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20 Best Data Analytics Software for 2019

What is the best data analytics software for 2019?

The best data analytics software for 2019 is Sisense because of its simple yet powerful functionalities that let you aggregate, visualize, and analyze data quickly. Moreover, this platform has a scalable architecture that allows it to handle a wide range of data volumes, making it great for small and large businesses alike.

In this digital age, it has become easier to access data that would allow you to optimize your business performance. By having this information at your fingertips, you can make smarter decisions, keep up with business trends, and even find ways to further improve your overall operations.

The only problem now is that collecting and processing such data can take up a huge chunk of your productive hours. To accelerate the process, you will have to use data analytics programs. These solutions use AI and advanced algorithms so that you can transform raw data into valuable insights in just a few clicks of a button.

However, there are plenty of factors involved in finding the right analytics tool for a particular business. From checking its performance and learning the strong suit of the platform to figuring out how well it plays with other systems and identifying its TCO, the whole research process can be overwhelming. To help you out, we have compiled some of the leading products available on the market and assessed their functionalities and usability. This way, it will be easier for you to determine the best possible data analytics platform for your operations.

Handling data from collection to visualization is a challenge in itself, even more so as it the amount of data you process grows. Furthermore, a scalable system is a requirement as data grows in volume each day, hence, you’d want to have an application equipped with the architecture and technology that can support your data analytics processes.

In Kaggle’s survey on the State of Data Science and Machine Learning, more than 16,000 data professionals from 171 countries and territories revealed the challenges they encountered in the field. Respondents were asked to choose all factors that apply to the difficulties they faced.

The challenges encountered by data professionals. (Source: Business Over Broadway)

On the first spot is dirty data which is voted by 36% of respondents. Next comes the lack of data science talent (30%), company politics (27%), not having clear questions (22%), inaccessible data (22%), and results not used by decision makers (18%).

There are also problems with the difficulty of explaining data to others with 16% and privacy issues with 14%. Meanwhile, 13% of data professionals revealed their small organization can’t afford to have a data science team.

These issues call for the importance of maximizing the capabilities of technology doing so much more beyond recognizing your organization’s bottlenecks. Today’s data analytics tools are easier to use and more affordable for all sizes of companies. This is vital, especially when considering the value it can bring to your organization.

For instance, content companies can use a data analytics tool to keep their audiences by clicking and watching their content. Another example is for gaming companies to get their hands on relevant data to keep players active in the game by providing rewards.

Of course, it’s not new for companies trying to be data-driven in their decision-making processes. According to a report by NVP, 85% of companies are transitioning to become more data-driven. However, only 37% of them become successful.

To ensure effective implementation, one of the first things to prioritize is choosing the right data analytics software. A good place to start is getting to know the leading products in the niche by checking out our list of the 20 best data analytics software. You can also read our article on how to use analytics in HR to improve your brand. This way you can further assess exactly how you can leverage analytics to refine your workflows and boost your products and services.

Data Analytics Software

1. Sisense

Sisense is a robust data analytics software that brings analytics not just to data scientists, but to all business users as well. It simplifies business data analytics even to non-technical users through its set of tools and features. Insights are extracted instantly by any user using self-service analytics without hard coding and aggregating modeling. Some of its top features that enable you to do so include its personalized dashboards, interactive visualizations, and analytical capabilities.

Its dashboard is one of its top features that enable you to filter, explore, and mine data in just a few clicks to get instant answers to your questions. With its in-chip technology, data analytics can be performed faster with richer insights. Furthermore, it provides you with advanced analytics through an improved, advanced BI reporting and predictive analytics by integrating R functions in your formulas.

It is best to test the features and functionalities of the tool first so that you’ll know if it matches your requirements. To do so, you may sign up for Sisense free demo here.

This award is given to the best product in our business intelligence software category. It highlights its superior quality and underlines the fact that it's a leader on the market.
USER SATISFACTION 99%
OUR SCORE 9.7
Sisense won our Best BI Software of 2018 Award

Why choose Sisense?

