The World Utility & Energy Analytics Market 2019-2024: Leading Players are Oracle, Capgemini …

The “Utility and Energy Analytics Market – Growth, Trends, and Forecast … It is important to mine the values of these data fully and Big Data analytics …

DUBLIN–(BUSINESS WIRE)–

The “Utility and Energy Analytics Market – Growth, Trends, and Forecast (2019 – 2024)” report has been added to ResearchAndMarkets.com’s offering.

The utility and energy analytics market was valued at USD 286.8 million in 2018, and it is expected to reach a value of USD 980.5 million by 2024, at a CAGR of 24.9%, over the forecast period (2019-2024).

Developed countries continue to consume vast amounts of energy, while the demand is increasing in developing countries. The increased demand is caused in part by the rise in population and by economic development.

In a utility and energy company, there are many sources of Big Data, such as grid equipment, weather data, smart meters, measurements from power systems, GIS data, and data related to asset management. Companies are using this data to bring in operational efficiencies and manage energy demand for end consumers.

The unprecedented explosion of data from the smart meter and smart grid programs, combined with the increasingly complex data retention requirements from regulators and a changing competitive landscape have created new challenges and opportunities for the transmission and distribution companies.

Poor data quality and integration, patchy ownership of data across processes, and fragmented use of analytics are among the prominent challenges that trouble any energy company. Thus, finding a proper analytics approach to a problem and embedding its core competencies for decision making is a challenge of its own.

Key Market Trends

Meter Operation Accounts for a Significant Share of Analytics Application

Earlier, utilities used to read meters once per month. However, some have transitioned to capturing meter data every 15 minutes, as well as every hour of every day. As a result, the terabyte of data containing valuable behavioral aspects of the consumer is generated every hour. It is important to mine the values of these data fully and Big Data analytics has become a significant contributor to this industry.

The insights uncovered through analytics will help in creating behavioral patterns of the consumers, which will in turn help with developing new meter rate plans and services for customers.

Moreover, with the advent of smart meters, the demand for analytics in the industry has increased with smart meters automating and stimulating usage data generation. In the United Kingdom, with the foundation for smart metering in place, energy utilities are favoring installation, and support mandated interactions with the Data Communications Company (DCC).

North America Dominates the Market

North America is one of the leading adopters of analytics solutions and is considered to be one of the largest markets for utility and energy analytics. The demand in North America is mainly driven by a higher focus on innovations through R&D and technology advancement in the developed economies, such as the United States and Canada.

The region’s large consumption of energy is also supporting the growth of the market. Moreover, the region has a strong foothold of vendors in the market. Some of them include IBM Corporation, Oracle Corporation, BuildingIQ, and SAS Institute Inc., among others.

Competitive Landscape

The market is highly fragmented with the emergence of new startups offering a broad range of innovative solutions, catering to diverse energy and utility sector requirements. The market is witnessing intensifying competitive rivalry, which is expected to further rise over and beyond the forecast period. The major players dominating the market are Oracle Corporation, Capgemini SE, ABB Corporation, IBM Corporation, and CA Technologies.

Recent Developments

  • March 2019 – Capgemini launched the Unified Commerce Solution for Grocery, a new Salesforce Fullforce solution designed to help grocers capture loyalty in the rapidly expanding online grocery market, by improving the ordering experience.
  • March 2019 – GE announced the launch order for its new GT26 HE (high efficiency) gas turbine upgrade with Uniper for the utility’s Enfield Power Station in greater London.
  • February 2019 – Siemens Canada and Nova Scotia Power (NSP) collaborated to develop and demonstrate smart grid technology to better manage the province’s electricity and reduce greenhouse gas emissions.

