Best Analytics Platforms

What are analytics software platforms?

What types of analytics tools and platforms exist?

What are the common features of analytics solutions?

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Replace old or disparate software: Businesses can replace outdated data storage solutions and reporting tools and migrate to an all-inclusive business cloud as an analytics platform.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) 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 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 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: 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 a 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 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: 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: 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: 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: 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 that can later be accessed and analyzed by analytics tools. Database software includes everything from big data software to traditional table-based 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 such as natural language processing (NLP) software 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) 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.