  1. NLG technology. With NLG (Natural Language Detection) technology, you can easily detect trends and patterns when you are interacting with every single widget on the dashboard.
  2. Data Visualization. It has a rich widget library with a wide collection of predesigned data visualization widgets. You also have the option to submit your own open source designs or receive recommendations on how to best view your data.
  3. Anomaly detection. If there are any anomalies in your data, the system uses machine learning to instantly detect and alert you with any potential issues.

2. Looker

Looker is a data analytics platform that allows anyone to ask sophisticated questions using familiar business terms. It delivers data directly to the tools and applications used by your team, including custom ones.

In simple terms, the platform gathers and extracts data from various sources and then loads it into an SQL database. From there, it undergoes the platform’s agile modeling layer for custom business logic and, finally, makes it available for all users through dashboards, shared insights, and explorations.

As a browser-based solution, data is conveniently accessible in your existing systems and easily shareable to everyone in your team. Exporting can also be done both locally and directly to platforms such as Google Drive and Dropbox.

If you are interested to know this solution’s features better, you may sign up for Looker free demo here.

An award given to products our B2B experts find especially valuable for companies
USER SATISFACTION 95%
OUR SCORE 9.6
Looker won our Expert's Choice Award for 2017

Why choose Looker?

  1. Accessible data. The app doesn’t lock your data in your data analytics tool. Instead, it can be accessed through systems such as Slack and Salesforce.
  2. Data scheduling. Anyone in the team can schedule the delivery of data to FTP, S3, chat, webhooks, and email, among others.
  3. Web integration. This provides you with a responsive mobile design, a solution that’s embeddable with SSO, and a full RESTful API.

3. Zoho Analytics

Formerly Zoho Reports, business data analytics platform Zoho Analytics hosts different components, including KPI widgets, pivot tables, and tabular view components, allowing it to generate reports that offer very useful insights. The system promotes collaborative review and analysis, allowing users to work with colleagues on report development and decision-making. What’s good about the system is that businesses can embed just about any report or dashboard in their blogs, websites, and apps. The system even has state-of-the-art security practices that include connection encryption. It can also be used by ISVs and developers the solution in the building and integration of analytical and reporting functionalities into their systems.

To check if this product is ideal for your business, you can sign up for Zoho Analytics free trial here and test drive its features at no cost.

Why choose Zoho Analytics?

  1. Insightful reports. The solution makes use of a variety of components, allowing it to generate insightful reports, which can be used as a basis for data-driven decisions.
  2. Highly-secure system. It makes use of only the finest security measures, including encrypted connections.
  3. Collaboration. Zoho Analytics promotes collaboration, allowing colleagues to jointly develop reports and decisions.

4. Yellowfin

Yellowfin is an end-to-end business intelligence solution created to help companies make better sense of their data. Equipped with comprehensive analytics features, market-leading collaboration tools, and machine learning capabilities, this is great for getting actionable insights from your company’s performance. It allows you to get data-driven predictions that can be used to make smarter business decisions. The software even has multiple data visualization options so you can present your data however you prefer.

Accessible via desktop and mobile devices, Yellowfin also comes with a web API that lets it integrate with a wide variety of business systems, add-ons, and widgets. This means you can easily extend its functionalities depending on the changing needs of your business or merge it with your existing software solutions to streamline your workflow.

The vendor has an appealing free trial where you can tinker with the features at no cost. You can sign up for the Yellowfin free trial here.

Why choose Yellowfin?

  1. Fully integrated BI platform. Yellowfin was created to eliminate having to invest in different BI solutions. With this, you can address multiple analytics problems and consolidate all of your data discovery, reporting, and analytics in one platform.
  2. Data storytelling. Traditional graphs and charts can be boring and difficult to understand. Using Yellowfin’s data storytelling capability, you can create interactive presentations using different immersive analytics. This way, you can simplify the data visualization process and let your numbers do the talking.
  3. Customizable notifications. On top of presenting you with comprehensive reports, Yellowfin also makes sure you are in-the-loop at all times. Offering custom notifications, you will be alerted whenever there are notable changes in your data flow.

5. Domo

Domo is a data analytics solution that aims to provide a digitally connected environment for your data, people, and systems. Your business’ data is put into work for all employees in your organization through real-time data refresh and drag-and-drop data preparation capabilities. Furthermore, partners outside of your organization also get to engage with your data to increase productivity and the ability to act on them.

