SYNC. Big Data 10 April Inside Big Data Industry size Growth drivers Investment risks Market trends Supply chain Key players

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1 SYNC. Global investment themes: telecoms, media and technology Issue No. 43 Big 10 April 2012 Today, the world will create 2.5 exabytes (2.5 x bytes) of data. At current growth rates, that figure will reach 95.8 exabytes per day by is growing so fast that 90% of the stored data in the world today has been created in the last two years alone. This data is created from hundreds of sources, including s, documents, apps, pictures, videos, tweets, Facebook posts, and credit card transaction details, to name but a few. Geeks call this big data. Its typical characteristics high velocity, high volume and high variety make big data difficult to interpret. For investors, the big money lies in smart analytics. Just as Google developed the best algorithms for internet search, someone somewhere will develop a winning set of algorithms for analysing big data, converting zettabytes of unstructured garble into business intelligence. Traditional methods of analysing data are unable to cope with digital information produced on this scale. So the search is on for new database technologies that can interpret and analyse big data speedily and cost effectively. The incumbent database companies like IBM and Oracle are investing billions in building analytical engines. Several next generation database platforms such as Hadoop are open source, enabling new entrants like Cloudera to enter the database market without substantial investment. Meanwhile, many internet companies from Amazon to Baidu have become so impatient with the IT industry that they have developed custombuilt databases of their own to handle the big data flowing out of their ecosystems. Search engines like Google and social networks like Facebook already have sophisticated analytical black boxes capable of handling big data, but even they are struggling to commercialise rich, unstructured data such as video. Inside Big Industry size Growth drivers Investment risks Market trends Supply chain Key players Recent TMT themes Mobile payments Internet advertising Mobile bandwidth shortage Patent wars The app Internet HTML5 Emerging Mobile OS Quad-core processors Global slowdown scenario App revolution Music, video, social Cyber security Video games Chinese Internet Regulation Cloud computing All this data has to be stored somewhere, so storage companies like EMC, Dell, HP and NetApp will benefit, as will network infrastructure companies like Brocade, SGI and Cisco; it has to be accessed by corporations, so enterprise software companies like Citrix Systems, Red Hat, Salesforce and VMware will continue to grow too. Security is a big issue, so niche players like Trend Micro, Verint Systems and Check Point Software will play a profitable role; and much of the data will need to be passed reliably, securely and quickly from machine to machine (M2M), which is where telecom operators from AT&T to China Telecom to Vodafone are poised to make big gains, though regulators may cap profits. Big data will give IT services companies like Accenture, Infosys, and Tata Consulting Services a new product to sell, though specialists like Informatica may gain the lion s share of the market. centres like Rackspace and Telecity will also gain. However, leading players in the business information market like Pearson, McGraw-Hill and Thomson Reuters will need to stay one step ahead of big data technology as a new wave of digital data resellers emerges unless they want to suffer the fate of book publishers. Cyrus Mewawalla +44 (0) High quality research requires investment Please do not copy or forward CM Research 2012

2 Contents WHAT IS BIG DATA?... 3 GLOBAL MARKET FOR BIG DATA... 4 Industry size...4 Growth drivers...5 Investment risks...5 SUPPLY CHAIN... 6 Big data production...6 Big data management...8 Big data consumption How does it all fit together? INTERNET COMPANIES DATA STORAGE, NETWORKING AND HARDWARE COMPANIES ENTERPRISE SOFTWARE COMPANIES CYBER SECURITY COMPANIES TELECOM OPERATORS APPENDIX: BIG DATA PLAYERS Analyst disclosure: Prior to becoming an equity analyst within the investment banking sector in 2000, the author spent ten years at PricewaterhouseCoopers (PwC), much of that time at PwC Consulting in London where he worked on several enterprise resource management (ERP) system implementations involving a host of companies mentioned in this report, including SAP, Oracle, Cognos, Business Objects and others. In 2002, PwC Consulting was acquired by IBM. 2

