Big Data Analytics

What is Big Data?

What is Big Data Analytics?

Big Data Analytics Helps You:

  • Reduce costs
  • Make decisions faster
  • Optimize business performance
  • Manage data
  • Analyze and predict trends

Big data analytics is a subset of business intelligence (BI), with a specific emphasis on large quantities of rich data. Many big data analytics tools source their data from a variety of sources, such as social media, web and additional databases, and then they perform detailed analysis on that data to uncover insights. What separates big data analytics from something such as business analytics, though, is the sheer volume of data being processed and the analytical techniques applied to said data. These tools often require advanced knowledge of data analysis techniques and make use of technologies like Apache Hadoop and cloud-based analytics.

Big data analytics takes that data and then presents it in meaningful ways by utilizing powerful visualizations and dashboards. Well-presented data can give decision-makers and managers the intel they need to, for example, move forward with product launches or scale back their marketing efforts.

This type of software often uses four types of analytics to help generate and uncover insights:

  • Prescriptive analytics
  • Descriptive analytics
  • Predictive analytics
  • Diagnostic analytics

Reduce costs

It might be hard to believe, but big data analytics can help users reduce costs in their business. Hadoop and cloud-based analytics provide cheap and efficient ways of storing users’ data. The added benefit of having a high-level overview is that you can see under and overperforming facets of your business.

For example, let’s say that your marketing department is bringing in a large number of leads, but your customers are abandoning their carts during the checkout phase. By utilizing big data analytics, you can investigate what factors are contributing to your lost sales and treat them. Maybe your checkout page isn’t well optimized? You could spend additional resources on engineering in order to remedy the problem.

Make decisions faster

With wide availability of data, users are often able to make critical decisions quickly. Less time needs to be spent mulling over small data points that aren’t yet analyzed or compiled.

Optimize business performance

Seeing every facet of your business has significant benefits because it allows users to diagnose pain points or deficiencies, and then treat them. When certain departments aren’t performing or meeting KPIs, users can investigate why. Most big data analytics products will help provide at least some diagnostic information, such as corroborating factors or associated data points.

Manage data

Data management — often known as data governance — is a critical feature of big data. As regulations such as the General Data Protection Regulation continue to have an impact on the way businesses handle data, controlling the flow of that data is a matter of critical importance. Data quality management usually includes cleaning, harvesting, distribution and contextualizing of the data.

Analyze and predict trends

Predicting trends and analyzing behaviors are among the most coveted features of big data analytics. Working off of historical data and evidence, big data analytics will then attempt to make projections and predictions while also accounting for a number of additional factors that can influence outcomes. Factors such as seasonality, price fluctuations, discrepancies, consumer behavior, brand interaction and more are usually accounted for in making predictions.

As an added benefit, predictions help leaders prepare for the future. Let’s say a certain product such as plastic Easter eggs historically sell well in the spring, according to historical data. Managers can then make sure they have plenty of them in stock for the seasonal boom.

Share insights

Sharing insights with others in your organization is a critical function of any analytics suite, not just big data analytics. Almost always, these data discoveries are communicated through the use of dashboards, reports and visualizations — each of which serves their own unique purposes.

Dashboards are live-updating, interactive windows into raw data. Dashboards are often highly tailored for specific use-cases, such as marketing, sales or management. While reports are generated and then considered “complete,” a dashboard is technically never complete. It shows information in real-time, utilizing visualizations to meaningfully convey information. Dashboards can more often than not be manipulated and explored by the user.

On the other hand, reports are static pieces of content that compile designated information and then deliver it using figures, visualizations or both. Often times, reports are generated at the end of a workday or any set period of time and serve as benchmarks for performance.

Visualizations refer to the vital, illustrative components that are often utilized by dashboards and reports. Visualizations help tell data’s story by communicating in efficient and meaningful ways. Some visualization tools include:

  • Charts
  • Graphs
  • Heatmaps
  • Flowcharts
  • Word clouds
  • Timelines


How Can Big Data Improve Business Performance?

Big data tools are going to pull massive quantities of data across the entire spectrum of your business. When decision-makers and managers can see the breadth and scope of their enterprise — and how it’s performing — they can take steps to capitalize on wins and optimize their losses.

Am I Ready For Big Data?

There are a couple of key factors that play into whether or not your business is ready to start taking advantage of big data analytics. Consider:

  • Your organization’s goals in utilizing data
  • Pain points and weaknesses of your business
  • Unexplained and erratic customer behavior
  • Your organization’s need to track certain facets of business
  • Lack of progress

How Do I Select a Big Data Analytics Solution?

Picking a big data analytics tool that fits your businesses’ needs is no small task. Just be sure to keep a few essentials in mind when you’re browsing for software.

Think about your enterprise’s needs, first and foremost. Decide what big data analytics features you’re going to need, what you want, and then start shortlisting products. We’ve got curated product pages with features and benefits lists to help make this process a bit easier. We also have a helpful tool called Requirements Hub that can assist users in creating a requirements list for their business.

Next, think about your budget. How much are you willing to spend, and are you willing to go higher for necessary features?

When you feel like you’ve got enough of the necessary information down, it’s time to start an RFP, which is a task in of itself. If you’ve never done an analytics RFP before, head on over to our article, which explains the process in depth.

