Let’s Analyze Descriptive Analytics Tools

By TechDogs Editorial Team

Overall Rating

Overview

Imagine you work at a retail store and your manager asks you to analyze and understand which products are likely to be the most popular in the coming few weeks. So, naturally, you consolidate data and visit your local crystal ball gazer. Just kidding – but honestly, how would you go about it?

You will need to gather and organize previous sales and customer data, analyze it to extract the patterns of customer preferences and then map it to your current catalog of products. Once the hard part is over you will also need to report your findings to the manager, so he can ensure the inventory is stocked accordingly – sounds like a lot of work, right?

Well, not if you use Descriptive Analytics Tools! These tools are akin to a magnifying glass, examining past data in detail to reveal trends, patterns and preferences as well as providing a detailed snapshot of the historical performance. This will help the manager and other business decision-makers to make informed choices for optimizing the inventory, enhancing customer satisfaction and streamlining the store's operations in general.

Whether it's retail or another industry, Descriptive Analytics Tools are critical in empowering stakeholders to navigate the complex landscape of enterprise data. We mean, who doesn’t want a single solution that can analyze information and deliver data-driven insights to make strategic decisions for the future?

Read on and acquire essential insights into Descriptive Analytics Tools!

There’s no bigger sin in the business world than not knowing how to leverage the data your business owns. After all, there’s no point in gathering and sitting on vast volumes of raw information! You also need robust analytics to gain value from your business data. However, most people wonder if data from the past can really be used to make decisions about the future.

The answer is a resounding yes!

With in-depth descriptive analytics, business decision-makers can understand what their data means from a decision-making perspective. This style of data analytics essentially answers the question “what happened?” and hence, is termed as “descriptive” analytics. Naturally, the tool that drives the analytical processes, extracts patterns and trends and organizes the insights found within them are called Descriptive Analytics Tools.

Read on to understand how these tools work, their evolution, features, benefits, future and more!

Understanding Descriptive Analytics Tools

Let’s first understand what descriptive analytics means: it is a statistical interpretation that analyzes historical data to identify patterns, trends and relationships within it. Fun fact: it is one of the four main types of analytics, namely: Descriptive, Diagnostic, Prescriptive and Predictive. For now, let’s focus on descriptive analytics.

You may have realized that descriptive analytics seek to describe an event, trend or other phenomenon, by analyzing historical data. Hence, it helps in understanding what has happened in the past, providing businesses with a baseline to track current trends and outcomes. For instance, businesses may track daily social media engagement but descriptive analytics adds more context. We mean, 30,000 likes on a social media post sounds incredible – but comparing it with the historical average tells if it’s truly popular or not.

This is where Descriptive Analytics Tools step in! They offer features that simplify the entire analytics process and provide various ways to visualize the raw data to see patterns and trends within it. Generally, these tools perform various statistical transformations, offering metrics such as averages, frequencies, variations and deviations, etc., that can be visually depicted for quick, digestible insights.

Some common examples of Descriptive Analytics Tools include statistical software, such as Excel or SPSS3, data visualization tools, such as Tableau as well as data wrangling tools, such as Alteryx, Trifacta or Talend.

These tools did not pop up overnight; here’s how Descriptive Analytics Tools evolved with time!

Origins Of Descriptive Analytics Tools

Source

As you know, the need for analytics has grown as the amount of data available to businesses has increased. This has driven the innovations and advancements in business intelligence, culminating with the development of Descriptive Analytics Tools that simplify how we understand data. Here’s a brief timeline:

Pre-2000s

The emergence of Business Intelligence (BI) Tools was inevitable but they focused on basic reporting and querying to provide businesses with a way to analyze, organize and understand their data.
2000-2010

As data warehousing solutions became prominent, they allowed businesses to consolidate large volumes of data from various sources for analysis. Tools such as SAP BusinessObjects and IBM Cognos gained popularity in analytics operations.
2010-2015

