Data Monetization Market | Insight, Outlook, Industry Analysis By Accenture, Viavi Solutions, Infosys …

Data Monetization Market Report 2019-2024 Industry research report offers an in-depth and decision-making market analysis prospects for size, share …

Data Monetization Market Report 2019-2024 Industry research report offers an in-depth and decision-making market analysis prospects for size, share, growth, proportion, emerging trends, demand, and Data Monetization Industry growth. It also encompasses through business profiles of some of the prime vendors in the market. The report comprises of a massive database concerning to the recent discovery and technological expansions witnessed in the market, complete with an examination of the impact of these interferences on the market future development.

Data monetization, a form of monetization, is the act of generating measurable economic benefits from available data sources. Typically these benefits accrue as revenue or expense savings, but may also include market share or corporate market value gains. Data monetization leverages data generated through business operations, available exogenous data or content, as well as data associated with individual actors such as that collected via electronic devices and sensors participating in the internet of things.

Major Players in Data Monetization Market are: Accenture, Viavi Solutions, Infosys, SAP, Adastra, Mahindra Comviva, Alepo, EMC, ALC, Redknee, SAS, Monetize Solutions and more

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Market Type:On-Premises, Cloud

Market Applications:Telecom, Finance & Banking, E-Commerce & Retail, Network & Software, Manufacturing, Others

Report Data

Data Monetization Market provides strategy of mergers and executions to enhance their Market share and product assortment. The main goal of Global Data Monetization Market report is to provide a clear picture and a better understanding of the market. Additionally, it also covers the overall market situation along with future lookout around the world. The report evaluated key market features, including revenue, capacity, capacity utilization rate, price, production, production rate, CAGR, consumption, import/export, supply/demand, cost, market share, and gross margin. In addition, This Report study offers a comprehensive study of the key market dynamics and their latest trends, along with applicable market segments and sub-segments.

The global Data Monetization market is valued at 330 million USD in 2018 and is expected to reach 4350 million USD by the end of 2024, growing at a CAGR of 54.0% between 2019 and 2024.

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GlobalData Monetization Market Size, Status and Forecast 2019-2024

1 Industry Overview of Individual Data Monetization

Product Overview and Scope of Data Monetization

1.2 Classification of Data Monetization by Types

1.2.1 Global Data Monetization Revenue Comparison by Types

2 Manufacturers Profiles

2.1 ACS Group

2.1.1 Business Overview

2.1.2 Data Monetization Type and Applications

3 Global Data Monetization Market Competition, by Players

3.1 Global Data Monetization Revenue and Share by Players

3.2 Market Concentration Rate

3.2.1 Top 5 Data Monetization Players Market Share

4 Global Data Monetization Market Size by Regions

4.1 Global Data Monetization Revenue and Market Share by Regions

5 Global Data Monetization Market Segment by Type

5.1 Global Data Monetization Revenue and Market Share by Type

5.2 Global Data Monetization Market Forecast by Type

Reasons to Buy

  • To gain insightful analyses of the Data Monetization market and have comprehensive understanding of the global market and its commercial landscape.
  • To assess the production processes, major issues, and solutions to reduce the development risk.
  • To understand the most affecting driving and restraining forces in the Data Monetization market and its impact in the global market.
  • Learn about the market policies that are being adopted by leading respective organizations.
  • To understand the future outlook and prospects for the Data Monetization market.

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Global Data Monetization Market future Trends and Key Players like Adastra Group, CellOS …

Adastra Group, CellOS Software Ltd, Connectiva Analytics and Insights Ltd., Dawex Systems, Infosys Limited, Mahindra ComViva, Mnubo, Netscout …

The Data Monetization market research report gives the idea about the overall market trends and patterns and highlights all through probability concepts and furthermore includes an exploration conclusion. The level of competition inside the Data Monetization market is disclosed in this report so as to give you a familiarity with what’s going on in this industry. The report divides the market on the basis the top maker, end clients, and their application as per their particular information including business sector estimate and figure, utilization, deals income, value, net edge, supply, and request.

