Gartner‘s 2020 Magic Quadrant Reveals the Top BI and Analytics Platforms

Selecting the right business intelligence (BI) and analytics platform is one of the most critical decisions an organization can make. These strategic tools play a key role in enabling companies to extract insights from their data assets to drive smarter, faster decisions. But the BI market is crowded and complex. There are dozens of platforms to choose from, each with different capabilities, use cases, and price points.

To help companies make sense of their options, Gartner publishes a yearly Magic Quadrant report that evaluates leading vendors based on their "completeness of vision" and "ability to execute." The 2020 Magic Quadrant for Analytics and Business Intelligence Platforms provides an in-depth look at 22 top vendors, their strengths and weaknesses, and key market trends.

Key Takeaways from the 2020 Magic Quadrant

Gartner‘s 2020 report reveals a dynamic BI and analytics market being reshaped by innovation in artificial intelligence (AI), machine learning (ML) and the cloud. Here are some of the key takeaways:

• Augmented analytics is the future: Capabilities like automated insights, natural language processing (NLP), and conversational analytics are quickly becoming table stakes. Gartner predicts that by 2025, data stories will be the most common way of consuming analytics and 75% will be automatically generated using augmented analytics techniques.

• Cloud ecosystem integrations are a must-have: Organizations are increasingly looking for BI platforms that integrate seamlessly with their cloud database and ML services. By 2023, Gartner expects cloud ecosystems to influence 65% of BI buying decisions.

• BI is becoming essential to digital transformation: The COVID-19 pandemic has accelerated companies‘ digital transformation efforts. BI and analytics platforms are a key enabler, providing the data-driven insights to navigate disruption. Gartner expects 90% of corporate strategies to explicitly mention data as a critical enterprise asset by 2023.

• Data literacy and user adoption remain challenges: Despite innovation in ease of use and augmented analytics, many organizations still struggle to drive BI adoption beyond power users. Developing a data-driven culture requires more than just deploying a platform.

Augmented Analytics Powers the Future of BI

Historically, working with data required deep expertise in statistics and data modeling. But as the volume and complexity of data continues to grow, it‘s impossible for human analysts to keep up. That‘s why augmented analytics, which leverages AI and ML to automate many aspects of data analytics, has become so critical.

Leading BI platforms like Microsoft Power BI, Tableau, and ThoughtSpot are investing heavily in augmented analytics capabilities. Here are some of the key use cases:

• Natural language query and generation: Enables users to ask questions about their data in plain language and receive explanations of the results in narratives that are easy to understand. Example: "What were sales by region last quarter and why?"

• Automated insights: Continuously analyzes data to uncover insights and anomalies proactively, without requiring the user to build models or dashboards. Directs users to the most important changes in the data.

• Augmented data prep: Uses ML to automate and accelerate data integration, profiling, and transformation. Detects data quality issues and recommends how to combine different data sources.

The impact of augmented analytics on organizations is significant. By automating routine tasks and insight generation, it enables companies to scale data science capabilities and empowers business users to be more self-sufficient in their analyses. And the ROI can be substantial.

For example, TDWI research found that organizations using automated ML and augmented analytics tools were 2.6x more likely to report a transformational business impact from their analytics efforts vs. those not using them. And Gartner predicts that by 2023, organizations leveraging augmented analytics will see 2X the data-driven business decisions.

Evaluating the Leaders in BI and Analytics

While augmented analytics is a key trend shaping the future of BI, there are many other important capabilities to consider when evaluating platforms. Let‘s take a closer look at how the leaders in Gartner‘s Magic Quadrant stack up across key areas:

Source: Gartner Magic Quadrant for Analytics and BI Platforms, 2020

As the table shows, these leading platforms have different strengths. Microsoft Power BI offers powerful functionality at a competitive price and integrates with Azure‘s cloud and AI/ML services. Tableau is known for its intuitive, visual-first user experience and community. Qlik provides strong associative analytics capabilities. And ThoughtSpot is a leader in augmented analytics with search-driven insights.

The Impact of Big Data and the Cloud

Another key trend impacting the BI market is the massive growth of big data and the shift to the cloud. Companies are dealing with exponentially more data coming from more sources than ever before – operational databases, SaaS applications, IoT sensors, social media, and more. At the same time, data is increasingly moving to the cloud to take advantage of greater scalability and flexibility.

