Best Big Data Analytics Tools
Buyer's Guide
Every organization worth its money invests in big data analytics to monetize its digital assets. With massive volumes of business information lying unused in silos, businesses are aware that they lose out on opportunities to the competition. If this is you, you should look for the best big data tools in the market.
This buyer’s guide provides the information to assist you in your software search, with handy resources and tips to ask the right questions before you decide.
Executive Summary
- Big data analytics software has the edge over traditional systems in processing large volumes of information, including unstructured digital assets.
- Creating a requirements checklist by identifying your implementation goals assists in software selection.
- Separating requirements into must-have and nice-to-have features helps stay on track with budget and business needs.
- Data sharing and IoT analytics are some current big data analytics trends.
- Ask the right questions within your organization and vendors for the tool comparison.
- What Is Big Data Analytics Software?
- Deployment Methods
- Primary Benefits
- Implementation Goals
- Key Features & Functionality
- Advanced Features to Consider
- Current Trends
- How to Begin a Software Comparison
- Cost & Pricing Considerations
- The Best Big Data Analytics Software
- Questions to Ask Yourself
- Questions to Ask Vendors
- In Conclusion
- Additional Resources
What Is Big Data Analytics Software?
Big data analytics solutions are software that enables analyzing complex and varied information to gain meaningful insight that’ll benefit your organization. High volume, velocity and variety of information overwhelm traditional processing and analytics systems, creating the demand for modern big data software.
These tools have an advantage over traditional software by integrating and analyzing unstructured information. In an economy ruled by big data and business analytics, a good piece of software that can process vast amounts of information is priceless. Organizations can't do without big data tools with sources as disparate as social media comments, images, and audio and video feeds.
Mobile insight, connected IoT devices, cloud computing traffic and technologies like artificial intelligence (AI) contribute to the big data boom.
Fortune Business Insights predicts the big data analytics market will grow to $655.53 billion by 2029.
Big data analytics is a discipline that falls under the umbrella term business intelligence, with business analytics, embedded analytics and enterprise reporting. Descriptive, predictive and prescriptive analytics are different ways of looking at information for business analytics.
Deployment Methods
Would you prefer to deploy on-premise or move to the cloud? Or would a hybrid solution work better, considering your legacy infrastructure?
On-premise
Installing on local infrastructure means you pay once for the cost of ownership. Plus, internet connectivity isn’t a deal-breaker when the software is in-house. You have complete control with full autonomy to plan maintenance, downtimes and upgrades. You can buy more storage when needed.
But setup and maintenance don’t come cheap. Scaling can be a pain, and the cost of technical resources for upgrades and patches adds to your overhead.
Cloud-based
Cloud software vendors offer flexible subscription plans with the option to pick and choose features, like the number of licenses and storage capacity. Managed analysis tools make you worry-free as the vendor handles upgrades and fixes.
Information security is a primary enterprise concern when considering cloud-based software tools. But not to worry, all leading vendors provide encryption and authorization protocols.
But, slow internet can cause latency issues and performance lag. Besides, a periodic subscription might be less than the cost of owning the software, but costs can pile on fast when opting for additional licenses, add-ons and upgrades.
Hybrid
A hybrid solution combines the benefits of information security with the perks of the cloud. Hybrid analytics solutions maintain consistent performance, scaling with larger workloads.
Pay-as-you-go plans are available, though at slightly higher prices than the cost of a public cloud. They prove viable for companies with fluctuating workloads.
Whatever your preferred deployment method, whether the software integrates with your existing software and hardware systems can be a deal-breaker.
So, which model fits you best?
A SaaS analytics solution might be a good fit for small businesses with limited infrastructure and IT resources. Larger enterprises can afford greater control over their information, so they might find on-premise or hybrid solutions appealing.
However, these aren’t set-in-stone rules, so consider your business’ unique situation when choosing a deployment method.
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Primary Benefits
With big data tools, you can pivot with fluctuating market trends by realigning strategies and redesigning campaigns. In the process, you can look closely at your business’ performance to identify inefficiencies and successes.
Let’s see how.