The Data Monetization market report is likewise secured through including advance energy, demand and supply structure, and utilization situation by the application. Item’s interest from various application regions and its future utilization are additionally contemplated in this report. The report is a rich hotspot for featuring organization profile, their market procedures, and challenges. Enterprises/customers will comprehend the current worldwide focused market status through this key report. All major geological districts and sub-areas are shrouded in this report.

The Global Data Monetization Market accounted for USD 1.3 billion in 2017 and is projected to grow at a CAGR of 17.6% the forecast period of 2018 to 2025.

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Key Players: Global Data Monetization Market

Adastra Group, CellOS Software Ltd, Connectiva Analytics and Insights Ltd., Dawex Systems, Infosys Limited, Mahindra ComViva, Mnubo, Netscout Systems, Inc, Virtusa Corporation, Infosys, Google, IBM, Cisco, 101 data, Accenture, Monetize Solutions, Narrative, NESS, NETSCOUT,Paxata, Inc., Optiva Inc., (Redknee Solutions Inc.), ALC, SAP SE, SQLstream, Inc., Openwave Mobility among others.

Table of Contents: Global Data Monetization Market

Executive Summary

Scope/opportunities of the Report

Research Methodology

Market Landscape

Pipeline Analysis

Market Sizing

Porter’s Five Forces Analysis

Market Segmentation

Customer Landscape

Regional Landscape

Business Decision Framework

Drivers And Challenges

Market Key Trends

Players Landscape

Players Analysis


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Market Segmentation: Global Data Monetization Market

Based on component, the global data monetization market is segmented into

Tools and


The services segment can further be sub segmented into support and maintenance, consulting and implementation.

Based on data type, the market can be segmented into

Customer data,

Product data,

Financial data and supplier data

Based on business segments, the market can be segmented into

Sales and marketing,

Supply chain management,


Finance and others (R&D, HR, and legal)

Market segment on basis of Regions/Countries Regions/Countries

North America & South America,


Asia-Pacific, and

Middle East & Africa.

Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa, and Brazil among others. In 2017, North America is expected to dominate the market.

Based on deployment model, the market can be segmented into

Cloud and

On premises.

Based on organization size, the market can be segmented into small and medium-sized enterprises (SMES) and large enterprises.

Based on industry vertical, the market can be segmented into banking, financial services, and insurance (BFSI), telecom, consumer goods and retail, media and entertainment, government and defense, manufacturing, transportation and logistics, energy and utilities, healthcare and others (real estate, education, and travel and hospitality).

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Dollars in the detail: banks pan for gold in ‘data lakes’

Europe’s largest bank has struck a deal with Element AI, a Canadian company, to help it tap this so-called ‘data lake’. JPMorgan, meanwhile, is …

By Iain Withers and Lawrence White

LONDON (Reuters) – From sending special offers on restaurants to burger-loving current account holders to selling anonymized credit card records, banks are racing to monetize the huge troves of data they hold.

Wall Street trails Silicon Valley in using customer information to boost revenue but with tech giants such as Amazon and Google wading onto their turf with forays into lending and payments, banks including JPMorgan, HSBC and Barclays are moving to narrow the gap.

Mining mountains of trading data to predict stock moves; partnering with retailers on marketing campaigns and using artificial intelligence (AI) tools to try and speed up credit decisions are some of the areas banks are focusing on.

In the digital era, knowing how much people earn, where they spend it and what they buy – information some wouldn’t divulge to their closest confidants – is valuable, particularly when banks’ earnings from lending and trading are under pressure from persistently low interest rates and tougher regulation.

“We are now seeing some amazing uses of data in banking, and the reason is pretty simple: they know their clients better than anyone, they have a name and address, information about what you’re buying and once you have those you can do so much,” said Craig Macdonald, head of data monetization at Accenture.

The surge in data mining is happening against a changed regulatory backdrop. New European Union (EU) rules introduced last year allow technology companies to access banks’ customer data if they have customers’ permission.

The EU has also toughened its privacy laws. Companies now have to get permission before they can collect and use personal information gleaned online from people living in the bloc.

But even with the extra protections, sensitive data is still at risk of being exploited because many people are not aware of how they can shield themselves.