3 What is big data? Big data is data that cannot be analysed on a traditional database Companies that develop the database platforms to analyse big data will make a fortune Big data is the next technology problem looking for a solution When Google came up with its successful search algorithms over ten years ago, it transformed the internet search industry and secured a decade of supernormal profits for itself. Today, there is a deluge of data on the internet. It comes from web crawlers (spiders), web robots (bots), web logs (blogs), s, videos, tweet streams, genome sequences, traffic-flow sensor data, banking transactions, GPS trails and much more. This data, if properly interpreted can be used defensively to combat theft, fraud, cyber-attacks or terrorism; it can also be used commercially to target sales or provide business intelligence. So it is valuable to governments, banks, marketing agencies, social networks, retailers and business information providers. But there is a problem: it is so complex that it cannot be processed using conventional methods. The digital unit scale Unit Symbol Size Bit b 0 or 1 Byte B 8 bits Kilobyte KB 1,000 B Megabyte MB 10 6 B Gigabyte GB 10 9 B Terabyte TB B Petabyte PB B Exabyte EB B Zettabyte ZB B Yottabyte YB B Source: CM Research In the digital age, the companies that provide the solution to this big data problem will spawn a new, multi-billion dollar industry. Given the complexity of the task, first mover advantage is likely to last for a considerable period before new entrants can enter the market. That is why the world s largest technology companies from Amazon, Google and Facebook to IBM, Cisco and Oracle to China Telecom, Verizon and Vodafone are all investing heavily in researching this space. The big money lies in developing the analytical engine that can intelligently interpret big data. Autonomy was a British software company that was the closest thing to a pure-play stock in this growth industry, but last year it was acquired by Hewlett Packard. Given that most successful business intelligence companies are based in the US and much of the funding for new ones comes from Silicon Valley, it s a fair bet that tomorrow s big data analytics engine will be designed in America. But, given the many cultural and language barriers across the worldwide web that play a part in interpreting big data, it is also possible that a Chinese and an Indian one could emerge simultaneously. Defining big data Big data refers to any data that cannot be analysed by a traditional database due to three typical characteristics: high volume, high velocity and high variety: High volume: big data s sheer volume slows down traditional database racks High velocity: big data often streams in at high speed and can be time-sensitive High variety: big data tends to be a mix of several data types, typically with an element of unstructured data (e.g. video), which is difficult to analyse Much of this data, if properly analysed, can provide companies a competitive advantage. But traditional relational databases such as Oracle, Microsoft s SQL Server or IBM s DB2 are not capable of handling this kind of data. So new technology platforms are required. These new platforms are likely to be built around four pillars: Big data s characteristics make it difficult to analyse V 3 = High Volume, High Velocity and High Variety Source: IBM Deep Analytics: sophisticated algorithms that can uncover patterns, knowledge and insights High Scalability: data handling capabilities that can scale to hundreds of petabytes or even exabytes High Flexibility: the agility to cope with several data types, from to text streams to video Real-time: the processing power to analyse and act on dynamic, real-time data 3

4 Global market for big data Digital information is growing at 57% per annum globally With global social network penetration and mobile internet penetration both under 20% this growth has only just begun All the data generated is valuable, but only if it can be interpreted in a timely and costeffective manner IDC expects revenues for big data technology infrastructure to grow by 40% per annum for the next three years Industry size In 2006, IDC estimates that the world produced 0.18 zettabytes of digital information. It grew to 1.8 zettabytes in 2011 and will reach 35 zettabytes by That translates to a ten-fold increase over the last five years and an astounding 29-fold increase over the next ten years. This year, the world s digital information is expected to grow by 57%. Two subsets of this digital data are internet traffic, which is growing by 35%, and mobile data traffic, which is growing at 110%, according to Cisco. Globally, all kinds of data are growing fast Digital information is growing at 57% IP traffic is growing at 35% Mobile data traffic is growing at 110% Total stored digital information in world Source: IDC, Cisco, CM Research Zettabytes PB/month Global IP traffic by type 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10, VoIP Online gaming Video calling Web, Internet video File sharing Business PB/month Global mobile data traffic by application type 12,000 10,000 Video 8,000 6,000 4,000 2, File sharing Other (M2M, gaming, VOIP) The big data industry is worth somewhere between $30bn and $200bn Analysing big data requires a host of technologies, ranging from data storage to server infrastructure to security applications to business intelligence software to cloud-based services. This, coupled with the fact that the industry is still nascent, makes it difficult to estimate market size. Estimates for the industry size today depend on definitions and range anywhere from around $30bn to $200bn. In the chart opposite, we take an average figure from several sources. According to IBM, the total market for big data analytics (including software, hardware and services) in 2010 was worth $155bn. That is forecast to grow at 6.1% to $208bn by In 2011, the big data market was worth $82bn Software and services account for 84% 2011 global market for Big ($82bn) Software 35% Services (eg IDC looks at big data from a different perspective. cloud) It expects the technology infrastructure section of 49% the industry to grow from $3.2bn in 2010 to Hardware $16.9bn in 2015, a CAGR of 40%. Within that 16% figure, IDC expects big data storage revenues to grow at 61% between 2010 and Separately, Source: IBM, IDC, Gartner, GigaOm, CM Research it expects the software portion of the market to grow at 7.5% CAGR from $25bn in 2010 to $34bn by

5 Growth drivers Smartphones, tablets, sensors, social networks, online games, video streams and mobile payments will all drive big data for many years to come. Growth drivers include social networks, the mobile internet and apps In 2011, there were 1.2bn social 1.2bn mobile internet subscribers, network users, growing at 19% growing at 41% Social network user penetration (% pop) 28% 26% 24% 22% 20% 18% 16% 14% 12% 10% Source: emarketer, CM Research Global Energy Model 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Mobile internet subscribers as a % of population and 32bn app downloads, growing at 185% No. of apps (bn) No. of app downloads worldwide As the digital age shifts into overdrive, demand for analytical tools will grow: government intelligence agencies will demand better analytical tools to sift through surveillance data to combat terrorism, crime and cyber warfare, whilst corporations will pay for business intelligence software to give them a competitive advantage. Investment risks Whilst big data industry revenues are certain to grow, investors face significant risks. Bandwidth risk Today, internet bandwidth prices are capped by most regulators, effectively making internet bandwidth a free resource for big data companies. But, without substantial investment by the world s mobile operators, big data is likely to grow far faster than the ability of the network to carry it. On mobile networks, many of which are close to capacity, traffic will rise from 0.6 to 10.8 EB/month between 2011 and 2016, according to Cisco. As networks get overloaded, network latency rises, reducing the speed and efficiency of analytical engines, especially those powered through the cloud. In the medium term, the coming mobile bandwidth shortage will shift competitive advantage within the internet economy from technology companies to telecom operators. Open source risk As we explain in the Supply Chain section on pages 6 to 11, the most commonly used big data technology platform today is Hadoop, based on open source software. Even the world s leading big data players from IBM to Oracle use Hadoop as the basic framework for their big data appliances, though they add value by writing the applications that run on it. Nonetheless, with the source code free, barriers to entry remain low. In the longer term, this may depress the database industry s margins. Patent risk Ever since Apple took on the mobile phone industry and won with barely a handful of mobile patents to its name, a patent war has erupted across the technology sector. Were a patent war to break out in the big data space, technological progress could be slowed down. Whilst regulators are unlikely to allow any hoarding of patents on anti-competitive grounds, the risk remains. Oracle, a leader in big data, is well known for filing multi-billion dollar patent infringement lawsuits against its competitors. Cyber risk Last month Global Payments, a credit card transaction processor, admitted that hackers had stolen the details of 1.5m North American card holders. This is the latest in a string of security breaches that have hit companies dealing in big data. Apple, EMC, Google, Oracle and Sony are all recent hacking victims. As the level of cyber-crime rises, so does the risk of dealing with big data. Just as the Fukushima incident dampened prospects for the nuclear sector, so a large cyber-attack could adversely impact big data industry profits. Regulatory risk In addition to security risks, regulators are clamping down on data privacy. The US, Europe and several Asian countries are looking at revising their data compliance and data privacy laws. That could limit the production and consumption of data by both businesses and governments. Big data can also fall fowl of copyright laws. As the amount of digital data flowing through analytical engines grows, so do the risks of bigger regulatory breaches and fines. 5

