Analytics Platforms reviews by real, verified users. Find unbiased ratings on user satisfaction, features, and price based on the most reviews available anywhere.
Analytics platforms, sometimes known as business intelligence (BI) platforms, provide a tool set for businesses to absorb, organize, discover, and analyze data to reveal actionable insights that can help improve decision-making and inform business strategy. Some of these products require IT implementation to build the analytical environment, connect necessary data sources, and help prepare the data for usage; others are designed to be primarily configured and used by non-expert users, without the help of IT for deployment (known as self-service). Business and data analysts, data scientists, or other business stakeholders can utilize this software to prepare, model, and transform data to better understand the day-to-day performance of the company and inform decision-making. Fundamentally, for a product to be categorized as an analytics platform it must be an end-to-end analytics solution, which incorporates five elements: data preparation, data modeling, data blending, data visualization, and insights delivery.
Although standalone data preparation tools exist that assist in the process of discovering, blending, combining, cleansing, and enriching data—so large datasets can be easily integrated, consumed, and analyzed—analytics platforms must incorporate these functionalities into their core offering.
Analytics platforms must support data blending and data modeling, giving the end user the ability to combine data across different databases and other data sources and allowing the end user to develop robust data models of this data.
The reports, dashboards, and visualizations created using analytics platforms can break down data to a granular level, depict connections and trends between multiple datasets, and create data visualizations that make the data easier to understand for non-expert stakeholders. Products which only provide the visualization component are categorized as data visualization software, which includes products primarily designed to create charts, graphs, and benchmark visualizations.
Some analytics platforms offer embedding functionality to place dashboards or other analytics capabilities inside applications; these products are considered embedded analytics software. Products specifically designed for ingesting and integrating big data clusters are categorized as big data analytics software. Other features of analytics platforms can include natural language search functionality and augmented analytics. Natural language search refers to the ability to query data using intuitive language, frequently in the form of a question. Augmented analytics refers to the process of using machine learning for deriving insights from the data and supporting non-expert users in working with and visualizing data, such as automated data preparation and discovering hidden patterns in the data.
To qualify for inclusion in the Analytics Platforms category, a product must:
Tableau Desktop allows people to make data-driven decisions with confidence, by helping them answer questions more quickly, solve harder problems more easily, and uncover new insights more frequently. With a couple of clicks, Tableau Desktop connects directly to hundreds of data sources, both on-premises or in the cloud, making it easier to start analysis. Interactive dashboards, drag and drop functionality, and natural language queries help people of all skill levels quickly discover actionab
Domo’s mission is to be the operating system for business, digitally connecting all your people, your data and your systems, empowering them to collaborate better, make better decisions and be more efficient, right from their phones. Domo works with many of the world’s leading and most progressive brands across multiple industries including retail, media and entertainment, manufacturing, finance and more. For more information about Domo (Nasdaq: DOMO), visit www.domo.com.
Qlik Sense is a platform for modern, self-service analytics, driving discovery and data literacy for all types of users across an organization. It supports the full range of analytics use cases - from self-service visualization and exploration to guided analytics apps and dashboards, custom and embedded analytics, mobile analytics, and reporting. And, it does this within a governed, multi-cloud architecture that delivers maximum trust, scale, and flexibility for the organization. Qlik Sense pro
Power BI Desktop puts visual analytics at your fingertips. With this powerful authoring tool, you can create interactive data visualizations and reports. Connect, mash up, model, and visualize your data. Place visuals exactly where you want them, analyze and explore your data, and share content with others by publishing to the Power BI web service. Power BI Desktop is part of the Power BI product suite. To monitor key data and share dashboards and reports, use the Power BI web service. To view
Looker is reinventing business intelligence for the modern company. Looker works the way the web does: browser-based, its unique modeling language lets any employee leverage the work of your best data analysts. Operating 100% in-database, Looker capitalizes on the newest, fastest analytic databases—to get real results, in real time.
