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Thursday, 11 October 2018

20 Best Data Analytics Software or website to hold in

20 Best Software for Data Analytics 


In this digital era, it is easier to access data– in large numbers in most cases– in order to gather insights that will lead to your business performance being optimized. The challenge now is to extract key data from your data, used for taking decisions and analyzing trends.
Since this process is likely to lose the metrics and trends in information mass, it is logical that companies use tools and software programs to automate mechanical algorithms and processes in order to convert raw data for their human consumption. We have compiled the leading data analysis software products in the category for review with the multitude of solutions available to you.
Data analysis is a complex process. The process of handling data is a challenge for any organization, even more so as it is enormous. Add to the mix the difficulty of creating a visually comprehensible representation of data. A scalable system is also a necessity as data increases in volume each day, so you want an application with the architecture and technology to support your data analysis processes.
More than 16,000 data professionals from 170 countries and territories revealed their challenges in the field in a survey called Kaggle 2017 State of Data Science and Machine Learning. Respondents were asked to select all factors relevant to their problems.

At the first place are dirty data, which 36% of respondents have voted for. The next issue is the lack of data- science talent( 30%), company policy( 27%), lack of clear issues( 22%), inaccessible data( 22%) and non- influential results( 18%). 

Data with 16 percent and privacy with 14 percent are also difficult to explaine to others. In the mean time, a data science team of 13 percent of data professionals has disclosed that their small business can not afford.
These issues require maximizing technological capabilities, far beyond the recognition of the bottlenecks of your organization. Today's platforms for data analytics are easier to use and are more affordable to use such a tool by all companies. This is crucial, especially in view of the value your organization can bring. For example, content companies can use a tool for data analytics, by clicking on and viewing the content for their audiences. Another example is the gaming companies gaining relevant information to keep players active in the game by offering rewards. You can also use HR analytics to use your brand.
It is, of course, not new in their decision making processes for companies trying to be data- driven. According to a NVP report, 85% of businesses are moved to data- driven. But only 37 percent of them succeed. 

In order to ensure an efficient execution, the right data analytics software is one of the first priorities. The leading products in the niche are a good place to start. This is 20 of the best software for data analysis.

20 Best Data Analytics Software

1. Sisense


Sisense is a robust data analytics software that provides analytics to all business users and not merely data scientists. Even for non- technical users, it simplifies business analysis by means of its tools and functions. Any user who uses autoservice analytics without hard coding and aggregating modelling extracts insights instantly. Some of its best features include its custom dashboards, interactive visualizations and analytical capabilities.
The dashboard is a top feature that allows you to filter, explore and collect data in just a handful of clicks to get answers to your questions instantly. Data analysis can be done quicker with a wealth of insight with its in- chip technology. 

In addition, it offers you advanced analysis by integrating R functions into your formulas through improved, advanced BI reporting and predictive analysis. The best way to first test the tool's features and functions is to know when it meets your needs. You can register here for free trial in Sisense to do that.
Why choose Sisense?
  1. Data Visualization. It has a large library of widgets with a wide range of designed display widgets. You can also submit your own designs of your open source or receive recommendations on how to view your information best.
  2. Anomaly detection:- If your information has anomalies, the system uses a machine to detect and alert you instantly to any possible problems.
  3. NLG technology. The NLG technology can be used to easily detect patterns and trends when interacting with each widget on the dashboard.

2. Looker

Looker is a platform for data analysis that allows people to ask sophisticated questions with familiar terms of business. It provides data directly for your team's tools and applications, including your customers. 

The platform collects, extracts and loads the data from different sources into a SQL database. It undergoes the agile modelling layer of the platform for customized business logic and finally offers it to users through dashboards, shared insights and explorations.

