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Big Data Analytics reviews by real, verified users. Find unbiased ratings on user satisfaction, features, and price based on the most reviews available anywhere.

Best Big Data Analytics Software for Medium-Sized Businesses

    Products classified in the overall Big Data Analytics category are similar in many regards and help companies of all sizes solve their business problems. However, medium-sized business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Medium-Sized Business Big Data Analytics to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Medium-Sized Business Big Data Analytics category.

    In addition to qualifying for inclusion in the Big Data Analytics Software category, to qualify for inclusion in the Medium-Sized Business Big Data Analytics Software category, a product must have at least 10 reviews left by a reviewer from a medium-sized business.

    Top 5 Big Data Analytics Software for Medium-Sized Businesses

    • Qlik Sense
    • Azure Databricks
    • Qubole
    • BigQuery
    • Alteryx

    Compare Medium-Sized Business Big Data Analytics Software

    G2 takes pride in showing unbiased reviews on user satisfaction in our ratings and reports. We do not allow paid placements in any of our ratings, rankings, or reports. Learn about our scoring methodologies.
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    (410)4.4 out of 5

    Qlik Sense is a platform for modern, self-service analytics, driving discovery and data literacy for all types of users across an organization. It supports the full range of analytics use cases - from self-service visualization and exploration to guided analytics apps and dashboards, custom and embedded analytics, mobile analytics, and reporting. And, it does this within a governed, multi-cloud architecture that delivers maximum trust, scale, and flexibility for the organization. Qlik Sense pro

    (106)4.4 out of 5

    Accelerate innovation by enabling data science with a high-performance analytics platform that's optimized for Azure.

    (256)4.0 out of 5
    Optimized for quick response
    Entry Level Price:30 day free trial

    Qubole is the open data lake company that provides a simple and secure data lake platform for machine learning, streaming, and ad-hoc analytics. No other platform provides the openness and data workload flexibility of Qubole while radically accelerating data lake adoption, reducing time to value, and lowering cloud data lake costs by 50 percent. Qubole’s Platform provides end-to-end data lake services such as cloud infrastructure management, data management, continuous data engineering, analytic

    (282)4.4 out of 5
    Entry Level Price:$0.02 per GB, per month.

    BigQuery is Google's fully managed, petabyte scale, low cost enterprise data warehouse for analytics. BigQuery is serverless. There is no infrastructure to manage and you don't need a database administrator, so you can focus on analyzing data to find meaningful insights using familiar SQL. BigQuery is a powerful Big Data analytics platform used by all types of organizations, from startups to Fortune 500 companies.

    (142)4.5 out of 5

    Alteryx, Inc. is a leader in self-service data analytics. Alteryx Analytics provides analysts with the unique ability to easily prep, blend, and analyze all of their data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours, not weeks. Analysts love the Alteryx Analytics platform because they can connect to and cleanse data from data warehouses, cloud applications, spreadsheets, and other sources, easily join this data together, then perform analytic

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    Check out the G2 Grid® for the top Big Data Analytics Software products. G2 scores products and sellers based on reviews gathered from our user community, as well as data aggregated from online sources and social networks. Together, these scores are mapped on our proprietary G2 Grid®, which you can use to compare products, streamline the buying process, and quickly identify the best products based on the experiences of your peers.
    High Performers
    Qlik Sense
    Azure Databricks
    Market Presence

    Learn More About Big Data Analytics Software

    What is Big Data Analytics Software?

    With the amount of data accessible to businesses today, it is a near necessity that they implement some type of analytics software to better understand and act on that data. Implementing big data analytics software has been a major initiative for companies undergoing digital transformation, as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large data sets collected from big data clusters.

    Because of the unstructured nature of big data clusters, these analytics solutions require a query language to pull the data out of the file system. Most commercial table databases allow SQL queries; however, big data analytics tools do not necessarily offer such SQL language capabilities and may require a more intricate knowledge of querying from a data scientist. As an alternative, some solutions may offer self-service features so that the average employee can assemble their own charts and graphs from big data sets.

