“Information is the oil of the 21st century, and analytics is the combustion engine”
This statement by Gartner’s former Executive Vice President, Research & Advisory Peter Sondergaard signifies the power held by analytics in rendering big data more meaningful, insightful, and relevant to the current business context. The world today is driven by voluminous data streaming in from countless structured and unstructured sources.
This “big data” fuels most of 21st century’s technology innovations such as Cloud Computing, Artificial Intelligence (AI), Machine Learning (ML), Blockchain, the Internet of Things (IoT), etc. While data itself is the backbone of business intelligence, in its raw form it is just an elephant in the room. To really work its charm, it has to be mined and processed to garner specific patterns and meaningful insights.
What is Big Data Analytics and Why Is It So Relevant in Today’s Context?
In its most comprehensive definition, big data analytics is essentially advanced analytics involving complex tools and applications, statistical algorithms, and predictive modeling propelled by high performance analytical systems. Simply put, analytics is the process of minutely analyzing large and complex datasets gathered from varied sources such as social networks, digital platforms, internet data, web logs, customer surveys, sales records, IoT data captured by sensors, etc.
The main objective of analytics is to derive critical information such as customer preferences, hidden data patterns and correlations, and current market trends to help organizations make informed business decisions.
Organizations the world over are realizing the importance of running analytics applications to interpret moving through the enterprise in different forms and silos. Applications across big data analytics help data analysts and scientists, statisticians, and predictive modeling professionals to expertly analyze all forms of untapped data floating across the organization.
An integration and critical analysis of structured, semi-structured, and unstructured enterprise data enables organizations to acquire the necessary actionable insights and leverage these for making strategic business decisions.
History and Evolution of Big Data and Analytics
The concept of big data was initially introduced somewhere in the mid-nineties and referred to increasing volumes of data. In the early 2000s, thus the term was expanded to encompass variety as well as velocity in data creation.
Consequently, three key dimensions of big data were identified – volume (amount of data gathered), variety (types of data gathered), and velocity (speed of data processing). This came to be known as the 3Vs of big data – volume, variety, and velocity – a concept widely popularized by Gartner in the 2000s.
Big data analytics reached a different level with the introduction of the Hadoop framework in 2006. Launched as an Apache open source distributed processing framework, Hadoop enabled organizations to run complex big data applications on a clustered platform built using commodity hardware.
As Hadoop and related technologies continued to mature and evolve within the ecosystem, advanced analytics brought in more speed and agility, enabling organizations to stay ahead of the competitive curve.
Importance of Big Data Analytics for Global Organizations
Advanced data analytics encompasses highly specialized software and solutions backed by powerful cloud-based computing systems. This enables organizations to harness enterprise data in the right manner; validate existing data models; and leverage new information to make smarter business decisions – in turn maximizing profitability.
Big data analytics helps organizations derive value in terms of:
- Increased growth opportunities
- Improved operational efficiencies
- Enhanced go-to-market initiatives
- Superior customer service
- Reduced costs of storing large volumes of data
- Speedy and instantaneous decision making using in-memory analytics
- Targeted launch of new products and services in alignment with customer needs
- Increased competitive edge in the market
Big Data Analytics: Typical Industry Use Cases
Big data analytics has found widespread application across multiple industry sectors including banking, manufacturing, healthcare, retail, government, etc.
Banking & Financial Services
Big data analytics enables banks and financial institutions to make sound financial decisions by providing robust analytical insights on large volumes of unstructured customer data.
Analytics helps manufacturers save costs and augment revenues by providing deep insights on complex supply chains, IoT systems, and equipment health and maintenance.
Management of patient health records, medical insurance information, and other patient health data can be overwhelming given the enormity of the available information.
Application of advanced analytics enables healthcare professionals to garner useful insights that can then be used to provide faster diagnoses and treatment options.
Customer satisfaction is a key retail success imperative and customers today have become more demanding in terms of their personal needs and brand preferences. By leveraging big data and analytics, retailers can now study consumer buying behavior and predict key purchase trends enabling them to send out personalized product recommendations and thereby boost the customer satisfaction index.
Most government institutions especially law enforcement agencies are often challenged with boosting productivity while maintaining tight budgets. Big data analytics tools help government agencies by streamlining core operations and providing comprehensive insights that facilitate speedy and accurate decision making.
