# Best Analytics Platforms - Page 17

  *By [Tian Lin](https://research.g2.com/insights/author/tian-lin)*

   Analytics platforms provide a tool set for businesses to transform raw data into meaningful, actionable insights. They enable organizations to explore data, uncover trends, forecast future outcomes, and support informed decision making.

Unlike tools limited to reporting on past performance, analytics platforms often include advanced capabilities such as predictive modeling, statistical analysis, and machine learning (ML). These platforms are designed to be flexible and scalable, supporting a wide range of use cases across the business.

These platforms are used in nearly every business function, from marketing and sales to finance, operations, and HR, supporting both strategic planning and day-to-day performance monitoring. From data analysts and scientists to business stakeholders and executives, analytics platforms are used by a wide range of personas. While analysts focus on exploring data and generating insights, self-service tools now enable non-technical users to interact directly with data. IT teams support platform integration and security, reflecting the growing push to democratize data access and embed analytics into daily decision-making across the organization.

Analytics platforms support critical functions such as data blending and modeling, enabling users to combine data from diverse sources and build robust, interconnected data models. The visual outputs — dashboards, reports, and interactive charts — help users explore trends, drill down into granular details, and communicate insights clearly.

Unlike standalone data visualization tools, which are limited to presenting information, analytics platforms encompass the full analytical workflow. Many also offer advanced capabilities such as embedded analytics, natural language query, and augmented analytics, which leverage ML to automate insight discovery and make data exploration more accessible to a broader audience.

Analytics platforms and [business intelligence (BI) software](https://www.g2.com/categories/business-intelligence) often work in tandem to support data-driven organizations. While BI tools focus on tracking and reporting historical performance through dashboards and key performance indicators (KPI), analytics platforms provide broader capabilities that support exploratory analysis and strategic planning. BI answers "what happened," while analytics platforms help users understand why it happened and what might happen next. Rather than replacing BI, analytics platforms complement it by enabling deeper insights and empowering a wider range of users across the organization.

To qualify for inclusion in the Analytics Platforms category, a product must:

- Ingest and integrate data from a wide range of structured and semi-structured sources
- Prepare and transform data using built-in tools for cleaning, enrichment, and formatting
- Support connections to diverse data sources, including file uploads, databases, application programming interfaces (API), and SaaS apps
- Enable users to model data relationships, join datasets, and explore data interactively
- Offer tools to build meaningful business reports, dashboards, and visualizations
- Allow creation and sharing of internal analytics applications or embedded insights across teams




  
## How Many Analytics Platforms Products Does G2 Track?
**Total Products under this Category:** 337

### Category Stats (May 2026)
- **Average Rating**: 4.48/5
- **New Reviews This Quarter**: 516
- **Buyer Segments**: Mid-Market 43% │ Small-Business 29% │ Enterprise 28%
- **Top Trending Product**: Myriade (+0.5)
*Last updated: May 18, 2026*

  
## How Does G2 Rank Analytics Platforms Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 27,500+ Authentic Reviews
- 337+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

  
## Top Analytics Platforms at a Glance
| # | Product | Rating | Best For | What Users Say |
|---|---------|--------|----------|----------------|
| 1 | [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) | 4.5/5.0 (1,572 reviews) | Microsoft-connected interactive dashboards | "[Microsoft Power BI Turns Raw Data into Clear, Interactive Dashboards Fast](https://www.g2.com/survey_responses/microsoft-power-bi-review-12678340)" |
| 2 | [Tableau](https://www.g2.com/products/tableau/reviews) | 4.4/5.0 (3,546 reviews) | Flexible visual dashboard exploration | "[Effortless Data Visualization, High Licensing Costs](https://www.g2.com/survey_responses/tableau-review-12793833)" |
| 3 | [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) | 4.3/5.0 (755 reviews) | Cloud analytics for governed data science | "[SAS Viya is a Powerful Analytics](https://www.g2.com/survey_responses/sas-viya-review-11702846)" |
| 4 | [Alteryx](https://www.g2.com/products/alteryx/reviews) | 4.6/5.0 (773 reviews) | No-code data preparation and automation | "[Easy, Time-Saving Data Automation with Alteryx’s Drag-and-Drop Workflows](https://www.g2.com/survey_responses/alteryx-review-12594796)" |
| 5 | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5.0 (755 reviews) | Governed lakehouse analytics and ML workflows | "[All-in-One Delta Lake Platform That Makes ETL Fast and Cost-Efficient](https://www.g2.com/survey_responses/databricks-review-12878359)" |
| 6 | [Looker](https://www.g2.com/products/looker/reviews) | 4.4/5.0 (1,579 reviews) | Governed shared BI metrics | "[Transforms Data, But Challenging for Beginners](https://www.g2.com/survey_responses/looker-review-12784757)" |
| 7 | [Domo](https://www.g2.com/products/domo/reviews) | 4.3/5.0 (987 reviews) | Centralized self-service business dashboards | "[All-in-One Platform for Real-Time Analytics and Dashboards](https://www.g2.com/survey_responses/domo-review-12676104)" |
| 8 | [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) | 4.8/5.0 (252 reviews) | Semantic-layer acceleration for enterprise BI | "[Kyvos Unified Our Business Logic with a Single Semantic Model](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12797024)" |
| 9 | [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) | 4.4/5.0 (543 reviews) | Warehouse-native spreadsheet-style analytics | "[Easiest BI Tool: Live Snowflake Data in a Spreadsheet-Like Experience](https://www.g2.com/survey_responses/sigma-review-12573150)" |
| 10 | [Amazon QuickSight](https://www.g2.com/products/amazon-quicksight/reviews) | 4.3/5.0 (673 reviews) | AWS-native serverless BI dashboards | "[Turns Raw Data into Interactive Dashboards for Better Trend Monitoring](https://www.g2.com/survey_responses/amazon-quicksight-review-12740199)" |