    With the ability to visualize and understand business data, employees can make informed decisions and impact the company in a positive way. For example, retailers can use these tools to better understand inventory distribution across their channels and make data-driven decisions based on this data. Some big data analytics solutions may offer artificial intelligence or machine learning features, such as natural language processing, as an interface capability to further aid non-technical users.

    Key Benefits of Big Data Analytics Software

    • Ingest, integrate, and prepare big data sources
    • Connect all company data sources into a single platform to make cross-department connections
    • Visualize and understand company data
    • Encourage data-driven decision-making for business optimization
    • Discover new insights that can enhance the bottom line

    What are the Different Kinds of Big Data Analytics Software?

    Many types of big data analytics solutions share overlapping functionality, while simultaneously catering to different users or providing unique services.

    Self-service — Self-service big data analytics tools do not require coding knowledge, so business end users can take advantage of them for data needs. These solutions often provide drag-and-drop functionality for building dashboards, prebuilt templates for querying data, and, occasionally, natural language querying for data discovery. Similar to BI platforms, organizations use these tools to build interactive dashboards for discovering actionable insights. This enables business users like sales representatives, human resource managers, marketers, and other non-data team members to make decisions based on relevant business data.

    Embedded — Embedded analytics solutions offer the ability to integrate proprietary analytics functionality within other business applications. Businesses may choose an embedded product to promote user adoption; by placing the analytics inside regularly used software, companies enable employees to take advantage of available data. These solutions provide self-service functionality so average business end users can take advantage of data for improved decision-making.

    Why Use Big Data Analytics Software?

    In today’s big data world, data is both common and invaluable; within that data lies insights that could impact an organization's processes and performance. There are seemingly infinite insights a business can pull from their data, and numerous reasons to utilize big data analytics software.

    Enable data-driven decision-making — Businesses can use big data analytics software to fuel digital transformation by leveraging data to drive business decisions. Companies can leverage analytics and business intelligence tools to understand all aspects of the business, including hiring forecasts, which marketing campaign should be used to target certain demographics, which sales prospects to target first, supply chain optimization, and many others.

    Measure and understand company performance — Organizations often leverage data visualization tools to track company key performance indicators (KPIs) in real time. From there, big data analytics software can be used to determine why the business is either exceeding or falling short of those important company metrics. When stakeholders develop a keen understanding of why the business is performing the way it is, they can make adjustments and pivots; if a team is falling short of a goal, they can examine and adjust processes as needed. It is one thing to simply know the performance of sales or web traffic numbers, but it is another to dig into the reasons behind it and adapt based on what is successful and what is not.

    Discover new actionable insights — Analytics tools combine data from a variety of sources, including accounting software, enterprise resource planning (ERP) software, CRM software, marketing automation software, and others. Data analysts can leverage this integrated data to find correlations between different departments, and their processes and actions, to discover previously hidden insights. For example, it is possible that certain sales tactics have varying impacts on the numbers for one specific product versus another. Analysts can discover this by comparing the list of closed accounts from their company CRM with products shipped in their ERP system. Because teams are generally siloed and using disparate software, these insights have traditionally been much more difficult to discover. However, by using analytics software properly, companies are at an advantage they never had in the past.

    Who Uses Big Data Analytics Software?

    In a data-driven organization, various departments adopt analytics tools for a wide range of purposes. While data analysts and data scientists are the most common users of big data analytics software, self-service tools allow for a wider range of end users and can be leveraged by sales, marketing, and operations teams. Additionally, while there are point solutions that provide analytics for very specific departmental purposes, such as marketing analytics software, sales analytics software, and HR analytics software, among others, big data analytics tools can offer the same functionality on a much broader level and allow for cross-departmental insights.

    Data analysts and data scientists — These employees are generally the power users of analytics tools, creating complex queries inside the platforms to gather a deeper understanding of business-critical data. These teams may also be tasked with building self-service dashboards to distribute to other teams. In smaller companies, these employees are most likely tasked with providing all data requests and working closely with the sales, marketing, and operations teams to help offer insights and optimize processes.

