Analytics Without Engineering is Just Reporting 
Relying on traditional desktop BI tools can hinder competitiveness as your business evolves. Transitioning to a future-proof analytics solution with features like advanced automation, scalability, AI-driven insights, and strong data governance is essential for driving innovation and growth. This whitepaper breaks down the key features and explains why choosing a tool without them could be a missed opportunity.
Analytics as Code for Automation
Analytics as Code (AaC) is crucial for businesses aiming to stay agile and responsive. It treats analytics tasks — such as data transformations, model building, and reporting — just like software code. This means the processes can be scripted, versioned, and managed, making it easier to automate tasks such as deployment and testing. By reducing manual effort and minimizing human error, AaC streamlines workflows and boosts efficiency.
Keep in mind!
Legacy BI tools may lack Analytics as Code capabilities, resulting in more manual processes and less flexibility for data teams. This means slower turnaround times, increased risk of errors, and reduced ability to adapt quickly to changing business needs.
A primary benefit of AaC is its ability to facilitate collaboration among data teams. Concepts like version control and continuous integration/continuous deployment (CI/CD) allow data professionals to collaborate. Multiple team members can contribute to the same analytics project, track changes, and ensure that everyone is working with the most up-to-date version of the code. This speeds up the development process and ensures that analytics outputs are consistent, accurate, and aligned with your business objectives.
In modern analytics platforms, Analytics as Code is often augmented with AI-driven tools that further streamline the workflow. These tools can suggest optimizations, detect anomalies, and generate code snippets, making it even easier for teams to develop and deploy analytics solutions.
Multitenancy for Scalability
Multitenancy is a key feature of any future-proof analytics tool. It enables a single application instance to serve multiple customers or departments while keeping their data and configurations distinct and secure. A multi-tenant architecture will allow your organization to manage and analyze data efficiently across different units without needing separate systems, thereby reducing costs and simplifying management.
Keep in mind!
Legacy BI tools often lack multitenancy, resulting in fragmented systems that are difficult to manage. This limitation drives up operational expenses and licensing costs, as desktop BI tools typically charge per user — forcing you to purchase additional licenses for new teams. Ultimately, these challenges make it harder to scale your analytics infrastructure, potentially limiting your ability to grow.
In a multi-tenant platform, changes and updates can be easily administered from the parent workspace to other tenants. Each tenant operates in an isolated environment, ensuring data privacy while offering a customized analytics experience.
Multitenancy typically leads to lower operational costs and greater scalability. By consolidating infrastructure, your organization can avoid the expenses of maintaining separate systems and be confident that the analytics solution can grow to accommodate increases in data volume, assets, and users.
Custom Embedding for Optimal Data Delivery
A key feature of modern analytics tools is the ability to seamlessly distribute insights through various channels, ensuring that data reaches users wherever they need it. One of the most effective ways to do this is through embedding, which will allow your organization to integrate metrics, dashboards, and AI-powered insights directly into existing apps, data products, or web portals. This approach ensures your users can access real-time analytics without leaving the tools they use daily, enhancing both convenience and productivity.
Keep in mind!
Legacy BI tools often lack flexible embedding capabilities, limiting the ability to distribute insights where they’re needed most. This means missed opportunities to integrate data seamlessly into workflows or apps, resulting in less accessible insights and slower decision-making across teams.
A modern analytics platform will offer flexible embedding options, including React SDK, Web Components, or iFrame, to suit different technical environments. This flexibility makes it easy to incorporate analytics into a variety of platforms, from internal systems to customer-facing products. Note that embedding offers more than just convenience — it allows for complete customization and branding. The best analytics tools enable you to white-label the analytics, ensuring the entire experience matches your brand’s look and feel. This not only improves user experience but also strengthens your company’s identity across its digital ecosystem.
Robust Analytics Engine for Better Performance and Lower Costs
A robust analytics engine is essential for modern tools to deliver high performance while controlling costs. This engine should enable your organization to combine data from multiple sources, perform complex calculations without overburdening the infrastructure, and provide monetization opportunities (we’ll discuss this in more detail later).
An optimal platform might achieve this through a smart in-memory data service layer, which federates and transforms data. Features like intelligent caching enable you to manage data warehouse costs more effectively, ensuring that resources are used only when necessary.
Keep in mind!
Legacy BI tools often struggle with limited processing power and inefficient data handling, leading to slower performance and higher infrastructure costs. This means longer wait times for insights and increased operational expenses.
The best analytics engines can connect to various data sources, whether on-premises, private clouds, or public cloud environments. This flexibility will allow your organization to deploy your analytics solution in the way that best suits your needs — enhancing performance and reducing costs.
Options for Turning Data Into Revenue
A modern analytics tool will allow your business to create monetizable experiences, turning your data into a revenue-generating asset. An example of this is packaging and offering analytics as a service, either through subscription models, tiered offerings, or embedded analytics products.
Keep in mind!
Legacy BI tools often fall short when it comes to supporting monetizable experiences, limiting your ability to generate revenue from data. This means missing out on new income streams and opportunities to boost customer engagement via value-added analytics services.
The best modern tools offer features designed for easy monetization, such as the ability to create customized dashboards, interactive reports, and AI-powered insights. These tools allow businesses to white-label their analytics products, providing branded experiences that add value to customers and drive revenue.
For business leaders, this capability transforms analytics from a cost center into a revenue driver. By leveraging data in this way, you can not only deliver deeper insights but also create sustainable business models that capitalize on the increasing demand for data-driven solutions.
Augmented Analytics for Faster and Deeper Insights
Augmented analytics transforms the way you interact with your data by integrating artificial intelligence (AI), business intelligence (BI), and machine learning (ML). This powerful combination enhances the analytical process, enabling you to ask questions, detect anomalies, and forecast trends with greater accuracy. By leveraging AI and ML, modern analytics tools provide deeper, more actionable insights that go beyond traditional reporting and data visualization.
Modern tools use augmented analytics to automate complex tasks such as data preparation, pattern recognition, and predictive modeling. This speeds up the process of gaining insights and democratizes data analysis by making advanced capabilities accessible to all users, regardless of their technical expertise.
Keep in mind!
Legacy BI tools often lack a semantic layer, which is crucial for unlocking AI's full potential. Modern tools equipped with a semantic layer offer advanced AI capabilities, enabling faster, more accurate decision-making and the ability to proactively address trends and anomalies — keeping you ahead of the competition.
Why Upgrading From Traditional BI Tools Can’t Wait
Switching to a modern analytics tool with some or all of the above-mentioned features will position your organization to seize future opportunities. GoodData offers all these features and more. Request a personalized demo to discover how the platform can drive success for your organization now and in the future.
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