The early days of analytics were targeted at individuals with a wealth of technical skills. Organizations need business analysts and data scientists to handle, process, and interpret data. Today, business data is shifting to a democracy.
Sure, you still need your data experts to handle the back end of your information management. However, when it comes to social media campaigns, you want your marketing managers involved in metric tracking. When you come up with customer service KPIs, you know that the best place to get ideas is from your support team.
Collaborative analytics is a data-driven approach. It leverages the knowledge and skills of your entire organization.
What is Collaborative Analytics?
Collaborative analytics is an analytical approach that involves multiple departments and team members. Employees break down siloes and work together to build data strategies and gain better insights.
Collaboration enables organizations to analyze data with greater efficiency and creativity. You glean new understanding and valuable insights, and arrive at decisions more quickly. Data analysis is based on sharing. It gives everyone access to valuable information that allows them to perform better.
Collaborative Analytics vs Collaboration Analytics
Although collaborative analytics and collaboration analytics sound very similar, they are different concepts.
Collaboration analytics is the gathering of data to understand how your team works together. Data analyzed include email, messaging, and meeting metrics. You collect data on how, when, and why your team collaborates. Data insights highlight strengths and weaknesses.
Collaborative analytics is an organizational approach used to democratize the use of information. It also produces deeper insights in all business areas. A collaborative strategy enables access to data and helps non-technical users interpret it.
What are the Key Components of Effective Collaborative Analytics?
Data analytics always come with associated complexities. Collaborative analytics involves more moving parts than a standard analytical ivory tower approach. With that being said, collaboration efforts make data handling much simpler for the average user.
Collaborative analytics empowers cross-functional teams to work together for the benefit of the business.
Let’s take a look at the key elements that build effective data collaboration.
Communication and Collaboration Tools
In-person meetings are a great way for people to collaborate. However, they can be inefficient and do not involve remote team members. You can optimize communication and collaboration with the use of collaborative tools like project management software and instant messaging apps.
When it comes to sensitive information and online security, you can also consider internet fax online services, as they’ll allow your teams to share the information they need without compromising any customer or business data.
Integrated Data
Data integration platforms gather all of your data into a central space. Every team member has access to the same, consistent data. Some of your apps might export data better than others. You can solve this problem using integration tools that can handle multiple data sources and formats. Your team can access structured and instructed data while building analyses.
Data Management
With so much data coming in, you need a system that keeps information accurate and reliable. Your data engineers oversee repositories such as data warehouses and data lakes.
Using a central platform, they can manage the following processes:
- Data validation: maintaining the accuracy, consistency, completeness, and security of data.
- Data cleaning: identifying and eliminating errors such as duplicates and missing information.
- Data governance: creating and enforcing data-handling policies and complying with data protection laws.
Additionally, implementing application performance monitoring ensures that all these data management processes run smoothly, by continuously assessing system performance and promptly addressing any issues that arise.
Project Management
Project management software like Asana streamlines the implementation of your collaborative approach. Your team has full visibility of who is doing what. It’s simple to coordinate tasks and complete goals on time.
For example, a sales manager wants to collect customer feedback. As data pours in from call logs and customer surveys, sales can work with CS to adjust survey tactics. Collaboration ensures both teams get what they need so they know what to work on. Support can hit their KPIs, and sales get to improve their funnel.
What are the Challenges of Collaborative Data Analytics?
Collaborating effectively with your data usage doesn’t happen overnight. Even when you start working together for better analysis, obstacles can pop up.
The challenges of implementing collaborative analytics include the following:
A lack of integrations
Your existing tech stack has apps that don’t want to play nice and connect data. Others might be difficult to use, slowing down adoption by non-technical users.
When adding a new collaborative application, make sure you have a long-term plan. Prioritize flexibility and fit for tools you use and those you may implement in the future.
