Facing increasingly high customer expectations, new competitive pressures, and an uncertain economic outlook, business leaders need their data to deliver more value than ever before. So, how do you maximize value from data?
Here’s the good news: Big data analytics tools can empower everyone in your organization to gain meaningful insights from relentless waves of data. In fact, research by McKinsey shows that companies using a data-driven approach report above-market growth and an increase of 15-20% in profits. But with so many options, how do you choose a tool that aligns with your business needs? The answer lies below.
Table of contents:
1. Handles large volumes of data
As the name applies, the ability to handle large data volumes is a key requirement for big data analytics tools. That’s because you need a scalable big data analytics solution with real-time processing features to handle your organization’s current and future data volume. Moreover, the platform should support connectivity with multiple databases and cloud data platforms. This will help you analyze the dormant data in your data warehouses and identify hidden growth opportunities.
2. Supports interactive data visualization
Spreadsheets are boring and quite frankly hard to read. Turning complex datasets into data visualizations helps you present data in a digestible, contextual, and actionable way. As you qualify solutions, ensure that your big data analytics tool has a simple and intuitive user interface that allows users to customize their dashboards and create interactive data visualizations so everyone can understand data and make timely decisions.
3. Features innovative AI capabilities
Big data analytics tools aren’t limited to running ad-hoc queries and creating visualizations. AI-powered big data solutions offer capabilities like natural language search and machine learning to help business users and data teams get into the nitty-gritty of data and find valuable insights.
For instance, business users can use AI-powered solutions like ThoughtSpot to ask questions in natural language and get instant AI-assisted answers, visualizations, and recommendations. On the other hand, data teams can leverage conversation AI to dig deeper into their data—asking questions and follow-up questions just as easily as talking to a coworker on Slack. These features significantly reduce time-to-insight and promote timely decisions across the organization.
4. Has real-world use cases
How can you tell if a product is effective in the real world? You have to put it to the test. To establish a vendor’s credibility and demonstrate the solution’s ability to solve complex business problems, check out customer case studies and reviews for up-to-date information about the platform’s capabilities, performance, and customer support. We also recommend exploring the product beforehand through demos, as it will allow you to understand the platform’s capabilities.
5. Delivers self-service analytics
Instead of gatekeeping insights to a select few analysts and IT specialists, your big data analytics tool should deliver self-service analytics. By democratizing data and allowing every business user to find insights on their own, you empower every decision-maker to find the information they need when they need it. With self-service analytics, users can create detailed reports and live dashboards, and monitor business performance on the go, enabling data-driven decisions rather than relying on their gut feelings.
This may be a shameless plug, but the truth is—our entire team and our customers rely on ThoughtSpot to make strategic business decisions at scale. ThoughtSpot offers a AI-powered, search-driven approach to data analysis that empowers all users to ask questions in natural language and get instant answers through interactive data visualizations.
And we haven’t forgotten about your data team, they can leverage features such as AI-assisted data modeling, human-the-loop feedback, verified Liveboards, and a centralized governance system to prevent unauthorized access and promote security.
By replacing traditional analytics tools with ThoughtSpot, Wellthy saved over $200K in costs by streamlining the analytics pipeline. Now the data team has more time to work on what’s important, and the partners, customers, and internal team can find all the answers they need through natural-language search or verified Liveboards.
Here’s a quote a Kelly Burdine, Head of Data Science and Analytics, at Wellthy, highlighting the same:
Core features of ThoughtSpot:
AI-powered insights, answers, and recommendations with Spotter, the AI Analyst for everyone
Interactive, verifiable, personalized Liveboards for KPI reporting
Predictive data analytics to forecast future outcomes
Multi-modal experience that pushes insights into your business apps
Enhanced data modeling and exploration
Easy set-up and connection with multiple cloud data sources
Seamless collaboration and sharing
Experience the power of AI-Powered Analytics with a free ThoughtSpot trial.
