Big Data Analytics – The Definitive Guide
Content Marketing Manager, Sigma
The era of Big Data—and big data analytics—is upon us. By 2025 the global datasphere will grow to an estimated 175 zettabytes. If you don’t know what a zettabyte is, don’t worry. Suffice it to say, it’s a lot. For context, from the dawn of the Internet to 2016, the web created a single zettabyte of data.
Of course, internet traffic is only one slice of the data pie created and stored worldwide—which includes all personal and business data as well. Today, the world sits somewhere between 10-50 zettabytes of total data. Which begs the question: what do we do with all this data? And what good will come from the constant collection of data across the web, personal devices, the Internet of Things, and more?
If you said, “analyze it for insights,” you’re on the right track. Somewhere in this endless sea of data lie the answers to the questions that will fuel the future decisions of business, government, and society at large. But with so much data, where do you start?
In this guide, I will walk you through the basics of big data analytics, and help you understand why it’s so important. You’ll also learn about the benefits it brings, the challenges ahead, how to analyze data, and what the future may hold for the field of Big Data Analytics.
So, what is Big Data Analytics anyway?
Let’s start with Big Data—a term we have all likely heard by now. But what does the term mean? Here’s a hint: when it comes to big data, it’s all about scale.
Big data has one or more of the following characteristics: high volume, high velocity, or high variety. Artificial intelligence (AI), mobile, social, and the Internet of Things (IoT) are driving data complexity through new forms and sources of data. For example, big data comes from sensors, devices, video & audio, networks, log files, transactional applications, web, and social media — much of it generated in real time and at an enormous scale.
Big data analytics uses advanced analytic techniques against vast, diverse data sets that include multiple forms of data (structured, semi-structured, and unstructured data), collected from different sources—ranging in size from terabytes to petabytes.
Benefits of big data analytics
Big data insights can have significant benefits for companies’ top and bottom lines. From helping uncover underlying issues to understanding customers and operations better, to informing communications, there is almost no end to the impact big data insights can make for an organization.
Big data analytics benefits in business
Faster and more informed decisions – The ability to process and analyze data as it’s created in real-time means that companies can act immediately to solve problems, adjust strategies, or decipher market trends.
Efficient operations – Many companies use big data analytics to generate insights about internal supply chains or services—allowing them make changes and streamline operations based on up-to-the-minute information.
Reduced costs – Not only can companies reduce costs by increasing operational efficiency, but today’s big data analytics infrastructures cost much less than data systems of the past. Thanks to the cloud, companies no longer have to build out entire data centers, manage hardware, or hire large teams of IT talent to keep the lights on. These cloud-based analytics “stacks” mean they can get more from their data without breaking the bank.
Improved product or service development – Real-time market, customer, or industry insights can help companies build the next great product, or create services that customers desperately need. This information can give companies the confidence to take bigger leaps in research and development without the same amount of risks.
“In God we trust, all others bring data.”
W. Edwards Deming
Engineer, Statistician, and Author
Big data analytics benefits in government
The impacts of big data analytics don’t stop in the private sector. Today, federal and state governments in the US harness big data to inform new policy agendas, make sweeping improvements to infrastructure, and invest in new social programs. Here are some recent examples of big data analytics at work in the public sector.
Public Education – At the federal level, the Department of Education uses big data to improve teaching methods and student learning. Higher education institutions apply analytics to ramp up services that increase student grades and retention.
Economic Regulation – Big data analysis helps create financial models from historical economic data to craft future policy. And the Securities and Exchange Commission uses big data to regulate financial activity, catch bad actors, and detect financial fraud.
Environmental Protection – For more than two decades, NASA and the US Department of Energy have used data analytics in its research better predict weather patterns, forest fires, and other environmental risks.
Learn more about the use of big data analytics by government agencies from the IBM Center for the Business of Government.
The challenges facing big data analytics
Despite the ubiquity of big data applications in business, there are still many challenges facing companies and governments that deploy big data analytics strategies. Here are some of the most common challenges organizations face when working with big data.
As I mentioned earlier, the rate of data creation is staggering. One of the biggest challenges organizations face with big data analytics is storing and analyzing all the data collected each day. What makes this particularly difficult is the amount of unstructured data (more on this later) that must undergo analysis.
If companies want to make use of data, it must get stored in an analytical database of some kind, such as a data warehouse. And with the rise of artificial intelligence (AI) and machine learning (ML) applications, data lakes are often used too. Of course, storage is just part of the equation. Maintaining a healthy database that is free of errors, duplications, and outdated or “bad” data also requires human resources to manage. That’s why some of today’s most data-driven companies have large data teams with engineers, data scientists, and analysts. As a company scales and creates more data, the more complicated the data infrastructure becomes over time.
Today, data is collected from a variety of disparate sources—enterprise applications, third-party software, social media, email servers, and more—making it difficult to centralize data in a single database for analysis.
