Big Data Analytics

What is Big Data?

What is Big Data Analytics?

Big Data Analytics Helps You:

  • check_circle_outlineReduce costs
  • check_circle_outlineMake decisions faster
  • check_circle_outlineOptimize business performance
  • check_circle_outlineManage data
  • check_circle_outlineAnalyze 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

FAQs

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:

Bergen Adair

Bergen Adair

Market Research Associate

Bergen Adair is a Market Research Associate at SelectHub who writes content on CMMS, FM, EAM, Business Intelligence and Enterprise Reporting. She has had a love of the written word since she started reading voraciously at three years old, and studied creative writing at Colorado State University and Swansea University. In her free time she’s usually reading (or napping) in a hammock, hiking with her dog, or listening to horror and true crime podcasts.

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Jason Keller

Jason Keller

Market Research Associate

Jason Keller is a Market Research Associate at SelectHub who writes content on Business Analytics Tools, Big Data Tools, Facility Management Software, Marketing Automation Software, Field Service Software and Endpoint Security Software. He studied journalism at the University of Northern Colorado, and in his free time likes reading and writing creatively, listening to music, hiking and hanging out with his dogs.

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Kim O’Shaughnessy

Kim O’Shaughnessy

Lead Editor

Kim is the Lead Editor at SelectHub, where she edits and manages content for over 40 different software categories. She discovered her zeal for writing while earning her BA in Communications at the University of Michigan. Kim enjoys working on any project that offers a creative outlet, and you can often find her blogging about video games in her spare time. If someone manages to pry her away from the game controller, she also enjoys spending time with family and friends.

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Learn the Basics: A Big Data Analytics Crash Course

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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.

Bergen AdairLearn the Basics: A Big Data Analytics Crash Course
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Data Analytics vs Business Analytics vs Web Analytics vs Big Data vs BI: What are the Differences?

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In the world of business software, it can feel like there are way too many buzzwords and industry terms to keep up with. As the software industry continues to grow and advance, more new concepts and trends are added, and the problem compounding all of this is that many of them sound incredibly similar. For example: web analytics vs business analytics. What’s the difference? I’ll answer that question in addition to many more about the different types of analytics and how they compare to each other (such as examining data analytics vs business analytics).

SelectHubData Analytics vs Business Analytics vs Web Analytics vs Big Data vs BI: What are the Differences?
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BI vs Big Data vs Data Mining: A Comprehensive 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. One of the most rapidly growing technologies in this sphere is business intelligence, and associated concepts such as big data and data mining.

To help you understand the various business data processes towards leveraging business intelligence tools, it is important to know the differences between big data vs data mining vs business intelligence. We’ve outlined the definitions of each, and detailed how they relate and compare to each other.

Alainia ConradBI vs Big Data vs Data Mining: A Comprehensive Comparison of the Difference Between Them
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Features of Big Data Analytics and Requirements

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What is Big Data analytics? Why is it big? These were my questions when coming across the term Big Data for the first time. Luckily for both of us, 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. This data can be anything from customer preferences to market trends, and is used to help business owners make more informed, data-driven decisions.

Bergen AdairFeatures of Big Data Analytics and Requirements
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