Maximize Your ROI
Customer feedback and operational performance metrics help downstream inventory, supply chain and workforce management processes. You come closer to identifying what customers want and can realign internal processes accordingly. It saves a lot of heartburn later trying to catch up with the competition.
You can boost revenue by consistently delivering to buyers, which helps establish a reputation as a serious player in the market. Analytics tools enable you to base your business strategy on hardcore metrics, irrespective of their volume and type.
Innovate
Big data analytics drives innovation by providing easy access to hidden insight through self-service methods like natural language processing. Data democratization increases the chances of finding previously undiscovered correlations between metrics.
Big data means more comprehensive information — and harnessing it gives a clearer picture of what’s working and not. This insight into the business’s strengths and weaknesses can help you decide which new products to launch and whether it’s the right time to diversify.
Improve Customer Experience
Customer service calls, chats, surveys, and social media comments and impressions drive the consumer experience. Big data software can capture this information to provide clear and current market insight.
Was the setup process intuitive, and did the customers find the help documentation useful? Were they able to generate the desired reports? For consumer products, the feedback could range from a star-based rating to text-based comments about whether the buyer would recommend the product to others.
Customer feedback is the linchpin around which you arrange your service or product design. It's how you build and retain a customer base and acquire new buyers.
Manage Risk
All business decision-making involves risk, especially when diversifying or growing your business. Risk management identifies and assesses these potential blockers and reduces their adverse impact through mitigation and monitoring. Big data combined with analytics systems can highlight hurdles in your business journey, reducing decision-making uncertainty.
Its applications include vendor risk management, money laundering, fraud prevention, credit risk assessment and identifying customer churn.
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Implementation Goals
Listing what you want the product to achieve will give you the clarity to shortlist suitable software. Here’s a list of common implementation goals to add to yours.
| Goal 1 Stay Ahead in the Market |
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|---|---|
| Goal 2 Improve Business Performance |
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| Goal 3 Increase Customer Engagement |
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| Goal 4 Manage Digital Assets | Poor information management practices cost enterprises thousands of dollars every year.
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| Goal 5 Secure Business Information |
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Key Features & Functionality
Vendors advertise solutions with many shiny, new features, but do you need them? If your stakeholders approve, sure, go for them. But, if you’re working within strict budget constraints, it helps to focus on the basic features.
| Digital Asset Integration | Connect to web and local files, and information warehouses and lakes in the cloud and on-premise. You should have access to near real-time information when you need it. |
|---|---|
| Dashboarding and Visualization | Create visualizations and personalize them with custom themes, formatting, styles, colors and fonts. Insights are helpful when consumers can understand them, irrespective of their skill levels. |
| Report Sharing | Share information with your teams and clients with big data reporting tools. Does the tool provide report bursting? Automated refreshes and interactive controls give you a clear view of critical metrics. |
| Security and Information Governance | Check if the software complies with PCI DSS, HIPAA and ISO 27001. Restrict access by assigning permissions at the column and row levels. Audit usage and access with activity logs and versioning. |
| Embeddability | Add analytics to user consoles by embedding the software tool into business applications. Personalize the dashboards and reports to give users a familiar interface and establish your brand identity with company logos, themes and colors. |
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Advanced Features & Functionality
At some point, the core features mentioned above won’t be enough. Eventually, your business will outgrow its basic software needs and require something more specialized. Or you might want to get advanced features at the onset to prepare for the future.
Even if you’re not planning for them now, it helps to know about the advanced features these tools offer.
| Forecasting | Any big data analytics tool worth its name will provide forecasting features. Predictive analytics lets you plan for upcoming opportunities through modeling and regression techniques. Ask the vendor if the solution supports machine learning and automated recommendations for guided analysis. |
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| Scalability | Your analytics tool should seamlessly connect to a greater number and variety of sources and integrate with newer add-ons. It should scale to take regular vendor updates with minimal downtime and zero impact on end users. |
| Collaboration | The best big data software should let you collaborate with others on specific dashboards and visualizations through comments and mentions. It’ll help avoid switching between different apps to get the desired metrics. How much work you can get done as a team within your dashboards will determine the tool’s efficacy. It’ll reduce the need for many run-of-the-mill reports, saving your team’s time. |
| Mobility | The best big data analytics software vendors offer mobile insight. Check which specific features they offer, like interactivity, sharing, in-app alerts and push notifications. |
| Augmented Analytics | Many enterprises want to reduce the time to insight by delegating analytics tasks to respective teams. It reduces the burden on managers and top stakeholders and encourages shared ownership of project goals. If this is you, augmented analytics can help analyze metrics using machine learning and natural language. These modules might be á la carte and so cost extra. Do check with vendors before you decide. |
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Current Trends
Knowing the latest software trends helps fine-tune your requirements checklist and compare products.
