Best Data Analysis Software for 2020
Data Analysis Software also commonly known as Analytics platforms or business intelligence (BI) platforms, offers a set of tools for businesses to engross, organize, determine, and analyze data to reveal actionable intuitions that helps to enhance decision-making and update business strategy. Some of these products need IT implementation to form the analytical environment, connect essential data sources, and help prepare the data for usage; others are intended to be mainly arranged and used by non-expert users, without the help of IT for deployment (known as self-service). Business organizations, data analysts, data scientists, or other business stakeholders use this software to make, model, and transform data to better know the day-to-day performance of the company and inform decision-making.
Though separate data preparation tools are available that support in the process of discovering, blending, merging, cleansing, and enriching data—so large datasets can be easily combined, consumed, and analyzed—data analysis solution must integrate these functionalities into their core offering.
The worldwide Data Analysis Software Market is projected to grow from USD 25.71 billion in 2016 to USD 78.26 billion by 2021, at a CAGR of 24.9 %. Data Analysis software has quickly gained traction in the market and is considered to be the fastest-growing business in IT. Business organizations are in very much need of tools that update their business process by rapidly analyzing, optimizing, and managing the exponential growth in data. All these functionalities are included within single support which allows organizations to analyze the correct data at the correct time and helps to understand the behavior and changing taste of the customer and view the performance of each and every resource of the organization. Thus, the usage of these tools is universally seen across all the industry verticals. BFSI and retail sectors are capturing the maximum of the market, while healthcare and media & entertainment are quickly gaining traction in the market. Additionally, players are showing their distinguishing competencies by presenting specific solutions and applications to emphasize the need and demand of a particular department or process. This has ramped up the interest of organizations in capitalizing on this business optimizing tools.
The global data analysis solution market is segmented based on the segment and services, type of deployment, organization size, industry verticals catered, and regions. The analytics segments comprise BI platforms, CPM suites, advanced and predictive analytics, content analytics, and analytics application. The services are categorized as professional services and managed services. Moreover, the deployment type is segmented into on-premises and on- cloud. The organization size is categorized into small-medium businesses and large enterprises. Further, the data analytics market caters to industry verticals such as BFSI, healthcare, IT and telecom, energy and power, retail, manufacturing, education, government, media & entertainment, and others.
The robust reports, dashboards, and visualizations tools created using the best data analysis software can drill down data to a granular level, portray connections and trends between multiple datasets, and generate data visualizations that make the data easier to understand for non-expert stakeholders as well. Products that provide only visualization components are considered as data visualization software, which comprises products primarily intended to generate charts, graphs, and benchmark visualizations.
Few analytics platforms provide embedding functionality to place dashboards or other analytics competencies inside applications; these products are known as embedded analytics software. Products explicitly designed for ingesting and integrating big data collections are known as big data analytics software. Other features of the data analysis tool can comprise natural language search functionality and augmented analytics. Natural language search means the ability to query data using instinctive language, commonly in the form of a question. Augmented analytics means the process of using machine learning for generating perspectives from the data and supporting non-expert users in working with and visualizing data, such as automated data preparation and determining hidden patterns in the data.
The MicroQuadrant matrix provides detailed information about the best Data Analysis Software companies. It describes the conclusions and analysis of how well each market player performs within the predefined MicroQuadrant standards. The vendor evaluations are based on 2 broad categories,i.e. Product Maturity and Company Maturity. Each category carries diverse criteria, based on which the vendors have been assessed. The evaluation criteria considered under the Product Maturity include product offering (based on the industries that the vendors cater to and their service offerings), essential features, type of deployment, and support services. The evaluation criteria considered under the Company Maturity include the overall geographic presence, industry verticals catered, partner ecosystem, and inorganic strategies like mergers & acquisitions.
Visionary Leaders have a robust portfolio of products and services to their clients. The products and services of these companies are highly accessible and can be customized as per the requirements of the clients. These companies are known to be investing significantly in R&D to introduce innovations in the best Data Analysis Software. Moreover, these companies have also undertaken various organic and inorganic strategies to achieve constant and progressive growth in the Data Analysis solution market.
Dynamic Differentiators have a substantial network of channel partners and resellers to expand the deployment of their solutions across a multitude of industry verticals. These vendors have been constantly creating positive revenue growth in the best Data Analysis Software market and their market position has emerged by their organic and inorganic undertakings.
