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.
That being said, many business professionals don’t have time or money to spare to take courses on big data analytics in a university. We get that, and we’re here to help. We can’t all be data analysts! So for those who are uninitiated into the world of big data analytics, here’s your big data crash course:
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What is Big Data?
Big data refers to the massive amount of data that companies collect and analyze. Some people use the term to simply describe the volume of data collected. However, most of the time it’s used to describe the systems and processes used to collect, store, analyze and output data.
Big data can use data of any kind — structured or unstructured, from email clicks to in-store purchases. The end goal of big data analytics is to find actionable insights like data trends that reveal changes you can implement to improve your business. These insights are the basis of data-driven decision-making — they help users make objective business decisions, such as whether or not to change suppliers, based on data trends that’ll give them a competitive advantage.
In order to produce more detailed and powerful insights, new technologies like machine learning and artificial intelligence are being used by more industries. These technologies help sort through the seemingly overwhelming amount of data points and find patterns that even some of the most skilled data scientists can’t.
Source: Asigra.com
When defining big data more in-depth, there are certain characteristics that you can sink your teeth into to get a better understanding. These are known as the 4 V’s of big data:
Volume
The term “big data” wasn’t a random choice; the word “big” is used to describe the massive amount of information used in big data analysis. To give you some perspective: big data datasets aren’t measured in megabytes or gigabytes. Instead, they’re measured in terabytes and petabytes of data. In other words, a little analysis on an Excel spreadsheet with 100 rows isn’t big data because it just isn’t big enough. However, an analysis of every Facebook user’s ad clicks is very much big data analysis.
Setting up an effective big data environment involves utilizing infrastructural technologies that process, store and facilitate data analysis. Today, businesses often use more than one infrastructural deployment to manage various aspects of their data.
Variety
Variety is more or less self-explanatory. To qualify as big data, a specific data analysis has to use a variety of data in that analysis. As we mentioned earlier, this could be any kind of data that’s relevant to your business.
What’s more interesting about variety, however, is the use of structured and unstructured data. Structured data is what most people typically think of when they think of data: numbers and information such as dates, money, names, etc. neatly organized into tables of columns and rows. This easy organization makes structured data easy for computers to analyze.
Unstructured data, on the other hand, is the data that isn’t so neat. This data is more abstract, making it much harder to analyze. Examples of unstructured data include pictures, blogs, text messages, voice recordings and other things “based on a human understanding,” as Dummies.com describes. This kind of analysis wasn’t even possible for computers to perform until recently, and the increased use of AI in the field has launched a revolution in how companies perform big data analysis.
Big data often provides companies with answers to the questions they did not know they wanted to ask. Therefore, there is an inherent usefulness to the information being collected in big data. Businesses must set relevant objectives and parameters in place to glean valuable insights from big data.
Velocity
Velocity refers to the speed at which data is collected. Big data doesn’t include any kind of data analysis where you collect a few data points per day. Big data refers to analysis that collects data on a constant basis. Things like social media posts, retail transactions and app usage are just a few examples of the type of high-velocity activities that big data tracks.
Veracity
Veracity involves the accuracy of the data. In other words, how much can you trust the data you’re using? Big data analysis is worthless if you’re using inaccurate data because any insights you gain from it are false and misleading. Therefore, the veracity of your data is absolutely essential. Duplicate and missing data are two of the biggest culprits when it comes to inaccurate data.
Source: Wipro.com
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Big Data vs Business Analytics vs BI
Big data and business analytics are elements of the larger business intelligence framework, but need to be understood and discussed as their own entities. While we won’t go super in-depth into each tool, we’ll give you a data analytics crash course so you can know the difference going forward.
Business intelligence is the collection of systems and products that have been implemented in various business practices, but not the information derived from the systems and products. Business analytics software is used to explore and analyze historical and current data. It utilizes statistical analysis, data mining and quantitative analysis to identify past business trends. Click here for our comprehensive comparison article to find out how to choose between business intelligence and business analytics tools.
Big data is a term utilized for specific types of data analysis practices performed by BI software or during BI processes. For large businesses or those looking for overarching insights into customer habits and preferences, big data is a much more useful analysis method than simple BI analytics or reporting.
For a more in-depth comparison of BI, big data and data mining, check out our article on the differences between these tools.
Hadoop
With the massive amount of data that needs to be analyzed, how are you supposed to do so in any kind of reasonable timeframe? After all, the computing power required to process a large data set can easily shut down your average laptop. Enter Apache Hadoop, an open-source software framework that efficiently distributes the storage of large data sets across clusters of computers and servers, called Hadoop clusters.
When using Hadoop, you set up your own physical servers and computers, called NoSQL databases, which are networked together. After you have the infrastructure, you reach out to a Hadoop vendor such as Hortonworks or Cloudera to install, configure and manage the Hadoop framework on those computers.
The Hadoop setup stores data in chunks across different computers, so when you process the data, it’s pulled from several different sources. One of the main advantages of this, in addition to efficient storage, is that if a computer shuts down, you don’t lose all of your data. In order to process data with Hadoop, different Hadoop tools are used, such as MapReduce, Hive, Pig, Impala, Mahout and the newer Scala. For a more in-depth look at Hadoop, check out this article from opensource.com.
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Case Studies
Need a few examples of how companies are using big data today? Mark Schaefer collected several case studies on the use and subsequent success of big data by some of the biggest companies in the world. British Airways, for example, combined data from their customer loyalty program with that of their customers’ online behavior. This combination helped the airline create more targeted, relevant offers for their customers, creating a more positive overall experience and improving brand loyalty.
Schaefer also highlighted American Express, which put big data to work to predict customer loyalty. By analyzing historical transactions as well as 115 other variables, the financial giant uses predictive analytics to identify which accounts will close within the next four months.
Needs Analysis
Once you’ve developed a familiarity with big data analytics basics, you can start implementing it in your organization. An easy place to start is making sure you know your business’ unique requirements. Big data analytics tools offer a range of features including data mining, data processing, predictive analytics, fraud analysis, reporting features and much more.
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The Future
So that’s the past and present of the basics of big data, but what does the future hold? I-Scoop.eu has put together some predictions about where big data will be by 2020.
According to IDC, the Worldwide big data and Business Analytics Market is poised to grow from $130.1 billion to over $203 billion in 2020. I know you can do math as well as I can, but that’s a 64 percent increase over only four years.
Big data isn’t going anywhere: in fact, it’s only going to increase. According to insideBIGDATA, there is going to be exponential growth in data towards 2020 and beyond. 90 percent of the world’s total data has been generated in last two years, and with the increase in the use of devices and technologies like machine learning and IOT, there really can’t be a degradation in the big data and its technologies.
Next Steps
So now you’ve graduated from our crash course on big data basics, which means you know what it means and how it works. That means the big question is: how will you use big data? You could use it like many companies do — identifying sales patterns, forecasting future demand, etc. But you just may find a use that nobody else has before. After all, there are practically no limits to what you can improve using big data. The only limit lies in how creative you can get with your analysis.
How do you hope to utilize big data analytics to better your business? What trends do you see in the coming years? Answer in the comments!