Industry-driven bots capture user information and execute tasks, generating job reports and driving processing pipelines. Virtual assistants like Siri and Google Assistant, and social media platforms capture and generate information by the second, producing huge repositories to fuel the analytics market.
IoT Will Raise the Bar for Data Management
Due to the growth in machine learning, AI and IoT analytics, there is an upsurge/increase in the number of connected devices like machine sensors. Statista predicts that IoT-connected devices will grow to 29 billion by 2030.
IoT is in use in all industries like gas, steam, water supply and waste management utility companies, among others. According to Statista (linked above), consumer companies like retail, manufacturing, real estate and healthcare accounted for 60 percent of all IoT-connected devices in 2020.
IoT big data differs from traditional IoT as it needs more agile, scalable platforms, flexible storage and robust analytical tools than conventional infrastructure. Managing this information securely and providing transparency to users and regulatory bodies will be challenging for enterprises going ahead.
Edge Computing and AI Will Drive Security Standards
Edge computing refers to information processing at or near the source, which lightens the workload burden on servers and networks. Edge devices like smartphones, autonomous vehicles and smart grids add to the already ballooning information online.
Edge computing raises logistical challenges like migrating network security protocols and complying with privacy and governance regulations. The secure access service edge (SASE) framework is a network protection protocol for information processed in edge devices. But it's new and needs to become mainstream to have a large-scale impact on information security.
IoT and machine learning are a formidable combination. With edge devices added to the mix, secure data access and governance compliance will be the next challenge for businesses.
Information Sharing Will Fuel Industry Verticals and Consumer Markets
Previous privacy protocols restricted sharing of consumer information with third parties. But, CEOs realize that siloed information benefits only a select few, while shared information helps everyone. Decrypting shared information isn’t even an issue now that processing is possible on encrypted sets.
Data marketplaces monetize information sharing with subscription-based licensing, offering connectivity through the data fabric. They build separate databases with on-demand connectivity for entities willing to store and share their digital assets.
With data sharing promising to be a trend next year and beyond, big data in verticals is a commodity that adds to enterprises’ bottom line. Big data analytics tools with open architectures will be in demand.
Augmented Insights Will Be a Primary Business Requirement
Verified Market Research predicts the augmented analytics industry will be worth $66.61 billion by 2030. The increase in information volumes and complexity drives the need for self-service BI and analytics, which drives augmented tech.
The software sector is the largest consumer of artificial intelligence and machine learning technologies. Modern big data analytics software with machine learning speeds up information discovery through model building, clustering and regression techniques. Automation support is a primary advantage of augmented technologies.
Augmented analytics is a primary requirement for many enterprises when buying software. It’ll be interesting to see how it evolves in tandem with regulatory and data quality concerns.
How To Perform a Software Comparison
Comparing software solutions can be difficult without the right tools and resources. We are here to help.
Start your software selection process with our requirements template, or create your own checklist listing your business needs. Compare vendors and big data analytics solutions with our software comparison report.
Cost & Pricing Considerations
The cost of a software solution can make or break your buying decision. While building your product list, check the deployment cost, especially whether implementation support is available. If not, you should pencil in the cost of technical resources.
Ask about necessary and optional add-ons and which ones are free. Verify whether support is free with the product and what it entails, and check into paid support plans.
Get the pricing details from the vendor’s website or ask for a quote. Our pricing guide helps you determine the best big data software per your budget.
Expert recommendations and analysis on the top software
The Best Big Data Analytics Software
Check out the big data tools list on our analytics leaderboard, curated by the SelectHub analyst team. It ranks the software solutions based on how well they deliver on essential requirements.