Innovators in the best Data Analysis Software offer innovative solutions and they also have an extensive range of product offerings. The innovators have been leading in deploying their solutions for niche and custom software requirements of the clients.
The emerging players are focused on delivering highly niche and customized mind mapping solutions and services to their clients. These vendors plan new ways of working to drive business results in the market.
What is the importance of Data Analysis Software?
Data Analysis solution allows companies to improve visibility into their data via data integration, cleansing, blending, enrichment, discovery, and more. These tools are strong systems that occasionally require IT and data science skills to access and interpret company data via custom queries. The data analysis tool offers a complete look into a company’s data by extracting from both structured and unstructured data sources through a sequence of detailed queries. Business users also benefit from the best data analysis software with customizable dashboards and can drill into particular data points and trends.
The initial step in selecting a data analytics tool is determining what problems or issues the user needs the product to solve. If a business requires a platform to build complex models of data metrics, then the ideal solution would be a data analysis tool as compared to data visualization software, which doesn’t provide the same integration and querying competences as a data analysis tool. The potential for the software is endless, but once the user emphasizes the variety of business problems they hope to solve, it becomes easier to decide which tool is best to use. Whatever the use may be, from requiring a tool to help forecast financials or to help examine through unstructured big datasets to demonstrate growth, the user wants to narrow it down before buying. Improving the focus of the data analytics tool also helps encourage user acceptance by proving its worth to employees.
What are the Key Benefits of Data Analysis Software?
- The software helps you to understand the present condition of operation from various quantifiable angles
- With data analysis tool you can identify and manage departmental problems on the basis of data findings
- It enables you to improve develop communication between executives, employees, and customers in a data-driven manner
- The best data analysis software allows you to communicate and transfer appropriate data by integrating to other intelligence tools and data sources
Why Use Data Analysis Software?
There are numerous complications that software can solve for businesses across almost every division including sales, marketing, and product development teams. These solutions may need a data expert, such as a data analyst, to initially prepare datasets before they are handed over to the rest of the company. Once the data is in a more understandable form, it can be used to enhance transparency and inform strategy. But, with self-service competences, most tools on the market permit business users to acquire access to data visualizations and data analysis without an IT expert or data support.
Two of the major business problems solved by data analysis tools are the capability to create one unified source for entire company data that can be cross-referenced and correctly managed (also referred to as a business cloud) and the capability to then take that data and use it to enhance business success. Both of these features are vital and core to the proper functioning of the data analysis solution. Without strong data integration and data management, analysis won’t succeed at best and be incorrect at worst and without the capability to derive deep understandings from data, business users are left sitting on banks of data points.
Create a single source of truth - Irrespective of company size, data is difficult to manage. Among all the different departments, tools, and channels, companies quickly accumulate large amounts of data that is housed separately, mostly with poor or little organization. Data analytics tools use connectors to bring that entire data together in one place. A company’s data, right from marketing analytics to financial records or customer feedback, can all be collected in dashboards through connectors—APIs that data analysis software vendors create through partnerships among their products and other software products across groupings.
After these connections are recognized, users start to connect the dots between all of the data that can possibly impact a company’s key performance indicators (KPIs). Then, they can provide other stakeholders with the ability to access reports, analyze data, and share documents on the basis of their queries. Without the data analysis solution, companies would have to collect all of their data manually and try to draw their own inferences and connections, which can be a painstaking process.
Turn data into actionable information - It becomes useless if a company collects huge amounts of data and lets it sit untouched. The data analysis tool allows analysts to deep dive into a company’s data to distribute information to the people and teams that use it to make forecasts, strategies, and actionable understandings. The reports and dashboards developed using software drill down data to a granular level, portray connections and trends between multiple datasets, and generate elaborate data visualizations that make the data easier to understand for the non-expert persons as well.
As analytics platform pulls in a company’s data from different sources, all of the functional areas in an organization line up to develop unified strategies, set objectives, and view the organization’s performance. Visibility into company-wide data enables a company to make timely, business decisions that are consistent and potentially transformative. This confirms that resources are allocated properly, and impact is maximized.
Who Uses Data Analysis Software?
