The market size of the Global Big Data Analytics market is estimated to be USD 11.02 Billion in 2018 and is expected to rapidly grow at a CAGR of 29.7% and is expected to reach a market size of USD 68.04 Billion in 2025.
- Definition / Scope
- Market Overview
- Market Risks
- Top Market Opportunities
- Market Drivers
- Market Restraints
- Industry Challenges
- Technology Trends
- Regulatory Trends
- Other Key Market Trends
- Market Size and Forecast
- Market Outlook
- Technology Roadmap
- Distribution Chain Analysis
- Competitive Landscape
- Competitive Factors
- Key Market Players
- Strategic Conclusion
Definition / Scope
Big data is generally termed as large and varied sets of data and examining the data for identifying customer trends and preferences, data patterns, relations for the companies to take better business decisions.
Companies use advances analytics techniques for large, and diversified data sets, allowing the researchers, analysts and business owners to make decisions based on the data that was previously inaccessible. Big data comprises of software and hardware components and the data set ranges from Megabytes to Terabytes.
A many number of companies are investing in the big data analytics, which has led banking and manufacturing industry to invest more in big data analytics majorly due to security and compliance issues. Many other industries other than banking and manufacturing are more inclined towards finding the customer experience information to better use the data for customer retention and customer delight.
On a broad scale, data analytics technologies and techniques provide a means to analyze data sets and draw conclusions about them to help organizations make informed business decisions. BI queries answer basic questions about business operations and performance.
Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by high-performance analytics systems.
Big data analytics finds its application in almost all fields across the globe from healthcare to finance, from Science and Research to Law Enforcement.
The value of Big data analytics is being felt across all verticals and horizontals of businesses and services across the globe and is gaining prominence because of the benefits offered by it including new revenue opportunities, more effective marketing, better customer service, improved operational efficiency and competitive advantages over rivals.
Big Data Statistics
- Google is more than 1 million petabytes in size and processes more than 24 petabytes of data a day, a volume that is thousands of times the quantity of all printed material in the U.S. Library of Congress.
- 32 billion searches are performed each month on Twitter.
- More than 1 billion unique users visit YouTube each month and over 6 billion hours of video are watched each month on YouTube – that’s almost an hour for every person on Earth, and 50% more than last year.
- 90 percent of the data in the world today has been created in the past two years.
- In 2012, data was forecasted to double every two years through the year 2020.
- In 2020, the amount of digital data produced will exceed 40 zettabytes, which is the equivalent of 5,200 gigabytes for every man, woman and child on planet earth.
- 1 Gigabyte = Approximately 1 full-length feature film in digital format; 1 Petabyte= One Million Gigabytes or a Quadrillion Bytes; 1 Exabyte = One Billion Gigabytes; 1 Zettabyte = One Trillion Gigabytes or One Million Petabytes.
The Big Data Analytics market is classified into two categories: Data Discovery and Visualisation (DDV) and Advanced Analytics (AA).
- The market size of the Global Big Data Analytics market is estimated to be USD 11.02 Billion in 2018
- The estimated CAGR growth rate of the Global Big Data Analytics market is 29.7%
- The market size of the Global Data Discovery and Visualisation market is estimated to be USD 6.06 Billion
- The estimated CAGR growth rate of the Global Data Discovery and Visualisation market is 32.3%
- The market size of the Global Advanced Analytics market is estimated to be USD 4.96 Billion
- The estimated CAGR growth rate of the Global Advanced Analytics market is estimated to be 27.4%
- The Advanced and Predictive Analytics (APA) software worth is estimated to grow from $2.2B in 2013 to $3.4B in 2018, achieving a 9.9% CAGR in the forecast epoch
IDC stated that simplified APA tools that provide smaller amount flexibility than individual statistical models tools hitherto have additional intuitive graphical consumer interfaces and easier-to-use features are fuelling business analysts’ adoption.
A.T. Kearney forecasts international expenditure on Big Data hardware, software and services will rise at a CAGR of 30% through 2018, attaining a total market mass of $114B. The average business expects to utilize $8M on large data-related initiatives this year.
Cloud-based Business Intelligence (BI) is estimated to increase from $.75B in 2013 to $2.94B in 2018, achieving a CAGR of 31%. Redwood Capital’s recent Sector Report on Business Intelligence (free, no opt in) provides a complete analysis of the existing and future direction of BI. Redwood Capital segments the BI market into traditional, mobile, cloud and social industry intelligence.
The universal market for Big Data associated hardware, software and professional services are estimated to attain USD 52 Billion in 2018. Signals and System Telecom forecasts the market will achieve a Compound Annual Growth Rate (CAGR) of 17% spread over the next 6 years. Signals and Systems Telecom’s report forecasts Big Data will be a $76B market by 2020.