Not using the right tools
Your collaborative analytics strategy initiates but hits a bottleneck. During implementation, you discover that you can’t centralize structured and unstructured data in the same platform. The team is trying to reach business objectives but they don’t have the right tools for the job.
You don’t have to replace your entire suite of applications. Even legacy systems can work with collaborative analytics. You do need to use an integrated data platform that gathers and validates relevant data types as well as provides availability to stakeholders.
Data quality isn’t consistent
Data management becomes more complicated as more stakeholders and departments are involved. Establish protocols and use tools to prevent inconsistent and inaccurate data from building up.
Data privacy
Moving to a cloud-based data solution presents new opportunities for cyber attackers. Manage risk with digital credential and user permissions to ensure compliance and security.
Poor infrastructure
Out-of-date infrastructure can be overwhelmed by modern collaborative data requirements. Collaborate with experts like your infrastructure architects to build a robust, modern solution.
To enhance your data management and development capabilities, you might look to hire Ruby on Rails developers, to ensure that your technical infrastructure meets the needs of your collaborative analytics strategy.
What are the Four Types of Collaborative Data Analysis?
Like most things in business, collaboration has no one-size-fits-all approach. You can tailor your collaborative analytics strategy to fit organizational needs. Luckily, you don’t have to figure everything out yourself, as there are several models to serve as your guide.
Real-time Collaboration
Real-time collaborative analytics is where cross-departmental team members work together simultaneously. They share data, build visualizations, and produce insights. Tools like Google Sheets and Miro enable you to collaborate instantly.
Real-time collaborative analytics assists in making agile business decisions. You get feedback and insights as soon as they’re ready.
Asynchronous Collaboration
Asynchronous collaboration involves team members working together but at different times. This works well for multi-national or remote teams with different work hours. Asynchronous collaborative analytics allows people to add input at separate times.
In asynchronous collaborative analytics, one team member contributes to the initial data analysis. One or more members then manipulate data and give feedback, producing collaborative insights.
Crowdsourced Collaboration
Collaborative analytics don’t only involve the members of your team. Crowdsourced platforms like Clickworker pool analytical input from people with diverse backgrounds. You gain a wider interpretation of industry and organizational data.
However, crowdsourced analytics may not have the same internal focus as other methods. Therefore, it can produce results less specific to your organization.
Hybrid Collaborative Analytics
Why only use one form of collaborative analytics? A hybrid approach uses two or more types of collaborative analytics to accomplish your business objectives. You can tailor your data strategy to fit different projects for increased flexibility.
7 Ways Collaborative Analytics Improves Workplace Dynamics
Establishing effective collaborative analytics takes some work; what makes it worth it to organizations like yours? All the many ways in which a collaborative approach enhances your business.
Let’s examine some of the top ways collaborative analytics improves your workplace.
1. Democratizes Data
A collaborative approach to data usage democratizes data. All key stakeholders have input on processes such as data collection and data handling. More importantly, you break down departmental data silos.
Everyone has access to the same data. Your team is empowered to give feedback and assist in data-driven insights.
Each employee can actively participate in the analytical process and add their expertise. You get to leverage the knowledge of your domain experts to improve decision making. Gain a deeper understanding of what the data is telling you and get a leg up on the competition.
2. Increases Transparency and Builds Trust
Greater transparency is the standard expectation of employees today. A Deloitte report found that 86% of workers think it’s critical to increase organizational focus on trust and transparency. A collaborative approach ensures every team member knows what data is available. Everyone also knows who’s responsible for datasets and how they are used.
Deloitte also found that only 52% of organizations are doing anything to improve trust and transparency. The same report found that organizations “doing great things” to build trust are twice as likely to achieve desired outcomes.
3. Promotes Collaboration
Collaborative analytics promotes workplace digitalization, which fosters teamwork. Gone are the old days of file cabinets and dialing department extensions with a traditional business phone system.
Collaboration tools streamline the sharing of data analysis between departments. Once teamwork starts producing results, a positive feedback loop reinforces collaboration. Over time this can lead to greater results.