The Sisense data & analytics platform makes it incredibly easy to mashup data across your entire data landscape and transform it into powerful, actionable analytics applications that can be embedded anywhere. From innovative startups to global brands like GE, Wix, Nasdaq, and Philips, thousands of organizations worldwide use Sisense to accelerate innovation and drive digital transformation by embedding Sisense into their businesses. Whether your data is in the cloud, on-prem, or a mix of bo
Tableau Server is an enterprise analytics platform that is easy to deploy and scale and helps enable data-driven decision making throughout your organization. Deploy the way that makes the most sense for your organization - on-premises or in the cloud, on Windows or Linux, while integrating with your existing security and authentication protocols. Provide governed data access while promoting sharing and collaboration with data, dashboards and insights, all with the scalability and security re
Mode helps companies make better decisions and answer new questions by unlocking the value in data. Mode’s collaborative analytics platform combines SQL, Python/R notebooks, interactive visualizations—both out-of-the-box and custom using HTML, CSS, and D3.js—and shareable live reports. Analysts, data professionals, and stakeholders can collaborate, analyze, and explore.
The MicroStrategy platform offers a complete set of business intelligence and analytics capabilities that enable organizations of any size or maturity to get value from their business data. Organizations use MicroStrategy to build and deploy analytical and data discovery applications in the form of personalized reports, real-time dashboards, pixel-perfect documents, mobile applications, and more. MicroStrategy also brings organizations HyperIntelligence®, the first ever tool that brings insights
Board is the #1 decision-making platform. Founded in 1994 and Headquartered in Chiasso, Switzerland, and Boston, MA, Board International has enabled more than 3,000 companies worldwide to drive digital transformation by effectively deploying Business Intelligence, Integrated Business Planning, and Predictive Analytics applications on a single platform. Board enables companies to achieve full transparency of business information and gain complete control of performance across the entire organiz
Simple enough for everyone. Powerful enough for the data team. Chartio is a cloud-based business analytics solution on a mission to enable everyone within an organization to access, explore, transform and visualize their data. With Chartio, every team member can now answer their own questions.
WebFOCUS provides an intuitive, immersive experience inviting users to begin creating analytical content, portal pages/ dashboards and reports in minutes. This same platform empowers data scientists, developers, and administrators to leverage powerful capabilities to manipulate and transform data within an unparalleled data prep and governance foundation. * Business users need complete self-service analytics capabilities that help them create, use, and share analytical content * Data scient
Klipfolio is a cloud-based BI solution that enables users to connect to any data source, create beautiful data visualizations and easily share them when and where needed. Whether you need dashboards, snapshot emails, PDF reports, TV data boards or mobile monitoring, thousands of customers rely on Klipfolio every day to help drive data-informed decisions. Klipfolio recently added a new way to get started and visualize your data with PowerMetrics, which offer single click visualizations, data ex
TIBCO Spotfire® is Analytics Accelerated, a self-service data visualization platform that speeds individual time to insight and analytics adoptions across the organization. Spotfire helps users quickly and easily generate insights with three new ways to support their analytical preferences: NLQ powered search, AI-driven recommendations, and direct manipulation - all wrapped in a streamlined, beautiful interface. Users can add context with native streaming for integrated analyses of real-time a
QlikView, Qlik’s classic analytics solution and the game-changing Associative Engine it is built on kicked-off the modern analytics era. It revolutionized the way organizations use data with intuitive visual discovery that put business intelligence in the hands of more people than ever. Qlik continues to lead the way with Qlik Sense, our next-generation analytics platform. With the Cognitive Engine powering augmented intelligence, combined with the Associative Engine; and a governed, scalable cl
Toucan Toco is the Data Storytelling platform that communicates clear and compelling insights to business users, no matter their data literacy levels or device used, and without the need for training, coding or design skills, or hardware requirements. Toucan is the first analytics platform to include a built-in UX that packages the best practices of design and storytelling for the sake of speed of deployment, consistent clarity and unconditional accessibility; leading to some of the highest ad
InsightSquared offers the industry's most complete and flexible Revenue Intelligence Platform, featuring 6 integrated solutions in one. We empower revenue professionals to make better decisions by equipping them with actionable, real-time intelligence that drives predictable growth. B2B organizations worldwide rely on InsightSquared to build stronger, healthier pipelines, improve conversion rates, target rep coaching, boost forecast accuracy and significantly increase competitive win rates. For
ThoughtSpot is a next-generation search & AI-driven analytics platform. With ThoughtSpot, users type questions into a search box like they would with Google or Bing and instantly get precise answers. The analytics platform presents best-fit visualizations so users can easily analyze data, create reports and build dashboards without relying on IT. ThoughtSpot connects with and combines any on-premise, cloud, big data, or desktop data source. Search gives everyone fast and easy access to gran
Yaguara is a connected operating platform that helps commerce companies create a single source of truth for their teams, using real-time data and predictive analytics. We empower companies to make better decisions because they see their data in context and get individualized insights and recommendations about it.