The Primary Security issue associated with big data analytics is the hidden collection of sensitive data. The current privacy issues associated with big data analytics includes identity theft, security breaches, financial fraud, and the increasing use of predictive analytics in big data analysis.While there are many benefits to the growth of big data analytics, traditional methods of privacy protections often fail. Many notions of privacy rely on informed consent for the disclosure and use of an individual’s private data.
However, big data means that data is a resource that can be used and reused, often in ways that were inconceivable at the time the data was collected. Anonymity is also eroded in a big data paradigm. Even if every individual piece of information is stripped of personal information, the relationships between the individual pieces can reveal the individual’s identity.
The Uncertainty of Data Management
One disruptive facet of big data management is the use of a wide range of innovative data management tools and frameworks whose designs are dedicated to supporting operational and analytical processing. The NoSQL (not only SQL) frameworks are used that differentiate it from traditional relational database management systems and are also largely designed to fulfil performance demands of big data applications such as managing a large amount of data and quick response times.
There are a variety of NoSQL approaches such as hierarchical object representation (such as JSON, XML and BSON) and the concept of a key-value storage. The wide range of NoSQL tools, developers and the status of the market are creating uncertainty with the data management.
High cost of switching from legacy to new infrastructure suited for big data flow
Storing, analyzing and accessing data is a growing problem for organizations. Competitive pressures and new regulations are requiring organizations to efficiently handle increasing volumes and varieties of data, but this doesn’t come cheap. And as the demands of Big Data exceed the constraints of traditional relational databases, evaluating legacy infrastructure and assessing new technology has become a necessity for most organizations, not only to gain competitive advantage, but also for compliance purposes.
The challenge is how well the organization’s legacy infrastructure integrates Big Data and the costs associated with IT Infrastructure in migrating from legacy data stores to modern data source. The implementation of Big Data Analytics infrastructure may cost uphill of 6-7 figure numbers in dollar terms and implementation of open source big data analytics solutions cost upto USD 1,000.
Lack of a Big Data implementation strategy
A study out of SAS in 2018 demonstrates a lack of understanding surrounding Big Data. The article states, “Despite industry hype, most organizations have yet to develop and implement a big data strategy. Business analytics leader SAS and SourceMedia surveyed 339 data management professionals about their organizations’ use of data management technology.
The results show few organizations taking advantage of product, customer and other data sources. Just 32 percent of organizations surveyed are currently executing against a big data strategy in daily operations. The most common reasons others are not fully exploiting their big data: 14 percent don’t know enough about big data; 8 percent don’t understand the benefits; 6 percent lack business support; 6 percent lack data quality in existing systems.”
Issues surrounding data redundancy and inconsistency
Data redundancy means duplication of data and inconsistency means that the duplicated values are different which causes inconsistency caused by duplication of data files.
Data integrity means that the data values in the data base should be accurate in the sense that the value must satisfy some rules. The issues associated with Data Integrity includes software bugs, design flaws and human errors.
Top Market Opportunities
Fighting Credit Card Fraud
The most common use of big data to combat financial fraud is in the credit card industry. And it’s an interesting problem, because it requires balancing two conflicting priorities. On the one hand, companies want to catch as many instances of fraud as possible; on the other, they want their customers to have a hassle-free experience.
To try to catch all instances of fraud, a credit card company would have to stop every transaction and put them through a detailed examination before approving. This would of course create a terrible experience for cardholders.
On the other hand, if the credit card company were to simply approve all transactions without checking for fraud, it would likely go out of business with the steady losses, and cardholders would be forced to look elsewhere. At the end of the day, it’s important to optimize for these competing demands and reach for a happy medium.
Companies that utilize big data can dramatically enhance their fraud detection. For example, credit card companies can use data analytics to compare the geographical locations of in-person card swipes with the amount of time elapsed between them, a method called geotiming.
If two in-person card payments occur in different locations without enough time having elapsed for the customer to travel between them, the credit card company can automatically flag the activity as indicative of fraud. By including additional types of data in the model, companies can further improve fraud detection accuracy.
Detecting Internal Fraud
The second most common implementation of big data in financial fraud prevention is in detecting fraud within a company, usually initiated and monitored by a company’s compliance department. Typically, we see this in regulated financial entities that make investment decisions.
The mechanism for detecting this kind of fraud is similar to that of credit card fraud models, but it is focused on the actions of internal employees. It involves gathering data on employee transactions, phone conversations, website visits, and other relevant work-related activities.
Models are then built to define an acceptable pattern of behavior for a particular role. A key difference between this scenario and the payment card example is that these reviews generally have less of a time restriction: internal fraud detection often occurs within minutes or hours, whereas payment card fraud detection typically must occur in a matter of seconds.