Using a holistic view in your system, you can make more informed actions with the tool’s 7 platform components working together. You are notified with predictive alerts to bring crucial matters and issues into your attention with enough time before they make an impact on your organization.

Why choose Domo?

  1. Connected data. Bring your data directly together with over 500 data connectors from any third-party source such as on the cloud, on-premise, and proprietary systems.
  2. Instant data-driven chat. It has more than 300 interactive charts and dashboards both for desktop and mobile use.
  3. Native mobile apps. Manage responsibilities on your mobile devices both for Android and iOS that’s intuitive, real-time, and designed for on-the-go usage.

6. Qlik Sense

Qlik Sense is powered by the associative engine to deeply extract insights commonly missed by other query-based data analytics tools. It does so by indexing each possible relationship between data and combining them from various data sources into a centralized view. The cloud-based data analytics platform provides you with flexibility in providing the right solution for various cases for analysts, teams, and global enterprises.

The absence of pre-aggregated data from common query-based tools paves the way for asking new questions and generating analytics even without waiting for the help of experts in building new queries. Sharing of insights is made with ease regardless of your organization’s size as the system enables work collaboration in a secure, unified hub.

Why choose Qlik Sense?

  1. Smart visualization. Find insights visually by the fully interactive interface of the platform that enables you to pan, zoom, and make selections to explore and pinpoint your data effectively.
  2. Interactive analysis. Interactive selection and global search let you explore data and ask any question to with no limits to exploration. Each click also updates analytics instantly to ensure the most up-to-date version is available.
  3. Flexible for any device. Touch interaction and a responsive mobile design ensure you can work on any device once you build the analytics app once to enable exploration, collaboration, and creation of analytics.

7. GoodData

GoodData is an end-to-end, secure cloud data analytics system that caters to your entire data pipeline—from the moment you take in data to the time you deliver the insights you generated. It is not only available to enterprises, but to partnerships and software companies as well. The product is mostly used in industries such as insurance, retail, financial services, and ISV.

This smart business application integrates insights directly into your point of work to expedite the decision-making process. Improvements are also automated over time as it learns from user actions and is capable of making data-driven predictions. On top of that, the tool ensures enterprise-grade security in HIPAA, GDPR, SOC II, and ISO 27001, among others.

Why choose GoodData?

  1. Industry-specific solutions. The tool has solutions specifically built to cater to the needs of ISV, retail, financial services, and insurance industries.
  2. Quick implementation. Deployment is done quickly so you can immediately use the system within 8 to 10 weeks.
  3. Embedded analytics. Analytics is embedded to your application so you can extend it for any use case such as machine learning, benchmarking, basic reports, and advanced analytics.

8. Birst

Birst is a solution that utilizes data analytics in a network that connects your insights for making smarter business decisions. These networked analytics solutions combine the speed, agility, and usability of consumer-grade desktop tools with the needs of IT specialists for data governance and scalability. It has a multi-tenant cloud architecture that enables the expansion of data analytics across departments, product lines, and regions.

Its specialty lies on its 2-tier approach for end-user data visualization, querying, and production-oriented business intelligence. You can extract data and maximize connectivity options in various databases and cloud or on-premise applications. Developers can build their own connections or they may use its numerous integrations with third-party systems.

Why choose Birst?

  1. User data tier. This lets you enable data as a service for data governance by both centralized and de-centralized teams. It is used for aggregating and governing a complex mix of enterprise data with agility and speed.
  2. Adaptive user experience. Users get a broad choice in how they interact with data that adapts to modern work styles. Regardless of the device used, you will see the same consistent interface with tools that you prefer.
  3. Multi-tenant cloud architecture. It connects everyone in a single, networked view of data through the virtualization of the entire data analytics ecosystem.

9. IBM Analytics


IBM Analytics is a data analytics tool specializing in evidence-based insights to support crucial decision-making for your business. It simplifies how you collect, organizes and analyze your data to allow an optimized procurement, management, and scale. You even get freedom on how you collect all types of data from various data sources.