Highly scalable, elastic, and secure cloud analytics architecture capable of supporting the entire data pipeline for companies with hundreds of internal or external users: SaaS’ customers/users, external partners (other companies), or internal teams. Best for companies looking for a seamless, secure, embeddable, and highly customizable data analytics platform for hundreds or thousands of internal or external users. These users can range from internal teams to outside organizations (business par
Grow is a no-code full-stack business intelligence (BI) platform that empowers everyone in your organization to make data-driven decisions. By combining ETL, data warehousing and visualization in one easy-to-use platform, any organization can connect and explore its data to surface insights. And our unlimited-user license model gives everyone access to the answers they need without waiting in line for an analyst. Now everyone can make great decisions in real-time to accelerate their growth.
Sisense for Cloud Data Teams empowers organizations to go beyond standard business intelligence to unlock untapped business potential. Leverage the power of SQL, R & Python to experiment with predictive analytics and explore data ad hoc. Build custom analytic experiences from interactive visualizations to user-friendly applications so that everyone can uncover insights that move the business forward.
Oracle Analytics Cloud is a comprehensive cloud analytics platform that empowers you to fundamentally change how you analyze and act on information. Empower leaders, analysts, and IT to access data from wherever they are, even using mobile devices. Oracle Analytics Cloud helps organizations discover unique insights faster with machine learning. With augmented analytics, combine data from across your organization with third-party data and automate important and time-consuming tasks such as dat
Successful Business Intelligence - Turn your Data Into Results Whether you’re a big picture thinker or go straight to the detail, with Phocas you lead the way in discovering data - with confidence that results will be in real time and accurate. Phocas follows your train of thought to answer critical business questions as fast as your brain (or your boss) comes up with them. Phocas not only answers your questions; it uncovers new questions and opportunities you had never even thought of! Design
Yellowfin is the only analytics suite that successfully combines action based dashboards with industry-leading automated analysis and data storytelling. By delivering the best analytical experience, Yellowfin provides your users with unique ways to engage with and act on their data, and addresses the needs of data analysts, business users, customers and developers who want to build, deploy or use amazing analytical experiences. Analytics for software companies Integrate and embed analytics wit
Dundas BI is an enterprise business intelligence and visual data analytics platform that allows you to easily connect, prepare and analyze your data so you can turn it into pixel-perfect dashboards and reports. It provides users the ability to create interactive, customizable dashboards, build their own reports, run ad-hoc queries, perform visual data discovery and collaborate with others. The Dundas BI platform is extremely flexible with massive out-of-the-box capabilities and open API across t
Unlock insights in your data with powerful analysis. NVivo helps you discover more from your qualitative and mixed methods data. Uncover richer insights and produce clearly articulated, defensible findings backed by rigorous evidence. NVivo is a place to organize, store and analyze your data. Work more efficiently, conduct deeper analysis from more sources, and defend your findings with NVivo: - Import data from virtually any source - Analyze data with advanced management, query and visualizat
Cognos Analytics is an AI-fueled business intelligence platform that supports the entire analytics cycle, from discovery to operationalization. Cognos Analytics gives every user — whether data scientist, business analyst or non-specialist — more power to perform relevant analysis in a way that ties back to organizational objectives. It shortens each user’s journey from simple to sophisticated analytics, allowing them to harness data to explore the unknown, identify new relationships, get a deep
Salesforce Analytics Cloud is a secure cloud-based analytics program developed to help medium-sized businesses to large enterprises to implement rapid, iterative exploration of data, with results displayed via layers of dynamic visualization over underlying data sets.
Cumul.io's embedded analytics platform lets you add interactive dashboards to your own software application or customer portal in no time. It is used by SaaS companies & enterprises who value data-driven insights, but don't want to waste time on developing a dashboard component from scratch. Cumul.io's dashboard integration allows you to offer advanced, personalized dashboards to all of your customers, by simply dragging & dropping your dashboard together. The embedded dashboards integr
Analytics platforms, also known as business intelligence (BI) platforms, enable companies to gain visibility into their data through data integration, cleansing, blending, enrichment, discovery, and more. These tools are robust systems that sometimes require IT and data science skills to access and decipher company data through custom queries.
Analytics platforms offer a comprehensive look into a company’s data by pulling from both structured and unstructured data sources through a series of detailed queries. Casual business users also benefit from analytics platforms with customizable dashboards and the ability to drill into particular data points and trends.
