Analytics Tools - Your Complete and Easy Guide

Analytics Tools: A Definition

Analytics tools are a core part of any BI and analytics platform and can help you understand and interpret data. With the right analytics tools, you can identify new opportunities to generate revenue, optimize costs, mitigate risk, improve compliance, improve decision-making, and gain a competitive edge. Analytics tools provide different insights, depending on your organization’s priorities, size, complexity, growth rate, and maturity. Your BI and analytics platform of choice offers these insights to help your organization grow. In the following overview, learn more about the benefits and the types of analytics tools.

The Benefits of Analytics Tools

Why analytics tools are so important for your organization.

Great data analytics tools help you make the most of your data. They also ensure that the decisions you make for your organization are derived from insights based on the best and most relevant data analysis. The benefits of analytics tools are as follows:

Make data accessible and comprehensible to everyone
Transform complex data into more and better insights, and actionable results
Accelerate time to insights
Improve decision-making, planning, productivity, and efficiency
Enhance data sharing and collaboration

Enable customized, white-labeled and embedded analytics applications
Maximize the value of your products and services
Enhance customer experience, satisfaction, and loyalty
Increase revenue streams and boosts profitability

Types of Analytics Tools

Analytics tools fall into four main types or categories: descriptive, diagnostic, predictive, and prescriptive. Here are descriptions of these tools, to help you and establish which of them are right for your current and future needs.

Descriptive Analytics

Descriptive analytics tell you what has happened or what is currently happening by summarizing historical results and metrics. Using business intelligence (BI) tools, raw, big data is analyzed and converted into key insights to summarize patterns. These metrics help to identify strengths, weaknesses, threats, and build better strategies for the future. It usually involves data aggregation and data mining. This kind of analytics is most often used by organizations. Typical examples are profit and loss analysis, customer demographics, and reach of marketing campaigns.

Diagnostic Analytics

Diagnostic analytics tell you why things happened or are currently happening. It drills-down into data, performs data discovery, data mining, and correlations to understand the causes of events. It identifies what factors led to specific outcomes, by examining probabilities and analyzing patterns of historical data. It enables you to investigate further than descriptive analytics and understand the causes and effects of events. Descriptive and diagnostic analytics are often deployed together. Analysis of this kind is often displayed in BI dashboards.

Predictive Analytics

Predictive analytics tell you what is likely to happen. It forecasts possible future outcomes based on current events and enables you to make better decisions. It uses statistical models, machine learning techniques and algorithms to evaluate the probability of an event. Predictive analytics can be used to make forecasts in sales and marketing. Or, in healthcare analytics, for instance, potential health risks can be predicted based on an individual’s habits/diet/genetic composition. Usually, companies need trained data scientists and machine learning experts to build these models, using popular coding languages for predictive analytics such as Python and R.

Prescriptive Analytics

Prescriptive analytics tell you what you need to do by recommending courses of action after analyzing data. It goes beyond predictive analytics to suggest future solutions. Prescriptive analytics constantly learns and updates the relationship between actions and outcomes. It understands what has happened and why, and then factors in predictive analysis to help identify what to do next. Sometimes it involves simulation that includes key performance indicators and metrics to recommend an optimal solution. As such, prescriptive analytics is the most sophisticated type of analytics that is generally available. A good example of this is navigational software like Waze that recommends the best routes in real-time, which can change according to traffic conditions, by learning from GPS tracking and user input.

Key Considerations about Analytics Tools

While companies and organizations share many features and requirements, each has its own specific characteristics and needs. So, bear in mind the following considerations:

Are the analytics tools of your choice future-proofed?

The amount of data you need to manage, the number of connected devices generating data, and the variety of types of data are rapidly growing all the time. It’s vital that your choice of analytics tools can not only handle your current data, but a massive increase in future data volume.

Are your analytics tools comprehensive?

Do your analytics tools have descriptive and diagnostic, predictive and prescriptive capabilities? Make sure that your analytics tools support this range of uses.

Are they scalable?

As your organization grows and changes, you should be sure that you can scale up the capability of your analytics tools so that they can grow with you. They should have the power and technology for scaling to handle larger quantities and increasing varieties of data, without extra infrastructure.

What do analysts say about our Analytics Tools?

See how the leading vendors of analytics tools stack up in the most respected industry analyst reports:

Value delivered, early innovation, and customer success are what make us the leading Visionary in the Magic Quadrant.
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Sisense earned top rankings from customers for ease of use, ease of doing business with and quality of support.
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Forrester Wave Report Names Sisense a Leader

Leader for Enterprise BI for both on-prem and in the cloud. Recognized for complex data, innovation in augmented analytics, and breadth of use cases.
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“Overall Leader” for customer experience and credibility with high scores for value & integrity put us consistently above the rest.
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The value of the Sisense platform

Product teams, cloud data teams, and business analysts from the world’s leading companies and global enterprises use the power of Sisense to effortlessly combine complex data from a variety of sources and build analytics apps that deliver insights to everyone in the organization.

These power users who we call builders enable other business users from a wide range of functions. . Sisense gives them the power to identify, analyze, and visualize the data that influence the course of your organization, with powerful decision-making capabilities that are potentially game-changing.

Power to the builders

The Sisense BI and analytics platform dramatically accelerates the time it takes to build, embed, and deploy intelligent analytics apps that unleash user creativity and engagement. Whether it’s interactive dashboards, self-service analytics, or white-labeled BI apps, Sisense delivers the industry’s lowest TCO at scale.

Insights for everyone

Sisense empowers your users to make decisions and challenge assumptions by equipping them with the insights they need, when and where they need them. We help everyone in your organization drive change through powerful AI and ML solutions that deliver BI with optimal impact.

Rated #1 in customer success by our customers

It’s not just what we do, it’s how we do it. It takes more than patented technology to make your business successful. You need the product to work in your network, with your requirements, under your constraints. And that’s when our customer-centric culture kicks in. We work with you on every installation, upgrade, and project to make sure you feel the value of your BI platform in your business.

Your Easy Guide to Analytics Tools

Analytics Tools: A Definition

The Benefits of Analytics Tools