Data Analysis tool sometimes needs complete development work by engineers or data scientists. Ordinary users, even analysts, and managers, sometimes cannot manipulate the data to appropriately find what they need to solve their business issues. It is crucial to think about the complication of data that a business has, also the types of data models that the business needs to build.
Business analytics that is produced by analytics tools are very useful for company-wide departments. Users can inspect big data, real-time data, and unstructured data and fix areas for improvement and other notable trends. Interactive dashboards permit users to easily navigate via different metrics and share precise analytics with others in the organization to support in their decision-making process. Generally, team leaders, managers, and the executive team can benefit maximum directly from the data discovery made possible with BI tools.
Data analysts and data scientists - These users are usually the power users of analytics tools, making complex queries inside the software to gather a deeper understanding of business-critical data. These users may also be tasked with creating self-service dashboards to distribute to other teams. In smaller companies, these users are most likely tasked with providing all data requests and working closely with the other departments like sales, marketing, and operations teams to help offer understandings and enhance processes.
Sales teams - Sales teams mostly use both self-service analytics tools and embedded analytics solutions to gain insights into potential accounts, sales performance, and pipeline forecasting, amongst many other use cases. Usually, sales operations executives or sales data analysts are the sales team members that use analytics on a day-to-day basis. But these tools are very useful for both sales managers and representatives. Sales Managers can track the performance of each representative and keep a clear picture of the potential pipeline. Moreover, sales teams can use data visualization tools to track previous tasks and quotas to give the entire company visibility into top sales numbers. Eventually, using data analysis solution in a sales team can help business organizations to improve their sales processes and influence revenue.
Marketing teams - Marketing teams generally run different types of campaigns, comprising email marketing, digital advertising, or even traditional advertising campaigns. The best data analysis software permits marketing teams to track and record the performance of those campaigns in one central location. With data visualization, for instance, marketing teams can track campaigns in real-time; by computing the performance of each effort, teams can plan for upcoming campaigns and estimate how much revenue they can set aside to said efforts. Data analysis tool enables analysts to dive deeper into marketing efforts by segmenting customers based on a variety of demographics, thus offering an understanding of which campaigns reverberate with varying segments of their customer base. This helps marketing teams to develop campaigns and exact products for preferred market segments. Similar to sales use cases, marketing teams can also utilize the best data analysis software to help progress the bottom line and provide transparency and visibility into overall performance.
Finance teams - Finance teams use data analysis solutionto get insight and understanding into the factors that influence an organization's bottom line. Integrating financial data with sales, marketing, and other operations data helps accounting and finance teams to gain actionable insights that may not have been revealed through the use of traditional tools. Moreover, they decide the "right and wrong" places to spend money. For instance, if a particular advertising campaign was the reason for a revenue spike, a company may consider that money well spent. If a specific product is generating less profit than others based on the effort of the sales team, then finance teams can inform their sales managers and correct accordingly. Data analytics tools also offer actionable insights into how all the other business factors impact profit and loss.
Operations and supply chain teams - Analytics solutions generally use a company's enterprise resource planning (ERP) system as a data source. These applications can track the whole thing from accounting to supply chain and distribution; by entering supply chain data into data analysis solution, supply chain managers can improve a number of processes to save time and resources. For instance, businesses can improve inventory to ensure that they are not over-producing or under-producing or influence location intelligence software to control the optimal location for their next warehouse. These platforms help to optimize distribution routes and ensure service level agreements (SLAs) are on time. Moreover, data visualization software allows warehouse workers to track their daily tasks to ensure all operations are running smoothly. All of these improvisations help businesses be on track and achieve higher company-wide goals.
What are the different types of Data Analysis Software?
Many types of data analytics tools share overlying functionality, while concurrently catering to different users or providing unique services.
Self-service data analysis software - Self-service analytics platforms don’t need coding knowledge, so business end users can take benefit of them for data requirements. These solutions usually provide drag-and-drop functionality for creating dashboards, prebuilt templates for querying data, and rarely, natural language querying for data discovery. Organizations use this type of software to create interactive dashboards for discovering actionable understandings. This allows business users such as sales representatives, human resource managers, marketers, and other non-data team members to make informed decisions based on appropriate business data.
Embedded data analysis software - Embedded business intelligence software can integrate proprietary analytics functionality within other business applications. Business organizations may choose an embedded product to endorse user adoption; by integrating the analytics inside regularly used software, companies allow employees to take benefit of available data. These solutions also offer self-service functionality so average business end-users get benefitted from data for improved decision making.