Key Topics Covered

1 INTRODUCTION

1.1 Study Deliverables

1.2 Study Assumptions

1.3 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET DYNAMICS

4.1 Market Overview

4.2 Introduction to Market Drivers and Restraints

4.3 Market Drivers

4.3.1 Rising Demand for Energy & Increasing Emphasis on a Greener Environment

4.3.2 Growing Consumer Focus on Energy Consumption Pattern Analysis

4.4 Market Restraints

4.4.1 Security Concerns & Compatibility Issues to Challenge the Market Growth

4.5 Value Chain Analysis

4.6 Industry Attractiveness – Porter’s Five Forces Analysis

5 MARKET SEGMENTATION

5.1 By Deployment

5.1.1 Cloud

5.1.2 On-Premise

5.1.3 Hybrid Cloud

5.2 By Type

5.2.1 Software

5.2.2 Services

5.3 By Application

5.3.1 Meter Operation

5.3.2 Load Forecasting

5.3.3 Demand Response

5.3.4 Distribution Planning

5.3.5 Other Applications

5.4 Geography

5.4.1 North America

5.4.2 Europe

5.4.3 Asia-Pacific

5.4.4 South America

5.4.5 Middle East & Africa

6 COMPETITIVE LANDSCAPE

6.1 Company Profiles

6.1.1 Oracle Corporation

6.1.2 Capgemini SE

6.1.3 ABB Corporation

6.1.4 IBM Corporation

6.1.5 CA Technologies

6.1.6 SAS Institute Inc.

6.1.7 Siemens AG

6.1.8 Schneider Electric SE

6.1.9 SAP SE

6.1.10 BuildingIQ Inc.

6.1.11 Teradata Corporation

7 INVESTMENT OUTLOOK

8 MARKET OPPORTUNITIES AND FUTURE TRENDS

For more information about this report visit https://www.researchandmarkets.com/r/3e894

View source version on businesswire.com: https://www.businesswire.com/news/home/20190510005201/en/

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Hadoop Big Data Analytics Market Summary, Demand, Size, Growth, Worldwide Analysis And …

Hadoop Big Data Analytics Market Forecast 2019-2025 report include Porter’s Five Forces Analysis (potential entrants, suppliers, substitutes, buyers, …

Hadoop Big Data Analytics Market

Hadoop Big Data Analytics MarketForecast 2019-2025 report include Porter’s Five Forces Analysis (potential entrants, suppliers, substitutes, buyers, and industry competitors) which provides crucial information for knowing the Hadoop Big Data Analytics industry. This report includes Overview, Classification, Industry Value, Price, Cost and Gross Profit. It also offers in-intensity insight of the Hadoop Big Data Analytics industry masking all vital parameters along with, Drivers, Market Trends, Market Dynamics, Opportunities, Competitive Landscape, Price and Gross Margin, Hadoop Big Data Analytics market Share via Region, New Challenge Feasibility Evaluation, Analysis and Guidelines on New mission Investment.

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Hadoop Big Data Analytics Market Competition by Manufacturers (2019-2025)

Hadoop Big Data Analytics Market Share of Top 3 and Top 5 Manufacturers, Hadoop Big Data Analytics Market by Capacity, Production and Share by Manufacturers, Revenue and Share by Manufacturers, Manufacturers Manufacturing Base Distribution, Sales Area, Product Type, Market Competitive Situation and Trends, Market Concentration Rate

Instantaneous of Hadoop Big Data Analytics Market: The increasing volume of structured & instructed data and the need to store, manage, and analyze data are factors driving the growth of the Hadoop big data analytics market.

Based onProduct Type, Hadoop Big Data Analytics market report displays the manufacture, profits, value, and market segment and growth rate of each type, covers:

  • Suite Software
  • Management Software
  • Training And Support Services
  • Operation And Management Services

Based onend users/applications, Hadoop Big Data Analytics market report focuses on the status and outlook for major applications/end users, sales volume, market share and growth rate for each application, this can be divided into:

  • Medical
  • Manufacturing
  • Retail
  • The Media
  • Energy
  • Transport
  • IT
  • Education
  • Other

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The Key Insights Data of Hadoop Big Data Analytics Market is Available in This Report:

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Global Big Data Analytics In Healthcare Market Share Revenue To Surge To USD 68.03 Billion By …

The Latest Report Available at Zion Market Research, “Big Data Analytics in Healthcare Market: by Component (Software [Electronic Health Record …

