Big data analytics is quickly gaining adoption. Enterprises have awakened to the reality that their big data stores represent a largely untapped gold mine that could help them lower costs, increase revenue and become more competitive. They don’t just want to store their vast quantities of data, they want to convert that data into valuable insights that can help improve their companies.
As a result, investment in big data analytics tools is seeing remarkable gains. According to IDC, worldwide sales of big data and business analytics tools are likely to reach $150.8 billion in 2017, which is 12.4 percent higher than in 2016. And the market research firm doesn’t see that trend stopping anytime soon. It forecasts 11.9 percent annual growth through 2020 when revenues will top $210 billion.
Clearly, the trend toward big data analytics is here to stay. IT professionals need to familiarize themselves with the topic if they want to remain relevant within their companies.
What is Big Data Analytics?
The term “big data” refers to digital stores of information that have a high volume, velocity and variety. Big data analytics is the process of using software to uncover trends, patterns, correlations or other useful insights in those large stores of data.
Data analytics isn’t new. It has been around for decades in the form of business intelligence and data mining software. Over the years, that software has improved dramatically so that it can handle much larger data volumes, run queries more quickly and perform more advanced algorithms.
The market research firm Gartner categories big data analytics tools into four different categories:
- Descriptive Analytics: These tools tell companies what happened. They create simple reports and visualizations that show what occurred at a particular point in time or over a period of time. These are the least advanced analytics tools.
- Diagnostic Analytics: Diagnostic tools explain why something happened. More advanced than descriptive reporting tools, they allow analysts to dive deep into the data and determine root causes for a given situation.
- Predictive Analytics: Among the most popular big data analytics tools available today, predictive analytics tools use highly advanced algorithms to forecast what might happen next. Often these tools make use of artificial intelligence and machine learning technology.
- Prescriptive Analytics: A step above predictive analytics, prescriptive analytics tell organizations what they should do in order to achieve a desired result. These tools require very advanced machine learning capabilities, and few solutions on the market today offer true prescriptive capabilities.
Source: Gartner and others
Benefits of Big Data Analytics
Organizations decide to deploy big data analytics for a wide variety of reasons, including the following:
- Business Transformation In general, executives believe that big data analytics offers tremendous potential to revolution their organizations. In the 2016 Data & Analytics Survey from IDGE, 78 percent of people surveyed agreed that over the next one to three years the collection and analysis of big data could fundamentally change the way their companies do business.
- Competitive Advantage In the MIT Sloan Management Review Research Report Analytics as a Source of Business Innovation, sponsored by SAS, 57 percent of enterprises surveyed said their use of analytics was helping them achieve competitive advantage, up from 51 percent who said the same thing in 2015.
- InnovationBig data analytics can help companies develop products and services that appeal to their customers, as well as helping them identify new opportunities for revenue generation. Also in the MIT Sloan Management survey, 68 percent of respondents agreed that analytics has helped their company innovate. That’s an increase from 52 percent in 2015.
- Lower Costs In the NewVantage Partners Big Data Executive Survey 2017, 49.2 percent of companies surveyed said that they had successfully decreased expenses as a result of a big data project.
- Improved Customer Service Organizations often use big data analytics to examine social media, customer service, sales and marketing data. This can help them better gauge customer sentiment and respond to customers in real time.
- Increased Security Another key area for big data analytics is IT security. Security software creates an enormous amount of log data. By applying big data analytics techniques to this data, organizations can sometimes identify and thwart cyberattacks that would otherwise have gone unnoticed.
Big data analytics can offer key advantages across many verticals.
Big Data Analytics Challenges
Implementing a big data analytics solution isn’t always as straightforward as companies hope it will be. In fact, most surveys find that the number of organizations experiencing a measurable financial benefit from their big data analytics lags behind the number of organizations implementing big data analytics. Several different obstacles can make it difficult to achieve the benefits promised by big data analytics vendors:
- Data Growth One of the biggest challenges of big data analytics is the explosive rate of data growth. According to IDC, the amount of data in the world’s servers is roughly doubling every two years. By 2020, those servers will likely hold 44 zettabytes of digital information. To put that in perspective, that is enough data to fill a stack of iPads stretching from the earth to the moon 6.6 times. Big data analytics solutions must be able to perform well at scale if they are going to be useful to enterprises.
- Unstructured Data Must of the data stored in an enterprise’s systems doesn’t reside in structured databases. Instead, it is unstructured data, such as email messages, images, reports, audio files, videos and other types of files. This unstructured data can be very difficult to search—unless you have advanced artificial intelligence capabilities. Vendors are constantly updating their big data analytics tools to make them better at examining and extracting insights from unstructured data.