Self-service analytics platforms
Self-service analytics platforms do not require coding knowledge, so business end users can take advantage of them for data needs. These solutions often provide drag-and-drop functionality for building dashboards, prebuilt templates for querying data, and occasionally, natural language querying for data discovery.
Embedded BI software
Embedded BI software offers the ability to integrate proprietary analytics functionality within other business applications. Businesses may choose an embedded product to promote user adoption; by placing the analytics inside regularly used software, companies enable employees to take advantage of available data. These solutions provide self-service functionality so average business end users can take advantage of data for improved decision making.
Root cause analysis
Companies of all sizes produce vast amounts of data from a host of different sources. It can be difficult to keep track of the ebbs and flows of data and to spot when there are outliers in the data and when trends are occurring across tens, if not hundreds (sometimes even thousands) of data sources. Some solutions provide the user with a birds-eye view of their data and intelligently alert them to changes in real time. Once alerted, they are able to dive in to evaluate the situation and solve it.
Analytics platforms are a great aid to any organization with the need for timely data visualization of high-level analytics. The following are some core features within analytics platforms that can help users make the most of them:
Data preparation: Although standalone data preparation software exist that assist in the process of discovering, blending, combining, cleansing, and enriching data—so large datasets can be easily integrated, consumed, and analyzed—analytics platforms must incorporate these functionalities into their core offering. In particular, analytics platforms must support data blending and data modeling, giving the end user the ability to combine data across different databases and other data sources and allowing them to develop robust data models of this data. This is a critical step in making meaning out of the chaos, through the combination of data from various sources.
Data management: Once the data is properly integrated, the data must be managed. This includes the ability for data access to be restricted to certain users, for example. Although some companies opt for a standalone data management solution, such as a data warehouse, analytics platforms must provide some level of data management by definition.
Data modeling and blending: As mentioned, it is not efficient, and often not effective, to examine data when it is sprawled across many systems. As a business cloud, analytics platforms help businesses to consolidate data and combine data points to understand the relationship between data and derive deep insights.
Reports and dashboards: Multilayered, real-time dashboards are a central feature of analytics platforms. Users can program their analytics software to display metrics of their choice and create multiple dashboards that show analytics related to specific teams or initiatives. From predictive analytics of website traffic to customer conversion rates over a specified period of time, users can pick and choose their preferred metrics to feature in dashboards and create as many dashboards as necessary.
Administrators can adjust the permissions of different dashboards so they are accessible by the users in the company who need them the most. Users can choose to share certain dashboards on office monitors or take screengrabs of dashboards to save and share as needed. Some analytics platform products may allow users to explore dashboards on their mobile devices.
Self service: Organizations use these tools to build interactive dashboards for discovering actionable insights. This enables business users like sales representatives, human resource managers, marketers, and other non-data team members to make decisions based on relevant business data.
Advanced analytics: Many analytics platforms are incorporating advanced features, sometimes called augmented analytics, to better understand a business’ data, even without IT support. This can include predictive analytics capabilities, as well as data discovery, which includes intelligent suggestions for data visualization and machine learning-powered suggestions for deeper insights.
Replace old or disparate software: Businesses can replace their old, outdated data storage solutions and reporting tools and migrate to an all-inclusive business cloud, in the form of an analytics platform. However, data migration is not absolutely essential for deploying an analytics solution, as businesses may not have the time or resources to do so. Therefore, it should be noted that these platforms provide the ability to integrate with a whole host of solutions, such as enterprise resource planning (ERP) and customer relationship management (CRM) software.
Improve productivity: The days of sorting through tens, if not hundreds, of systems and needing immense support from IT have passed. With analytics platforms (especially those which are of self service nature and having features such as natural language search), anyone looking for data and data analysis, including average business users can derive insights from their data.
Save time (automation): For most analytics platforms, users no longer need a strong background in query languages. Instead, features such as data discovery and root cause analysis allow users to automatically receive alerts and insights into their data and get notified if the data has changed in any meaningful way.
Reduce errors: Although standalone data preparation tools may be the right solution for businesses with particularly complex data, analytics platforms give users the ability to clean and prepare their data, through methods such as data mapping and deduplication.
Consolidate data: In this data-driven era, essentially every program and device a business has produces a massive amount of data. To understand this diverse data in the best way possible, it is often necessary to combine it through methods such as data blending, which allows users to combine data from multiple sources into a functioning dataset.