Data can be easily accessed in your existing systems and can easily be shared with everyone in your team as a browser- based solution. Exports to platforms like Google Drive and Dropbox can also take place locally and directly.
If you are interested to know this solution’s features better, you may sign up for Looker free demo here.
Why choose Looker?
  1. Web integration. This provides you with a responsive mobile design, a solution that’s embeddable with SSO, and a full REST API.
  2. Accessible data. Your data analysis tool is not locked by the app. Instead, systems such as Extra work and Salesforce can access it.
  3. Data scheduling. The delivery of data to FTP, S3, Chat, Webhooks, and emails is planned by all members of the team.

3. Zoho Reports


Zoho Reports is a platform for business data analytics that allows fully customized accounts with flexible and different options in a range of modules. Examples include scheduling reports, column grouping and link- up modules at the 3-level. It is designed for small and medium- sized companies with their own data analysts, custom applicators or tablet users seeking a more efficient system.
Even Non- Zoho users can receive insights from the Tool via email to ensure that all team members have access to critical information on data analysis. More than 40 standard reports, packaged in different models, can also be used to improve your learning experience and to provide a ready reference.

Why choose Zoho Reports?
  1. Robust dashboard.  It gives an in- person snapshot of key organizational metrics and an easy view of trends, patterns and comparisons in sales, marketing, inventory and support.
  2. Macromedia Flash technology. This creates and produces 2D and 3D charts with your analytics dynamically
  3. Funnel chart. A unique chart used by the system to visualize the sales pipeline intuitively in various stages.

4. Domo


Domo is a data analytics solution to provide your data, people and systems with a digitally connected environment. Your business data is used to refresh and drag- and- down data processing capabilities for all employees of your organization in real time. In order to increase productivity and the ability to deal with your information, partners outside your organisation also can contact you.
You can take more informed actions with the 7 platform components of the tool by using a holistic view on your system. Predictive alerts inform you of important issues and issues with sufficient time before they affect your organization.
Why choose Domo?
  1. Native mobile apps. Management of responsibilities for Android and iOS on your mobile devices is intuitive, time- efficient and on- the- go designed.
  2. Instant data-driven chat. It has more than 300 interactive desktop and mobile tablets and dashboards.
  3. Connected data. Bring your data to any third- party source such as a cloud, on- site and proprietary systems with over 500 data connectors directly.

5. Qlik Sense


The Associative Engine powered Qlik Sense to deeply exploit insights that other query- based data analytics tools often miss. It does this by indexing every possible link between data and combining it with a centralized view from various data sources. The cloud- based data analytical platform allows you to offer the correct solution for analysts, teams and global businesses in different cases.

The absence of pre- aggregated data from the common query- based tools provides the basis for questions and analysis even when experts in creating new queries are not waiting for help. The sharing of views is easier irrespective of the size of your company since the system enables a safe and unified hub to cooperate.

Why choose Qlik Sense?
  1. Interactive analysis. You can browse data interactively and search globally and ask any questions without exploration limits. The most up to date version is available, every click will also update the analytics at once.

  2. Smart visualization. Find visual insights through the fully interactive platform's interface which allows you to pan, zoom and select your data effectively.

  3. Flexible for any device. You can work with any device once you build your analytics app to allow exploration, collaboration and analytics to be done by interaction and responsive mobile design.

6. GoodData


GoodData is a comprehensive, secure architecture of cloud data analysis that covers the full range of your data, starting with data up to the insights you have generated. It's not just for companies, but also for partnerships and software firms. The product is mainly used in the insurance, retail, financial and ISV industries.
The intelligent business application integrates insights into your work in order to speed up smart decisions. Improvements are also automated over time, since they can learn from user actions and predict data. In addition, the tool guarantees company level safety in, among others, HIPAA, GDPR, SOC II and ISO 27001.
Why choose GoodData?
  1. Quick implementation. Deployment is carried out quickly, so that the system can be used in 8 to 10 weeks immediately.
  2. Industry-specific solutions. The tool has solutions designed to meet ISV, retail, financial services and insurance needs.
  3. Embedded analytics. Analytics is incorporated in your application so that you can extend it for any application, including machine learning, benchmarking, fundamental reports and advanced analysis.