- Data Siloes Enterprise data is created by a wide variety of different applications, such as enterprise resource planning (ERP) solutions, customer relationship management (CRM) solutions, supply chain management software, ecommerce solutions, office productivity programs, etc. Integrating the data from all these different sources is one of the most difficult challenges in any big data analytics project.
- Cultural Challenges Although big data analytics is becoming commonplace, it hasn’t infiltrated the corporate culture everywhere yet. In the NewVantage Partners Survey, 52.5 percent of executives said that organizational hurdles like lack of alignment, internal resistance or lack of
Big Data Analytics Trends
What’s coming next for the big data analytics market? Experts offer a number of predictions.
Open Source
As big data analytics increases its momentum, the focus is on open-source tools that help break down and analyze data. Hadoop, Spark and NoSQL databases are the winners here. Even proprietary tools now incorporate leading open source technologies and/or support those technologies. That seems unlikely to change for the foreseeable future.
Market Segmentation
Plenty of general-purpose big data analytics platforms have hit the market, but expect even more to emerge that focus on specific niches, such as security, marketing, CRM, application performance monitoring and hiring. Analytics tools are also being integrated into existing enterprise software at a rapid rate.
Artificial Intelligence and Machine Learning
As interest in AI has skyrocketed, vendors have rushed to incorporate machine learning and cognitive capabilities into their big data analytics tools. According to Gartner, by 2020, almost every new software product, including big data analytics, will incorporate AI technologies. In addition, the company says, “By 2020, AI will be a top five investment priority for more than 30 percent of CIOs.”
Prescriptive Analytics
Fueled by this rush to AI, expect companies to become more interested in prescriptive analytics. Seen by many as the “ultimate” type of big data analytics, these tools will not only be able to predict the future, they will be able to suggest courses of action that might lead to desirable results for organizations. But before these types of solutions can become mainstream, vendors will need to make advancements in both hardware and software.
Refocusing on the Human Decision-Making?
As machine learning improves and becomes a table stakes feature in analytics suites, don’t be surprised if the human element initially gets downplayed, before coming back into vogue.
Two of the most famous Big Data prognosticators/pioneers are Billy Beane and Nate Silver. Beane popularized the idea of correlating various statistics with under-valued player traits in order to field an A’s baseball team on the cheap that could compete with deep-pocketed teams like the Yankees.
Meanwhile, Nate Silver’s effect was so strong that people who didn’t want to believe his predictions created all sorts of analysis-free zones, such as Unskewed Polls (which, ironically, were ridiculously skewed). Many think of Silver as a polling expert, but Silver is also a master at Big Data analysis.
In each case, what mattered most was not the machinery that gathered in the data and formed the initial analysis, but the human on top analyzing what this all means. People can look at polling data and pretty much treat them as Rorscharch tests. Silver, on the other hand, pours over reams of data, looks at how various polls have performed historically, factors in things that could influence the margin of error (such as the fact that younger voters are often under-counted since they don’t have landline phones) and emerges with incredibly accurate predictions.
Similarly, every baseball GM now values on-base percentage and other advanced stats, but few are able to compete as consistently on as little money as Beane’s A’s teams can. There’s more to finding under-valued players than crunching numbers. You also need to know how to push the right buttons in order to negotiate trades with other GMs, and you need to find players who will fit into your system.
As Big Data analytics becomes mainstream, it will be like many earlier technologies. Big Data analytics will be just another tool. What you do with it, though, will be what matters.
Big Data Analytics Tools
Big data analytics has become so trendy that nearly every major technology company sells a product with the “big data analytics” label on it, and a huge crop of startups also offers similar tools. Cloud-based big data analytics have become particularly popular. In fact, the 2016 Big Data Maturity Survey conducted by AtScale found that 53 percent of those surveyed planned to use cloud-based big data solutions, and 72 percent planned to do so in the future. Open source tools like Hadoop are also very important, often providing the backbone to commercial solution.
The lists below are not exhaustive, but do include a sampling of some of better known big data analytics solutions.
Open Source Big Data Analytics Tools
Big Data Analytics Vendors
- Zoho Analytics (free trial available)
- Alpine Data
- Alteryx
- Amazon Web Services
- Cloudera
- Datameer
- Domo
- Google Cloud Platform
- HPE
- IBM
- Microsoft
- MicroStrategy
- Oracle
- Pentaho
- SAP
- SAS
- SiSense
- Splunk
- Tableau
- Teradata
- TIBCO Jaspersoft
- VMware
SEE ALLBIG DATA ARTICLES
FAQs
What is big data and analytics? ›
What is big data analytics? Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.