Improve processes: Without an analytics platform in place to be used across a business, processes can be slow and inefficient as interested parties seek data from disparate sources and request data from various people. Analytics platforms can help a business user easily access data and data analysis and share it with internal and external stakeholders.
Analytics platforms can have both internal and external users.
Data analysts and data scientists: These employees are generally the power users of analytics tools, creating complex queries inside the platforms to gather a deeper understanding of business-critical data. These teams may also be tasked with building self-service dashboards to distribute to other teams.
Sales teams: Sales teams use both self-service analytics tools and embedded analytics solutions to obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue.
Marketing teams: Marketing teams often run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns. Analytics tools allow marketing teams to track the performance of those campaigns in one central location.
Finance teams: Finance teams leverage analytics software to gain insight and understanding into the factors that impact an organization's bottom line. By integrating financial data with sales, marketing, and other operations data, accounting and finance teams pull actionable insights that might not have been uncovered through the use of traditional tools.
Operations and supply chain teams: Analytics solutions often utilize a company's ERP system as a data source. These applications track everything from accounting to supply chain and distribution; by inputting supply chain data into an analytics platform, supply chain managers can optimize a number of processes to save time and resources.
Consultants: Businesses, especially larger ones, do not always understand the breadth and depth of their data, perhaps not even knowing where to begin. An external consultant wielding a powerful analytics platform can help businesses better understand their data, and as a result, make more informed business decisions.
Users may consider reaching out to BI consulting partners to help determine the most relevant analytics and data to capture in relation to their company’s overall success. Following a proper consultation, these agencies may offer assistance with setting up or choosing BI tools. A number of these agencies can assist businesses with the entire BI process, from complete data analysis to the shaping of processes or protocols related to data collection. For users who have never performed data analysis before or those desiring to optimize their company’s reporting, a relationship with these consultants can prove extremely beneficial.
Partners: Partnerships between companies often involve data sharing and cross-company collaboration. As a result, a centralized repository of data, which would allow for data management, data querying, and data insights can provide an important tool for these businesses to succeed together, providing them with a birds-eye view of their data.
Alternatives to analytics platforms can replace this type of software, either partially or completely:
Marketing analytics software: Businesses looking for tools geared toward marketing use cases and marketing data (e.g., related to targeting prospects) should look at marketing analytics solutions that are purpose built for this.
Sales analytics software: Although sales data such as revenue forecasts and closed deals can be imported and analyzed in general-purpose analytics platforms, sales analytics platforms can provide more granular analysis of sales-related data and might have better integrations with sales tools such as CRMs.
Log analysis software: If a business wants to focus on analyzing their log data from applications and systems, they could benefit from log analysis software, which helps enable the documentation of application log files for records and analytics.
Predictive analytics software: Broad purpose analytics platforms allow businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Since analytics platforms allow for these various types of analyses, they might not provide the most robust features for any one type. Therefore, businesses that are focused on looking at their past and present data to predict future outcomes can use predictive analytics software for a more fine-tuned solution.
Text analysis software: Analytics platforms are focused on structured or numerical data, allowing users to drill down and dig into numbers to inform business decisions. If the user is looking to focus on unstructured or text data, text analysis solutions are the best bet. These tools help users quickly understand and pull sentiment analysis, key phrases, themes, and other insights from unstructured text data.
Data visualization software: Data visualization tools can be a great place for businesses to start when looking to better understand their data. With capabilities including dashboards and reporting, data visualization software can often be quick and easy to set up and is frequently cheaper than more robust analytics platforms.
However, it is important to recognize their limitations. Data visualization solutions do what they say on the box: visualization. They do not give the user an end-to-end analytics solution from data preparation to data insights, nor do they provide significant data management capabilities.
Related solutions that can be used together with analytics platforms include:
Embedded business intelligence software: Analytics platforms are standalone platforms that help companies analyze data. Businesses who want to build analytics capabilities into applications, whether that be for internal or external use, can use embedded BI software to accomplish this goal.
Database software: There are a plethora of solutions for storing, organizing, and sharing large amounts of data to later be accessed and analyzed by analytics tools. Database software includes everything from big data software to traditional table-based relational databases software. Businesses should research and implement whichever database tools make the most sense for their particular data types or analytical needs.
When considering an analytics solution, users should investigate which of these databases can integrate with the tool to make the most logical product choice for their particular situation. Analytics products would not serve much purpose without one or more company databases to pull data from when the time comes.