Less than a third of Europeans were aware of all their data rights and only 13 percent said they read privacy statements fully, according to a poll this year of 27,000 EU citizens.

Banks do not disclose how much they earn from analyzing and marketing customer data or other ways in which they monetize the information they hold. But, in comparison to the billions earned from lending and trading, the amounts generated are likely to be small.

“If there was a gold mine people would probably have found it by now,” said Benjamin Ensor, an analyst at Forrester. “But if you can generate some marginal incremental revenue at relatively little cost why wouldn’t you do that?”


Tie-ups with retail firms is one way banks are monetizing their data.

Customers of Britain’s Lloyds and Spain’s Santander can get special offers from a range of retailers after the banks joined a digital loyalty scheme run by US-based data advertising firm Cardlytics.

The scheme uses spending data to give customers discounts at shops they already frequent or which are in their neighborhood. So, burger-aficionados get deals at local burger restaurants and fashion fans get ads about discounts at clothing stores.

The banks get a percentage of the fee charged by Cardlytics for running the campaign. Cardlytics gets insights on consumer behavior which help the retailers tailor and fund the offers and discounts.

Cardlytics, Lloyds and Santander declined to comment on what percentage of the fee banks get.

“We leverage transaction data that’s created every time the card is tapped, every time a direct debit is made by a customer, in an anonymized way,” said Campbell Shaw, London-based head of bank partnerships at Cardlytics.

“We only need to know it’s customer 12345, we don’t need to know the name of the customer for any reason.”

Bank clients have to enroll in the rewards program.

A spokesman for Santander said their customer spending data was only shared with Cardlytics if customers choose to receive retail offers. The bank said the information was shared on an anonymized basis meaning the customer’s name is replaced by a unique identifying number.

Lloyds declined to comment on the specifics of the deal. Its privacy policy said the scheme would use customers’ mobile location data only with their permission.

Even with the tougher regulations around big data, privacy experts warn there is still scope for abuse, for example, if highly-indebted people are targeted with unsuitable offers for high interest loans or credit cards.

“If you can use data to get a customer to buy something that they otherwise wouldn’t, it’s good for the bank but not necessarily for the customer and the potential for misuse is significant,” said Paul Bernal, an expert in data privacy at University of East Anglia.

Ashok Vaswani, global head of consumer and payments at Barclays, told attendees at AI conference CogX in London this month that the bank would crunch data in an ethical way.

“We’re going to do it in a transparent and understandable fashion,” he said. “If I can’t explain it [to a customer] I’m not going to offer it.”

Like many banks, Barclays markets anonymized spending data to a range of businesses including mall operators who can see from the information which retail chains attract the most customers and are therefore worth targeting as tenants.

Barclays said it doesn’t share personally identifiable information and it sends privacy notices to customers through a combination of email, text, post and via mobile apps. It also has a page on its website explaining its data privacy policy.


Using data to improve risk analysis, make faster credit decisions and anticipate customer needs is particularly appealing for banks looking to cut costs.

HSBC plans to use AI tools to rake through its 10 petabytes of data – roughly equivalent to the storage capacity of 2 million DVDs – from investment banking clients in 66 countries.

Europe’s largest bank has struck a deal with Element AI, a Canadian company, to help it tap this so-called ‘data lake’.

JPMorgan, meanwhile, is developing a raft of AI applications to better predict stock moves and to map and mine 3 billion transactions it handles annually.

The bank hired Manuela Veloso, the head of the machine learning department at Carnegie Mellon University, to be its head of AI research last year.

In comparison to newer, tech-focused companies, banks are often at a disadvantage when they look to extract value from their data – they lack inhouse experts and their businesses are often siloed with legacy IT systems.

To speed things up, lenders are set to spend $26 billion on big data and business analytics this year, according to analysis by International Data Corporation, up from $23 billion last year and $19.7 billion in 2017.

Hires for senior leaders with digital experience at financial firms have doubled year on year for the last five years, according to London-based headhunters Heidrick & Struggles.

“These skills are now a necessity within senior leadership teams,” says Marcus De Luca, UK financial services practice leader at the recruiter.