Then came tools like Tableau and QlikView, introducing the analytics world with intuitive, self-service features. Users could now analyze data and create visualizations without extensive technical expertise, enhancing the understanding of insights for non-technical professionals.
2016-2018

The Big Data era was critical in driving the development of Descriptive Analytics Tools, as its analytics needed capabilities to handle large volumes of unstructured data. Businesses started integrating such tools with Big Data technologies like Hadoop and Spark. Moreover, the adoption of cloud-based analytics solutions rose, offering businesses higher scalability and accessibility to insights. Google Data Studio and Microsoft Power BI were among the pioneers of the cloud-native analytics revolution.

Today, businesses need advanced data discovery abilities and hence, AI-driven insights have gained prominence. Tools with machine learning algorithms for automatic pattern recognition and Natural Language Processing (NLP) for conversational intelligence are among some features that are becoming more common.

Throughout this evolution, the focus has always shifted from static insight reporting to dynamic, user-friendly interfaces that give businesses the power to glean real-time, actionable insights from their data. To know how this happens, we need to know how these tools work, right?

How Do Descriptive Analytics Tools Work?

Descriptive Analytics Tools work by analyzing historical business data to provide a complete understanding of how the business performed in the past. To do this, the tools use various statistical analysis techniques that essentially slice and dice the raw data to unearth patterns, anomalies, trends, etc. and improve planning. More importantly, they help businesses compare metrics from two time periods. For instance, a team manager might want to compare customer queries resolved this month versus last month.

Despite the large volume of data, Descriptive Analytics Tools can help if you follow these steps:

Identify Relevant Business Data

The process of descriptive analytics must start by identifying the specific business metrics that correlate to business performance, such as sales-per-region, cost-per-sale or customer engagement, depending on the business and its industry. This allows users to identify the business data that needs to be analyzed, assuming it has buried insights and trends.

Organize The Data

The tools can then acquire data from different sources, clean and normalize them to enhance the analytics’ accuracy. As Descriptive Analytics Tools organize, summarize and visualize data in an easily interpretable way through intuitive dashboards, raw data is pre-processed before analytics

Perform Descriptive Analytics

Next, the tool performs various statistical techniques to combine, summarize and compare the raw data in different ways to generate data features, that is, measurable attributes of the dataset. Then, using the data features, Descriptive Analytics Tools can identify patterns, trends and other relationships within the complex data.

Present Visual Insights

As data features are numerical information, the tools can transform them into actionable insights via dashboards or visualizations, including bar charts, pie charts, line charts, bubble charts and histograms.

This process allows users, even those without technical data expertise, to gain valuable insights into the company's historical performance and map it to the day-to-day for informed decision-making.

Think of Descriptive Analytics Tools as a detective, who helps you piece together hard-to-find clues to make informed decisions based on a clear understanding of business data. Well, employing this data detective at your workplace is always a win – here’s how!

Advantages Of Descriptive Analytics Tools

Source

Yes, dear internet grandma – but it’s so much more than that!

Here’s what you need to know:

Enhances Business Performance

Descriptive Analytics Tools help businesses identify trends and patterns in their data, leading to informed decisions that enhance business performance. For example, it can allow retail stores to track historic sales data to identify when certain products have a spike in demand to keep their inventory prepared.
Leverages Historical Data

Yes, we know that the past does not equal the future – but it’s a damn good indicator! Descriptive Analytics Tools can help users gain insights into the root causes of their current situation. For example, a company can use Descriptive Analytics Tools to analyze historical sales data and understand whether the sales have fallen or risen since they introduced changes to a product.
Improves Internal Communications

Descriptive Analytics Tools can work wonders for cross-functional teams as they can unpackage complex data into something more digestible. For instance, a team leader can understand the project’s progress by summarizing an extensive report.
Promotes Data-driven Decisions

Descriptive Analytics Tools also empower businesses to make well-informed decisions by providing solid data-backed takeaways. For instance, a restaurant owner can examine customer reviews across apps and review sites to gauge a dish’s popularity.
Monitors Business Outcomes

Descriptive Analytics Tools allow users to track the various key performance indicators (KPIs) that give a clear picture of the business’s health. For instance, businesses can analyze sales trends over a span of years to determine which products have been the most popular.