If there is any segment in financial institutions or banks that has turned to big data and analytics increasingly, then it is none other than “Risk Department”. Big data and analytics applications are being deployed to ascertain credit risk, market risk and any other non-financial risk. With an increase in the number of financial crimes and frauds, it is crucial to be proactive rather than embrace a reactive approach to unforeseen events.
Risk function department in financial institutions uses vast amount of data and data models. Value at Risk (VaR), Expected Shortfall (ES), Stressed VaR, and VaR Backtesting are some of the methods adopted to measure risk.
In addition to these, other simulation methods like Monte Carlo Simulation, Historical Simulation; scenario analysis and sensitivity analysis, all use huge amount of data and different types of data modelling techniques. Financial institutions turning to Big data, analytics and machine learning is thus inevitable.
Customer analytics, also called customer data analytics, is the systematic examination of a company’s customer information and customer behavior to identify, attract and retain the most profitable customers.
Big Data Analytics can Differentiate the customer experience by understanding the needs of the customer. It helps understand the needs and the views of the customer in a single view and thus helps provide better service to the customer on a holistic basis.
Content analytics can be defined as unlocking business value from unstructured content via semantic technologies to find answers to important questions or discover causes to certain trends. Companies can use content analytics to understand the content that is created, how it is used, the context it is in and the nature of that content.
Content analytics is all about unstructured data and it can be used to explain trends in structured data and provide valuable insights to organisations.Content analytics is especially relevant for organisations where knowledge is at the core of their business.
When that is the case, ordinary business intelligence is not sufficient anymore. Knowing who read what content, when, how often it was shared, the number of clicks, the location of visitors, etc., so-called metadata, is simply not sufficient for a deep understanding.
Operational Advantages of Big Data Analytics
Growing number of organisations are realizing the operational advantages offered by Big Data Analytics and is driving market spend. Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers.
Big Data Analytics offers Operational Advantages in the following ways
Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
Faster, better decision making.
With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.
New products and services.
With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that with big data analytics, more companies are creating new products to meet customers’ needs.
Increased access to cloud-based subscription models
Companies are turning to cloud-based analytics for easier access to increasing amounts of data, greater data sharing and collaboration, faster insights and time to value, and to reduce operational costs.
The components of the data analytics process that can be found in the cloud include the following:
- Usage-based compute and storage resources
- Structured and unstructured data sources, such as hosted data warehouses and repositories
- Data models and complex event processing applications
- Analytic models and business intelligence (BI) tools
- Collaboration applications for sharing results
- Enterprise information and performance management
- Governance risk and compliance solutions
Identify Threats and Prevent Fraud
Big Data Analytics solutions are helping progressively helping organisations identify security threts and prevent fraud, increasing the uptake of analytics across industry verticals. The use of data analytics helps banking institutions understand activity patterns among their own customers and the broader industry. This is why the sharing of data, especially about emerging attack vectors and threats, is so critical.
Banking institutions are increasingly relying on data to predict attacks, based on trends that are targeting the industry. They also are enhancing their abilities to detect cross-channel schemes by reviewing data across numerous banking platforms rather than just monitoring them in isolation.Big data also supports what’s known as continuous or behavioral authentication, which can help prevent fraud.
Using big data to track such factors as how often a user typically accesses an account from a mobile device or PC, how quickly the user types in a username and password, and the geographic location from which the user most often accesses an account can substantially improve fraud detection.
High spending on data architectures to identify opportunities for future growth
Due to the Operational Advantages offered by Big Data Analytics organisations are migrating from legacy data stores to Big Data Source and hence are ripe for opportunities offered such as Cost reduction, Faster, better decision making, smarter business moves, more efficient operations, higher profits and happier customers.
Affordability of tried and tested open-source big data computing frameworks
Apache Hadoop is an open source software framework used for the distributed storage and processing of large data sets using the MapReduce programming model. It consists of computer clusters built using commodity hardware. All the different modules in Hadoop are actually designed with the assumption that different hardware failures are commonly observed occurrences and they should be automatically handled by the framework.
Dark data is a subset of big data but it constitutes the biggest portion of the total volume of big data collected by organizations in a year. Dark data is not usually analysed or processed because of various reasons by companies but that does not lessen its importance in the context of business value.
There are two ways to view the importance of dark data. One view is that unanalysed data contains undiscovered, important insights and represents an opportunity lost. The other view is that unanalysed data, if not handled well, can result in a lot of problems such as legal and security problems.
Data Governance and Management
- For managing data, its quality is the first issue that needs to be addressed. Poor quality data can lead to several erroneous results during the analyses processes. Proper data quality management is mandatory to extract valuable insights; without data-cleansing it is almost impossible to provide a single view of the customers and their behavior, and to map all their points of interaction with the organization.
