What is Big Data Analytics?

Decades before the term ‘big data’ came into existence, businesses used basic data analytics – numbers in spreadsheets – to gain meaningful insights on customer behaviour. Today, the advancement of technology has reached a point where the volumes of data have become considerably massive. And as enterprises – large and small – are well-aware of the fact that data empowers most of their business operations, they are adopting big data analytics to capture and gain actionable insights from all the data that streams into their businesses.

Unlike the yesteryears, big data analytics now brings new benefits such as agility and efficiency; transforming the way enterprises delve deep into the ocean of ‘data’. Precisely speaking, Big data analytics is described as the use of advanced analytic techniques such as machine learning, text analytics, predictive analytics, data mining, statistics, and language processing to declutter and arrange massive, diverse data sets. This is because these data sets have become complex, making it impossible for traditional databases to capture, manage, and process with agility.

Endowed with the efficiency of numerous sensors, devices, applications, networks, log files, web, and social media, big data analytics processes data sets in real time; and on a large scale. Eventually, it is this streamlined analysis that enables data analysts, researchers, and enterprises to make better and faster business decisions.

What are the Basic Characteristics of Big Data?

Due to the complexity of data and analytics procedures involved, big data is plainly categorized into three V’s:

Volume: The massiveness of data has caused enterprises to face numerous challenges when it comes to its storage and processing. In fact, today’s ongoing popular trends such as e-commerce, social media and the Internet of Things (IoT) are constantly producing large amounts of complex information; making the volume a matter of concern for almost every organization across the globe.

Velocity: Velocity is another key feature associated with big data. Organizations generating new data at rapid speeds oftentimes need to make business-critical decisions in real time. Injecting the power of analytics, big data offers this velocity with its fast-paced data processing capabilities.

Variety: When it comes to big data, it is quite obvious that the massive volumes of data come in a variety of formats — such as email messages, images, videos, spreadsheets and the data that resides on structured databases.

Why is Big Data Analytics so Important for Businesses?

As we already know, big data paves a productive path towards greater profits and happy customers for organizations worldwide. Although the benefits are numerous, below mentioned are the key ones that make the maximum impact.

  • improved efficiency

    Exceptional Accuracy

    In an ideal scenario, working with data brings the imminent risk of inaccurate or incomplete data that misleads businesses to make bad decisions. But, when it comes to accuracy, big data analytics has incredibly reduced these risks by generating more accurate and holistic view of business data. With the right analytics platform, businesses can gather data from a plethora of trusted sources.

  • improved efficiency

    Cost Reduction

    Ever emerging technologies such as Hadoop and cloud-based analytics is cost-effective when it comes to data storage in massive amounts. Additionally, these technologies help identify smarter ways of doing business; boosting the overall revenue.

  • improved efficiency

    Scope for New Products and Services

    Big Data analytics closely analyzes the customer’s behavior data, preferences and browsing habits. In an innovative manner, this process helps organizations identify their customer’s fluctuating needs and enables them to introduce new products and services — to give the customers exactly what they want.

Big Data Challenges

  • improved efficiency

    Staying Ahead of the Data Growth

    As we already know, dealing with big data clearly means constantly dealing with the exponential data growth. This obviously involves monitoring and processing the massive pool of data on a regular basis; also keeping the security aspects in mind.

    Considering the current situation, enterprises are trying to adopt a wide array of technologies — to tackle data growth. Talking about storage, they are implementing converged and hyper-converged infrastructure, and software-defined storage that streamlines the way companies scale their hardware. Moreover, technologies such as compression, deduplication and tiering help them reduce the amount of space and costs associated with big data storage. Focusing on management and analysis, enterprises are deploying NoSQL databases, Hadoop, Spark, big data analytics software, business intelligence applications, artificial intelligence and machine learning tools.

  • improved efficiency

    Generating Real-time Insights

    When it comes to big data, the final goal of every organization is - to make quick and better decisions that drive business processes. And, generating insights based on reports is the first step to achieve that feat. Organizations are constantly in search of the best-of-breed analytics tools that can effectively reduce the timeframe of report generation. This can only be made possible with software integrated with real-time analytics capabilities that empower agility.