Till now, we have talked about how this tool analyzes historical data to enhance business decisions – now, let’s talk about the future!

What’s The Future Of Descriptive Analytics Tools?

Source

Well, the future of Descriptive Analytics Tools is poised for continued growth and innovation, enabling more proactive and accurate decision-making within businesses. We have two key trends:

First, Descriptive Analytics Tools will increasingly integrate with artificial intelligence (AI) and machine learning (ML) to improve their ability to understand relationships between data points and derive more meaningful insights. This integration will also address the demand for real-time insights, as AI-powered Descriptive Analytics Tools will evolve to provide instant access to data to make quick decisions in a dynamic environment.

Second, as data visualization will continue to play a crucial role in descriptive analytics, these tools will see the development of sophisticated dashboards, capable of presenting complex information in easily understandable and visually appealing formats. Moreover, these dashboards will be customizable to suit specific industry needs. Thus, tailored analytics will enable organizations to extract insights that are highly relevant to their unique challenges, goals and strategies.

In summary, as more businesses recognize the value of data-driven decision-making, Descriptive Analytics Tools will rise to play a crucial role in the landscape of business intelligence!

To Conclude

Descriptive Analytics Tools have become an essential part of the modern business world, providing detailed and unique insights into business data. These tools can help users discover key trends, analyze correlations in datasets and identify areas of opportunities through robust insights. If you want to extract valuable information from historical business data and use it to make more informed decisions in a rapidly changing market, Descriptive Analytics Tools are what you need!

Frequently Asked Questions

What Is Descriptive Analytics And How Does It Benefit Businesses?

Descriptive analytics involves analyzing historical data to identify patterns, trends and relationships, providing businesses with insights into past events. It helps in understanding what happened in the past, offering a baseline to track current trends and outcomes. By leveraging descriptive analytics, businesses can enhance performance, improve internal communications, make data-driven decisions and monitor business outcomes.

What Are Some Examples Of Descriptive Analytics Tools?

Descriptive Analytics Tools include statistical software like Excel or SPSS, data visualization tools such as Tableau and data wrangling tools like Alteryx, Trifacta or Talend.

How Do Descriptive Analytics Tools Work?

Descriptive Analytics Tools analyze historical business data using statistical techniques to uncover patterns, anomalies and trends. They help in comparing metrics from different time periods and present visual insights through dashboards or visualizations, facilitating informed decision-making

Enjoyed what you've read so far? Great news - there's more to explore!

Stay up to date with the latest news, a vast collection of tech articles including introductory guides, product reviews, trends and more, thought-provoking interviews, hottest AI blogs and entertaining tech memes.

Plus, get access to branded insights such as informative white papers, intriguing case studies, in-depth reports, enlightening videos and exciting events and webinars from industry-leading global brands.

Dive into TechDogs' treasure trove today and Know Your World of technology!

Disclaimer - Reference to any specific product, software or entity does not constitute an endorsement or recommendation by TechDogs nor should any data or content published be relied upon. The views expressed by TechDogs' members and guests are their own and their appearance on our site does not imply an endorsement of them or any entity they represent. Views and opinions expressed by TechDogs' Authors are those of the Authors and do not necessarily reflect the view of TechDogs or any of its officials. All information / content found on TechDogs' site may not necessarily be reviewed by individuals with the expertise to validate its completeness, accuracy and reliability.

Tags:

Data AnalyticsDescriptive Analytics Tools Descriptive Analytics Descriptive Data Analysis Narrative Analytics Predictive Analytics Prescriptive Analytics Descriptive Analytics Diagnostic Analytics

Join The Discussion