- Due to the large variety of data gathered, security breaches are more frequent than previously. Big Data technologies were not developed with security in mind; considering that, it is imperative that approach toward security be changed and it be given top priority.
- People generate thousands of data points every day and become susceptible to exposure. Companies and governments need to ensure that the collection, use, and disclosure of data occur in a transparent way taking into consideration the context in which consumers/citizens provided the data. Individuals should have the right to restrict indiscriminate usage of their personal data
Big Data Definitions and Concepts Incomprehension Generates Skepticism Among Adopters
Companies may have already heard about Big Data, but most of them still do not know how to begin using it to their benefit. One of the main factors is the use of different taxonomies by different providers. This makes companies confused about investments. In addition, most of them do not have much expertise in using analytics on the company data which can help them take business decisions.
Big Data requires a new approach toward data integration. It involves an interconnection between different data warehouses, business intelligence, and operational systems, which makes it crucial that all sources of business data be integrated accurately and that organizations be able to easily shift to new data types and sources. The systems are still very different from each other and their languages are not suitable to the Big Data environment yet.
Lack of Use Cases to Assure Return on Investments
- Considering that Big Data is less tangible, it is not easy to find real use cases to boost the market. Big Data requires high investment and convincing the executive boards that the return on investments will appear in the long-term is difficult.
- With the early adopters not yet achieving any measurable benefits, the lack of exemplar use cases can deter others from deploying technologies.
- IT vendors need to disclose the results of the first projects and provide a factual and reasonable argument for the current and future customers to convince them to allocate funds for Big Data solutions.
Once business enterprises discover how to use Big Data, it brings them a wide range of possibilities and opportunities. However, it also involves the potential risks associated with big data when it comes to the privacy and the security of the data.
The Big Data tools used for analysis and storage utilizes the data disparate sources. This eventually leads to a high risk of exposure of the data, making it vulnerable. Thus, the rise of voluminous amount of data increases privacy and security concerns.
Uncertainty Of Data Management Landscape
With the rise of Big Data, new technologies and companies are being developed every day. However, a big challenge faced by the companies in the Big Data analytics is to find out which technology will be best suited to them without the introduction of new problems and potential risks.
Getting Voluminous Data Into The Big Data Platform
It is hardly surprising that data is growing with every passing day. This simply indicates that business organizations need to handle a large amount of data on daily basis. The amount and variety of data available these days can overwhelm any data engineer and that is why it is considered vital to make data accessibility easy and convenient for brand owners and managers.
Difficulty with getting Meaningful Insights Through The Use Of Big Data Analytics
It is imperative for business organizations to gain important insights from Big Data analytics, and also it is important that only the relevant department has access to this information. A big challenge faced by the companies in the Big Data analytics is mending this wide gap in an effective manner.
Association rule learning
Association rule learning is rule-based machine learning used to discover interesting relationships between variables in large databases. It uses a set of techniques for discovering the interesting relationships, also called ‘association rules’, among different variables present in large databases. All these techniques use a variety of algorithms to generate and test different possible rules.
One of its common applications is market basket analysis. This enables a retailer to determine the products frequently bought together and hence use that information for marketing (for example, the discovery that many supermarket shoppers who buy diapers also tend to buy beer). Association rules are being used today in Web usage mining, continuous production, intrusion detection and bioinformatics. These rules do not consider the order of different items either within the same transaction or across different transactions.
Natural language processing
This is a field of computational linguistics and artificial intelligence concerned with the interactions between computers and human languages. It is used to program computers to process large natural language corpora. The major challenges involved in natural language processing (NLP) are natural language generation (frequently from machine-readable logical forms), natural language understanding, connecting the language and machine perception, or some combination thereof.
NLP research has relied mostly on machine learning. Initially, many language-processing tasks involved direct hand coding of the rules, which is not suited to natural language variation. Machine-learning pattern calls are now being used instead of statistical inferences to automatically learn different rules through the analysis of large different real-life examples.
Many different classes of machine learning algorithms have been applied to NLP tasks. These algorithms take large sets of ‘features’ as input. These features are generated from the input data.
Edge Computing is gaining prominence in technological space streaming network performance for quite a while now. All credit to edge computing that data analytics is partly reliant on the network bandwidth to save data locally close to the data source. Edge Computing makes data to be handled and stored away from the silo setup closer to end users with processing taking place either in the device itself or in the fog layer or in the edge data center.
Tech giants like IBM, Microsoft, Google and Intel, race against each other to work rigorously in a bid to build the first quantum computer. Quantum Computing enables seamless data encryption, weather prediction, solving complex medical problems, real conversations and better financial modelling to make organizations develop quantum computing components, algorithms, applications and software tools on qubit cloud services.