6 Supply chain Traditional database companies like Oracle and IBM face disruptive threats from open source and cloud platforms The real money is likely to be in business intelligence, rather than databases Much of the innovation especially in terms of database business models is in the cloud As the big data industry evolves, four trends are emerging. First, data is moving from structured to unstructured format, raising the costs of analysis. This creates a highly lucrative market for analytical search engines that can interpret this unstructured data. Second, proprietary database standards are giving way to new, open source big data technology platforms such as Hadoop. This means that barriers to entry may remain low for some time. Third, many corporations are opting to use cloud services to access big data analytical tools instead of building expensive data warehouses themselves. This implies that most of the money in big data will be made from selling hybrid cloud-based services rather than selling big databases. Finally, in future, a growing proportion of big data will be generated from machine to machine (M2M) using sensors. M2M data, much of which is business-critical and time-sensitive, could give telecom operators a way to profit from the big data boom. In this section, we look at some of these trends. But, first, we summarise the supply chain for the big data industry, splitting it into three sub-sectors: Big data production: raw data is gathered from several sources on an industrial scale Big data management: the raw data is cleansed, filtered and analysed, improving data quality Big data consumption: this high quality data is used commercially as business intelligence What does the big data supply chain look like? Big Production Big Management Big Consumption Social media Documents bases Web crawlers Web robots (bots) Sensors Voice Music & video RFID Call records Payment details GPS Volume Velocity Variety Storage Big quality bases Security Analytics Mining Search Digital Marketing Re-selling Gather raw data on industrial scale Improve big data quality Commercialise big data Source: CM Research Big data production In this section of the value chain, raw data is gathered from a variety of sources on an industrial scale. The sources include documents, corporate databases, search requests, social network posts, , video, call records and credit card transactions. Whilst much of this data is generated directly by humans and posted onto the World Wide Web, an increasing amount is coming from sensors that collect data autonomously from machines and send it to other machines for processing, analysis and retention. Examples include apps that 6

7 continuously update themselves, synchronising software such as icloud or smart meters. Much of the growth in big data will come from this kind of M2M data. Structured vs. unstructured data Industry commentators normally classify big data into two categories: structured data and unstructured data. Structured data such as that found in a corporate database is relatively easy to analyse. Unstructured data, which includes voice, video, and documents, can be difficult and expensive to analyse. Structured data can be compartmentalised in accordance with its properties. For example, an Oracle database an example of structured data segregates data by field (e.g. user name, user address, etc.) at the input stage, making it easy to handle when used for other purposes. Structured data, no matter what it is used for, has a high degree of integrity, accuracy and completeness. Unstructured data will never have the same degree of data integrity because it often depends on a machine interpreting nuances Unstructured data is difficult and expensive to analyse Big data classification Ease of use Classification type Easy and cheap to analyse Difficult or expensive to analyse Requires extensive infrastructure Source: CM Research Structured data Unstructured data Sensor data (machine-to-machine) bases XML data warehouses Enterprise systems Social media Voice, music & video Documents RFID GPS QR Temperature that only humans can understand. Anyone who has ever used voice recognition software can see this point in action; no matter how well a computer is programmed, it can never interpret every voice, accent and emotion. So unstructured data must be tagged. This can be a labour-intensive process that is open to manipulation. A tag is a form of metadata (i.e. data about other data) attached to a web item by its creator that helps describe the item and allows it to be found again by browsing or searching. Hashtags on social networks like Twitter (e.g. #OccupyWallStreet) allow its micro-bloggers to accurately target their tweets to those who most want to