The Latest Report Available at Zion Market Research, “Big Data Analytics in Healthcare Market: by Component (Software [Electronic Health Record Software, Practice Management, and Workforce Management], Hardware [Data Storage, Routers, Firewalls, Virtual Private Networks, E-Mail Servers, and Others], and Services), By Deployment Type (On-Demand and On-Premise), By Analytics Type (Descriptive, Predictive, and Prescriptive), and By Application (Financial Analytics [Claim Processing, Revenue Cycle Management, Risk Adjustment & Assessment, and Others], Clinical Data Analytics [Quality Control, Population Health Management, Clinical Decision Support, Reporting and Compliance, and Precision Health], and Others): Global Industry Perspective, Comprehensive Analysis and Forecast, 2017 – 2024” provides pin-point analysis for changing competitive dynamics and a forward looking perspective on different factors driving or restraining industry growth. The Big Data Analytics in Healthcare Market is the most blooming and promising sector of the industry. This overall Big Data Analytics in Healthcare Market has been ascending at a higher rate with the enhancement of inventive strategies and a raising customer tendency. The wide-coming to Big Data Analytics in Healthcare Market is a wide field for players offering gigantic entryways for advancement. The overall Big Data Analytics in Healthcare Market is the establishment of the all-inclusive enhancement perspectives and prospects, as the headway of a specific thought requires diverse mechanically supported considerations, theories, and techniques.

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Some of the Major Big Data Analytics in Healthcare Market Players Are:

  • Cerner Corporation
  • Dell
  • McKesson Corporation
  • Hewlett-Packard Co.
  • GE Healthcare
  • Mckesson
  • Koninklijke Philips N.V.
  • Epic System Corporation

The Big Data Analytics in Healthcare Market is widely partitioned reliant on the predictable updates in the enhancement of parameters, for example, quality, trustworthiness, end customer solicitations, applications, and others. The Big Data Analytics in Healthcare Market report contains general successful parameters, confinements, and besides has in detail illumination of the noteworthy data close by the present and future examples that may concern the advancement. The comprehensive Big Data Analytics in Healthcare Market report elucidates within and outside representation of current advancements, parameters, and establishments. The worldwide Big Data Analytics in Healthcare Market also gives knowledge related to the monetary circumstances that would be useful for businesses and startups.

The global Big Data Analytics in Healthcare Market report offers point by point viewpoints on the major and furthermore minor factors that may impact up or limit the market expansion. The Big Data Analytics in Healthcare Market report gives explanatory data that can change the forceful components in the market and will give a topographical division [Latin America, North America, Asia Pacific, Middle & East Africa, and Europe] of the general market on an overall measurement. The Big Data Analytics in Healthcare Market report gives in-detail data to grasp the critical market parts that help with settling on business decisions dependent on creation, demand, and supply of the thing as demonstrated by the examination of Big Data Analytics in Healthcare Market segments at regional and application preface. It gives Big Data Analytics in Healthcare Market estimates data for the upcoming years subjected to the advancement desire and structure of the market.

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Promising Regions & Countries Mentioned In The Big Data Analytics in Healthcare Market Report:

  • North America ( United States)
  • Europe ( Germany, France, UK)
  • Asia-Pacific ( China, Japan, India)
  • Latin America ( Brazil)
  • The Middle East & Africa

Key Topics Covered:

1. Introduction

2. Executive Summarya

3. Big Data Analytics in Healthcare Market Market Dynamics

4. Global Big Data Analytics in Healthcare Market Competitive Landscape

5. Global Big Data Analytics in Healthcare Market Therapy Type Segment Analysis

6. Global Big Data Analytics in Healthcare Market Therapeutic Area Segment Analysis

7. Global Big Data Analytics in Healthcare Market End-User Segment Analysis

8. Global Big Data Analytics in Healthcare Market Regional Segment Analysis

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Key Highlights of the Big Data Analytics in Healthcare Market Report:

  • To analyze and research the global Big Data Analytics in Healthcare Market capacity, production, value, consumption, status and forecast;
  • To focus on the key Big Data Analytics in Healthcare Market manufacturers and study the capacity, production, value, market share and development plans in next few years.
  • To focuses on the global key manufacturers, to define, describe and analyze the market competition landscape, SWOT analysis.
  • To define, describe and forecast the market by type, application and region.
  • To analyze the global and key regions market potential and advantage, opportunity and challenge, restraints and risks.
  • To identify significant trends and factors driving or inhibiting the market growth.
  • To analyze the opportunities in the market for stakeholders by identifying the high growth segments.
  • To strategically analyze each submarket with respect to individual growth trend and their contribution to the market.
  • To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the market.
  • To strategically profile the key players and comprehensively analyze their growth strategies.