The tool also lets you build a secure foundation for your analytics and organize your data in a business-aligned, single source of truth. Furthermore, it lets you scale your insights by incorporating evidence-based insights into your decisions that were previously unobtainable. This can help you analyze data in a smarter way.

Why choose IBM Analytics?

  1. Machine learning. Expedite the deployment of data science projects by maximizing intelligence into your enterprise application.
  2. Prescriptive analytics. Consider business constraints and the optimization of business trade-offs to determine the best course of action according to plan, design, schedule, and configuration.
  3. Predictive analytics. This brings together data mining, text analytics, predictive modeling, ad-hoc statistical analytics, and advanced analytics capabilities, among others, to spot patterns in data and anticipate what is most likely to happen next.

10. IBM Cognos

IBM Cognos is the solution to consider when you’re looking to make business decisions quickly with the use of smart self-service capabilities. This tool provides IT a solution to deploy in the cloud or on-premise according to the architecture they require. Additionally, it caters to business users who want to create and configure dashboards and reports on their own.

One of its top features is its self-service functionality that enables users to interact and access reports on mobile devices both online and offline. When it comes to analytics, the tool also offers a wide selection of analysis methods ranging from trend analysis, analytical reporting, trend analysis, and what-if analysis.

Why choose IBM Cognos?

  1. Smart self-service. As an integrated solution, you can efficiently deliver mission-critical analytics and generate insights from data in an impressive presentation and visualization.
  2. Robust automation. To increase productivity across teams, ecosystems, and organizations, it uses smart technology to automate the analytics process, offer recommendations, and predict user intent.
  3. Complete cloud-based experience. The user experience remains consistent whether you are using the tool via desktop or mobile device as it doesn’t require any desktop tool. This also eliminates the need for you to transfer data as it lives in the cloud.

11. IBM Watson

IBM Watson is an analytics platform that streamlines leveraging interactions, predicting disruptions, and accelerating research through the use of artificial intelligence. This advanced data analysis and visualization solution lives in the cloud and provides a reliable guide to users over the discovery and analysis of their data.

Independently unravel patterns and meaning in your data through guided data discovery and automated predictive analytics. Even without the help of a professional data analyst, you can interact with data and gather answers that you can understand using the tool’s cognitive capabilities like natural language dialogue. This means any business user can immediately determine a trend and visualize the data report in the dashboard for an effective presentation.

Why choose IBM Watson?

  1. Smart data discovery. Using your own words, you can easily type a question that will add or connect to data for you to gather understandable insights on the go. Whether you’re on desktop or iPad, you immediately get a roster of starting points.
  2. Analysis of trusted data. Since data analytics comes in many forms, the tool helps you stay in synch when exploring, predicting, and assembling data for a trusted insight.
  3. Simplified analysis. You can be prepared to act with confidence when you identify patterns and factors that can potentially drive business outcomes.

12. MATLAB

MATLAB is a data analytics platform commonly used by engineering and IT teams to support their big data analytics processes. It enables you to access data from various sources and formats such as IoT devices, OPC servers, File I/O, databases, data warehouses, and distributed file systems (Hadoop) in a single, integrated environment.

Before the development of predictive models, the system empowers you to preprocess and prepare your data by automating tasks ranging from cleaning data, handling missing data, and removing noise from sensor data. You can then directly forecast and predict outcomes by building predictive models and prototypes. Furthermore, the system lets you integrate the tool with production IT environments even without recoding or building a custom infrastructure.

Why choose MATLAB?

  1. Machine learning. Offering a full set of statistics and ML functionalities, it provides you with advanced methods like system identification, prebuilt algorithms, financial modeling, and nonlinear optimization.
  2. Online deployment. The tool integrates with enterprise systems, clouds, and clusters. Additionally, it can be targeted for real-time embedded hardware.
  3. Physical-world data. It provides native support for binary, image, sensor, telemetry, video, and other real-time formats.

13. Google Analytics

Google Analytics is one of the most popular and widely-used data analytics tools to summarize data on high-level dashboards and function with a variety of funnel visualization techniques. At its core, it is a web analytics service that is used for tracking and reporting about website traffic. The freemium product provides an analysis of poorly performing pages using various data-driven techniques.

