Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.
Big Data Analytics deals with the use of a collection of statistical techniques, tools, and procedures 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. Big data analytics is a form of advanced analytics, which involve complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by analytics systems. Organizations can use big data analytics systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization and improved operational efficiency. It is because of the emergence of new data sources such as social media, and IoT data that big data and analytics have become popular.
Intelligence and military applications rely on massive data pipelines to drive intelligence gathering and mission-critical decision-making. With future conflicts likely to take place in megacities and highly populated areas, deployed military commanders will need access to local sensors, including not only cameras but also sensors or data feeds related to other essential activities in or around a city. Modern warfare on the big data battlefield relies on insights extracted from ever-growing volumes of unstructured, time-critical Big data. The speed at which the warfighter is able to collect, process, analyze, and understand data directly impacts mission success.
The challenges of big data
Big data is a combination of structured, semistructured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications. The challenges of big data break down into five fundamental areas – volume, variety, velocity, veracity and value, also known as the five Vs.
Volume: Datasets are often massive. Storing and moving this data without inundating existing IT infrastructure becomes a challenge without the proper hardware. Volumes of data are growing exponentially, necessitating scalable solutions.
Velocity: Analysis is most useful when it’s timely, driving real-time critical thinking and decisions. Important factors that can hamper data processing include insufficient bandwidth, improper communications infrastructure, weather, and outdated hardware.
Variety: Data comes from a variety of sources and arrives in both structured and unstructured forms. Unstructured data – such as surveillance imagery, sensor readings, and human-generated content – is the most challenging to analyze. Without the proper software tools and analysis techniques, critical information may never surface from the chaotic mix of collected data.
Veracity: Collected data must be clean and accurate. The hardware that safeguards data contributes to veracity by ensuring all data is reliably and securely stored. Information warfare (IW) and cyberattacks represent growing threats to veracity because they pose the risk of lost or altered mission-critical data.
Value: The most important of the 5 Vs. Data is useless unless you can gain insight into its value. For example, users cannot deploy resources or make key operational decisions without understanding the risks, costs, and benefits to the mission.
Big Data Sources
There are primarily three sources of Big Data. These are enlisted below:
- Social Data: Data generated because of social media use. This data helps in understanding the sentiments and behavior of customers and can be useful in marketing analytics.
- Machine Data: This data is captured from industrial equipment and applications using IoT sensors. It helps in understanding people’s behavior and provides insights on processes.
- Transactional Data: It is generated as a result of both offline and online activities of users regarding payment orders, invoices, receipts, etc. Most of this kind of data needs pre-processing and cleaning before it can be used for analytics.
Big Data Analytics Uses
1) Customer Analytics
Big Data Analytics is useful for various purposes, such as micro-marketing, one-to-one marketing, finer-segmentation, and mass customization for the customers of a business. Businesses can create strategies to personalize their products and services according to customer propensities to up-sell or cross-sell a similar or different range of products and services.
2) Operation Analytics
Operation analytics helps in improving the overall decision making and business results by leveraging existing data and enriching it with the machine and IoT data. For example, big data analytics in healthcare have made it possible to face challenges and new opportunities related to optimization of healthcare spending, improving the monitoring of clinical trials, predicting and planning of responses to disease epidemics such as COVID-19.
3) Fraud Prevention
Big data analytics is seen with the potential to deliver a massive benefit by helping to anticipate and reduce fraud attempts, primarily in the financial and insurance sectors. For example, Insurance companies capture real-time data on demography, earnings, medical claims, attorney expenses, weather, voice recordings of a customer, and call center notes. Specific real-time details help derive predictive models by combining the information mentioned above with historical data to identify speculated fraudulent claims early.
4) Price Optimization
Companies use big data analytics to increase profit margins by finding the best price at the product level, and not at the category level. Large companies find it too overwhelming to get the granular details and complexity of pricing variables, which change regularly for thousands of products. An analytics-driven price optimization strategy, such as dynamic deal scoring, allows companies to set prices for clusters of products and segments based on their data and insights on individual deal levels to score quick wins from demanding clients.
The global big data market size to grow from USD 138.9 billion in 2020 to USD 229.4 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 10.6% during the forecast period.