  • improved efficiency

    Hiring Skilled Big Data Analysts

    Most business decisions rely on big data analytics, enterprises are exploring new ways to make the most out of the technology. However, as the data gets more and more complex, big data analytics requires being handled by experienced professionals. Today, enterprises – apart from training their employees – are also hiring big data analysts who can manage data in a streamlined manner. Consequently, this has paved a path for numerous job opportunities worldwide — filling the void for fresh talents.

  • improved efficiency

    Data Integration

    Technically, the variety of data formats associated with big data oftentimes lead to challenges in data integration; meaning - combining that pool of data to generate reports is an incredibly difficult task. To tackle this, numerous big data solution vendors are offering the best-in-class data integration tools to simplify and add agility to the overall process.

  • improved efficiency

    Data Validation

    In big data analytics, with large data streams, comes the risk of inaccuracy and incompleteness of data sources. To be precise, the trustworthiness of big data is negatively impacted by these 4 key factors:

    • Errors in incoming data that goes undetected
    • Desynchronized data sources
    • Operating on multiple big data platforms
    • Structural change in the data

  • improved efficiency

    Data Security

    Data security is another obvious challenge when it comes to dealing with massive amounts of data. Analytic tools that handle unstructured big data and databases like NoSQL are newer technologies that are still undergoing development. Hence, protecting the new data sets can turn out to be a strenuous process for most security software.

    So, in a bid to avoid potential breaches of business-critical data, enterprises are now trying to adopt and deploy big data solutions that are integrated with the latest security features.

Open Source Big Data Analytics Tools

Although the availability of big data analytics tools – focused on specific tasks – is enormous, open source tools are also flexible in all aspects. As for today, the below mentioned open source tools are widely implemented by enterprises:

  •  Apache Hadoop
  •  Apache Storm
  •  Apache Samoa
  •  HPCC
  •  Lumify
  •  Elasticsearch
  •  RapidMiner
  •  R-Programming
  •  MongoDB
  •  Talend Open Studio

Techniques used in Big Data Analytics

Enterprises are leveraging the efficiency of big data analytics by deploying some innovative techniques; such as:

  •  Association Rule Learning
  •  Classification tree analysis
  •  Machine learning
  •  Genetic algorithms
  •  Regression analysis
  •  Sentimental Analysis
  •  Social network analysis

Applications of Big Data Analytics in Diverse Industries

As big data analytics continues to evolve, industries are gradually accepting the fact that this can be a game-changer for them. Over the past few years, big data analytics has been used in the diverse industrial realms to achieve a common goal - declutter their underlying data and gain meaningful insights that could lead them towards higher productivity.

The industries are:

  •  Banking
  •  Communications, Media, and Entertainment
  •  Healthcare
  •  Manufacturing
  •  Education
  •  Government
  •  Retail and wholesale Trading
  •  Insurance
  •  Energy and Utility
  •  Transportation

Things to Consider Before Implementing Big Data Analytics

Analyze your Enterprise Needs

Before you even start considering big data analytics solution, try asking this simple question: Do we really need this? 

If the answer is yes, you might also want to consider the following questions:

  •  What issues are we trying to address the solution?
  •  Does the solution support/streamline business-critical operations?
  •  Will the solution drive profits in the long run?

What Does the Future Hold for Big Data Analytics?

According to IDC, the worldwide revenues for big data and analytics is expected to reach a staggering $203 billion mark in 2020, at a compound annual growth rate (CAGR) of 11.7 percent. The firm further adds - industries such as banking, manufacturing, government, and professional services will drive a major part of this growth.

Dan Vesset, Group Vice President of Analytics and Information Management at IDC highlights:

The availability of data, a new generation of technology, and a cultural shift toward data-driven decision making continue to drive demand for the big data and analytics technology and services market.

The Final Say

Data – as we know – is transforming the way enterprises deal with business operations; due to the rapid advancements in three major areas - bandwidth, processing power, and storage. Looking into the future, today’s “big data” will definitely be incomparable with respect to the massiveness of data that will be generated — by billions of devices, and ever-evolving tech products such as smartphones, wearables, and Internet of Things (IoT) devices.

Originally published , updated March 15 2018