Because data integration remains a challenge for companies, there’s been a rise in modern ETL and ELT tools that simplify data pipelines by automating data collection and transfer to the data warehouse. This technology makes data centralization possible and eliminates data silos that aren’t accessible to business teams.
Like most things in this world, data expires. And with the rate that new data gets created today, it’s not only necessary but imperative that teams utilize the latest information to make decisions. Otherwise, they risk operating on outdated assumptions. If you think data doesn’t have an expiration date, think again. The CGOC estimates that 60% of data collected today has lost some—or even all— its business, legal or regulatory value.
Because data has a relatively short shelf life, organizations must analyze data in real time—or near time—as it’s collected. This requires a robust data pipeline to collect data immediately after it’s created and transforming and storing it in an analytical database so that it’s queryable in minutes.
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Managing business data can be challenging. As stated earlier, it’s constantly changing, aging, and moving across multiple systems. This can make it difficult to ensure data integrity, usability, accessibility, and security across an organization. That’s where the governance process comes in. With the right big data governance strategy, data is centralized, consistent, accurate, available, and secure. Big data governance (and data modeling) also allows for a common set of data formats and definitions.
Data governance is essential. If data isn’t available or accurate to business units, they can’t make informed decisions. And if it falls into the wrong hands, the results can be catastrophic.
The increase in data privacy regulations also requires additional governance practices to meet compliance. These regulations are driving a significant amount of future governance strategy.
Data security will always present challenges to businesses. Data is extremely valuable, and with the amount of sensitive information collected, there will always be security threats to mitigate.
Some of the more common challenges come from the need to keep up with a quickly changing regulatory and security landscape. This requires updating security patches, and IT systems as new threats emerge. The vulnerabilities inherent in today’s distributed technology frameworks can open up opportunities for bad actors to breach systems. There is also pervasive use of false data, or counterintelligence information, that can be used to corrupt databases and hinder a company’s ability to decipher fact from fiction.
Types of data
There is a seemingly endless amount of data collected in our modern world. But what kinds of data do organizations collect? At a high level, there are two main types of data: Quantitative data and qualitative data. Let’s dig in.
Quantitative data consists of hard numbers–think of it as things that you can count. Quantitative analysis techniques include:
- Regression – Predicts the relationship between a dependent variable and one or more independent variables.
- Classification (probability estimation) – Predicts, or calculates a score of the probability of how likely an individual belongs to a class. In the content marketing example from earlier, you could think of two classes: “Will consume” and “Will not consume” and predict the likelihood of the success of an individual piece of marketing collateral.
- Clustering – The grouping of individuals in a population based on similarities.
Qualitative data is more subjective and less structured than quantitative data. In the realm of business, you encounter qualitative data from customer surveys and interviews. Common analysis methods include:
- Content analysis – Used to classify and categorize different types of text and media.
- Narrative analysis – Analyzes content from various sources, including interviews and field observations. As you conduct your analysis, make sure your metrics are in the format that your company already using. For example, if your company budgets quarterly, your metrics should reflect the same.
Structured data vs. unstructured data
Data—whether quantitative or qualitative—can take multiple shapes depending on the nature of the information, how it’s collected, where it’s stored, and whether humans or machines created it. There are two primary levels of data structures to take into account: structured data and unstructured data.
Structured data is information that is rigidly formatted so that it’s easily searchable in a relational database. It’s usually quantitative information. Examples include names, dates, emails, prices, and other information we’re used to seeing stored in spreadsheets under column titles organized by category. Think of a company’s CRM, ERP, or email database information.
Structured data is organized and readable by machine code, making it easy to add, search, or manipulate it within a relational database using SQL. Consider an e-commerce purchase, for example. The information collected at the point of sale may include the product name, date of purchase, price, UPC number, payment method, and customer information—all of which is easy to search or analyze later to spot a trend or answer a question.
At first glance, it can be hard to extract insights from structured data alone. But using an analytics tool, you might decipher interesting trends, such as customers in Boston tend to buy a specific product at a higher rate in February and March. This insight could lead you to increase your retail store’s stock of that item during those months to meet regional demand.
Unstructured data is quite the opposite of structured data. It’s usually qualitative data, and it’s challenging to search, manipulate, and analyze using a traditional database or spreadsheet. Common examples include images, audio files, document formats, or someone’s social media activity.
Unstructured data isn’t easily readable or analyzed in a relational database because it lacks a pre-defined data model—meaning it requires a non-relational (or NoSQL) database or data lake to search. To extract insights from this type of data requires the use advanced analytics techniques such as data mining, data stacking, and statistics.
Unstructured data insights help companies understand things like customer sentiment and preferences, buying habits, and more. It’s more challenging to analyze these types of data. But with the right resources, you can uncover intelligence that can give you a competitive advantage.
Semi-structured data falls somewhere between structured and unstructured data formats. This data has clearly defined characteristics but lacks a rigid, relational structure. It includes semantic tags or metadata that create hierarchies of classification—making it more machine readable during analysis.
