It’s no accident that industry increasingly depends on deep analytical talent. Our world is being reshaped in profound ways as Big Data remakes entire industries in its own image. Really, it’s difficult to overstate how “big” Big Data’s influence is turning out to be.
Big data is impacting almost every major industry with the ever-improving capabilities and amount of data that they’re collecting from sensors, online and other online sources.
Big Data is getting bigger. In its wake, we’re all looking for ways to understand it and put it to use. In order to talk about the Big Data phenomenon, we first have to consider what brought us here in the first place. From there, we’ll gain a better perspective of where we’re going.
After a very brief overview of data analytics and Big Data, we can start talking about practical applications for industry. Big Data can be very useful if you know what to do with it.
Data Overload: Why Big Data’s So Big
Key technological advances are converging now to create a lot of Big Data. A century or two ago, people might’ve thought of a single book as a useful measurement for information’s size. Libraries carry a lot of books, so they have a lot of data. This worked pretty well for most applications until computers and data science became more comfortable with petabytes:
- Megabyte: A book
- Gigabyte: A shelf of books
- Terabyte: A library
- Petabyte: 1,000 libraries
- Exabyte: 1,000,000 libraries
An Exabyte is truly a lot of data. Then again, there’s also a lot of data to measure. Pretty soon, we’ll have to move on to even bigger measurements.
One widely cited statistic from IBM Marketing Cloud claimed in 2016 that 90 percent of the world’s data was created within the previous two years. The Internet’s traffic in a year was said to equal several hundred exabytes back in 2012, and it’s a lot bigger now.
Increasingly, the world’s data is being collected and stored without an immediate application; we collect it now and ask questions later. Big Data, it seems, is always getting bigger–we don’t entirely always know what to do with it.
The way we got here is a bit complicated. Essentially, computers became much better and the Internet of Things (IoT) accelerated some important trends already happening in data collection, storage, management, and use.
There’s more data to collect, but there’s also more reasons than ever to collect it and more ways to get it done. As a consequence, we’re experiencing something akin to data overload. But, naturally, there’s more to the story than too much data.
Distinctions Between Big Data and The Traditional Data Ecosystem
Big Data is more than just a huge amount of traditional data. It’s makeup and behavior is often completely different from anything resembling the traditional data ecosystem.
There’s this perception that Big Data and Data Analytics are just new words for old ideas. That couldn’t be further from the truth, however. Big Data is on everyone’s radar today because Big Data happens to take on very different characteristics and behaviors.
Just like the unique transformation the laws of physics demonstrate at the quantum level, data gets really weird when it gets really big. The world of Big Data is quite a bit different, it turns out, from how traditional data looks. That fact is creating a whole new Big Data ecosystem and it’s even working to transform our global economy.
Let’s consider these defining characteristics of Big Data, for instance:
- Data collection often starts without a hypothesis, plan, or theory
- The volume of data increases exponentially as data collection continues
- Often lacks structure
- Resource-intensive to work with
- There’s a high rate of change requiring some dynamic analysis
- It’s not all useful
- At least some of it is a complete mess
If we can understand and become intimately acquainted with the data set we’re looking at, there’s usually a story to tell. But is it worth telling?
Not always. But here’s how to find out.
Analysis and Big Data
Storing and Accessing Big Data for Analytics
All of this data is only useful if we can store and access it. In a nutshell, data must be collected, pre-processed, and stored. It often has to be cleaned before much processing can be done. So, there’s definitely a lot of work to do before analysis can begin.
Before we can begin to use a data set, we have to answer pre-processing questions such as:
- Is this error-free, or can we economically remove the errors?
- Is the data accurate?
- Do we need more data, or do we have enough?
- How much work do we need to do before the data’s in good enough shape to use?
While we’re considering these questions, it helps to have a model for approaching the data set. And once we start developing a model, it’s important to have a sandbox where we can test it out before trying it on the actual data.
After that step, we’re ready to start analysis. Now, let’s get down to the good stuff–data analytics.
Types of Data Analytics
There are different ways to approach data once we have it, and we don’t always initially know what the best type of data analytics will be until we start looking at the actual data. If you don’t know what to do yet, there are two basic strategies you can try:
- Exploratory Data Analysis: Data mining to determine what methodology to apply
- Repetitive Data Processing: By systematically trying a selection of different methods, we can try to find the right approaches to use
From there, it’ll gradually become clear what a data set needs. Here are just a few different approaches:
- Descriptive: Take the data and create a summary of what happened. Useful for social data.
