collaborate with the employees that use the data. This relationship, between those analysing the data and those using it within the business, is vital. When it’s strengthened, your data should generate additional business value.
And, to manage this data flow effectively, you need a robust data team. Let’s look at the necessary components of this team.
How to organise the data team for ultimate efficiency
A data team should be organised into specific roles to ensure the team is effective. These roles could include:
- Data engineers
- Data scientists
- Software engineer
- BI experts
- Project manager
Each of these roles needs to have defined responsibilities in the data team. And every team member should understand their role and contribution. Plus they should be aware of what their teammates are contributing to ensure clarity. Clearly defined roles and responsibilities will save time and cut down on confusion, especially if one of the team members encounters a roadblock.
Once the roles are set, you could look at how to streamline the flow of data.
Here are a few key questions to help organise the data flow:
- Who will source the data?
- Who will manipulate and prepare it?
- Who is going to clean up the data?
- Who is going to present it?
- Who is managing the overall project?
With the data team in shipshape, let’s look at how they can collaborate like pros!
Tips for ensuring collaboration that boosts business value
By working together, you can achieve more! Here are 6 tips to improve collaboration with data teams.
1. Don’t jump the gun
Don’t dive straight into creating a dashboard as soon as you receive a request. You could be missing out on the bigger picture of the problem and the broader issue the data could resolve. If you understand the core of the data request, you can track back to an expanded view and ensure you maximise the data potential of the opportunity.
2. Avoid assumptions
You’re probably aware of the old expression about assumptions! Teams can avoid this pitfall by:
- Starting every data meeting with an open mind. Because something didn’t work in one project, it doesn’t mean the circumstances are exactly the same in another project.
- Understanding the data users needs and workflow. We’ll cover this in more depth in point 5.
- Letting the data lead the investigation. This is the best approach for nurturing innovation and value creation.
3. Collaboration begins with questions
Asking questions is a trusted route to successful collaboration. To get started:
- Establish what the objective of the data request is
- Next, brainstorm the questions leading from that objective
Pro tip: To expand the initial narrow data request, organise your questions from general to specific. Doing so will help structure the data story from general to detailed.
It will be beneficial to allow everyone to provide input in these data meetings. And, traditionally, these conversations have taken place in meeting rooms.
But now, there’s a game-changing opportunity to move these interactions from the meeting room to where the action is really happening – on the dashboard!
And, you can make the switch easily with Vizlib Collaboration. Instead of taking screenshots and sharing reports via email, then setting up meetings to discuss those points, data teams can now move faster and take instant action by keeping the conversation within Qlik Sense. Contextualised comments that are tied to specific selections minimise confusion and lead to action! Now that’s collaboration!
4. Break down the main goal into smaller targets
Once you’ve established the overarching goal of the data request, begin breaking it down into sub-goals or sub-questions.
These sub-questions should tie into the overall goal but focus on only one part of the project. Say the overall goal is to improve customer satisfaction, then perhaps a sub-question might be, how to improve the time it takes your business to reply to online customer feedback. Then, shape these sub-questions into measurable factors. In our example, you could measure the improvement in how quickly your team responds to customers. Remember the sub-questions must be measurable with specific metrics, like time, for instance.
5. Get to know the data user
A practical method to establish or enhance the relationship between the data science team and data users is for the data user to explain their typical workday. The data team will gain a better understanding of how they use data.
If you wish to speed up this process, you could pair data scientists with data users, or data citizens, to foster a more collaborative approach. A data citizen is an employee who doesn’t have a data science background, but uses BI tools to get insights from data. These partnerships can promote a personal and professional collaboration framework for both parties.
The NHS Wrightington, Wigan and Leigh NHS Foundation Trust (WWL), for example, ensures collaborative development by having their BI team work side-by-side with clinical services for the design and development of apps. And they encourage their analysts to get involved in the clinical services to develop a deeper understanding of the hospital staff’s needs. And what do all parties get out of it? Smoother collaboration and time saving!
Teams working on Vizlib Collaboration can benefit from a shortened data communication cycle and turn insights into fast action.
6. Promote data democracy
Everyone that works with data should feel empowered to use and share data to improve their output and that of the business. Since data and findings will be communicated to people of various data skill levels, including data citizens, the data science team must understand how to communicate the data story effectively.
An easy-to-use solution, like Vizlib Collaboration, can help teams get their data story across clearly. And, with the addition of a new feature, users can also categorise their comments.
Pro tip: Users can categorise their comments by priority, department or project to provide more context.
To get everyone on board, aim your message at your specific audience by asking questions such as:
- What do they need to know?
- What’s the easiest way to deliver the message?
- What medium will work best?
Augmented analytics and other easy-to-use BI solutions will help to build the confidence of data citizens in using data effectively. And, the more employees that are using data strategically, the greater your business’ agility and momentum!
Improving collaboration with the data team is easier when the business has a shared data vision and is transparent about their data. Leverage easy-to-use data solutions to nurture data democratisation and keep your business on the leading edge!