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Big data analytics articles are written and edited by:

Zachary Totah

Zachary Totah

Content Manager

As SelectHub’s Content Manager, Zachary Totah leads a team of more than 35 writers and editors in their quest to provide content that helps software buyers find the right system for their company.

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Hunter Lowe

Hunter Lowe

Content Editor and Senior Market Analyst

Hunter Lowe is a Content Editor and Senior Market Analyst at SelectHub. He writes content for Construction, Inventory, Warehouse, and Supply Chain Management.

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Ritinder Kaur

Ritinder Kaur

Market Analyst

Ritinder Kaur is a Market Analyst who writes content on Business Intelligence, Big Data Analytics, Business Analytics, Embedded Analytics and Enterprise Reporting.

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Conner Martin

Conner Martin

Writer and Researcher

Conner Martin is a writer and researcher with a passion for communication and education.

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A Comprehensive Crash Course in Big Data Basics

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November 10, 2023
The future is here, and it comes in the form of data. For businesses of every industry and size, the use of big data is only continuing to increase in the age of technology. After all, it’s been one of the most well-known buzzwords of the last few years for a reason. Despite how much it’s talked about, many people still don’t know what big data actually is.

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Business Analytics vs. Data Analytics: What’s the Difference?

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Would you believe that over 100,000 business analytics jobs were posted in the U.S. as of Feb. 2023? As new blood fills the industry and demand for BI skills skyrockets, understanding business analytics vs. data analytics is crucial.

This article breaks it down with the associated roles, courses and certifications. Plus, it has information for software buyers. Which tool should you choose?

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What Are The Types Of Big Data?

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As the Internet age surges on, we create an unfathomable amount of data every second. So much so that we’ve denoted it simply as big data. Naturally, businesses and analysts want to crack open all the different types of big data for the juicy information inside. But it’s not so simple. The different types leverage varying big data tools and have different complications that accompany working with each individual data point plucked out of the vast ether.

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Features Of Big Data Analytics And Requirements

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What is big data analytics? Why is it big? What are the key features of big data analytics? These were my questions when coming across the term big data for the first time. Luckily, it’s a pretty simple answer. Big data analytics tools are exactly what they sound like — they help users collect and analyze large and varied data sets to explore patterns and draw insights.

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Enterprise Big Data: A Comprehensive Guide

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In an era where data is king, and big data analytics and related tools are the king of kings, enterprise big data analytics is no longer a differentiator, but a password at the door for some industries.

Enterprises in industries like banking, energy, transportation and others rely on big data to not just keep a competitive edge in their markets, but even tread water. Up to 60% of businesses have incorporated big data recently, a number that is sure to have only increased recently.

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What is Data Lifecycle Management? Key Components, Stages and Best Practices

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It’s frustrating. Your business intelligence strategy is fraught with data quality issues, and it’s tough keeping up with fast-changing data norms. Siloed repositories are the bane of your existence, unleashing the twin woes of dwindling profits and lost opportunities. Will a governance strategy be enough, or do you need data lifecycle management?

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What Is Data Management? A Comprehensive Guide

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Enterprise data is the linchpin of all business processes — companies around the world build their business strategies on insights derived from numerous, complex data points and big data analytics. Data management refers to end-to-end processes that include the sourcing, ingestion, storage and transformation of proprietary data for business intelligence (BI), reporting and analytics.

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Hadoop vs Spark: Who Is The Winner?

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Hadoop and Spark are open-source big data software meant to replace traditional information warehouses. They help organizations harness the power of big data for real-time analytics and business intelligence. When searching for big data solutions, the Hadoop vs. Spark comparison is common.

This article delves into a comparative analysis of the two platforms. Though Spark comes out as the winner, we can’t dismiss Hadoop altogether.

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BI vs. Big Data vs. Data Mining: A Comparison of the Difference Between Them

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Lately, there have been tremendous shifts in the business technology landscape. Advances in cloud technology and mobile applications have enabled businesses and IT users to interact in entirely new ways.

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Getting smarter is always a good thing. Making informed decisions and capitalizing on inefficiencies and opportunities have always been crucial components of getting ahead of the pack in commerce. In the golden age of information, that means big data analytics tools. In 2021 and beyond, the field has diffused enough to get to free and open source analytics.

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Big data comprises large volumes of information in the form of simple to complex data sets at tremendous velocity. Big data analytics tools are equipped to ingest information in all forms – structured to semi-structured to unstructured – and transform it for visualization and analysis so that organizations from small startups to large corporations can make sense of their data. In this article, we will look closely at big data and the best big data solutions on the market.

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Big Data Integration: A Comprehensive Guide

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So, you want to add big data tools to your business. And why wouldn’t you? Big data analytics gives you a competitive edge, helps you optimize your operations and gives you a broader overview of your company. However, it’s not as simple as snapping your fingers and telling your staff to implement BDA. Big data integration is a complex process with high rewards.

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The world of business intelligence software shifted acutely over the past couple of decades. While the overall goal to achieve smarter, optimized business has not changed, the methods of doing so are like baseball players in the Steroid Era: they’ve grown immensely. Two areas of business intelligence, big data and business analytics, are the very definition of this new world of business data.

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