In all the countries around the globe there are no set regulations on Big Data Analytics market and the Big Data Analytics market is regulated by Privacy Laws of the respective countries for regulations on the usage of Sensitive data and Private Information
Other Key Market Trends
Talent Gap in Big Data
The analysis of data is important to make this voluminous amount of data being produced in every minute, useful. With the exponential rise of data, a huge demand for big data scientists and Big Data analysts has been created in the market.
It is important for business organizations to hire a data scientist having skills that are varied as the job of a data scientist is multidisciplinary. Another major challenge faced by businesses is the shortage of professionals who understand Big Data analysis. There is a sharp shortage of data scientists in comparison to the massive amount of data being produced.
It is found that the United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of Big Data It is also noted that the average salary for Big Data Engineers is a notch up from other technologies at USD 92,000
Market Size and Forecast
North America accounted for a sizeable revenue share exceeding 28% of the global Big Data Analytics market in 2018, which is attributed to the high penetration, primarily, across the IT & telecommunication, BFSI, and retail sectors.
USA Big Data Analytics market size
- USD 1.4 Billion – The estimated market size of the USA Big Data Analytics market in 2018
- USD 727 Million – The estimated market size of Data Discovery and Visualisation market of USA in 2018
- USD 595 Million – The market size of USA Advanced Analytics market in 2018
- USD 352 Million – The estimated market size of the USA Cloud-based Business Intelligence (BI) market in 2018
- USD 6.4 Billion – The market size of the USA Big Data associated hardware, software and professional services in 2018
Canada Big Data Analytics market size
- USD 657 Million – The estimated market size of the Canada Big Data Analytics market in 2018
- USD 341.69 Million – The estimated market size of Data Discovery and Visualisation market of Canada in 2018
- USD 273.7 Million – The market size of Canada Advanced Analytics market in 2018
- USD 161.92 Million – The estimated market size of the Canada Cloud-based Business Intelligence (BI) market in 2018
- USD 3.03 Billion – The market size of the Canada Big Data associated hardware, software and professional services in 2018
Mexico Big Data Analytics market size
- USD 459.7 Million – The estimated market size of the Mexico Big Data Analytics market in 2018
- USD 238.46 Million – The estimated market size of Data Discovery and Visualisation market of Mexico in 2018
- USD 195.16 Million – The market size of Mexico Advanced Analytics market in 2018
- USD 115.16 Million – The estimated market size of the Mexico Cloud-based Business Intelligence (BI) market in 2018
- USD 2.12 Billion – The market size of the Mexico Big Data associated hardware, software and professional services in 2018
South America’s big data and analytics market is expected to triple in the next five years, with revenues increasing by 200% during the period, IDC said in a release. The region’s big data and analytics market is also expected to grow above the global average this year
Brazil Big Data Analytics market size
- USD 565.3 Million – The estimated market size of the Brazil Big Data Analytics market in 2018
- USD 293.3 Million – The estimated market size of Data Discovery and Visualisation market of Brazil in 2018
- USD 240.04 Million – The market size of Brazil Advanced Analytics market in 2018
- USD 141.64 Million – The estimated market size of the Brazil Cloud-based Business Intelligence (BI) market in 2018
- USD 2.6 Billion – The market size of the Brazil Big Data associated hardware, software and professional services in 2018
Colombia Big Data Analytics market size
- USD 111 Million – The estimated market size of the Colombia Big Data Analytics market in 2018
- USD 57.49 Million – The estimated market size of Data Discovery and Visualisation market of Colombia in 2018
- USD 47.04 Million – The market size of Colombia Advanced Analytics market in 2018
- USD 27.77 Million – The estimated market size of the Colombia Cloud-based Business Intelligence (BI) market in 2018
- USD 510 Million – The market size of the Colombia Big Data associated hardware, software and professional services in 2018
Argentina Big Data Analytics market size
- USD 77.7 Million – The estimated market size of the Argentina Big Data Analytics market in 2018
- USD 40.24 Million – The estimated market size of Data Discovery and Visualisation market of Argentina in 2018
- USD 32.92 Million – The market size of Argentina Advanced Analytics market in 2018
- USD 19.43 Million – The estimated market size of the Argentina Cloud-based Business Intelligence (BI) market in 2018
- USD 357 Million – The market size of the Argentina Big Data associated hardware, software and professional services in 2018
Russia Big Data Analytics market size
- USD 149.18 Million – The estimated market size of the Russia Big Data Analytics market in 2018
- USD 77.26 Million – The estimated market size of Data Discovery and Visualisation market of Russia in 2018
- USD 63.2 Million – The market size of Russia Advanced Analytics market in 2018
- USD 76.19 Million – The estimated market size of the Russia Cloud-based Business Intelligence (BI) market in 2018
- USD 1.4 Billion – The market size of the Russia Big Data associated hardware, software and professional services in 2018
Germany Big Data Analytics market size
- USD 287.9 Million – The estimated market size of the Germany Big Data Analytics market in 2018
- USD 149.12 Million – The estimated market size of Data Discovery and Visualisation market of Germany in 2018
- USD 121.98 Million – The market size of Germany Advanced Analytics market in 2018
- USD 147.04 Million – The estimated market size of the Germany Cloud-based Business Intelligence (BI) market in 2018
- USD 2.7 Billion – The market size of the Germany Big Data associated hardware, software and professional services in 2018
UK Big Data Analytics market size
- USD 345.48 Million – The estimated market size of the UK Big Data Analytics market in 2018
- USD 178.94 Million – The estimated market size of Data Discovery and Visualisation market of UK in 2018
- USD 146.37 Million – The market size of UK Advanced Analytics market in 2018
- USD 176.45 Million – The estimated market size of the UK Cloud-based Business Intelligence (BI) market in 2018
- USD 3.24 Billion – The market size of the UK Big Data associated hardware, software and professional services in 2018
It is estimated that revenues from big data and analytics in the Asia-Pacific region will reach US$18..7 billion with an increase of nearly 15% from 2017.