What are the key features of Data Analysis Software?
Data Analytics platforms are of great help to any organization with a need for timely data visualization of high-level analytics. Here are some core features within analytics platforms that can help users make the most of them.
1. Integrations – Data Analysis Software function as designed with an important portfolio of software integrations. The data mining achieved within these tools depends on deep, precise information and big data scrapped from multiple locations. These comprise company data warehouse software, sales platforms such as CRM software, website monitoring software, and other additional platforms containing some data appropriate to business performance. Analytics platforms collect precise fields of data from external software and translate this information as required for the numerous findings or predictive analytics a business considers worthwhile.
When considering the best data analysis software, it is vital to research the integrations offered with a product and decide which components of the business software stack are accounted for. If current software can’t integrate with a preferred data analysis tool, users have to decide how essential that data would be to the business’ analytics creativity. Users may require considering a replacement solution or a different analytics tool if a vital integration is missing. It is always helpful to reach out to an individual vendor with questions about any integrations.
2. Data preparation - Even though standalone data preparation software are available that assist in the process of discovering, blending, combining, cleaning, and enriching data—so vast datasets can be easily integrated, consumed, and analyzed—data analysis tool should incorporate these functionalities into their essential offering. Specifically, analytics platforms must support data blending and data modeling, offering the end-user the ability to merge data across different databases and other data sources and enabling them to develop strong data models of this data. This is an important step in making meaning out of the chaos, through the merging of data from various sources.
3. Data management - After the data is properly integrated, the data must be managed properly. This comprises data access to authorized users, for instance. However, some companies choose a standalone data management solution, as a data warehouse, analytics platforms should provide some level of data management by definition.
4. Definable business metrics – Business organizations have their own version of success, and along with that, distinct data analytics that is appropriate to measure their performance. While planning a data mining campaign with the best data analysis software, users program the tool to capture and organize the most important analytics from any data sources that are available (e.g., data warehouses). Users can also identify the relationships between different tables, create data models, blend data from different sources, and then label these resulting analytics based on the success metric they are tracking. Few examples of valuable analytics can include the average time customers spend in a store and lifetime customer value.
Users may reach out to BI consulting partners to help decide the most appropriate analytics and data to capture in relation to their company’s overall success. After a proper consultation, these agencies may help with setting up or choosing BI tools. Many agencies can help businesses with the entire BI process, right from entire data analysis to the shaping of processes or protocols associated with data collection. For users who have never done data analysis before or those keen to enhance their company’s reporting, a relationship with these consultants can prove very beneficial.
5. Customizable dashboards and reports - Multi-layered, real-time dashboards are a vital feature of the tools in this category. Users can program their analytics software to show whichever metrics they would like and create multiple dashboards that show analytics associated with specific teams or initiatives. Users can pick and choose their preferred metrics from predictive analytics of website traffic to customer conversion rates over a stated period of time to feature in dashboards and build as many dashboards as required. Admins can regulate the permissions of different dashboards, so they are easily accessible by authorized users in the company who need them the most. Users can also choose to share few dashboards on office monitors or take screenshots of dashboards to save and share as required. Few products in this category may permit users to explore dashboards on their mobile devices as well.
Responding to a single business query in these platforms and separating a specific data point associated with the question, is recognized as ad-hoc reporting. The IT team can help assign users across an organization and enable them with ad-hoc reporting features so they can make the maximum of this technology without continuous support. Users can also pull metrics from the past with in-memory processing, which processes data from stored databases. This is useful for comparative analysis and defining trends over a period of time.
6. Data querying – Apart from dashboards and reports, users like data analysts may need to conduct more thorough-going analysis on the data via queries. Based on the platform, this can be done in a natural, conversational manner or via query languages like SQL. By means of queries, users can slice and dice the data and deep dive into particular points of interest.
Even though the above-mentioned features are core and vital to the very definition of data analysis software, there are few distinctive features, mentioned below, which provide advanced functionality to the end-user.
1. Root cause analysis – Many companies produce huge amounts of data from different sources. But it can be difficult to keep track of the ebbs and flows of data and to identify when there are outliers in the data and when trends are happening across tens, if not hundreds of data sources. Similarly, some platforms provide the user with a sharp view of their data and logically alert them to fluctuations in real-time. Once alerted, they can dive and drill down, to assess the condition and resolve it.