7. Birst

In a network that can connect your insights for smarter business decisions, Birst is specializing in data analysis. Networked analytics combine the speed, agility and usability of consumer design tools and the data management and scalability requirements of IT specialists. The Cloud architecture has a multi rental network that allows data analysis to be expanded across departments, product lines and regions.

His particularity is his 2-tier approach to visualizing, querying and manufacturing data by end- user users. In several databases and cloud or on- site application, you can extract and maximize data and connectivity options. Developers can establish their own connections or use their numerous third- party systems integrations.

Why choose Birst?
  1. Adaptive user experience.  Users have a wide choice in how they interact with data that fits in with modern styles of work. Whatever device you use, you can see the same interface that you prefer with the tools.
  2. User data tier. This makes it possible for both centralized and decentralized teams to enable data as a data governance service. It is used to aggressively and rapidly aggregate and control a complex mix of business data.
  3. Multi-tenant cloud architecture. It connects all through the virtualisation of the whole data analytics ecosystem in a single networked view of data.

8. IBM Analytics


In order to support critical decisions for your company, iBM Analytics is an data analytics tool specializing in proving insight. It simplifies the way in which you collect, organize, and analyze your data in an optimized way. You are free to gather all kinds of data from different data sources.
It also enables you to build a secure basis for your analytics with agility in how data is supplied and organized into a single source of truth in business. In addition, it allows you to scale your insights into your previously unachievable decisions by incorporating evidentiary insights. This helps you to smartly analyze data.
Why choose IBM Analytics?
  1. Prescriptive analytics. Consider business constraints and optimizing the trade offs to identify the best action plan, plan, schedule and configuration accordingly.
  2. Predictive analytics. This combines data mining, text analysis, predictive modeling, ad- hoc statistical analytics, advanced analytics, and more, to spot data patterns and anticipate next- generation developments.
  3. Machine learning. Maximize intelligence in your business application to accelerate the deployment of data science projects.

9. IBM Cognos

When making intelligent decisions quickly using intelligent self- service capability, IBM Cognos is the solution to consider. This tool provides IT with an architecture- based solution to deploy in the cloud, or on site. It also provides business users with dashboards and reports that they want to create and configure themselves.
One of its highlights is its own service capability, which allows users both online and off- line to interact and access reports on mobile devices. The tool also offers a wide range of methods of analysis from trend analysis, analytical reporting, trend analysis and whatever analysis is available in the analysis.
Why choose IBM Cognos?
  1. Complete cloud-based experience. Whether you utilize the tool via desktop or mobile device, the user experience is consistent because no desktop tool is needed. This also prevents you from transferring data as you live in the cloud.
  2. Smart self-service. As integrated solution, you can perform task- critical analytics in a compelling presentation and visualization and generate insights from the data.
  3. Robust automation. Smart technology automates the analytical process, provides recommendations and predicts user intentions to increase productivity across teams, ecosystems and organisations.

10. IBM Watson

IBM Watson is an analytical platform for optimizing the use of artificial intelligence to leverage interactions, predict disruptions and accelerate research. This advanced data analysis and visualization solution lives in the cloud and provides users with a trustworthy guide to their data discovery and analysis.
Unlock patterns and significance in your data independently with a guided data detection and automated predictive analysis. You can interact with data and collect answers that you can understand using the cognitive capabilities of the tool, such as the natural language dialogue, even without the help of a professional analyser. This allows every company user to identify a trend and view the data report for an effective presentation in the dashboard.
Why choose IBM Watson?
  1. Smart data discovery.  You can easily type a question that adds or links to the data with your own words to provide you with understandable insights. You get a roster of starting points immediately, whether you're on your desktop or iPad.
  2. Simplified analysis. When identification of patterns and factors that can potentially result in business results you can be ready to act with confidence.
  3. Analysis of trusted data. Due to many forms of data analysis, the tool helps you keep synchronized when you explore, predict and assemble data for confident insight.