    Sales teams — Sales teams can use both self-service analytics tools and embedded analytics solutions to obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Generally, sales operations managers or sales data analysts are the sales team members that use analytics on a daily basis. However, these tools are extremely useful for both sales managers and representatives. Managers can help track the performance of each representative and maintain a clear picture of the potential pipeline. Additionally, sales teams can use data visualization software to track year-long goals and quotas to give the entire company visibility into high-level sales numbers. Ultimately, using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue.

    Marketing teams — Marketing teams often run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns. Analytics tools allow marketing teams to track the performance of those campaigns in one central location. With data visualization solutions, for example, marketing teams can track campaigns in real time; by measuring the performance of each effort, teams can plan for future campaigns and forecast how much revenue they can attribute to said efforts. Analytics and BI platforms enable analysts to dig deeper into marketing efforts by segmenting customers based on a range of demographics, thereby offering an understanding of which campaigns resonate with varying segments of their customer base. This can help marketing teams develop campaigns and specific products for preferred market segments. As with sales use cases, marketing teams can utilize analytics software to help improve the bottom line and provide transparency and visibility into overall performance.

    Finance teams — Finance teams leverage analytics software to gain insight and understanding into the factors that impact an organization's bottom line. By integrating financial data with sales, marketing, and other operations data, accounting and finance teams can pull actionable insights that might not have been uncovered through the use of traditional tools. Additionally, they can determine the "right and wrong" places to spend money. For example, if a specific advertising campaign was the cause for a revenue spike, a company might consider that money well spent. If a specific product is yielding less profit than others based on the effort of the sales team, then finance teams can inform their sales leaders and adjust accordingly. Data analytics tools not only offer insights into financial records, like accounting or corporate performance management software; instead, these solutions deliver actionable insights into how all the other business factors impact profit and loss.

    Operations and supply chain teams — Analytics solutions often utilize a company's enterprise resource planning (ERP) system as a data source. These applications track everything from accounting to supply chain and distribution; by inputting supply chain data into an analytics platform, supply chain managers can optimize a number of processes to save time and resources. For example, businesses can optimize inventory to ensure that they are not over- or under-producing or leverage location intelligence software companies to determine the optimal location for their next warehouse. These platforms can help optimize distribution routes and ensure service level agreements (SLAs) are hit on time. Additionally, data visualization software can help warehouse workers track their daily goals to ensure all operations are running smoothly. All of these optimizations can help businesses stay on track and achieve higher company-wide goals.

    Potential Issues with Big Data Analytics Software

    Need for skilled employees — Big data analytics software is not necessarily simple. Often, these tools require a dedicated administrator to help implement the solution and assist others with adoption. However, there is a shortage of skilled data scientists and analysts that are equipped to set up such solutions. Additionally, those same data scientists will be tasked with deriving the actionable insights from within the data. Without people skilled in these areas, businesses cannot effectively leverage the tools or their data. Even the self-service tools, which are to be used by the average business user, require someone to help deploy them. Companies can turn to vendor support teams or third-party consultants to assist if they are unable to bring someone in house.

    Data organization — Analytics solutions are only as good as the data that they consume. To get the most of the tool, that data needs to be organized. This means that databases should be set up correctly and integrated properly. This may require building a data warehouse, which can store data from a variety of applications and databases in a central location. Businesses may need to purchase a dedicated data preparation software tool as well to ensure that data is joined and clean for the analytics solution to consume in the right way. In the context of big data, a company might want to specifically consider big data processing and distribution software. This often requires a skilled data analyst, IT employee, or an outside consultant to help ensure data quality is at its finest for easy analysis.

    User adoption – It is not always easy to transform a business into a data-driven company. Particularly at older companies that have done things the same way for years, it is not simple to force analytics tools upon employees, especially if there are ways for them to avoid it. If there are other options, such as spreadsheets or existing tools that employees can use instead of analytics software, they will most likely go that route. However, if managers and leaders ensure that analytics tools are a necessity in an employee’s day to day, then adoption rates will increase.