The major growth factors of the big data market include the increasing awareness of Internet of Things (IoT) devices among organizations, increasing availability of data across the organization to gain deeper insights to remain competitive, and increasing government investments in various regions for enhancing digital technologies.
The elevating number of virtual online offices coupled with increasing popularity of social media producing an enormous amount of data is a major factor driving growth. Increased internet penetration owing to the several advantages including unlimited communication, abundant information and resources, easy sharing, and online services generates huge chunks of data in everyday life, which is also anticipated to propel demand over the coming years. Additionally, mounting demand for mobile devices and apps has further paved the way for big data significantly for the next few years.
The demand for technologies like big data, IoT, machine learning, artificial intelligence, and other integrated advanced technologies will help the market to grow rapidly and also open up new job opportunities in most industries. The big data market is rapidly becoming an emerging area of focus across numerous end-use industries. Big data can enable companies to increase operational efficiencies and reduce costs.
Big data solutions and services store and analyze structured and unstructured data from IT operations and turn it into relevant information and insights. Companies with the help of these solutions obtain both efficiency and quality in managing a large volume of raw information, ultimately resulting in significant cost reduction. Big data implementation assists companies to strike the right balance between operational cost, speed, flexibility, and quality. Numerous companies are implementing big data solutions and services to evaluate their internal processes and enhance operations. For instance, according to the report by World Economic Forum in 2018, 85% of companies will adopt data analytics and big data by 2022, which will generate jobs with related roles.
The growing volume of raw data from various sources will help to boost the growth of the data analytics market. For instance, the healthcare segment has a large amount of unstructured and structured data which need to be analyzed in order to make quick decisions. Other business segments where large amount of data need to be handled rapidly are retail industry, media & entertainment, IT sector, and others. However, the cost of data storage and data tools are high. Further, the privacy issues related to data extraction are restraining the growth of the market.
By component, the big data market is divided into the following segments: Solutions, Big Data Analytics, Data Discovery, Data Management
Data Visualization, Services (Managed Services, Profession Services, Consulting, Deployment and Integration, Support and Maintenance ).
The big data analytics segment is expected to hold the largest market size during the forecast period. Big data solutions enable more precise segmentation of potential buyers and facilitate a deeper understanding of those buyers, their needs, and motivations by analyzing the data generated from various sources, such as social media, call logs, and service forms. Big data solutions enable data experts to understand various trends, such as identifying financial growth opportunities, financial benchmarking against industry standards, and identifying financial implications.
Based on type, the data analytics market is segmented into predictive, prescriptive, descriptive, customer, and others. The rise in the adoption of machine learning and artificial intelligence in predicting financial scenarios will contribute to the growth of predictive analytics. The COVID-19 pandemic has also forced various business segments to adopt these advanced technologies to get the overview of future trends.
Based on solution, the data analytics market is segmented into data management, fraud and security intelligence, data monitoring, and data mining. The data management segment is expected to become the largest segment during the forecast period. The process of acquiring, storing, validating, protecting and processing raw data into reliable data is necessary in every business segment.
Based on deployment, the data analytics market is segmented into on-premises and cloud. Cloud deployment segment is expected to grow with the highest CAGR during the forecast period. The current COVID-19 pandemic is forcing the employers to work remotely with the help of cloud-based data analysis, which will fuel the growth of data analytics market. The managing of one or more business segments from single cloud service driving companies to adopt automated cloud orchestration and optimization. This in result will boost the growth of cloud segment in data analytics market.
Based on function, the data analytics market is segmented into marketing analytics, sales analytics, accounting & finance analytics, and others. Marketing analytics is expected to grow significantly in during the forecast period. Data analytics will help the companies to create business models to target their audience for marketing and sales purposes.
Based on organization size, the data analytics market is segmented into large enterprise and small & medium enterprises. Large enterprises are expected to grow continuously during the forecast period. Factors like huge amount of data generated from various business segments and customers, and quick delivery of services and products will boost the growth of data analytics in large organizations.