2. Predictive analytics - Though there is a market of tools that emphasize exclusively on predictive analytics, many data analysis solution have tools for applying predictive models to data. However, they vary in terms of the sophistication of this predictive analysis, such as the types and diversity of models users can deploy.
What are the current trends in Data Analysis Software?
With analytics platforms, many users across a business can easily find, access, and analyze the data. Moreover, artificial intelligence (AI) tools like natural language processing (NLP) software support in searching through and for data easier and more powerful, providing more accurate results.
1. Conversational interface - Earlier, to query data within an analytics solution, users required to master a query language like SQL. With the growth of conversational interfaces, users reveal the data and understandings they are looking for using instinctive language. Instinctive methods of querying data mean allowing a larger user base to access and make sense of company data.
2. Augmented analytics - AI is rapidly emerging as a promising feature of analytics solutions across the whole data journey, from ingestion to insights. From AI-powered data preparation to smart understandings, in which the platform suggests visualizations to the end-user, the analytics tool is quickly becoming much more powerful. End users can discover hidden insights using machine learning that allows them to make sense of data and help them understand what they are seeing.
What are the challenges faced in Data Analysis Software?
1. Difficulties with configuration - Analytics solutions may have an extremely technical setup process, which may require IT expert or developmental expertise. While trying to implement one of these platforms without an internal data scientist or IT professional, users may struggle with getting the technology off the ground, combining it with the suitable solutions, and generating queries for data collection. This could cause a substantial loss of resources and an inability to use the tool as planned. Users can reach out to BI consultants for support with setting up a program or, in some cases, for managing the entirety of BI reporting.
2. Incomplete integrations - Incomplete integration with existing software solutions becomes challenging to get a comprehensive view of a business’s operational performance. Likewise, if an integration experiences a communication fault or other issue during a data query, it causes an improper or partial reading. Users should make a point to look at these connections and any potential performance issues throughout their software stack to confirm that correct, comprehensive, and up-to-date information is being processed and displayed on dashboards.
3. Risk of data security - Companies must consider security options to ensure the authorized users see the accurate data, to ensure severe data security. Effective analytics solutions should provide security options that allow administrators to allocate authorized users different levels of access to the platform, on the basis of their security clearance or level of seniority.
4. Too much dependency on analytics - Focusing and depending too much on data and analytics can also be challenging. Data-driven decisions are important to a business’ success, but data-only decisions overlook the various issues from within and without the organization. While successful businesses merge rigorous analytics with anecdotal storytelling and thoughtful conversations around the success of the business and its components.
KPI dashboards can be created by users using various analytics tools like graphs, charts, spreadsheets, and many other metrics. Being able to create unlimited scorecards internally and extreme customization options available are really the attractive features of the software.
Kissmetrics Data Analysis Software keeps track of every single behavior of the user/customer even prior to their signing up. This helps to comprehend the origin and the behavior of the user and also the reason behind their conversion into a customer. The software helps to simplify the data that is needed by an entrepreneur in order to improve the marketing strategies of the company.
Zoho analytics data analysis software is a network that puts together AI and Analytics to deliver companies smart and tailored sales and marketing solutions. The intuitive AI and interface help users obtain measured insights into their clients and predictive analytics help produce detailed sales and marketing data.
Magellan data analysis software is considered to its mission to delight its users by exceeding their expectations, aiming for the highest customer satisfaction and retention. It contributes to the quality of life by enabling users to make the world a safer and healthier place. It promises to create continuous, sustainable shareholder value and to be the employer of choice in the industry. It commits to delivering quality service and putting the success of its users first. It constantly improves its offerings and empowers its users and partners to achieve their research and business goals. It commits to focus on detail like no other, consistently delivering quality engineering, flexibility, and reliability. It strives to meet the highest standards in every market it serves.
MapR Data Analysis Software works to collect and synchronize data from various sources globally and helps businesses access the data easily. It comes with an additional feature of cloud storage that helps businesses sync their data in the cloud and prevent it from any cyber disasters. The quick access to data ensures higher productivity, increases quality, and yield by leveraging data at the edge.