The Actual Working of Big Data Analytics
The world we live in is a big data warehouse. There are trillions of petabytes of data generated every day and brands leverage insights from this data to improve their product and service offerings and thereby enhance customer experiences. Technology has not only greatly influenced how we live and carry out our daily activities; it has further enabled a systematic collection and analysis of information capable of altering our lives dramatically.
almost every individual uses a smartphone and is connected to the internet through some device or the other. Social media has become a game changer in the way people interact with their family, friends, co-workers, and the brands they use every day. This complex interconnectivity has fueled a massive data explosion across digital and social channels. Brands gather this big data, apply advanced analytics, and extract pertinent insights that enable them to serve consumers better.
Technologies such as Artificial Intelligence and Machine Learning have created newer paradigms of customer satisfaction by helping brands create more personalized shopping experiences.
Organizations deploy big data analytics to study basic human behavior and intrinsic life patterns to improve their products and services thereby impacting every aspect of our lives.
Types of Big Data Analytics
Big data analytics can be broadly classified into the following types, and algorithms play a very important part in ensuring successful implementation of the right type of analytics relevant to the organization’s primary needs.
As the name indicates, the future path is predicted in advance by answering critical ‘why’ and ‘how’ questions that reveal specific data patterns. Advanced Machine Learning technologies are applied to learn on the go as new data patterns present themselves.
This involves studying past data and identifying the cause of the occurrence of specific events. Diagnostic analytics – also called behavioral analytics – identifies and eliminates analytical loopholes and provides actionable insights after answering the ‘why’ and ‘how’ questions systematically.
This type focuses on specific analyses based on a fixed set of rules and recommendations to prescribe a clear analytical model for the business. Prescriptive analytics facilitates automation of decision making – advanced heuristics and neural networks are applied to existing analytics algorithms to provide recommendations on the best actions capable of achieving desired business outcomes.
This type involves the mining of data coming into the enterprise and applying analytics to derive a description based on the type of data gathered. Descriptive analytics answers the ‘what happened’ question to provide a high-level overview of the business landscape.
Benefits and Challenges Associated with Big Data Analytics
Implementing a robust big data analytics solution has become an integral component of business strategy and enterprises worldwide are reaping the myriad advantages of data analytics. However, before actually running a full-fledged implementation, it is important to understand some of the inherent benefits and challenges associated with big data analytics deployment.
- Enhanced decision making backed by data-driven business insights
- Increased productivity and operational efficiency through advanced big data analytics tools and technologies
- Reduced costs of operation owing to increased efficiencies
- Superior customer service achieved using data insights to launch new products and send out personalized recommendations
- Easy detection of fraud especially in information sensitive industries such as banking and healthcare
- Increased organizational growth and revenue owing to superior decision making and enhanced customer service
- Focused innovation through timely and speedy insights into global market trends
- Lack of talent with necessary skillsets and high costs involved in hiring and training qualified data professionals (data scientists, data analysts, experts )
- Issues pertaining to data quality arising from deploying analytics on inaccurate, irrelevant data in an improper format
- Compliance issues on account of inability to meet industry standards and government regulations pertaining to sensitive personal data
- Risks pertaining to cybersecurity especially concerning the storing of sensitive data that may be subject to hacking
- Rapidly evolving technologies in the global ecosystem rendering erstwhile investments close to obsolete
- High costs pertaining to IT infrastructure (datacenters, network bandwidth, ), hardware maintenance, staffing, etc.
- Issues pertaining to integration of legacy enterprise systems incorporating siloed datasets with advanced analytics platforms
Big Data or Data Science or Data Analytics? Is There a Difference?
The massive data explosion especially over the last decade has opened newer vistas in the field such as data analytics and data science, and big data analytics is usually associated with data science. While these terminologies are used interchangeably, each concept functions in a unique manner within the data technology landscape.
|Big Data||Data Science||Data Analytics|
|Refers to the voluminous structured, semi- structured, and unstructured data generated through multiple social, digital, and online sources||Includes the process of slicing and dicing large volumes of data and deriving value- based insights and trends using advanced technologies||Provides actionable business intelligence by studying historical and current enterprise data to predict future outcomes|
All three concepts are relevant within the realm of data and are highly impacting global business operations significantly. Organizations are fast moving from being product-centric to data-centric – using every piece of available customer and market information to improve their products and services, provide superior customer service, and beat competition.
How Can You Grow Your Business Using Data Science?
The advent of new-age technologies such as IoT, AI, and ML has streamlined big data analytics and data science implementation across industries. Data science benefits all types of organizations – size and business notwithstanding – in several tangible ways.