By deployment mode, the big data market is divided into the following segments: Cloud, Hybrid Cloud, Private Cloud, Public Cloud, On-Premises. Big Data applications in the conventional on-premise environment, can be difficult to implement and manage. Add to this, the exponential growth of data along with the cost of implementing these technologies can be a huge burden. In such scenarios, the cloud can help alleviate some of these hurdles. Cloud’s promise of agility, scale, and flexibility, is expected to increase the Big Data and Business Analytics market size.
Most vendors in the big data market offer cloud-based big data solutions to maximize profits and automate the equipment maintenance process, effectively. The adoption of cloud-based big data solutions is expected to grow, owing to benefits, such as easy maintenance of generated data, cost-effectiveness, agility, flexibility, scalability, and effective management of these solutions. Companies prefer to adopt cloud-based big data solutions, as these support their regional, cross-regional or cross-country data recovery strategies. This enables them to ensure resilience in case of disasters.
North America is expected to hold the highest share in the global big data market, while Asia Pacific (APAC) is expected to grow at the highest CAGR during the forecast period. North America is the most significant revenue contributor in the global big data market. The region is witnessing significant developments in the big data market. In North America, the high growth rate can be attributed to increasing adoption of IoT devices by various businesses in the region. Organizations, especially in the US, have started using big data solutions to generate data insights for making strategic business decisions and remaining competitive in the market.
China’s big data market will continue to expand in the coming years, driven by the country’s economic development and digital transformation, an industry report shows. The size of China’s big data market was estimated to exceed 10 billion U.S. dollars for the first time in 2020, up 15.9 percent year on year, according to a report from global market research firm International Data Corporation (IDC). Banking, telecommunication and local governments accounted for 38 percent of total spending in the big data market last year, and these sectors will continue to lead in big data spending, read the report.
Key market players
Major vendors in the global big data market include Microsoft (US), Teradata (US), IBM (US), Oracle (US), SAS Institute (US), Google (US), Adobe (US), Talend (US), Qlik (US), TIBCO Software (US), Alteryx (US), Sisense (US), Informatica (US), Cloudera (US), Splunk (US), Palantir Technologies (US), 1010data (US), Hitachi Vantara (US), Fusionex (Malaysia), Information Builders (US), AWS (US), SAP (Germany), Salesforce (US), Micro Focus (UK), HPE (US), MicroStrategy (US), ThoughtSpot (US), and Yellowfin (Australia). These vendors have adopted various organic and inorganic growth strategies, such as new product launches, partnerships and collaborations, and mergers and acquisitions, to expand their presence in the global big data market.
Big Data Solution Providers
Some of the top big data solutions in the market are:
Amazon Web Services: AWS’s solutions for big data include cloud storage, databases, data warehousing, analytics, and machine learning services. The company offers a wide range of products and services to customers present in 190 countries. Amazon’s product portfolio comprises segments, such as compute, storage, database, migration, network and content delivery, developer tools, management tools, media services, Machine Learning (ML), and analytics. Additionally, the solutions segment offers website and web apps, mobile services, back-up, storage and archive, financial services, and digital media.
It caters to various industry verticals, such as media and entertainment, automotive, education, BFSI, game tech, government, healthcare and life sciences, manufacturing, retail, telecommunications, oil and gas, and power utilities. In big data market, its offerings include Amazon QuickSight, Amazon S3, Amazon Glacier, AWS Glue, Amazon Athena, Amazon EMR, Amazon Redshift, Amazon Kinesis, and Amazon Elasticsearch Service.
Hitachi Vantara: This lineup features big data storage, DataOps, IoT services, and big data analytics.
Tableau: The Salesforce-acquired tool offers big data analytics, business intelligence, and data visualizations
Cloudera: This big data platform offers a Hadoop distribution, plus data science and analytics tools.
Microsoft Azure: The cloud platform offers storage, big data analytics, machine learning, data warehousing, and data lakes.
IBM: IBM’s big data solutions include cloud services, database management, data warehousing, analytics, and machine learning.
Oracle: The Oracle suite of big data solutions includes cloud-based and on-premises database management, data integration, and analytics.
Splunk: This offering primarily focuses on analytics for log and security data.
Talend: The solution features a set of big data integration tools.
RapidMiner: The data science platform includes data mining, predictive analytics, and machine learning solutions
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