“We are often asked if there is someone who works at Amazon, Google, Netflix, or Facebook who could be tempted to join.”

(This story corrects to show banks get a percentage of a fee charged not purchases made in digital marketing scheme)

(Editing by Carmel Crimmins)

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Big Data’s Efficacy is out to Simply Policy and Claim Management

Big Data well-equips the insurers with analytical and procedural solutions, enhancing the industrial databases’ capacity to collect, store, manage and …

Big Data well-equips the insurers with analytical and procedural solutions, enhancing the industrial databases’ capacity to collect, store, manage and analyze.

FREMONT, CA: The insurance sector is progressively relying on predictive modeling and extensive data to succeed. Big data in the insurance sector is characterized, as the structured or unstructured data used to influence the processing of underwriting, rating, pricing, forms, marketing, and claims. The insurance industry has developed unique strategies to determine the quality of the data they are analyzing due to the sheer quantity of information available.

The word big data is vague as distinct data storage, and analytics capacities are available from different insurers. Big data generation velocity, collection, and refresh are the main problems that insurers will face with big data. Big data is produced today at an almost incomprehensible speed. As per Forbes, in just the last two years, 90 percent of all information in the globe has been developed, and we are presently creating about 2.5 quintillion bytes of data per day. Insurers need systems that can rapidly aggregate this information quantity and continue to update caches so that the optimum level of data analytics can be achieved. Big data’s more significant advantage is the unstructured content that is now accessible. Unstructured data is the information that unconventional insurers can collect and use to affect all company fields such as policy, fraud detection, and customer experience.

Value is probably the most significant of big data in insurance. Big data’s value extends beyond just nice and bad data; what insurers do with it is also about. Big data in insurance can be used to create precise choices such as pricing and risky choice, but it can also impact marketing projects and recognize emerging trends. Big data collection is costly–insurers need to make sure they get the value of their money.

There are many distinct programs and platforms to assist insurers to leverage their information efficiently. Irrespective of the programs they are using, insurers need to have the proper framework around data collection and processing to ensure that data of the highest quality is used as quickly as possible.

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Big Advantages of Big Data, Impacting Finance

FREMONT, CA: Big data and big data analytics are becoming instrumental for firms in the financial sector. The financial industry generates enormous …

By Banking CIO Outlook | Monday, June 17, 2019

Digitalization and Big Data are ushering in a new era for banking and financing firms. With the right technological benefits in place, the sector improves rapidly.

FREMONT, CA: Big data and big data analytics are becoming instrumental for firms in the financial sector. The financial industry generates enormous amounts of data, and big data is helping financial firms manage and make the most from it. Analytics is helping the industry to extract the right information and put it to use through actionable business plans and policies. Smarter decision making has become the new norm for banks as they adopt technological advancements and high-tech solutions.

• Customer-centric Banking

Competition is fierce in the financial industry. Big data can help companies gain an edge over their competitors by improving customer experiences. Well harnessed data allows companies to read their customers well and device customized services for individual customers. The better the quality of services offered, the better the customer satisfaction.

• Targeted Marketing

Individual spending and investments vary greatly. Hence, finance companies are utilizing big data and analytics to understand consumer spending dynamics. Insights gained from analyzed data create opportunities for companies to plan personalized marketing strategies. Personalized marketing is much more effective in influencing consumers when compared with general and collective marketing.

• Efficient Processes

Every process in the workflow stands a chance of being improvised and made more efficient. Data regarding the functioning of different departments and systems are accumulated and evaluated to check if they are competent enough. Subsequently, banks figure out how they can optimize specific processes that would lead to increased profitability and better resource management.

• Understanding Risks Better

Data plays an essential role in improving security in financial organizations. By monitoring and analyzing data, banks can track risks and detect vulnerabilities. Immediate action to plug the security loopholes prevents frauds and attacks.

Digitalization is at its peak. With big data, banks can keep a tab on the rising number of customers and their transactions. Resource management and profitability are becoming better, as well. Experts from the industry have predicted that the amount of data generated per second is about to grow 700 percent by 2020. With the right big data tools, financial firms can take advantage of this boost to a great extent.

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