- Enables the leadership to make informed business decisions
- Helps validate critical business decisions by providing deep data insights
- Identifies key market trends to stay ahead of competition
- Enhances operational efficiency and business productivity
- Enables the deployment of low-risk, data-enabled action plans
Top Big Data Analytics Tools and Technologies
Big data analytics does not incorporate any one single solution or technology. In fact, it is a combination of several advanced tools and technologies that work in tandem to derive maximum value from the data analyzed.
|Apache Technology Stack||Big Data Tools and Platforms||Programming Languages|
|Apache Spark||Splunk||R Programming|
Nowadays, professionals use Hadoop deep lake architectures that serve as a primary vault for storing incoming raw data. Data management is of crucial importance in the data analytics process and the gathered data should be well stored, organized, properly formatted and configured, and partitioned to achieve best performance. The stored data is then ready for analysis using advanced analytics software incorporating tools for the following:
Data Mining – sifting through large datasets to uncover patterns for further processing and analysis
Predictive Analytics – building advanced data models that forecast future customer behavior
Machine Learning – training machines to learn in real time for analyzing bigger, complex datasets
In-memory Analytics – analyzing voluminous data from system memory to test newer scenarios and create viable data models
Text Mining – analyzing textual data from books, surveys, the internet, and other text-based data sources
Data Analytics Software for 2020 and Beyond
Below is a list of some of the top data analytics software likely to be deployed by most organizations in the coming years.
- Apache Hadoop – open source solution for storage and processing of large datasets within huge complex computing clusters
- IBM Watson – AI enabled cloud analytics platform for automated predictive intelligence and data discovery
- Google Analytics – most popular dashboard-based web analytics tool for tracking and reporting website traffic
- SAP Business Intelligence Platform – an advanced business intelligence solution to monitor key customer metrics for analyzing customer behavior
- Zoho Analytics – a collaborative business data analytics platform for generating reports to arrive at data-driven decisions
- GoodData – an end-to-end cloud-based system with embedded analytics for providing industry-specific data analytics solutions
- IBM Analytics – a prescriptive and predictive data analytics tool for providing evidence- based insights to support crucial decision making
Trends in Big Data and Analytics: What Lies Ahead?
2019 witnessed an operationalization of enterprise systems with analytics largely driven by automation frameworks. Another notable development was the mass consolidation of vendors providing big data solutions, leaving the market open only for the innovators and true game-changers. Integration of AI and ML with traditional data analytics solutions reached significant heights to drive operational efficiencies across the business value chain.
While these trends continue to evolve, there are certain profound advancements anticipated to massively impact the world of big data analytics.
1. Burgeoning adoption of IoT and digital twins:
IoT data analytics continues to skyrocket at immense speed, with the emerging concept of digital twins reaching faster adoption among organizations. Digital twins are simply digital replicas of physical objects, systems, and people; and are powered by real-time sensor gathered data. Extracting value from all this data requires integration onto an advanced data platform and it is here that digital twins will create immense business opportunities in future.
2. Augmented analytics:
The future belongs to augmented data streams where analytics systems will deploy AI and ML technologies to preempt key insights. Gartner predicts the rise of ‘citizen data scientists’ with augmented analytics, making users easily query data using Natural Language Processing (NLP).
3. Monetization of dark data:
Gartner defines dark data as routine business information collected, processed, and recorded purely to meet compliance standards; and usually takes up a huge storage space. The coming years will witness organizations tapping into their dark data by digitizing analog enterprise records and integrating this data into their analytics platform to derive pertinent business insights.
4. Optimization of cloud costs by deploying cold storage:
The future is all about cost-optimized cloud systems with organizations moving towards cold data storage such as Google’s Nearline and Coldline and Azure Cool Blob to store historic and unused data leading to as much as 50% savings on data storage costs5.
Demand for integration and governance tools along with the inherent complexities in existing data pipelines have led to the emergence of DataOps. DataOps incorporates DevOps and Agile methodologies in the entire big data analytics lifecycle and deploys automated mechanisms for testing and delivery to provide quality insights.
The next chapter of evolution in big data and analytics is already presenting itself before the world. Organizations are quick in the adoption of newer technologies, tools, and concepts that promise enhanced data quality, more insightful metrics, and fact-based predictive analytics capable of fueling informed business decisions. Digital transformation will revolutionize big data strategies and organizations will invest in platforms and solutions that cater to multiple business use cases. Data will get larger than life in the coming years and analytics will play an important role in shaping future pathways in a densely interconnected digital ecosystem.