The most common everyday example most people have encountered is a smartphone photo. While an average photo taken with a smartphone contains unstructured image content, it’s timestamped, geotagged, and carries identifiable information about the device itself. Some common semi-structured data formats include JSON, CSV, and XML file types.
Semi-structured data makes up the majority of data generated in the world today. Just think about all the photos taken on a daily basis. Semi-structured data is most often associated with mobile applications, devices, and the Internet of Things (IoT).
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Types of data analytics
There are four main types of analysis that range in complexity and the level of insights they can generate for an organization. Despite these four categories, each is interlinked and can be used in conjunction with the other to unlock deeper, more meaningful understanding.
Descriptive analysis helps you answer the question, “what is happening?” It’s the most common form of analysis and the base of all other kinds of analytics.
Anyone that has seen a live dashboard or read a quarterly report should be familiar with descriptive analytics. It provides a snapshot of performance to date and is often associated with tracking key performance indicators within an organization. In practice, this might include measuring marketing and sales metrics, like the number of qualified leads and demo requests in Q4.
Once you know what’s happening, the natural follow up question is “why is it happening?” This is where diagnostic analytics shine.
This type of analysis requires drilling down ‘behind the dashboard’ to better understand the root cause of a specific outcome or ongoing trend. In practice, diagnostic analytics might help a marketing team understand which ad campaigns drove qualified leads—or help a sales team understand which emails resulted in the most demo requests in Q4.
Predictive analytics—as you may have guessed by its name—helps answer “what is most likely to happen in the future?”
Building on past trends, this type of analysis uses historical data to predict future outcomes. Predictive analysis builds on the insights found through descriptive and diagnostic analysis and uses statistical modeling to forecast the most likely scenario of the future.
This type of analysis would help a marketing team predict how many qualified leads they will collect in Q4, while a sales team can use it to forecast the sales pipeline in Q4.
Prescriptive analytics helps an organization understand “what should we do next?” to address a current trend or problem. It’s more complicated than the other forms of analysis, which means most businesses lack the resources to deploy it.
Prescriptive analysis often requires the use of advanced data science and artificial intelligence to digest massive amounts of information and propose decisions that will solve existing organizational problems.
The big data analytics process
Without the right processes in place, generating analytical insights from your organization’s data will be difficult. The process of collecting, processing, and analyzing data is just as important—if not more so—than raw data alone.
The right process can ensure the insights derived from data are accurate, consistent, and free of errors that yield false trends.
Understanding data goals and requirements
A clear understanding of company goals and needs will inform how you approach big data analytics from the start. What type of data will you collect? How will you store it? Who will analyze it? All of these questions matter and ultimately determine not only the data infrastructure you’ll need to put in place, but what types of analytics tools you will need to uncover and share insights. For more information, check out our implementation guide.
Collecting and centralizing data for analysis
Once you’ve got a clear understanding of your goals, you need to extract data from your systems and applications and transfer it to the data warehouse or data lake. That’s where an ELT and ETL solution comes into play. They help replicate data to the cloud warehouse for analysis. This centralized data store gives you a fuller picture of what’s happening across the company and eliminates any data silos that may exist along the way. You can capture data from applications, e-commerce events, other databases, and more.
Modeling Data for Analysis
Data can technically be analyzed once it’s in a central data store. But before you open the doors to the warehouse, you may want to consider a data model first. Data modeling defines how data is related, what it means, and how it flows together. An effective model makes data approachable and consumable and ensures people use the right information in the proper context—and it requires tight-knit collaboration between data and domain experts.
With data collected, processed, stored, and modeled in a queryable data warehouse, you will then need an analytics tool that is up to the task of searching through all that data and returning actionable insights to steer business decisions. It’s essential to get a good idea of what you need from a real-time analytics tool. Every company is unique, and needs will vary. We recommend evaluating internal requirements and aligning purchasing decisions with those goals.
It’s also important to note that not all analytics tools are the same (or solve the same problems). Often, companies will deploy multiple tools for different use cases, teams, or business units. With that in mind, here are some guidelines to consider when selecting an analytics tool.
Interpreting Insights and Informing Decisions
Using various types of analytics methods you can uncover all kinds of insights from company data. You can analyze the past, track operations in real-time, and even predict what might happen in the future. These trends can increase your competitive advantage, help you create better products and services, deliver a better customer experience, and more.
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The Future of Big Data Analytics
If you’re not sure about the future of data analytics, look no further than the nearest cloud. The question on everyone’s mind has shifted from “if” to “when” organizations will migrate to the cloud for computing, storage, and application management. As storage becomes even cheaper, and compute nears infinite levels, cloud adoption is expected to grow at more than six times the rate of general IT spending through 2020 to meet big data analytics goals.
While that growth may seem extraordinary, it makes sense. Organizations that use big data saw an 8 to 10 percent profit increase, and a 10 percent reduction in overall costs. It’s clear that the future of big data analytics holds great promise.
Curious about the future of data analytics? Explore the top 6 trends that will impact the next decade of data, or download our free eBook 2020 Data Trends and Predictions to Help You Kickstart the Next Decade.