11. MATLAB

MATLAB is a platform for data analytics commonly used by engineers and IT teams to support their processes for big data analysis. It enables you to access data in a single, integrated environment from a variety of sources and formats like IoT devices, OPC servers, File I / O, databases, databases and distributed file systems( Hadoop).
You can prepare your data before developing predictive models by automating tasks, such as cleaning data, handling of missing data, and noise removal from sensor data. Then, predictive modeling and prototyping can predict and predict results directly. The system allows you, even without coding or building a custom infrastructure, to integrate the tool with IT environments in production.
Why choose MATLAB?
  1. Online deployment. The tool incorporates business systems, clouds and clusters. It can also be used on embedded hardware in real- time.
  2. Machine learning. It provides you with advanced methods such as system identification, prebauthored algorithms, financial models, and nonlinear optimisation, providing a full set of statistical and ML functionalities.
  3. Physical-world data. They support binary, image, sensor, telemetry, video and other formats in real time.

12. Google Analytics

Google Analytics is one of the most known and widely used data analysis solutions in which high- level dashboard data and functions can be summarized with a variety of funnel viewing techniques. At its core is a web analytics service used for website tracking and reporting. The freemium product analyzes poorly functional pages using various techniques driven by data.
In addition, this tool gives you information to transform businesses of all sizes into actionable insights in order to obtain a stronger result across their websites, apps and offline channels. This tool is specialized in one of the most important aspects of data analysis and is essential for the construction of a tight framework for data analysis for your organization.
Why choose Google Analytics?
  1. Data collection and management. You have an overview of your customer that can be easily adapted to your business needs. It is also simplified to share this across your organization.
  2. Data analysis. Reporting and analysis tools are available to assist in segmenting and filtering data to better understand the lifecycle of your customers.
  3. Data activation. The data can be activated in marketing to explore new contents and channels and to leverage marketing campaigns.

13. Apache Hadoop

Apache Hadoop is a good location to start distributing storage and processing large datasets if you are looking for an open source platform. It also offers data access, administration, security and operational services. It is a collection of tools that enables a multi- computer network and data sets on computer clusters made of commodity hardware to solve problems.
This solution supports large computer clusters fundamentally. In the cluster failure of single nodes is seldom a problem, and when it does, the system replicates the data automatically and reroutes the other data in the cluster. It is a highly scalable platform for storing, handling and analyzing petabyte- sized data.
Why choose Apache Hadoop?
  1. Low cost. It works on low- cost commodity hardware because it is an open source platform, which makes it an affordable alternative to proprietary software.
  2. Flexible platform. Raw data can be stored, parsed and used for the scheme when reading in any format. Since the storage of data does not require structured schemes, you can even store data in semi- structured and structured formats.
  3. Data access and analysis. Data analysts may select their preferred tools because they are able to interact with platform data seamlessly by means of batch or interactive SQL or low latency NoSQL access.

14. Apache Spark

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

 To maximize this solution, you can use Hadoop to create applications to harness its power, gain a deeper understanding and improve workloads in data science in a single, shared database.
The Hadoop YARN- based architecture, making this tool a data access tool that works with YARN HDP, is expected to provide consistent response and service levels. This means that the solution can easily share a common dataset and cluster together with other applications.
Why choose Apache Spark?
  1. Data processing engine. In developing APIs that need fast access to datasets, data analysts can execute streams, machine learning and SQL workloads.
  2. Unified solution. It enables complex workflows to be created and combined with support for SQL queries, graphics processing, machine learning and higher level libraries.
  3. Easy-to-use APIs. The tool is easy to use because it can provide more than 100 data transformation operators and known data frame APIs for semi- structured data handling.

15. SAP Business Intelligence Platform

SAP is the adata analysis tool used to monitor key metrics and gain valuable insight into customer behavior while eliminating assumptions. In principle, it provides you with actionable information at your disposal as a BI solution. It is available both on site and in the cloud according to your needs.

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