  
## Which Analytics Platforms Is Best for Your Use Case?

- **Leader:** [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews)
- **Highest Performer:** [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews)
- **Easiest to Use:** [Databricks](https://www.g2.com/products/databricks/reviews)
- **Top Trending:** [Hex](https://www.g2.com/products/hex-tech-hex/reviews)
- **Best Free Software:** [Tableau](https://www.g2.com/products/tableau/reviews)

  
## Which Type of Analytics Platforms Tools Are You Looking For?
  - [Analytics Platforms](https://www.g2.com/categories/analytics-platforms) *(current)*
  - [Data Visualization Tools](https://www.g2.com/categories/data-visualization-tools)
  - [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics)
  - [Embedded Business Intelligence Software](https://www.g2.com/categories/embedded-business-intelligence)
  - [Marketing Analytics Software](https://www.g2.com/categories/marketing-analytics)
  - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms)
  - [ETL Tools](https://www.g2.com/categories/etl-tools)
  - [Data Preparation Software](https://www.g2.com/categories/data-preparation)

  
---

**Sponsored**

### ThoughtSpot

ThoughtSpot is the Agentic Analytics Platform company for the enterprise. With natural language and AI, ThoughtSpot empowers everyone in an organization to ask data questions, get answers, and take action. Code-first for data teams and code-free for business users, ThoughtSpot is intuitive enough for anyone to use, yet built to handle large, complex cloud data at scale. Customers like Coca-Cola, Hilton Worldwide, and Capital One are unlocking the full potential of their data with ThoughtSpot.



[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=620&secure%5Bdisplayable_resource_id%5D=620&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=620&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=6232&secure%5Bresource_id%5D=620&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fanalytics-platforms%3Fpage%3D2&secure%5Btoken%5D=46a9c18ab43b55c7ff4cbae2319cf3626a552a4e0ae80da82b68d03d709e8619&secure%5Burl%5D=https%3A%2F%2Fwww.thoughtspot.com%2Fdemo%3Futm_source%3Dg2%26utm_medium%3Daggregatorads%26utm_term%3Dcompete%26utm_content%3Dtext_ads%26utm_campaign%3Dppc_g2compete26&secure%5Burl_type%5D=book_demo)

---

  
## Buyer Guide: Key Questions for Choosing Analytics Platforms Software
  ### What do Analytics Platforms do?
  When I explain analytics platforms, I frame them as systems that help teams turn business data into usable insight. These platforms bring dashboards, reports, data modeling, metric exploration, visualizations, and insight sharing into one workflow. Instead of relying on scattered spreadsheets, static reports, or disconnected data views, teams can access information faster, understand performance more clearly, and make decisions with more confidence.


  ### Why do businesses use Analytics Platforms?
  From the G2 reviewer patterns I evaluated, businesses use Analytics Platforms because data sits across many systems and takes too much manual effort to organize. Users mention fragmented reports, delayed insight, inconsistent metrics, and hard-to-present data.