Next on the list of big data analytics tools is Mode. This versatile, big data analytics tool helps you collect, model, analyze, and visualize data. With its integrated notebook environment and intuitive drag-and-drop SQL editor, analysts can easily process queries, explore data, and run advanced data analytics techniques to find hidden insights. The code-free visualization capabilities also help business users build interactive visualizations, identify relevant trends, and share key findings with stakeholders.
Consider Shopify. Before, the e-commerce giant Shopify struggled to make sense of its enormous data volumes, and reporting was cumbersome and slow to load. In fact, it took team leads a half hour or more just to pull a simple status report. This left no bandwidth for analysts to focus on strategic initiatives.
With Mode, what took hours to build reports, now takes mere minutes. The interactive reports also allowed users to drill into the data and proactively identify insights. Here’s what Denis Zgonjanin, former Data Applications Lead at Shopify, has to say about Mode:
“If you're focused on getting data in front of people every day, you don't need anything else except Mode. To me, Mode is really the best first and foundational step a data team could make.”
Denis Zgonjanin, former Data Applications Lead at Shopify,
Core features of Mode:
Run advanced SQL queries with an intuitive SQL editor
Integrated notebook environment
Build interactive visualizations with Visual Explorer
Advanced data analysis
Easy set-up and connectivity with multiple data sources
Ready to join the ranks of successful, data-driven businesses? Sign up for a free Mode trial today.
Power BI is a big data analytics tool that integrates with Microsoft’s ecosystem, allowing businesses to analyze and visualize large datasets. The platform offers data analytics features, like data modeling, dashboards, and reporting. Despite its intuitive interface, leveraging Power BI’s advanced capabilities can be challenging, especially for business users who will likely require additional training.
Core features of Power BI:
Data visualization and reporting
Connects with Microsoft ecosystem
Data modeling
Data transformation
Sharing and collaboration
Qlik Sense allows business users to run ad-hoc queries and apply advanced data analytics techniques to large datasets. Users can build visualizations, get recommendations, and monitor real-time data. Qlik Sense can be deployed either on-premise or in the cloud.
Core features of Qlik Sense:
Data visualization and reporting
Real-time monitoring
Data modeling
Sharing and collaboration
Embedded analytics
Tableau is Salesforce’s legacy big data analytics tool that lets users visualize and analyze data. With a drag-and-drop interface, users can create interactive dashboards to gain insights. The platform integrates with multiple data sources, allowing you a high-level view of critical KPIs. However, navigating Tableau can be difficult for business users as the platform requires an analytical background.
Core features of Tableau:
Data visualization
Data connectivity
Available to view on different devices
Report sharing and collaboration
App-level security
Apache Hadoop is an open-source framework based on Java that manages the storage and processing of large datasets. Hadoop uses distributed storage and parallel processing to break down enormous amounts of data into smaller workloads, allowing analysts to store and process data quickly.
Core features of Apache Hadoop:
Scalable data storage
Data processing
Open-source framework
Distributed computing model
Apache Spark is another open-source data-processing big data analytics software that handles large amounts of semi-structured and structured datasets. In addition, the platform leverages in-memory computing to process data and includes several libraries to support SQL queries, stream processing, and AI/ML applications.
Core features of Apache Spark:
Stream data processing
Advanced data analytics
Supports SQL queries
Open-source framework
Data teams spend a lot of time and effort in modeling, transforming, and analyzing data. But if the data doesn’t help business users make timely decisions, you will never be able to drive meaningful growth at scale.
To become a data-driven organization, you need a true self-service data analytics tool that allows business users to ask questions in natural language and build interactive dashboards. That way, your data team isn’t stuck creating hundreds of reports and can focus on larger data initiatives.
ThoughtSpot’s AI-powered search experience empowers business users to visualize real-time data, drill into specific searches, and pin charts to custom Liveboards. With the business know-how and the right tools, your entire organization can make in-the-moment decisions when they count the most. See how ThoughtSpot reduces time-to-insight—book a free demo today.
