How to Bolster Collaborative Analytics with Cloud Data Pipelines

Cloud-native tools fuel greater collaboration and creativity with best-in-class technology that helps companies get insights quickly. However,   data and BI analysts need additional features to bring analytics projects to production. Here, Dave Lipowitz, Solution Architect at Matillion, highlights key steps that data science teams can take to streamline collaborative analytics projects in the age of the pandemic. Also, learn how cloud data warehouses can pave the way for better data management and data sharing capabilities.

In the new normal of remote work, collaboration and communications are imperative to keeping pace with workloads while supporting business efforts. In a recent survey from IDGOpens a new window , half of the data professionals surveyed stated that data control and ownership issues are the biggest challenges to data analytics projects. 

Collaborative analytics, using cloud-native technology, allows for more people within an organization to self-serve for their data initiatives. Many tools enable remote collaboration through sophisticated privilege management and data sharing capabilities. 

But for IT and business users to bring analytics projects to production frictionlessly, other additional features can be helpful such as those found in cloud data pipelining, warehousing, and visualization platforms. Here are some capabilities to look for when streamlining collaborative analytics projects.

Centralize Data in a Scalable Cloud Data Warehouse (CDW)

The first step to collaborative analytics is deciding where the data will live so different teams can access it. In that same IDG survey, 40% of respondents cited the lack of visibility and control into the data silos as a challenge when collaborating with business users. A CDW is a great place to centralize data due to its scalable, flexible nature that can grow along with your project. Ensure you consider all business needs before selecting the cloud data warehouse that will work for your company. 

A few things to consider, for example:

• Are there other parts of your business that already use a cloud data warehouse?
• What are the compliance and governance requirements you need to adhere to?
• Other than self-service, what use cases will the cloud data warehouse support? Some cloud data warehouse vendors provided differentiators that make data sharing or machine learning easier to manage, for example. 

Learn More: Remote-First is Driving a Collaborative Approach to BI

Create ELT-driven Data Pipelines That Favor Visual, Reusable Workflows

If you’re on board with the CDW recommendation above, then the ELT approach should be favored as well.  Cloud data warehouses have native, bulk load commands that can drastically speed data ingestion. Once loaded, data can be transformed into analytics-ready insights within the CDW itself, easily and at scale. The best practice for this approach is for data engineers to stage source data as-is, transform, and cleanse that newly staged data, and then publish the results for downstream consumers. There are a few different ways to publish the data and the method should be chosen with the intended audience in mind. The options  — truncate and load, data surgery, append, blue/green  — all have their benefits and drawbacks. 

Whichever you choose, it’s important to enable collaborative analytics and data self-service with a visual tool that supports reusable logic. This allows data and business users alike to speed up development by capitalizing on work that has already been done. Visual drag-and-drop workflows are easy to extend, even for the busy data engineering team.  Business stakeholders can self-serve as well given that visual interfaces offer code-free or code-optional tools for building ELT workflows. 

Implement Low Code/ No Code Data Visualization Tools

Data visualization tools provide business analysts and citizen data integrators the ability to understand and act on insights. To encourage the use of data, IT should incorporate tools that do not require technical proficiency and have the intuitive nature of SaaS solutions. This will help with the adoption of IT-approved data tools, keep project compliant, and enable users closest to business processes to visualize the data they need to see.

Best Practices for Data Self-Service:  

• Test a use case first – Make a plan for incremental data loads to ensure you can acquire data from a number of sources. Run a proof-of-concept (PoC) with a few key stakeholders to test and improve before rolling out to the wider team.
• Think through access for all – Understand how and when you will make data accessible to parts of the business
• Keep security top of mind– Ensure all cloud data tools follow the compliance and security standards of your company
• Train the team –  It is very likely that business users have had limited exposure and experience with cloud data analytics. Perform thorough training for end-users so that data teams and business users know how to use the tools. 
• Choose the right products– The great thing about cloud-native solutions is the flexibility, scalability, and ease of use they provide. Select tools that are graphical and intuitive for less technical users. 

Learn More: Cloud Collaboration Tools: Risks vs. Rewards

Collaborative Analytics Made Possible by Cloud Data Solutions

The cloud and cloud-native tools enable better collaboration and innovation with best in breed technologies that help organizations get to insights quickly. IT and business users alike can perform collaborative analytics using graphical, intuitive cloud solutions to bring together siloed data and transform it into a format usable for analytics. 

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