Common benefits include:

- Faster dashboard and report creation.
- Clearer visibility into KPIs, sales, marketing, operations, and product data.
- Stronger exploration through filters, drilldowns, SQL, and visual workflows.
- Easier sharing of insights with teams and stakeholders.
- Better integration with databases, cloud platforms, spreadsheets, CRM tools, and warehouses.


  ### Who uses Analytics Platforms primarily?
  After I evaluated G2 reviewer roles, I found that Analytics Platforms serve technical and business users.

- **Data analysts** build dashboards, reports, and recurring performance views.
- **BI teams** manage metrics, models, and governed workflows.
- **Data engineers** connect sources and prepare datasets.
- **Data scientists** use notebooks, models, and advanced analytics features.
- **Business analysts** translate data into operational decisions.
- **Executives** use dashboards to understand trends.


  ### What types of Analytics Platforms should I consider?
  From the way reviewers describe the category, buyers should compare several product types:

- **BI and dashboard platforms** for reports, visualization, and executive views.
- **Self-service analytics tools** for fast business exploration.
- **Cloud analytics platforms** for larger datasets and scalable processing.
- **Data science and notebook platforms** for SQL, Python, models, and collaboration.
- **Embedded analytics tools** for customer-facing dashboards.
- **Semantic layer platforms** for shared metrics and reusable definitions.


  ### What are the core features to look for in Analytics Platforms?
  When I break down G2 reviews for this category, I look closely at the themes users repeatedly mention:

- Dashboard and report builders with flexible layouts that help teams organize business data into views people can actually use.
- Data visualization, charting, and interactive filters that help users explore trends, compare performance, and understand metrics faster.
- Connectors for databases, spreadsheets, cloud tools, and business applications that help bring data from different systems into one place.
- SQL support, data modeling, and calculated metrics that help analysts define, structure, and customize business logic.
- AI-assisted search or automated insights that help users find patterns and answers without building every report manually.
- Collaboration, permissions, scheduled delivery, and sharing controls that help teams distribute insights securely and consistently.
- Performance controls for complex dashboards that help reports load reliably even when data volume or usage grows.
- Documentation, training resources, and responsive support that help teams onboard faster and troubleshoot issues with less friction.


  ### What trends are shaping Analytics Platforms right now?
  From the G2 review patterns I evaluated, several trends stand out:

- **AI-assisted analytics** is helping users ask questions, generate summaries, and discover insights faster.
- **Self-service reporting** is becoming more important as business teams look for answers without depending on analysts for every request.
- **Cloud and application integrations** are expanding as companies connect analytics platforms to more data sources and business systems.
- **Governed metrics and semantic layers** are gaining value as teams work to keep definitions consistent across dashboards and reports.
- **Performance optimization** remains a priority as users expect complex dashboards to load quickly and reliably.
- **Usability and guided setup** are shaping adoption as buyers look for platforms that are powerful without creating a steep learning curve.


  ### How should I choose Analytics Platforms?
  For me, the strongest Analytics Platforms fit depends on data sources, governance needs, and scale. I would prioritize products reviewers praise for intuitive dashboards, flexible visualization, strong integrations, reliable performance, and clear collaboration. I would also examine concerns around customization, complex setup, pricing, learning curve, and AI accuracy before making a final choice.



---

  
    ## What Is Analytics Platforms?
  [Analytics Tools & Software](https://www.g2.com/categories/analytics-tools-software)
  ## What Software Categories Are Similar to Analytics Platforms?
    - [Data Visualization Tools](https://www.g2.com/categories/data-visualization-tools)
    - [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics)
    - [Embedded Business Intelligence Software](https://www.g2.com/categories/embedded-business-intelligence)
    - [Marketing Analytics Software](https://www.g2.com/categories/marketing-analytics)
    - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms)
    - [ETL Tools](https://www.g2.com/categories/etl-tools)
    - [Data Preparation Software](https://www.g2.com/categories/data-preparation)

  
---

## How Do You Choose the Right Analytics Platforms?

### What You Should Know About Analytics Platforms

### What are analytics software platforms?

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 structured and unstructured data sources through detailed queries. Casual business users also benefit from analytics platforms, which offer customizable dashboards and the ability to drill into particular data points and trends.

### What types of analytics tools and platforms exist?

#### **All-in-one software**

##### **Self-service analytics platforms**

Self-service analytics platforms do not require coding knowledge, so business end users can use them for data needs. Cloud-based business analytics software often provides 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 can 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 use data for improved decision-making.