What is the benefit of data analytics? ›Data analytics helps businesses get real-time insights about sales, marketing, finance, product development, and more. It allows teams within businesses to collaborate and achieve better results. It is useful for businesses to analyse past business performance and optimize future business processes.
What is big data analytics simple? ›Big data analytics is the often complex process of examining big data to uncover information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions.
What are the benefit of data? ›Data Provides a Deeper Understanding of Your Market
When you know more about your customers, you can tweak everything about your business to better fit their needs. You can also improve the ways in which you communicate with your target market, optimize your website to improve the user experience, and much more.
- Prescriptive Analytics. ...
- Diagnostic Analytics. ...
- Descriptive Analytics. ...
- Predictive Analytics. ...
- Cyber Analytics. ...
- Interested in learning more about business analytics and data science?
Uses and Examples of Big Data Analytics
Here are some examples: Using analytics to understand customer behavior in order to optimize the customer experience. Predicting future trends in order to make better business decisions. Improving marketing campaigns by understanding what works and what doesn't.
Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't manage them. But these massive volumes of data can be used to address business problems you wouldn't have been able to tackle before.
What is big data analytics and how it works? ›Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions. These processes use familiar statistical analysis techniques—like clustering and regression—and apply them to more extensive datasets with the help of newer tools.
Why is big data important? ›Why is big data important? Companies use big data in their systems to improve operations, provide better customer service, create personalized marketing campaigns and take other actions that, ultimately, can increase revenue and profits.
What is the purpose of big data? ›Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively.
What is the purpose of analytics? ›
Analytics defined
Quite simply, analytics helps us see insights and meaningful data that we might not otherwise detect. Business analytics focuses on using insights derived from data to make more informed decisions that will help organizations increase sales, reduce costs, and make other business improvements.
- Data sources. ...
- Data storage. ...
- Batch processing. ...
- Real-time message ingestion. ...
- Stream processing. ...
- Analytical datastore. ...
- Analysis and reporting. ...
- Align with the business vision.
Big data is classified in three ways: Structured Data. Unstructured Data. Semi-Structured Data.
What are the characteristics of big data analytics? ›Big data is a collection of data from many different sources and is often describe by five characteristics: volume, value, variety, velocity, and veracity.
Where is data analytics used? ›Data Scientists and Analysts use data analytics technology and techniques in their research, and businesses also use it to inform their decisions. Data analysis can help companies better understand their customers, evaluate their ad campaigns, personalize content, create content strategies and develop products.
What is the future of big data? ›In the future, big data analytics will increasingly focus on data freshness with the ultimate goal of real-time analysis, enabling better-informed decisions and increased competitiveness.
What are the data types of big data? ›- Structured data. Structured data has certain predefined organizational properties and is present in structured or tabular schema, making it easier to analyze and sort. ...
- Unstructured data. ...
- Semi-structured data. ...
- Volume. ...
- Variety. ...
- Velocity. ...
- Value. ...
- Veracity.
There are four types of data analytics:
Predictive (forecasting) Descriptive (business intelligence and data mining) Prescriptive (optimization and simulation) Diagnostic analytics.
Big data technologies can be categorized into four main types: data storage, data mining, data analytics, and data visualization [2]. Each of these is associated with certain tools, and you'll want to choose the right tool for your business needs depending on the type of big data technology required.
What are the three characteristics of big data? ›What are the Characteristics of Big Data? Three characteristics define Big Data: volume, variety, and velocity. Together, these characteristics define “Big Data”.
What is an best example of big data? ›
Big Data powers the GPS smartphone applications most of us depend on to get from place to place in the least amount of time. GPS data sources include satellite images and government agencies. Airplanes generate enormous volumes of data, on the order of 1,000 gigabytes for transatlantic flights.
What are big data skills? ›In Big Data Market, a professional should be able to conduct and code Quantitative and Statistical Analysis. One should also have a sound knowledge of mathematics and logical thinking. Big Data Professional should have familiarity with sorting of data types, algorithms and many more.
What is introduction to big data? ›Big data is a collection of massive and complex data sets and data volume that include the huge quantities of data, data management capabilities, social media analytics and real-time data. Big data analytics is the process of examining large amounts of data. There exist large amounts of heterogeneous digital data.
What are the four characteristics of big data? ›- Volume. Volume refers to how much data is actually collected. ...
- Veracity. Veracity relates to how reliable data is. ...
- Velocity. Velocity in big data refers to how fast data can be generated, gathered and analyzed. ...
- Variety.
Big data analytics refers to the method of analyzing huge volumes of data, or big data. The big data is collected from a large assortment of sources, such as social networks, videos, digital images, and sensors.