Oracle Analytics Cloud (OAC)
OAC is a reporting and analytics tool that drives business decisions by integrating and analyzing big data from disparate sources. Personalized searches and automatic recommendations help generate interactive, up-to-date visualizations. Dashboards and reports are shareable and collaborative, and natural language explanations make them understandable for all.
The vendor offers a native mobile app.
Big data analytics with Oracle Data Integrator.
IBM Watson Analytics
Watson Analytics is a big data analytics solution with machine learning capabilities for data exploration and forecasting. The vendor offers customizable modules for lifecycle management, applications, APIs and industry-specific specializations. AI and pattern identification help remedy potential issues in workflows.
Role-based permissions and single sign-on (SSO) protocols secure data through restricted information access. You can train the solution to preempt information breaches before they impact performance.
An IBM Watson dashboard shows multiple visualizations with performance and risk metrics.
SAP HANA
SAP HANA is the in-memory database for SAP’s Business Technology platform, supporting businesses with robust processing and analytics. The platform’s lean model lets you perform advanced analytics on real-time information at the speed of thought.
Developers can build robust solutions through its library of pre-built functions. The platform allows you to incorporate location coordinates into business intelligence.
A performance analytics dashboard in SAP.
BIRT
BIRT from OpenText is an open-source big data analysis software for information exploration through visual analysis. The platform blends information from various sources, including Plain Old Java Objects (POJOs), Java Data Objects (JDO) datastores, SQL databases, JFire scripting objects, XML and web services.
Its charting engine enables embedding reports and charts into business systems. A single presentation can incorporate multiple reports in one document.
Viewing business insight in BIRT Report Viewer.
Qlik Sense
Qlik Sense is an AI analytics platform that processes big data for enterprises. It’s deployable on-premise, in private and public clouds, or as a hybrid solution. Qlik Sense is flexibly scalable with an enterprise-class, modular architecture.
The vendor offers conversational and location-based analytics and indexing through Qlik NPrinting, Qlik Insight Bot, Qlik DataMarket and Qlik GeoAnalytics.
On-the-go big data analytics with Qlik Sense.
Expert recommendations and analysis on the top software
Questions to Ask Yourself
Even with a requirements checklist, weighing the various big data analytics solutions’ pros and cons can be daunting. Having internal conversations and asking questions to identify stakeholder expectations can help.
Use these questions as a starting point:
- Are you using a big data analytics solution? What are the pain points?
- What’s the company budget? Is the current software cost-effective?
- What are your company's present and future goals? How does the analytics tool fit in with them?
- Who are the end users, and what gaps do you hope this platform fills?
- What databases should it integrate with, e.g., SQL Server, MySQL, MS Access, etc.?
- Which deployment method works best for your company?
- Is self-service a must-have feature, or do you want a system that performs the analysis and offers you recommendations for further action?
- How important is scalability?
- What databases should it integrate with, e.g., SQL Server, MySQL, MS-Access, etc.?
- Will you need a mobile application?
- Is technical expertise available to deploy and manage the software?
Questions to Ask Vendors
Use these questions as a starting point for conversations with vendors:
About the Software
- Does the solution provide visualization, data management, reporting and collaboration?
- Is it scalable? Can it integrate with add-ons and other applications?
- Does it need customization before deployment?
- Which mobile functionalities will you get?
- Can you track KPIs? Is this feature available on mobile?
- Is the software user-friendly?
About the Vendor
- How often does the vendor issue updates?
- Is training included in the purchase plan?
- Which features cost extra?
- Do they provide implementation support?
- Do they offer phone, email and chat support? Is it free or paid?
- How can you submit a support request? What is the average response time?
In Conclusion
Selecting a big data analytics tool is a task that needs careful thought and lots of research. Do your due diligence in evaluating business requirements and list them by holding in-depth discussions with stakeholders. It should tell you what you want in a big data analytics tool.
Asking the right questions internally and to potential vendors is an excellent way to fine-tune your requirements and compare software products. It will help you narrow down your product shortlist to present to your top-level management for the final decision.
Expert recommendations and analysis on the top software
Top 10 Big Data Analytics Tools Leaders by Analyst Rating (of 96 products)
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