#### **Point solutions**

##### **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 outliers and trends across tens if not hundreds (sometimes even thousands) of data sources. Some solutions provide the user with a bird' s-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.

### What are the common features of analytics solutions?

Analytics software platforms are a great aid to any organization needing 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](https://www.g2.com/categories/data-preparation) exists that assists in 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 modeling, allowing the end user to combine data across different databases and other data sources and to develop robust data models of this data. This is a critical step in making meaning out of the chaos by combining data from various sources.

**Data management:** Once the data is properly integrated, it must be managed. This includes restricting data access to certain users, for example. Although some companies opt for a standalone data management solution, such as a data warehouse, analytics platforms must, by definition, provide some level of data management.

**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 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 website traffic analytics to customer conversion rates over a specified period, users can 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 to the users in the company who need them the most. Users can share specific 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**](https://www.g2.com/categories/analytics-platforms/f/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 solutions are incorporating advanced features, sometimes called augmented analytics, to better understand a business’s data, even without IT support. These can include predictive analytics capabilities and data discovery, which includes intelligent suggestions for data visualization and machine learning-powered suggestions for deeper insights.

Other features include [Anomaly detection](https://www.g2.com/categories/analytics-platforms/f/anomaly-detection), [Query based](https://www.g2.com/categories/analytics-platforms/f/query-based), [Search](https://www.g2.com/categories/analytics-platforms/f/search), [Traditional](https://www.g2.com/categories/analytics-platforms/f/traditional)

### What are the benefits of using analytics platforms?

**Replace old or disparate software:** Businesses can replace outdated data storage solutions and reporting tools and migrate to an all-inclusive business cloud as an analytics platform. However, data migration is not 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 can integrate with a whole host of solutions, such as [enterprise resource planning (ERP)](https://www.g2.com/categories/erp-systems) and [customer relationship management (CRM) software](https://www.g2.com/categories/crm).

**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 that are self-service and have 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, 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 meaningfully.

**Reduce errors:** Although standalone data preparation tools may be the right solution for businesses with particularly complex data, analytics platforms allow users to clean and prepare their data through data mapping and deduplication methods.

**Consolidate data:** In this data-driven era, essentially every program and device a business has produces massive data. To understand this diverse data in the best way possible, combining it through methods such as data blending, which allows users to integrate data from multiple sources into a functioning dataset, is often necessary.

**Improve processes:** Without an analytics platform 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 quickly access data and data analysis and share it with internal and external stakeholders.

### **Who uses analytics tools?**

Analytics platforms can have both internal and external users. 

#### **Internal 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 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 into the factors impacting 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 using 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; supply chain managers can optimize several processes to save time and resources by inputting supply chain data into an analytics platform. 

#### **External users**

**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 contacting [BI consulting partners](https://www.g2.com/categories/business-intelligence-bi-consulting) to help determine the most relevant analytics and data to capture about 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. A relationship with these consultants can prove highly beneficial for users who have never performed data analysis before or want to optimize their company’s reporting.

**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 essential tool for these businesses to succeed together, providing them with a birds-eye view of their data.

### **What are the alternatives to analytics platforms?**

Alternatives to analytics platforms can replace this type of software, either partially or completely:

[**Marketing analytics software**](https://www.g2.com/categories/marketing-analytics) **:** 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**](https://www.g2.com/categories/sales-analytics) **:** 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 a more granular analysis of sales-related data and might have better integrations with sales tools such as CRMs. 

[**Log analysis software**](https://www.g2.com/categories/log-analysis) **:**  If a business wants to focus on analyzing its log data from applications and systems, it could benefit from log analysis software, which helps enable the documentation of application log files for records and analytics.

[**Predictive analytics software**](https://www.g2.com/categories/predictive-analytics) **:** Broad-purpose analytics platforms allow businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Since analytics platforms allow for these different types of analyses, they might not provide the most robust features for any type. Therefore, businesses focused on looking at past and present data to predict future outcomes can use predictive analytics software for a more fine-tuned solution. 

[**Text analysis software**](https://www.g2.com/categories/text-analysis) **:** Analytics platforms are focused on structured or numerical data, allowing users to drill down and dig into numbers to inform business decisions. Text analysis solutions are the best bet if the user is looking to focus on unstructured or text data. These tools help users quickly understand and pull sentiment analysis, key phrases, themes, and other insights from unstructured text data.

[**Data visualization software**](https://www.g2.com/categories/data-visualization) **:** Data visualization tools can be an excellent 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 essential 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.