Revenues from the commercial purchase of big data and analytics related hardware, software and services are likely to reach US$22.2 billion by 2021, with a compounded annual growth rate (CAGR) of 15% for the time period of 2016-2021.
China Big Data Analytics market size
- USD 583.86 Million – The estimated market size of the China Big Data Analytics market in 2018
- USD 302.16 Million – The estimated market size of Data Discovery and Visualisation market of China in 2018
- USD 248.83 Million – The market size of China Advanced Analytics market in 2018
- USD 299.97 Million – The estimated market size of the China Cloud-based Business Intelligence (BI) market in 2018
- USD 5.5 Billion – The market size of the China Big Data associated hardware, software and professional services in 2018
Japan Big Data Analytics market size
- USD 171.98 Million – The estimated market size of the Japan Big Data Analytics market in 2018
- USD 88.99 Million – The estimated market size of Data Discovery and Visualisation market of Japan in 2018
- USD 72.16 Million – The market size of Japan Advanced Analytics market in 2018
- USD 86.99 Million – The estimated market size of the Japan Cloud-based Business Intelligence (BI) market in 2018
- USD 1.62 Billion – The market size of the Japan Big Data associated hardware, software and professional services in 2018
India Big Data Analytics market size
- USD 594.5 Million – The estimated market size of the India Big Data Analytics market in 2018
- USD 307.9 Million – The estimated market size of Data Discovery and Visualisation market of India in 2018
- USD 249.67 Million – The market size of India Advanced Analytics market in 2018
- USD 300.98 Million – The estimated market size of the India Cloud-based Business Intelligence (BI) market in 2018
- USD 5.6 Billion – The market size of the India Big Data associated hardware, software and professional services in 2018
Middle East and Africa
Revenues for big data and analytics (BDA) in the Middle East and Africa (MEA) totalled $2.7 billion in 2018
Saudi Arabia Big Data Analytics market size
- USD 90.23 Million – The estimated market size of the Saudi Arabia Big Data Analytics market in 2018
- USD 49.26 Million – The estimated market size of Data Discovery and Visualisation market of Saudi Arabia in 2018
- USD 39.95 Million – The market size of Saudi Arabia Advanced Analytics market in 2018
- USD 48.16 Million – The estimated market size of the Saudi Arabia Cloud-based Business Intelligence (BI) market in 2018
- USD 0.85 Billion – The market size of the Saudi Arabia Big Data associated hardware, software and professional services in 2018
UAE Big Data Analytics market size
- USD 81.20 Million – The estimated market size of the UAE Big Data Analytics market in 2018
- USD 44.33 Million – The estimated market size of Data Discovery and Visualisation market of UAE in 2018
- USD 35.96 Million – The market size of UAE Advanced Analytics market in 2018
- USD 43.34 Million – The estimated market size of the UAE Cloud-based Business Intelligence (BI) market in 2018
- USD 0.765 Billion – The market size of the UAE Big Data associated hardware, software and professional services in 2018
South Africa Big Data Analytics market size
- USD 64.96 Million – The estimated market size of the South Africa Big Data Analytics market in 2018
- USD 35.46 Million – The estimated market size of Data Discovery and Visualisation market of South Africa in 2018
- USD 28.77 Million – The market size of South Africa Advanced Analytics market in 2018
- USD 34.67 Million – The estimated market size of the South Africa Cloud-based Business Intelligence (BI) market in 2018
- USD 0.612 Billion – The market size of the South Africa Big Data associated hardware, software and professional services in 2018
- The market size of the Global Big Data Analytics market is expected to be USD 68.04 Billion in 2025
- The estimated CAGR growth rate of the Global Big Data Analytics market is 29.7%
- The market size of the Global Data Discovery and Visualisation market is expected to be USD 42.99 Billion
- The estimated CAGR growth rate of the Global Data Discovery and Visualisation market is 32.3%
- The market size of the Global Advanced Analytics market is estimated to be USD 27.02 Billion
- The estimated CAGR growth rate of the Global Advanced Analytics market is estimated to be 27.4%
- The Advanced and Predictive Analytics (APA) software worth is estimated to grow from USD 3.4 Billion in 2018 to USD 6.58 Billion , achieving a 9.9% CAGR in the forecast epoch.