Furthermore, this tool provides you with data that you can transform into actionable insights for businesses of all sizes to garner a stronger result across their websites, applications, and offline channels. Specializing in one of the most important aspects of data analysis, this tool is essential for building a tight data analysis framework for your organization.

Why choose Google Analytics?

  1. Data collection and management. You get a single, comprehensive view of your customer that’s easily customizable according to your business needs. Sharing this across your organization is streamlined as well.
  2. Data activation. Used in marketing, data can be activated to leverage marketing campaigns and explore new content and channels.
  3. Data analysis. Reporting and analysis tools are available to help you segment and filter data according to your needs in order to have a better understanding of your customer’s lifecycle.

14. Apache Hadoop

If you are looking for an open source platform, Apache Hadoop is a good place to start for distributed storage and processing of large datasets. In addition, it offers services for data access, governance, security, and operations. It is a collection of utilities that facilitates a network of multiple computers and data sets on computer clusters built from commodity hardware to solve problems.

This solution is fundamentally resilient to support large computing clusters. Failure of individual nodes in the cluster is rarely an issue and if it does, the system automatically re-replicates the data and redirects the remaining ones in the cluster. It is a highly scalable platform that stores, handles, and analyzes data at a petabyte scale.

Why choose Apache Hadoop?

  1. Low cost. Since it is an open source platform, it runs on low-cost commodity hardware making it a more affordable solution compared to proprietary software.
  2. Flexible platform. Data can be stored in any format, parsed, and applied the schema to it when read. Since structured schemas are not required before storing data, you may even store data in semi-structured and unstructured formats.
  3. Data access and analysis. Data analysts have the option to choose their preferred tools as they can interact with data in the platform seamlessly using batch or interactive SQL or low-latency access with NoSQL.

15. Apache Spark

Apache Spark is a developer-friendly big data analytics platform that supports large SQL, stream processing, and batch processing. Like Apache Hadoop, it is an open source platform in data processing that supports a unified analytics engine for machine learning and big data.

To maximize this solution, you can run it on Hadoop to create applications that will leverage its power, derive deeper insights, and improve data science workloads in a single and shared database.

Consistent levels of response and service are expected with its Hadoop YARN-based architecture which makes the tool one of data access engines that work in YARN in HDP. This means the solution, along with other applications, can share a common dataset and cluster with ease.

Why choose Apache Spark?

  1. Unified solution. It caters to creating and combining complex workflows as it comes packaged with support for SQL queries, graph processing, machine learning, and higher-level libraries.
  2. Data processing engine. Data analysts can execute streaming, machine learning, and SQL workloads in development APIs needing fast access to datasets.
  3. Easy-to-use APIs. The tool is easy to use as it offers a collection of over 100 operators for data transformation and familiar data frame APIs for semi-structured data manipulation.

16. SAP Business Intelligence Platform

SAP Business Intelligence Platform is a data analytics tool for monitoring key metrics and gaining valuable insight into customer behavior while eliminating guesswork in the process. At its core, it serves as a BI solution for delivering actionable information at your reach. It is available both in the cloud or on-premise to suit your requirements.

The platform offers various tools including SAP Analytics Cloud and SAP BusinessObjects BI Suite. They are used for solving specific business needs and leveraging decision-making. By supporting the collecting IQ of your business, this tool is reliable in providing a high standard for enterprise data analytics and BI.

Why choose SAP Business Intelligence Platform?

  1. Dashboards and apps. Dashboards are compelling and insightful to drive an effective BI adoption to your organization.
  2. Data Visualization. To present the data in a way that’s understandable for everyone involved, the tool helps business users in various skills levels to easily understand and make use of data when making business decisions.
  3. Self-service. By having access to data anytime and anywhere, decision-making is made faster and more informed through real-time business data.

17. Minitab

Minitab provides smart data analysis for businesses to improve quality and drive efficiency in their performance. The flagship product, called Minitab Statistical Software, is used by companies to graph and analyze their data. Meanwhile, Minitab 18 is a solution that provides robust statistical software for all business users to find meaningful solutions to your tough business problems.