### **Software and services related to analytics platforms**

Related solutions that can be used together with analytics platforms include:

[**Embedded business intelligence software**](https://www.g2.com/categories/embedded-business-intelligence) **:** 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**](https://www.g2.com/categories/database-software) **:** There are a plethora of solutions for storing, organizing, and sharing large amounts of data that can later be accessed and analyzed by analytics tools. Database software includes everything from [big data software](https://www.g2.com/categories/big-data) to traditional table-based [relational databases](https://www.g2.com/categories/relational-databases). 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 databases can integrate with the tool to make the most logical product choice for their situation. Analytics products would not serve much purpose without one or more company databases to pull data from when the time comes.

### Challenges with analytics platforms

**Configuration:** Analytics solutions may have a highly technical setup process, requiring 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 contact BI consulting providers for assistance 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 critical to a business’s success, but data-only decisions ignore the various voices from within and without the organization. Successful companies combine rigorous analytics with anecdotal storytelling and thoughtful conversations about the business's success and components.

**Integrations:** If the analytics tool does not fully integrate with existing software, getting a complete view of a business’s operational performance becomes challenging. 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 and 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.

### How to choose the best analytics tools

#### Requirements Gathering (RFI/RFP) for Analytics Platforms

If a company is just starting 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.

The particular business pain points might be related to all the manual work that must be completed. If the company has amassed a lot of data, it needs 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 needing this software, as this drives the number of licenses they will likely 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 is a detailed guide with 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 deployment scope, producing an RFI, a one-page list with a few bullet points describing what is needed from an analytics platform might be helpful.

#### Compare Analytics Platforms Products

**Create a long list**

From meeting the business functionality needs to implementation, vendor evaluations are essential to 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 the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list, businesses can produce a matrix to compare the features and pricing of the various solutions.

**Conduct demos**

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. 

#### Selection of analytics platforms

**Choose a selection team**

Before getting started, creating a winning team that will work together throughout the process, from identifying pain points to implementation, is crucial. The software selection team should consist of organization members with the right interests, 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 primary decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. The vendor selection team may be more minor in smaller companies, with fewer participants, multitasking, and taking on more responsibilities.

**Analyze the data**

As analytics platforms are all about the data, the user must ensure that the selection process is also data-driven. The selection team should compare notes and facts and figures that they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities.

**Negotiation**

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 discount multiyear contracts or recommend the product to others.

**Final decision**

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 received, the buyer can be confident that the selection was correct. If not, it might be time to return to the drawing board.

### How much do analytics software platforms cost?

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 for 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 often 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 support, which might be unlimited or capped at a certain number of hours per billing cycle.

Once set up, analytics platforms, especially those deployed in the cloud, do not often require significant maintenance costs.

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.

#### Return on Investment (ROI)

Businesses deploy analytics platforms to derive a return on investment (ROI). As they are looking to recoup the losses they spent on the software, it is critical to understand its costs. As mentioned above, analytics platforms are typically billed per user, sometimes tiered, depending on the company size. More users will generally translate into more licenses, which means more money.

Users must consider how much is spent and compare that to what is gained in terms of efficiency and revenue. Therefore, businesses can compare processes between pre- and post-deployment software to understand better 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 using an analytics tool.

### Implementation of analytics software solutions

**How are analytics software 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 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 use the software efficiently and effectively.

**Who is responsible for analytics platform implementation?**

Properly deploying an analytics platform may require many people or teams. This is because, as mentioned, data can cut across teams and functions. As a result, one person or even one team rarely has a complete understanding of all of a company’s data assets. With a cross-functional team, a business can begin to piece together its data and begin the analytics journey, starting with proper data preparation and management.

### Emerging trends related to analytics platforms

**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](https://www.g2.com/categories/artificial-intelligence) such as [natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) help make searching through and for data more accessible and 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 understand and act on that data better. Implementing analytics software has been a significant 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 various sources.

**Shift from on-premises to cloud**

The move from on-premises data analytics to the cloud has been underway for several 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 insight. Moving 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 several 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)](https://learn.g2.com/health-insurance-portability-and-accountability-act) require data to be secure. Although it is possible to ensure this security in the cloud, it can be more complicated.

**Conversational AI**

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 seek using intuitive language. Intuitive methods of querying data enable a larger user base to access and make sense of company data.

**Machine learning**

AI is quickly becoming a promising feature of analytics solutions throughout the 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 understand what they are seeing.