Configuration: Analytics solutions may have a highly technical setup process, which may require IT or developmental expertise. When trying to implement one of these platforms without an in-house data scientist or IT professional, users may struggle with getting the technology off the ground, integrating it with the appropriate solutions, and creating queries for data collection. This could mean a significant loss of resources and an inability to use the tool as intended. Users can reach out to BI consulting providers for assistance with setting up a program or, in some cases, for handling the entirety of BI reporting.
Overreliance: Focusing too much on data and analytics can also be problematic. Data-driven decisions are key to a business’ success, but data-only decisions ignore the various voices from within and without the organization. Successful businesses combine rigorous analytics with anecdotal storytelling and thoughtful conversations around the success of the business and its components.
Integrations: If the analytics tool does not fully integrate with existing software, it becomes challenging to get a complete view of a business’s operational performance. Similarly, if an integration experiences a communication error or other issue during a data query, it causes an incorrect or incomplete reading. Users should make a point to monitor these connections and any potential performance issues throughout their software stack to ensure that correct, complete, and up-to-date information is being processed and displayed on dashboards.
Data security: Companies must consider security options to ensure the right users see the correct data, to guarantee strict data security. Effective analytics solutions should offer security options that enable administrators to assign verified users different levels of access to the platform, based on their security clearance or level of seniority.
If a company is just starting out and looking to purchase the first analytics platform, or maybe an organization needs to update a legacy system--wherever a business is in its buying process, g2.com can help select the best analytics platform for the business.
The particular business pain points might be related to all of the manual work that must be completed. If the company has amassed a lot of data, the need is to look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy.
Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.
Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from an analytics platform.
Create a long list
From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.
Create a short list
From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.
To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.
Choose a selection team
Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.
Analyze the data
As analytics platforms are all about the data, the user must make sure that the selection process is data driven as well. The selection team should compare notes and facts and figures which they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities.
Just because something is written on a company’s pricing page, does not mean it is gospel (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multiyear contracts or for recommending the product to others.
After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.
As mentioned above, analytics platforms come as both on-premises and cloud solutions. Pricing between the two might differ, with the former often coming with more upfront costs related to setting up the infrastructure.
As with any software, analytics platforms are frequently available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will frequently not have as many features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, which might be unlimited or capped at a certain number of hours per billing cycle.
Analytics platforms, once set up, do not often require significant maintenance costs, especially if deployed in the cloud.
As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software.
Businesses decide to deploy analytics platforms with the goal of deriving some degree of a return on investment (ROI). As they are looking to recoup their losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, analytics platforms typically are billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.
Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of an analytics tool.
How are Analytics Platforms Implemented?
Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.
Who is Responsible for Analytics Platforms Implementation?
It may require a lot of people, or many teams, to properly deploy an analytics platform. This is because, as mentioned, data can cut across teams and functions. As a result, it is rare that one person or even one team has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together their data and begin the journey of analytics, starting with proper data preparation and management.
Increase data accessibility
Business data is no longer locked up in silos. With analytics platforms, more users across a business can find, access, and analyze this data. In addition, artificial intelligence (AI) tools such as natural language processing (NLP) software help make searching through and for data easier and more powerful, providing more accurate results.
With the amount of data accessible to businesses today, it is a near necessity that they implement some type of analytics software to better understand and act on that data. Implementing analytics software has been a major initiative for companies undergoing digital transformation as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large datasets collected from all their various sources.
Shift from on-premises to cloud
The move from on-premises data analytics to the cloud has been underway for a number of years, with more and more businesses moving their data and data insights into the cloud. This is taking place for various reasons, such as time to insights. The move away from on-premises infrastructure has helped many companies enable data work anywhere one has access to the cloud—anywhere with internet access. However, not all data users have the luxury of working in the cloud for a number of reasons, including data security and issues related to latency. In industries such as health care, strict regulations such as the Health Insurance Portability and Accountability Act (HIPAA), require that data be secure. Although it is possible to ensure this security in the cloud, it can be more difficult and complicated to do so.
Historically, to query data within an analytics solution, users needed to master a query language like SQL. With the rise of conversational interfaces, users uncover the data and insights they are looking for using intuitive language. Intuitive methods of querying data means enabling a larger user base to access and make sense of company data.
AI is quickly becoming a promising feature of analytics solutions throughout the whole data journey, from ingestion to insights. From AI-powered data preparation to smart insights, in which the platform suggests visualizations to the end user, analytics platforms are quickly becoming more powerful. Machine learning is helping end users discover hidden insights, allowing them to make sense of data and helping them to understand what they are seeing.