Through these tools, you don’t need to be a statistics expert to understand and gain insights from your data since Minitab’s Assistance will guide you along the way. Its other modules include Quality Trainer for learning how to analyze data and companion for centralizing the tools you need in reporting and analysis such as process mapping, brainstorming tools, and quality function deployment.

Why choose Minitab?

  1. Cost-effective e-learning. To fully maximize the tool, you get e-learning materials on how to analyze data and statistics online.
  2. Project Roadmaps. This feature streamlines the execution, replication, and sharing of projects across your organization. Meanwhile, it also has process mapping for constructing high-level and detailed flow charts.
  3. Assistant feature. This walks you through data analysis and helps you interpret results with better accuracy. It can improve the quality of the product and process as well as boost efficiency.

18. Stata

Stata is a data analytics and statistics software for obtaining, exploring, and manipulating data. It enables you to visualize, model, and curate results in reproducible reports. It is developed by researchers for researchers to effectively support the needs of their fellow professional software developers. It is a complete and integrated software package for all tools needed in data management, analysis, and graphics.

The tool is known for being fast, easy, and secure. It has intuitive command syntax and point-and-click interface that streamlines how analyses are reproduced and documented for review and publication. Regardless of when they are written, version control ensures the analyses scripts are accurate and up-to-date to show the same results.

Why choose Stata?

  1. Data management. You gain complete control over all types of data as it works with byte, double, long, float, integer, and string variables. It can also combine and reshape datasets, monitor variables, and gather statistics across replicates or groups.
  2. Statistical tools. It packs hundreds of statistical features ranging from standard methods (basic tabulation and summaries, linear regression, and choice modeling) to advanced techniques (multilevel models, survey data, and structural equation modeling).
  3. Publication-quality graphics. Have the option to write scripts for producing a wide variety of graphs in a reproducible manner or use point and click to create your own custom graph. This enables you to build a publication-quality and distinctly-styled graph.

19. RapidMiner

RapidMiner specializes as a data science platform built for analytic teams to prepare data, build predictive models and deploy them in a single system. It offers a rich technology for working in various stages and phases of an advanced analytic project such as featuring scripting support for various languages.

The suite of solutions includes machine learning, application integration, data integration, and transformation. It also delivers true predictive analytics in addition to its unified approach in streamlining maintenance, learning, and standardization of your most crucial data processes. Furthermore, it supports data science at every scale through running workflows in-memory or in-Hadoop to match the ideal requirements of your project.

Why choose RapidMiner?

  1. Open source platform. It supports the evolving needs of data science through its robust marketplace, a large community of data science experts, and well-accepted open languages and technology. It also connects with more than 60 connectors for structured, unstructured, and big data.
  2. Unified platform. It caters to your complete workflow in a single platform, single user interface, and a single system.
  3. Visual workflow design. To leverage productivity, the platform offers easy-to-learn navigation and a drag-and-drop approach to accelerate data science processes.

20. Alteryx

A smart, advanced data analytics platform, Alteryx is designed to provide a self-service solution for the needs of business data analysts. It is a platform built for data science and analytics in preparing, blending, and analyzing all of your data so you can deploy and circulate it at scale. The process is accelerated as you can garner deep insights in only a few hours and not weeks like older, traditional systems.

Raw data is easily prepared for analysis from unlocking datasets—both big or small and clean or dirty—with no coding and waiting. The controlled and secure solution helps data workers to uncover data and analytic assets previously overlooked. Data is searchable and trackable for enhanced data discovery and data security.

Why choose Alteryx?

  1. Code-free system. The system doesn’t require coding as you can use its intuitive, drag-and-drop interface in customizing the tool to your needs. This means a code-free analytic modeling and code-friendly advanced modeling.
  2. Automated Workflow. Manual and time-consuming tasks can be transformed into automated, repeatable processes to expedite analytic workflows in real time.
  3. User-friendly. The solution is built to cater the needs of different types of users ranging from data analysts, IT and data management, data scientists, C-level executives, analytic leaders, BI directors, and academics, students, and non-profits.