Reason to Buy

  • Save and reduce time carrying out entry-level research by identifying the growth, size, leading players and segments in the global Big Data Analytics in Healthcare Market
  • Highlights key business priorities in order to assist companies to realign their business strategies.
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  • Scrutinize in-depth global market trends and outlook coupled with the factors driving the market, as well as those hindering it.
  • Enhance the decision-making process by understanding the strategies that underpin commercial interest with respect to products, segmentation and industry verticals.

The study objectives are:

  • We will give you an assessment of the extent to which the market possesses commercial characteristics (such as the presence of firms with primarily non-government business bases, the presence of business methods not consistent with public law/regulation/oversight including government acquisition) along with examples or instances of information that supports your assessment.
  • We will also help you identify standard/customary terms and conditions such as discounts, warranties, buyer financing, inspection, and acceptance for the Big Data Analytics in Healthcare Market industry.
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  • Furthermore, we will help you in identifying any historical trends to predict Big Data Analytics in Healthcare Market market growth rate up to 2025.
  • Lastly, we will predict the general tendency for supply and demand in the Big Data Analytics in Healthcare Market industry.

Also, Research Report Examines:

  • Competitive companies and manufacturers in global market
  • By Product Type, Applications & Growth Factors
  • Industry Status and Outlook for Major Applications / End Users / Usage Area

Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe or Asia.

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What is the IIoT?

Conversations about the IIoT often stray into discussions about Big Data, machine learning, and predictive analytics – all of which can aid the proper …

First things first – the term IIoT refers to the industrial internet of things, a network of smart sensors and analysis tools which enhance ongoing production processes by leveraging data. You may also have heard the term Industry 4.0, which is pretty much the same thing: the end of the era of so-called dumb machines, and the rise of smart, interconnected industrial equipment.

Conversations about the IIoT often stray into discussions about Big Data, machine learning, and predictive analytics – all of which can aid the proper use of the data gathered through machine-to-machine communication.

Back to the beginnings of IIoT

In their latest information technology report, Gartner have revealed that 53 per cent of oil and gas companies have failed to implement IIoT technology, and are not planning to do so. This is a massive issue for the oil and gas industry, with billions of dollars worth of revenue stuck in uncommunicative plants. With the majority of companies yet to jump on the bandwagon, the real changes in IIoT will occur in two place: with those companies at the forefront of the Fourth Industrial Revolution, and with those yet to be persuaded to commit to it.

Reading all this as a decision-maker in a company without a robust IIoT strategy may feel like being presented with an insurmountable obstacle – but beginning to work with IIoT is a simple transition. Many solution providers have entry-level packages available, and often, this is no more complicated than simply implementing a system of sensors and processors to allow equipment to communicate. The data provided by this system can then be stored, analysed, and accessed on an interface that is also connected to the network of sensors.

The IIoT tug-of-war

The evolving nature of IIoT’s leading edge is both a blessing and a curse for those oil and gas companies looking to implement new strategies. On the one hand, the ever-changing operating models mean that older and simpler technologies are becoming more comprehensive and affordable. On the other, many models are becoming obsolete at a faster and faster rate. Perhaps this constant drive to succeed is one of the reasons that so many companies, all of whom are aware of the IIoT, are simply refusing to start their journeys.

Without IIoT in place, decision-making units within oil and gas companies are relying on smaller data sets, which can often involve a significant amount of guesswork. Even within companies with huge volumes of data, the inability to properly leverage that information can significant stymie attempts to improve business processes.

Building up IIoT connections

We’ve already discussed the implementation of sensors as the first step in an IIoT strategy – but once you’ve connected all your assets – from those out in the field to those in your main facility – what happens next?