How data analytics can benefit to organization? ›Data analytics can help an organization understand risks and take preventive measures. For instance, a retail chain could run a propensity model — a statistical model that can predict future actions or events — to determine which stores are at the highest risk for theft.
Is big data analytics a good career? ›Choosing a career in the field of Big Data and Analytics will be a fantastic career move, and it could be just the type of role that you have been trying to find. Professionals who are working in this field can expect an impressive salary, with the median salary for Data Scientists being $116,000.
What is the most important concept of the big data? ›Big data defined
The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs. Put simply, big data is larger, more complex data sets, especially from new data sources.
Diagnostic Analysis, Predictive Analysis, Prescriptive Analysis, Text Analysis, and Statistical Analysis are the most commonly used data analytics types.
Who is the father of big data? ›Advance your data career
Some argue that it has been around since the early 1990s, crediting American computer scientist John R Mashey, considered the 'father of big data', for making it popular.
What are the 4 types of analytics? ›
Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics are the 4 types of analytics used by Business Analysts to unlock raw data's potential in order to improve business performance.
What are the basics of data analytics? ›- Descriptive analytics tell us what happened.
- Diagnostic analytics tell us why something happened.
- Predictive analytics tell us what will likely happen in the future.
- Prescriptive analytics tell us how to act.
Data Analytics is being increasingly leveraged by startups, SMEs, and large organizations to reduce costs, improve customer experience, optimize existing processes and achieve better-targeted marketing. In addition to these, many companies are showing interest in Big Data due to its ability to improve data security.
What are the benefits of business analytics? ›The Benefits of Business Analytics
Improve operational efficiency through their daily activities. Assist businesses to understand their customers more precisely. Business uses data visualization to offer projections for future outcomes. These insights help in decision making and planning for the future.
Benefits of big data:
Cost. Time reduction. Speeding up decision making. Analyze in real-time.
Cities can discover trends and requirements by analyzing data from IoT devices and sensors. The analysis can assist drivers to find a parking place and minimizing the number of road accidents and congestion. Data may also help with crime reduction, smart city lighting, and water and electricity systems.
What are the benefits of big data in healthcare? ›Some of the benefits of Big Data healthcare that the industry has experienced are translated into terms of improved patient experience, prediction of epidemics, avoidance of preventable deaths, improvement of the quality of life, effective surveillance of public health, educated decision-making of policies, and more.
What is an example of data analytics? ›A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it.
Where is data analytics used? ›Data Scientists and Analysts use data analytics technology and techniques in their research, and businesses also use it to inform their decisions. Data analysis can help companies better understand their customers, evaluate their ad campaigns, personalize content, create content strategies and develop products.
What are the advantages and disadvantages of big data? ›- Advantages of Big Data.
- Better Decision Making. ...
- Reduce costs of business processes. ...
- Fraud Detection. ...
- Increased productivity. ...
- Improved customer service. ...
- Increased agility. ...
- Disadvantages.
What are the main components of big data analytics? ›
- Data sources. ...
- Data storage. ...
- Batch processing. ...
- Real-time message ingestion. ...
- Stream processing. ...
- Analytical datastore. ...
- Analysis and reporting. ...
- Align with the business vision.
Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively.
What are the characteristics of big data analytics? ›Big data is a collection of data from many different sources and is often describe by five characteristics: volume, value, variety, velocity, and veracity.
How big data analytics helps businesses increase their revenue? ›The implementation of Big Data Analytics Solutions helps retail businesses to predict customers' demands and empower them to make customer-centric decisions to personalize marketing based on consumer data derived from applications.
How has big data changed the world of business? ›Big data has the power to reduce business costs. Specifically, companies are now using this information to find trends and accurately predict future events within their respective industries. Knowing when something might happen improves forecasts and planning.
Which one of the following options is the most important key benefit of data analysis? ›One of the main benefits of Big Data analytics is that it improves the decision-making process significantly. Rather than relying on intuition alone, companies are increasingly looking toward data before making a decision.
What is data analysis explain in detail? ›Data analysis, is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. Data, is collected and analyzed to answer questions, test hypotheses, or disprove theories.
What is data analyst job? ›A data analyst is a person whose job is to gather and interpret data in order to solve a specific problem. The role includes plenty of time spent with data but entails communicating findings too. Here's what many data analysts do on a day-to-day basis: Gather data: Analysts often collect data themselves.
What is an example of big data in healthcare? ›Medical researchers can use large amounts of data on treatment plans and recovery rates of cancer patients in order to find trends and treatments that have the highest rates of success in the real world. For example, researchers can examine tumor samples in biobanks that are linked up with patient treatment records.