- Predictive: Using different models, statistical analysis, machine learning, etc, you start looking for predictions or trends. This is probabilistic in nature.
- Prescriptive: A type of predictive analytics, prescriptive analytics creates actionable insights. Good for a lot of business data uses.
- Heterogeneous analytics: Starts with where it wants to end, then works backwards to get there. Useful for making business decisions.
With the right analytical techniques, we can start making use of all of that data.
Analytical Techniques for analysis and visualization
There are several different analytical techniques, such as forecasting, regression, and statistical analysis. Once we know what to do with the data, it’s time to begin doing something with it. With machine learning, this is in some respects becoming easier to do.
Large data-sets can be trickier to work with, but thanks to smart analytical techniques, we can use this data in interesting ways.
For instance, these analytical techniques are creating tremendous value for industry, even with Big Data.
Classification Tree Analysis
Sometimes, we’re presented with new data we can classify based on what we’ve observed before with older data. Using a historical data set, we can tag new data into categories.
Some basic examples:
- Building customer profiles with help from old behavior and transaction data
- Reviewing and categorizing documents
Best used with quantitative data, regression analysis allows us to study the relationship between an independent variable and a dependent variable. These relationships can be particularly interesting in business, for instance:
- Call center wait times and number of complaints
- Temperature baked goods are produced at and product shelf life
- Extended shopping hours and sales totals
The conclusions we can glean from regression analysis help us make better business decisions, improve operational efficiency, and decide which risks are worth taking as an organization.
A really common form of data analytics; many businesses use predictive forecasting without even thinking about it! Using trends, cycles, or notable patterns in past data, predictive forecasting makes predictions about what future trends will happen when more data is collected.
- A sales team forecasts their next quarter’s sales numbers using previous quarters’ data
- A warehouse plans to order a bigger shipment for next month based on last year’s sales
- VoIP (Voice over Internet Protocol) phone service plans are adjusted in time for higher call volumes during a holiday season, based on past years’ service needs
Of course, there are many ways we can study data and apply analytics techniques. Recently, AI technologies have grown to play a bigger role, too.
Analytics and Emerging Technologies
With Big Data, we often have large data sets that don’t easily and immediately conform to straightforward data analysis techniques. Thankfully, AI and machine learning are coming to the rescue.
Machine learning can help us sift through extraneous data and find only the important stuff. Using an initial set, machine learning can adapt and computers can learn to recognize the important and useful so the bad data is safely excluded.
Bigger data questions, like these, can be addressed with help from emerging tech:
- The probability of winning legal cases
- How to identify spam email
- How to safely navigate a self-driving car
How Data Analytics Helps Industry
Data analytics has a wide range of uses in industry, as one can imagine. With the right insights, businesses are able to make more effective decisions and manage uncertainty. Having methodology to effectively use Big Data is allowing more industries to benefit from today’s data overload.
Recent corporate case studies from Datameer, for instance, have cited some pretty significant results:
- Video Gaming: One gaming company doubled revenue to over $100 million after implementing behavioral analytics to help with monetization of the platform.
- Retail: Another company used data from social media profiles and behavior to target their customers with more appropriate offers and marketing, resulting in 30 percent lower customer acquisition costs.
- Security: By analyzing patterns of how computers around the globe were being infected with a virus, one company was able to effectively predict where the attack would hit next so they could prevent it from causing a data breach.
Data Analytics and Your Industry
How can data analytics make a difference for your organization? Even if the applications aren’t immediately obvious, it’s likely that your industry does face challenges related to data. And if you’re not there yet, you probably will be soon. Big Data is making its presence known throughout entire industries. It doesn’t have to be impossible to answer these questions.
Executive leader with a passion for Customer Success and the ability and desire to develop & refine business strategy. Sachin drives excellence in solutions from concept to delivery for large and complex consulting, strategic solutions and business operations backed by result driven client success. His strong focus on customer satisfaction and quality delivery with deep skills in business strategy, transformation change, portfolio program and project management is enabled by his 17+ years of experience in various geographical markets including APAC, Europe & the US.