With remote and automatic monitoring now possible, the next step is to establish a cloud environment that is sufficient to manage the data and provide some insights that would otherwise be difficult to arrive at. Predictive analytics within your enterprise should now be functioning holistically, with most of your equipment connected to your cloud or IT system.

However, as soon as materials, products, or fuel leave your facility – your IIoT strategy becomes obsolete. Connecting the entire supply chain should be the next priority. Predictive analytics can then apply to everything from the schedule according to which replacement parts are ordered, to when fuel transports should depart. The ultimate goal should be complete connectivity, including offshore plants, vehicles, logistics, and intercontinental data transfer.

The Cloud: an IIoT bastion

With data at the centre of all decision-making processes within any type of company, data should be centrally-accessible from all points along the chain. Once the glut of industrial data can be managed to the extent that IIoT applications can make recommendations on the back of it, the benefits to the operation should become swiftly apparent. Process implementation will take place in a fraction of the time, analytics engines will be able to work with multiple often seemingly-incomparable data types, and storing vast amounts of data for auditing and regulatory purposes far simpler.

According to Gartner’s report, some businesses are only harnessing 30 per cent of the value inherent to their collected and stored data – due primarily to not being able to asses their business processes supported by data. An easier way to describe this problem is that of data lakes becoming stagnant data swamps: bigger isn’t necessarily better – it depends on how you use it.

IIoT and the bottom line

We’ve already seen the extent to which IIoT can unlock revenue trapped in badly-utilised data. According to McKinsey, the total value that could be released in the next five years if all O&G make efforts to employ IIoT is somewhere between four and 11 trillion US dollars. So in what other ways can IIoT help release this?

Oft-overlooked, HS&E can cost companies a vast amount of money if improperly managed – not counting the enormous human price of serious breaches of health and safety. Oil and gas is frequently referred to as the most dangerous industry in which to work, and the remote nature of so many sites only exacerbates the risk to life, and the cost of safeguarding employees and assets. One major benefit of the IIoT is that predictive maintenance functions can completely negate costly, high-risk inspection journeys – and help plan the safest and most timely points during the year that travel should be attempted. With hundreds of incidents around extraction sites every year, minimising the human element as much as possible protects workers, those responsible for them, and the company coffers.

Transportation is another key area for IIoT improvements, as tracking the progress and location of vehicles can not only help with predictive analytics such as departures and fuel consumption, but can also help combat fuel adulteration and theft – as it becomes impossible for product to go off-grid. Real-time updates supported by multiple connections such as GPS and mobile, can also minimise downtime during failures and ensure maintenance and repair occurs as soon as possible.

IIoT classifications

As an all-encompassing, company-wide initiative, an IIoT strategy is often split into separate departments or areas. There are, of course, no consistent breakdowns, but a common distinction that we at Oil & Gas IQ use is as follows:

Intelligent maintenance

Intelligence maintenance is the element of IIoT that prioritises the management systems used to reduce unexpected downtime, and minimise breakdown frequency. This usually applies to the pre-existing management systems and assets.

Automation

This can apply both to buildings and machines, from auto-heating and lighting, to increasing precision during production. New solutions have been able to monitor temperature and humidity in both workspaces and pipelines, as well as transportation and wells: there really is no part of the business that cannot benefit from automation.

Machine-machine communication

This subset deals with the communication between assets without human intervention. With the rise of mobile devices and the mechanisms now in place for data transmission across series of networks, technologies such as telemetry, robotics, and low-code are now far more prevalent.

Logistics

Rather than simply relying on human discretion, or inferences drawn from incomplete data sets – IIoT can fully transform the logistics process – from orders of components and lubricants to predicting the point of failure and replacement of heavy machinery.

So, why do we need to invest in the IIoT?

Of all industries, oil and gas is one of the most asset-heavy and data-rich. It is high-risk, employs tens of thousands of people within individual companies, and has some of the longest supply chains in the world. Data is constantly being pulled and collected in hugely-varying packets from myriad sources, making it extremely difficult to standardise and analyse. IIoT strategies can provide data virtualisation to homogenise data sets, and can store vast amounts of information on the Cloud.