IoT – Internet of Things
Today, many people use Internet of Things (IoT) gadgets in numerous segments of their lives. Each device gathers and sends information in huge quantities. According to one estimate, the total data from IoT devices will reach 500 zettabytes per year by the end of 2019.
An assortment of fascinating use cases reveals that businesses are obtaining data from IoT devices to learn things they couldn’t have without those connected devices, such as the emotions people feel and whether the ways they rely on their IoT devices translates to more social media posts about those products.
As mentioned, company representatives are aware of how data analysis assists their enterprises. Those brands that also have IoT devices are missing opportunities by not mining through the collected data. Thus, the increase in IoT devices and the data generated from them is also helping the market’s success.
Distribution Chain Analysis
Big Data Analytics are offered on pay-as-you-go pricing methods and are offered both as on-premise solution and cloud-based solutions, It is also offered as a stand-alone solution and as an Integrated solution with other technologies such as Machine Learning and Cloud-Computing.
The Various types of Services offered by Big Data Analytics Service Providers include:
Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis.Descriptive analytics is sometimes said to provide information about happened. You might see, for example, an increase in Twitter followers after a particular tweet.
Diagnostic analytics is a form of advance analytics which examines data or content to answer the question “Why did it happen?”, and is characterized by techniques such as drill-down, data discovery, data mining and correlations. Diagnostic analytics takes a deeper look at data to attempt to understand the causes of events and behaviors.Diagnostic analytics lets you understand your data faster to answer critical workforce questions.
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.
Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and predictive analytics. Referred to as the “final frontier of analytic capabilities,”. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option.
Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities.
The big data market is a highly fragmented market with the presence of hundreds of local and global players catering to the need of the industry. The tremendous shift of traditional data processing methods towards more advanced solutions has changed the approach of big data solutions/service vendors to serve various industries.
Organizations have deployed more on-premises solutions as compared to on-demand solutions due to privacy and security threatsThe competitive landscape of the big data analytics market is formed by some major players and many of the new entrants. The leaders constantly keep innovating for the new technology and investing in research and development for the cost effective portfolio.
The widespread use of digital technologies has led to the emergence of big data analytics (BDA) as a critical business capability to provide companies with better opportunities, to obtain value from an increasingly huge amount of data and gain a commanding competitive advantage.
The Factors that serve as Competitive Factors in the Big Data Analytics market includes
With the technologies such as Predictive Analytics, Content Analytics and Advanced Analytics offered by Big Data Analytics Service Providers and the Operational advantages offered by Big Data Analytics the technology serves as a Competitive advantage, the players updating to latest technologies are already reaping the benefits in the Big Data Analytics market.
Scalability is a huge competitive advantage. A lack of it can discourage new competitors from entering a market or eliminate smaller competitors. This is why it can lead to an oligopoly, where only a few companies produce the majority of an industry’s output. Sometimes scalability can even lead one company to dominate an industry; this is called a natural monopoly.
Cost leadership is a business’ ability to produce a product or service that will be at a lower cost than other competitors. If the business is able to produce the same quality product but sell it for less, this gives them a competitive advantage over other businesses. Therefore, this provides a price value to the customers. Lower costs will result in higher profits as businesses are still making a reasonable profit on each good or service sold.
If businesses are not making a large enough profit, Porter recommends finding a lower-cost base such as labor, materials, and facilities. This gives businesses a lower manufacturing cost over those of other competitors.The company can add value to the customer via transfer of the cost benefit to them.
Key Market Players
International Business Machines Corporation (IBM) is an American multinational information technology company. IBM produces and sells computer hardware, middleware and software, and provides hosting and consulting services in areas ranging from mainframe computers to nanotechnology.
IBM is also a major research organization, holding the record for most U.S. patents generated by a business (as of 2019) for 26 consecutive years. Inventions by IBM include the automated teller machine (ATM), the floppy disk, the hard disk drive, the magnetic stripe card, the relational database, the SQL programming language, the UPC barcode, and dynamic random-access memory (DRAM).
Tableau Software is a software companythat produces interactive data visualization products focused on business intelligenceIt initially began in order to commercialize research which had been conducted at Stanford University’s Department of Computer Science between 1999 and 2002.
