Learn More About Analytics Platforms
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 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: 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, Query based, Search, 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) 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.
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.
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.