Compass Data Analysis Software software helps to measure and improve trade effectiveness of trade promotions with a combination of AI powers tools to predict and analyze data accurately. These aspects serve as key attractions to the software.
Databricks is a cloud-based source for collaborative data science and large scale data engineering. It helps organizations in making all their data ready for analytics, empowering data science and data-driven decisions across the organization, and rapidly adopting machine learning to outpace the competition. It helps the organizations innovate faster and tackle challenges easily through rapid drug discovery, improvement in energy efficiency, and protecting the financial markets.
Mammoth data analysis software gives liberty to perform numerable tasks with the help of a virtual interface so that users with no coding knowledge can easily work on this software. It follows a simple workflow and keeps a record of each and every modification made, thus making it easier for the users to keep track of their work and also make several attempts at shaping their data into a useful piece of information. It also provides a large storage space under a single platform to avoid missing data.
The USPs of ATLAS.ti Data Analysis Software include its support for major text, graphic and audio formats. It also supports the handling of large data sets and provides structured visualization in a semantically meaningful way. It is useful in a wide variety of fields like anthropology, economics, criminology, medicine, etc.
Bouquet Data Analysis Software is easy to use for both simple look up’s as well as complex analysis, supports search and filtering across many values, and also helps to manage user access rights to data. These varied uses of the software attract attention from the users more and more.
Informer Data Analysis Software by Entrinsik promises to deliver next-generation analytics software to cater to specific business needs. Its features come loaded with the assurance of lightning-fast delivery without any slacking from the product. It is a customer-driven organization fueled by customer feedback and believes that change is the only constant in this world. It emphasizes specifically, on the ease of use, modern technology, and high level of customer engagement. Its latest features make it one of the revered data analysis software choices for the business.
The software works to analyze customer engagement through various channels and generates insights for the same. It aids the marketers in highly targeted customer segmentation and aids small and large businesses alike. The service aids in data-driven marketing by using advanced analytics that studies customer behavior and keeps the marketer updated regarding the current trends in the market space.
Qubole data analysis software provides computer scientists, system developers, data analysts, and managers with an out-of-the-box workbench and notebook. It supports open-source platforms used for all application user styles including Apache Spark, Presto, Hive / Hadoop, TensorFlow, Airflow, and others. It offers faster access for Analytics and Machine Learning to petabytes of safe, accurate, and trustworthy data sets of structured and unstructured data.
Stata data analysis software is considered to be the perfect solution package for data science inclusive of analysis, modeling, statistics, data management, and visualization. It is a fast and powerful statistical package designed for researchers of all disciplines with tools and techniques put at the users’ fingertips. It is deemed more powerful than basic data analysis software and user-friendly that provides quality graphs with a professional finish. It is easy to use, automate, extend and to grow with. It helps users to master data with the help of a broad suite of statistical features with automated reporting and python integration. Its extensive features commit maximum customer satisfaction.
Tonic is considered to be an all-in-one platform for masking, subsetting, scaling, and simulating data across tables without coding. It preserves the constraints, statistical correlations, distributions, and interdependencies that make the users’ data unique by using pre-trained models and feature extraction. It unlocks data to accelerate growth. It promises to anonymize user data to diagnose issues effectively without breaching user privacy. It is a tool for masking data and elevating communication. It mitigates risks and minimizes data breaches and by streamlining data de-identification, it commits to safeguarding data access.
The use of intrinsic designs and computational techniques makes this software one of its kind. It is used in various fields of technical computing and thus includes thousands of features and functions that broaden the spectrum of the software. It uses simple language which makes it comprehensible to its users. It can easily be accessed online on any device which ensures the reachability of the software.
Workbench Data Analysis Software demands no prior knowledge of the coding world and is designed so to benefit every individual. It is capable of performing multiple queries without any lag and facilitates smooth transmission of data throughout. It helps in gaining rich insights into the database and produce efficient models to increase the credibility of the same. With easy export and import of data, it becomes feasible to establish a collaboration between different teams and reduce communication gaps.
The unique selling proposition of YouGov data analysis software lies in the simple structure of its software. The software does not necessitate the need to know standard jargon to perform analytical functions. The workspace is well organized and can easily be customized to suit the requirements and interests of the user. The feature of alerts and notifications keeps the user well informed of the different activities and data trends and thus helps in making informed decisions.