For example, your accounting team has all the budget data on your existing tech stack. Your IT managers look over the data and see the budget is nearing the limit. They find tools to consolidate capabilities and replace less-used apps. Now the accounting team knows that IT can help them hit their KPIs and they can ask for their advice in the future.
4. Improves Accountability
Accountability reinforces trust and fosters an open, collaborative work environment. However, a Culture Amp survey found that more than half of employees don’t believe leaders and fellow team members are being held accountable. A lack of accountability made respondents feel 13% less engaged at work.
A collaborative strategy brings all stakeholders to the same playing field. If someone drops the ball, it will affect others’ performance when it comes to data analysis. Knowing that one collaborator’s failure can produce a domino effect encourages self-accountability.
Task assignments, progress, and insights are available to stakeholders in real time. Every project member can see what everyone is up to, including managers and executives.
Even when the worst happens, teams have access to the data and are empowered to chase up incomplete work.
5. Foster Creativity
A recent report from the World Economic Forum found that analytical thinking was the most important core skill for workers. Creative thinking ranked as the number two vital core skill when acquiring top talent.
You invest many resources into recruiting, training, and developing employees. Collaborative analytics helps improve ROI on your efforts. Breaking down data silos gives access to more diverse talent and promotes creative solutions.
Data democratization encourages your team to think outside the box and try new things.
For instance, a team member gains access to datasets they’ve never used before. Using niche knowledge, they produce new insights that would otherwise stay hidden.
6. Increases Agility Through Shared Insights
Collaborative analysis produces faster results no matter which approach you take. Stakeholders from cross-functional teams know active progress is being made.
Communication enhances teamwork for faster problem-solving. Project management tools notify the right people when someone completes a task. Leaders can continually monitor results and access updates immediately.
When data analytics are locked away in ivory towers, key stakeholders miss out on information. Don’t keep your analytical insights in jail. Get everyone on board and benefit from rapid decision-making.
7. Promotes Data-Driven Decision-Making
You implement an analytics strategy to make better decisions and grow your business. S&P Global reports that only a little more than 25% of organizations use data to make “nearly all” of their decisions. Almost 1 out of 10 firms only make a few decisions based on data. They also report that 30% of organizations don’t use data to make most business decisions.
Data integration platforms like Apache Hadoop or NoSQL databases centralize large volumes of information. Integrating data produces more accurate analytics and predictive modeling. Communication tools ensure easier data sharing and enhanced teamwork. Your team will sprint to new and creative insights.
With more data on hand, stakeholders are better informed to make any and every business decision. The more decisions you make backed by data, the further you’ll get ahead of your competitors.
Take a Collaborative Analytics Approach and Reach for the Stars
Don’t get stuck using data in narrow, rigid ways. Build a collaborative analytics strategy that leverages the full knowledge of your organization. Your team will produce innovative ideas and discover insights that grow your business.
When markets change, you’ll have cross-functional teams collaborating on the analysis. They’ll find new opportunities as they develop to keep you ahead of your competition. There’s no better time than now to meet with key stakeholders and integrate your analytics data.
FAQs
How Does Collaborative Analytics Work?
Collaborative analytics brings stakeholders together, including non-technical users, to handle and interpret datasets. All data is integrated into a centralized platform for data governance and analysis. Organizations use communication and collaboration tools to work together and glean new insights.
What are the Benefits of Collaborative Analytics Tools?
Collaborative analytics tools streamline greater data accessibility and data sharing. Stakeholders can easily collaborate on projects, finding new ways to use and reuse data. You get faster insights and more data-driven decision-making.
What is Collaborative Data Collection?
Collaborative data collection is the process of cross-functional data sharing and gathering. Data is collected and stored in a centralized warehouse where data is validated, cleaned, and governed. Collaborative data collection empowers your team to use data in creative ways. They can use cross-functional data to enhance and enrich decision-making.