Other Recommended Solutions

  1. Actian. This platform features data management, analytics database solution and data integration that let you process queries of various data types, such as, SQL, embedded, relational, object and NoSQL. It integrates with a variety of apps and databases in both on-premise and cloud deployment model and is ideal for large and medium enterprises.
  2. Analyse-it. An Excel-based data visualization and statistical analysis tool, this software suits users who are familiar or comfortable with spreadsheets. Non-technical users with limited statistics skills will also find it intuitive especially that file formats are saved as an Excel workbook, making collaboration simple.
  3. Pyramid Analytics. A user-friendly high-end analytics platform, this comes with a self-service BI portal. It is also compatible with any browser or device and features tools to generate various data modeling that helps you understand complex business issues.
  4. Arcadia Data. A BI platform with real-time capabilities that can scale to a variety of data modeling demands, Arcadia Data is an ideal tool to help you understand complex statistics fast. The system has a strong orientation towards data security and, in fact, aims to set the standard on preventing data leaks.
  5. SigmaPlot. A data analysis tool to help you create graphs fast even for non-technical users, this software can also integrate with Excel for data organization and PowerPoint for presenting outputs.
  6. dxFeed Bookmap. A revolutionary data visualization solution, dxFeed Bookmap provides users with a crystal clear view of the market to equip them with essential data to make smarter business decisions. The platform is primarily designed for traders who are looking to track the dynamics of Depth of Market and explore the evolution of the order book.
  7. AtomLynx Insights Engine. This business intelligence solution and data analytics platform gathers all important metrics and information from disparate sources and places them on a single platform for fast and efficient data analysis. It also leverages AI to provide you with the relevant metrics you need.
  8. Displayr. An intuitive BI system for beginners yet powerful for pros, Displayr features tools for visualization, exploration, modeling, and reporting. It also utilizes drag-and-drop variable manipulation, full R Language support, and statistical testing.
  9. Statistix. Featuring a menu-driven interface and easy navigation for non-statisticians, Statistix features powerful data manipulation capabilities. The vendor also offers free technical support.

Maximizing Your Data Analytics Software

The process of analyzing big data to improve your business operations doesn’t stop at purchasing data analytics software. If you want to achieve your goals, you should also put some elbow grease into learning the ins and outs of your new data analytics system. Moreover, it is pertinent that you have a clearcut idea of how to use it to your advantage. To guide you, here are some ways you can maximize your data analytics software:

  1. Define What You Want to Achieve Using Data Analytics. Even before purchasing your data analytics program, try to list down what you plan to use it for. Understand what aspects of your operations will benefit from using data, specify the type of insights you would want the platform to provide, and determine what you want to measure. By doing so, you will be able to specify your KPIs and other metrics as well as establish a good framework as to how you can use your new investment.
  2. Determine Where to Source Your Data. Most analytics platforms have the capability to source data from multiple internal and external systems for a more convenient data collection and synchronization process. However, this doesn’t mean you should simply integrate your platform to all your existing business systems and call it a day. Be selective about where you source your data, choosing only those that will help you move your data analysis efforts forward. This way, you not only generate more targeted reports but also prevent your database from being cluttered by information that you won’t even use.
  3. Regularly Assess Your Data Models. You wouldn’t use outdated information to perform analysis so why should you utilize outdated data models? To prevent old data models from having a negative effect on your data analytics efforts, you need to make it a point to assess these models every now and then. Check if you are ignoring certain data sources or if you have overlooked how certain fields could affect your model. Perhaps, there are certain data sources containing poor naming standards that are affecting your data analytics model. By taking this extra step, you can ensure that you are generating accurate reports that can drive your business forward.

By taking advantage of these tips as you implement your data analytics software, you are only a few steps away from reaping all the benefits that this technology has to offer. Hopefully, our list of 20 best data analytics tools was able to guide you in finding the right platform for your operations. To sum it up, we highly recommend choosing Sisense as it offers a code-free self-service analytics system that is great for users who don’t have the extensive technical know-how. Furthermore, it offers highly customizable dashboards, allowing it to easily adapt to your business’ data analytics and visualization needs. You can sign up for Sisense free demo here.

Category: B2B News

One Comment »

  • Sunil Bhardwaj says:

    Was looking out for the name SAS . It is the industry leader in Analytics.

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