Tableau products query relational databases, online analytical processing cubes, cloud databases, and spreadsheets and then generates a number of graph types. The products can also extract data and store and retrieve from its in-memory data engine.
TIBCO Software Inc. is an American company that provides integration, analytics and event-processing software for companies to use on-premises or as part of cloud computing environments. The software manages information, decisions, processes and applications for over 10,000 customers
Splunk Inc. is an American public multinational corporation based in San Francisco, California, that produces software for searching, monitoring, and analyzing machine-generated big data, via a Web-style interfac. Splunk (the product) captures, indexes, and correlates real-time data in a searchable repository from which it can generate graphs, reports, alerts, dashboards, and visualizations.
Palantir Technologies is a private American software company that specializes in big data analytics. The company is known for three projects in particular: Palantir Gotham, Palantir Metropolis and Palantir Foundry.
Palantir Gotham is used by counter-terrorism analysts at offices in the United States Intelligence Community (USIC) and United States Department of Defense, fraud investigators at the Recovery Accountability and Transparency Board, and cyber analysts at Information Warfare Monitor, while Palantir Metropolis is used by hedge funds, banks, and financial services firms.
Dell is an American multinational computer technology company based in Round Rock, Texas, United States, that develops, sells, repairs, and supports computers and related products and services. Dell sells personal computers (PCs), servers, data storage devices, network switches, software, computer peripherals, HDTVs, cameras, printers, MP3 players, and electronics built by other manufacturers.
The company is well known for its innovations in supply chain management and electronic commerce, particularly its direct-sales model and its “build-to-order” or “configure to order” approach to manufacturing—delivering individual PCs configured to customer specifications
The Hewlett-Packard Company was an American multinational information technology company headquartered in Palo Alto, California. It developed and provided a wide variety of hardware components as well as software and related services to consumers, small- and medium-sized businesses (SMBs) and large enterprises, including customers in the government, health and education sectors
Datameer, Inc. is a big data Analytics and Visualization company based in San Francisco, California.Datameer offers self-service and schema-free Big Data analytics application for Hadoop. Founded by Hadoop veterans in 2009, Datameer scales up to thousands of nodes and is available for all major Hadoop distributions.
Datameer specializes in analysis of large volumes of data for business users of Apache Hadoop. The company’s product, Datameer Analytics Solution (DAS), is a business integration platform for Hadoop and includes data source integration, an analytics engine with a spreadsheet interface designed for business users with over 200 analytic functions and visualization including reports, charts, and dashboards.
Sisense is a business analytics software company with offices in New York City, Tel Aviv, and Scottsdale, Arizona. Its business intelligence product includes both a back-end powered by in-chip technology that enables non-technical users to join and analyze large data sets from multiple sources,nd a front-end for creating visualizations, like dashboards and reports, on any device, including mobile
RapidMiner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. It is used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process including data preparation, results visualization, model validation and optimization.
Guavus is a Big Data Analytics company situated in Santa Clara, California that provides an integrated end-end solution with decision making applications for network engineering, marketing, monetization and customer care. They fuse data from multiple sources and analyze them before its stored providing insights across networks, device, contents and subscriber statistics which helps in making the data relevant and actionable.
Qlik is a software company founded in 1993 in Lund, Sweden and now based in Radnor, Pennsylvania, United States whose main products are QlikView and Qlik Sense, both software for business intelligence & data visualization.
The market size of the Global Big Data Analytics market is estimated to be USD 11.02 Billion in 2018 and is expected to rapidly grow at a CAGR of 29.7% and is expected to reach a market size of USD 68.04 Billion in 2025.
Security Issues, Uncertainty Of Data Management Landscape, Getting Voluminous Data Into The Big Data Platform, Difficulty with Getting Meaningful Insights Through The Use Of Big Data Analytics are the Challenges being faced by the market players.
The Factors restraining the growth of the Global Big Data Analytics market are Dark Data, Data Governance and Management, Big Data Definitions and Concepts Incomprehension Generates Skepticism Among Adopters, Systems Integration, Lack of Use Cases to Assure Return on Investments
Operational Advantages of Big Data Analytics, Increased access to cloud-based subscription models, Identify Threats and Prevent Fraud, High spending on data architectures to identify opportunities for future growth, Affordability of tried and tested open-source big data computing frameworks are the factors driving the growth of the Global Big Data Analytics market.
- CAGR – Compounded Annual Growth Rate
- IoT – Internet of Things
- ML – Machine Learning
- NLP – National Language Processing
- DDV – Data Discovery and Visualisation
- AA – Advanced Analytics
- APA – Advanced and Predictive Analytics
- BI – Business Intelligence
- VaR – Value at Risk
- ES – Expected Shortfall