BI and analytics platforms need to evolve to enable companies to extract insights from these growing data assets. That means:

• Connecting to and combining a wide variety of data sources, both on-premises and in the cloud
• Processing massive, complex datasets with high performance
• Scaling cost-effectively to support large numbers of users
• Enabling real-time insights to power use cases like operational reporting and predictive maintenance

This is why integration with cloud database and AI/ML services has become so important in BI platform selection. Leading vendors are focused on making it seamless to access and analyze data across multi-cloud environments:

• Microsoft allows customers to leverage the Azure Synapse Analytics platform for data integration and Power BI‘s direct query capabilities to analyze data in Azure Data Lake Storage or Cosmos DB.
• Qlik offers Qlik Data Integration to automate streaming data pipelines across on-premises and multi-cloud environments.
• Tableau integrates with all major cloud data warehouses and launched the Tableau Data Management Add-On to simplify connecting to data across the enterprise.

Overcoming Challenges to Drive Adoption and Value

Despite significant advancements in capabilities, many companies still struggle to drive widespread adoption of BI tools and build a data-driven culture. Gartner estimates that only 30% of potential users in an organization on average actively use analytics.

Common challenges include data literacy, user experience, governance, and change management. To maximize the value of BI investments, data and analytics leaders should:

• Make data literacy an organizational priority: Formal training is key but it‘s equally important to integrate data into existing processes and promote data-driven decision making.
• Prioritize ease of use: Evaluate platforms through the lens of a business user. Augmented analytics will help but intuitive visualizations and collaboration are also key.
• Implement flexible governance: Balance control with business agility via a centralized/decentralized model. Consider governance features as part of platform evaluation.
• Treat it as change management: Focus on people and process, not just technology. Identify and support champions and communicate the value of data-driven decisions.

The Future of BI and Analytics

Looking ahead the next 3-5 years, I believe the trends around augmented analytics and cloud will accelerate. Natural language interfaces will become the primary way business users interact with data. And AI/ML will increasingly be applied to optimize the entire data pipeline, from ingestion to insight.

We‘ll also see BI and analytics continue to converge with adjacent markets like data science and embedded analytics:

• BI platforms will add more predictive and prescriptive capabilities to deliver forward-looking insights, not just historical reports
• Analytics will be embedded in every major enterprise application and workflow to provide in-context insights
• Data storytelling will emerge as a key feature to help communicate insights and drive action

As a result of these shifts, the barrier to entry for driving value from data will continue to fall. But at the same time, I expect the leading platforms to add more enterprise features around governance and security to meet the needs of large, global deployments.

Selecting the Right BI and Analytics Platform

With so many choices available, what‘s the best way to select a BI and analytics platform? Here‘s a framework I recommend:

1. Define your use cases: Prioritize the decisions and processes you want to support with analytics. Consider both strategic and operational use cases.

2. Determine key requirements: Develop criteria around critical capabilities like data source connectivity, performance, advanced analytics, and deployment model.

3. Assess organizational maturity: Be realistic about the skills and processes you have to support the platform. Fill gaps with augmented analytics and services.

4. Evaluate TCO: Look beyond licensing costs to understand the all-in cost of the platform, from implementation to migration. Develop an ROI model to quantify the business value.

5. Conduct a proof of concept: Put 2-3 platforms to the test with a real use case to assess capabilities and validate ease of use with stakeholders.

When evaluating platforms, these RFP questions can help:

• How does the platform ingest and integrate data from different sources (on-premises and cloud)? What connectors are available?
• Describe the platform‘s augmented analytics capabilities for automatically uncovering insights, generating explanations, and recommending visualizations.
• How can business users collaborate and share insights both inside and outside the platform? Is mobile supported?
• What is the scalability of the platform as data volumes and numbers of users grow? How does user experience and performance change at scale?
• How robust is security, including access controls, data encryption, and compliance certifications?

Conclusion

BI and analytics platforms have evolved significantly in recent years thanks to AI/ML innovation, the shift to cloud, and the rise of augmented analytics. Organizations have more choice than ever in platforms that range from visually-driven data discovery to automated insights powered by NLP.

Gartner‘s Magic Quadrant provides a comprehensive look at 22 leading vendors and the trends shaping the market. But selecting the right platform requires carefully defining requirements and rigorously evaluating capabilities through the lens of your business users.

One thing is certain: BI and analytics will only continue to grow more critical to organizations‘ ability to compete on data-driven decisions. By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.