Here’s Why BI Tools Should Be End-to-End


Your organization is likely flooded with big data. You have massive, complex, and high-velocity datasets coming in from many sources. And you want to use all this information to make better, data-driven decisions that move your business forward.

Data analytics alone is not enough to bridge the gap between that raw data and actionable insights. That’s why today’s business intelligence (BI) tools must take a holistic approach and bring together data integration and advanced data analytics.

Your BI tool should first be able to integrate data and offer a data catalog to make all data easily accessible to all users. It should then help you understand your data via augmented analytics and let you embed your analytics anywhere. Below we describe each of these four capabilities:

  1. Data Integration involves bringing together data from many sources to give you accurate, complete, and up-to-date data for analysis. The best BI tools offer key processes such as data replication, ingestion and transformation. These processes turn a variety of data types into a standardized format which is stored in a data warehouse, data lake or data lakehouse.
  2. A data catalog optimizes existing data assets and then transforms them into business-ready information. A simple marketplace interface makes it easier for you to manage, prepare and deliver analytics-ready data to you and other business users.
  3. Augmented analytics uses artificial intelligence to provide you with better insights, more quickly. The best business intelligence tools integrated with your data sources provide self-service intelligent alerts and advanced statistical trending, and anomaly detection that immediately notify you of material changes in your data.
  4. Embedded analytics puts actionable analytics features and data into any application, process, product, or workflow. This brings the analysis to where you and others make decisions. Your tool should make this easy by providing open APIs and an extensible platform.

So yes, data visualization, dashboards, and reporting are still important capabilities. The Gartner Magic Quadrant report for BI gives an unbiased comparison of BI vendors on one set of criteria. But today, your business intelligence service should be a holistic solution. Another way to break it down is between “data services” and “analytics services”.


The best BI tools allow for hybrid data delivery, which means they can ingest data from a wide range of both on-premises and SaaS databases and applications. They also support data warehouse automation by generating ETL code and ongoing updates, to reduce the time, cost, and risk of maintaining cloud data warehouses. They transform data by using a push-down ELT approach to converting, joining, enriching, consolidating, and standardizing data from heterogeneous structures and formats. Lastly, application automation streamlines workflows between SaaS applications and the BI tool to trigger action.


Here, top BI tools will support best-in-class, interactive data visualizations and dashboards which include guided discovery, free-form exploration, and search capabilities. As described above, augmented analytics leverages AI to make contextual suggestions, let you interact with natural language, and auto-generate visualizations and predictions. And your BI tool should have open APIs so you can embed analytics in other operational apps, bringing insights into where your users work. Your data is constantly changing, so your tool should provide alerting & action. This not only informs you of changes but triggers automatic event-driven actions in other apps.


There are many business intelligence tools on the market and finding the right one for your organization can be challenging. So, when evaluating tools, you should:

  • Find a true end-to-end solution that can bridge the gap between your raw data and actionable insights.
  • Determine which capabilities are most important to you and focus on them first.
  • Ensure that your tool’s deployment options are compatible with your overall data strategy; it should have a platform-agnostic, multi-cloud architecture.
  • Understand the total cost of ownership of the tool, which means digging into the extra resources and infrastructure needed to meet your expectations.
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