Cracking the Code to Scaling Analytics

Choosing the right data analytics platform is critical to the success of an organization's data analytics initiatives. With a mature and crowded data analytics marketplace that offers a dizzying array of features, it's tempting to believe that a flashy new data analytics platform can solve all your company's pain points quickly. But it's important to approach the matter with realistic expectations, especially considering that less than 20% of organizations have achieved analytics at scale, with people benefiting from insights enterprise-wide.

A key factor in scaling analytics is the number of people who can effectively make sense of data using the chosen data analytics platform. Data analytics initiatives often involve multiple stakeholders across different departments or teams within an organization, a mix of skill sets – data scientists, data engineers, business analysts, and line-of-business users. The project is more likely to succeed if your analytics tool can facilitate collaboration and knowledge-sharing among these stakeholders and at the same time enable more such stakeholders to self-sufficiently engage with data.

The right data analytics platform can provide more than just efficiency for data experts to perform their tasks. It can also play a vital role in advancing basic data understanding within the enterprise. By simplifying complex technologies and making them more transparent, the platform can increase data accessibility to users of various skill levels and encourage broader participation in data-driven decision-making processes.

After confirming that the data analytics platform can connect to all your data sources and types, as well as provide the necessary level of analytic sophistication, consider the following factors to ensure successful enterprise-scale adoption.

When evaluating a data analytics platform, it's essential to prioritize ease of use. A key feature to look for is the ability to quickly create and deploy models without needing prior coding knowledge. The true test of a platform's no-code capabilities is the extent to which users can build analytical models without writing a single line of code.

Some platforms make it easy to build models but require coding for certain production aspects, such as parameterizing the model for data applications. This is why the "no-code" capability of the platform is crucial, enabling users of all levels to build and deploy models with ease.

Readymade solution blueprints are a useful resource for users who want to start building analytics workflows and apply them to their business case. Instead of starting from scratch, users can download freely available blueprints and make their own customizations as they become familiar with the tool. This approach empowers technical and enablement teams to upskill non-technical teams faster, freeing them to tackle more complex tasks.

Providing users with ample pre-built examples enables them to begin using data analytics platforms swiftly and self-sufficiently. It encourages them to explore and experiment with the tool without being intimidated by a blank canvas, allowing them to start creating more sophisticated workflows faster.

In this regard, look for vendors with an active community of users so that you get access to the working examples that are constantly added and are freely available. A robust community lets you learn from experts, even those outside your organization, with contributors sharing knowledge, ideas, and answering queries.

Another important facet is the availability of learning resources like self-paced courses and certifications from the vendor. A well-defined learning path with courses covering topics for both beginners and advanced users can help you get up to speed quickly.

Data science success relies heavily on teamwork and collaboration. The use of no-code/low-code platforms presents an ideal setting for fostering efficient team collaboration.

One of the key aspects to look out for when selecting a vendor is their capacity to bundle and share expertise across various disciplines.This functionality ensures that technical specialists like Python experts and ML engineers can share their work in an accessible form with non-experts.

In addition, teams of similar expertise levels should be able to work together effortlessly on the same project, building on one another's progress. With the ability to share and version-control data solutions, team members can work collaboratively and productively.

The lack of transparency around data is a costly risk. No-code/low-code data analytics platforms model the data process visually, offering far greater transparency than scripted models.

However, when it comes to machine learning or other advanced techniques, it can be challenging even for a data analyst to understand what is happening to the data.

To comply with regulatory requirements and adhere to industry best practices, organizations need to capture what is being done to the data at each stage in a verifiable manner. They need to be aware of the precise libraries used during execution and any non-controllable actions that may have occurred, such as external package calls. This detailed knowledge is essential for further investigative activities if needed.

A key piece in the puzzle of choosing the right data analytics platform is a consistent, repeatable path to deployment - i.e. making the results of analysis available for end users to consume through dashboards or data apps, or making them available as APIs for integration with a third-party tool.

It is still too common for results of the analysis to be transferred to a separate environment, causing lots of friction, delays, and increased chances of errors. When evaluating a vendor, it’s important to consider how easy it is to take the model or data preparation routine that has been built and apply it to a production environment, and where models can be deployed — whether in the cloud, on-prem, or hybrid.

The ideal platform would let you deploy data science outcomes, whether through dashboards, data science services, or full-fledged analytical applications, in the same environment where the analysis was performed.

The adoption of a data analytics platform can be a costly investment. As such, it is essential to evaluate the total cost of ownership, especially with a growing internal data user community. By providing cost-effective solutions at the outset, businesses can build support and enthusiasm for investing in features that support large-scale data science productionization.

Anyone who builds analytic solutions with the low-code, no-code KNIME Analytics Platform can leverage KNIME Business Hub to scale these insights across the enterprise. With KNIME Business Hub, users get a single, scalable environment to share, reuse, and collaborate on visual workflows. Multiple users can work together effortlessly with version control and auditability.

The scalable, cloud-native architecture enables organizations to support an unlimited number of users, running any number of models, which are then deployed to any number of consumers. While IT has centralized control, teams can act independently and manage their own resources within parameters specified by IT. The result is high scalability, with minimal burden on IT.

This suite of features enables organizations to increase analytics adoption and accelerate the spread of data-driven decisioning.

KNIME has a vibrant open-source community of hundreds of thousands of users who share their data science solutions and extensions as components and workflows on KNIME Community Hub to help users accelerate their time to value. The solutions span across use cases and industries, with dedicated public spaces for manufacturing, financial services, retail & CPG, FP&A, marketing analytics, customer analytics, HR analytics, and much more.

Successfully creating and productionizing data science in an organization requires a collaborative end-to-end environment that allows everybody from the data analyst to the business owner to work closely together.

Moving data science into production does not simply mean moving a workflow to production but involves additional tasks beyond traditional deployment such as testing, validating, monitoring and updating the workflow to ensure that the output remains reliable.

While organizations may share common concerns about deployment practices, the actual process of deployment can vary significantly. Factors such as the organization's size, industry, available resources, and IT and governance requirements play a crucial role in shaping the deployment process. As a result, any features or frameworks provided by the vendor must be highly flexible and adaptable to accommodate the unique needs of each organization.

Check out our latest e-Guide to discover more about this and other factors you must take into account while choosing a data analytics platform.