Many of the greatest risks to both life and profits in oil and gas are time sensitive, but without the IIoT, it is impossible for solutions to be effectively implemented. The adoption of IIoT applications will improve business efficiency, safety, capex and opex, and reduce the negative effects of having assets spread over a wide area. It’s an inevitable transformation – let’s just hope none of us get left behind.

If you enjoyed this guide, then you may be interested in our upcoming digital event IIoT in Oil & Gas: Online 2019.

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Storage In Big Data Market is expected to register a CAGR of 20.4% over the forecast period (2016 …

Storage In Big Data Market is expected to register a CAGR of 20.4% over … Leading market players are integrating predictive analytics with storage …

Persistence Market Research delivers key insights on the global storage in big data market in a new report titled, “Storage in Big Data: Global Industry Analysis and Forecast, 2016–2026”. According to the report, the global storage in big data market is estimated to be valued at US$ 9,599.1 Mn by the end of 2016 and this is anticipated to increase to US$ 61.44 Bn by 2026 end. In terms of value, the global storage in big data market is expected to register a CAGR of 20.4% over the forecast period (2016–2026). This is attributed to various factors, regarding which Persistence Market Research offers vital insights in detail in this study.

Market dynamics

Digitization of records globally is the primary factor driving the global storage in big data market. Due to recent laws enforced by governments of various countries, companies are shifting towards digital maintenance of records, especially in the healthcare sector. Increasing digital data volumes is leading to companies increasingly adopting various data storage options. Further, increasing adoption of software-based storage options and an increase in the number of connected devices is expected to fuel the growth of the global storage in big data market over the forecast period. One of the major restraints for growth of the global storage in big data market to a certain extent are macroeconomic factors such as reduced budgets for data storage and high total cost of ownership of flash storage. Improper data representation is also posing a threat to the global storage in big data market.

Leading market players are integrating predictive analytics with storage systems and are providing storage servers close to end users to reduce latency time.

Market forecast

The global storage in big data market is segmented on the basis of – Segment (Hardware Segment, Software Segment, Services Segment); Industry (BFSI, IT and Telecommunications, Transportation, Logistics & Retail, Healthcare and Medical, Media and Entertainment, Others); and Region (North America, Latin America, Western Europe, Eastern Europe, Asia Pacific Excluding Japan (APEJ), Japan, and Middle East & Africa (MEA)).

The Hardware segment dominated the global storage in big data market with 49.1% market share in terms of value in 2015. The Hardware segment is estimated to create incremental $ opportunity of US$ 16.38 Bn between 2016 and 2026. The Software segment is estimated to create incremental $ opportunity of US$ 11.34 Bn between 2016 and 2026.

The BFSI segment was valued at US$ 1,694.1 Mn in 2015 and is estimated to reach US$ 2,071.2 Mn by the end of 2016, reflecting a Y-o-Y growth rate of 22.3%. The Media and Entertainment segment is expected to register high Y-o-Y growth rates throughout the forecast period.

Among regions, North America is expected to remain dominant in terms of revenue throughout the forecast period, registering a CAGR of 21.1% between 2016 and 2026. The North America storage in big data market is estimated to be valued at US$ 30.12 Bn by the end of 2026. The Western Europe storage in big data market is anticipated to register the highest CAGR of 21.8% during the forecast period. Latin America is projected to be the most attractive region in the global storage in big data market in terms of revenue during the forecast period.

A sample of this report is available upon request @ https://www.persistencemarketresearch.com/samples/12235

Competitive landscape

The global storage in big data market report covers various solution providers operating in the global storage in big data market. Top companies profiled in the report include Google Inc., Microsoft Corporation, Amazon Web Services Inc., VMware Inc., IBM Corporation, Dell EMC, SAS Institute Inc., Oracle Corporation, SAP SE, Teradata Corporation, Hewlett Packard Enterprise, Hitachi Data Systems Corporation, and MemSQL Inc. Some of these companies are adopting strategies such as increasing global investment in cloud services, providing hybrid storage solutions, enhancing flash storage product portfolio, integrating cloud and flash technology with storage products, and collaborating with various technology partners to expand their market presence and enhance customer base globally.

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