hear them, thereby converting unstructured data into semi-structured data. But tags can be manipulated, giving savvy internet users the power to fool sophisticated analytical engines. Indeed, a legitimate industry has sprung up in recent years to do just that. Search engine optimisation (SEO) companies can move their clients websites to the top of a Google search ranking by manipulating tags. Key players in big data production Big data is produced by governments, corporations and internet companies. Social networks like Facebook, Twitter and Sina Weibo are one of the largest creators. Browser software companies like Apple and Microsoft also collect a lot of data, as do search engines like Baidu and Google. Apps developers who create software for Apple s App Store and Google s Android Market create a significant amount of M2M data where data is passed from mobile devices to the app developer via the mobile operating system used by your connected device. This data is then sold on to third parties for marketing or further analysis. Banks collect big data from their payment transaction systems and electricity companies are increasingly using M2M data sent from smart meters to monitor energy usage. All these data producers need to analyse the data they collect. Some, like Facebook, Google and Microsoft build custom databases designed to handle data that is specific to their industry. Others, in the banking sector, electricity sector or government departments, use databases built by the likes of IBM and Oracle. Those who cannot afford to do either of the first two steps have the option of using cloud-based services where they gain temporary access to someone else s physical database and analytical engines. 7

8 Big data management In this section of the value chain, data quality is improved by cleansing, filtering and analysing it. Several players are involved in the process of improving data quality storage companies like NetApp and EMC warehouse the data; database companies like Oracle sort the data; business intelligence companies like Cognos (IBM) analyse this data; server manufacturers like Cisco, Dell and HP help build the data centre infrastructure; networking companies like Brocade, F5 Networks and Juniper Networks ensure all the data communicate with each other efficiently; cyber security companies like Websense and Verint Systems help screen the data from threats; cloud services providers like Salesforce lower the costs of accessing these enterprise software services; and IT services companies like Accenture and Informatica help corporations piece all of this together. The main trends in big data management are: bases: these are moving away from relational databases (e.g. Oracle or SQL Server) to new database technologies such as NoSQL Processing: new, distributed database platforms such as Hadoop are emerging, that can process semi-structured data far more cost-effectively than traditional database tools Analytics: the value-add has moved from databases to analytics all the big database companies (IBM, SAP, Oracle) have been on an M&A spree, buying up business intelligence software houses such as Netezza and Aster Appliances: many big data players are merging their software and hardware to create big data appliances that provide one-stop solutions for big data analytics Cloud services: companies are moving from building expensive databases in-house to accessing someone else s database infrastructure from the cloud The big data supply chain management section ction Volume Velocity Variety dustrial scale Source: CM Research Big Management Storage Big quality bases Security Analytics Improve big data quality Big D DATABASES Today, 90% of data warehouses hold less than 5 terabytes of data. Yet Twitter alone produces over 7 terabytes of data every day! As a result of this data deluge, the database industry is going through a significant transformation. The incumbents in the database market Oracle, IBM and Microsoft face several threats. Here is a quick update on the story so far of the global database industry. Historically, relational databases were the industry standard The most popular database technology used today for capturing business data is the relational database management system (RDBMS), which was first created in the 1970 s.these relational databases are made by the likes of Oracle, IBM and Microsoft and use a computer language called SQL (Structured Query Language) to define, query and update the database. but these databases were not capable of handling big data Over the last decade, business data has changed dramatically, creating two problems for traditional database makers: first the sheer size of the data has increased into the petabytes range; and second the majority of business data that needs to be analysed today comes in unstructured format, such as or video. To deal with the first problem, RDBMS platforms typically scaled up vertically, by adding more CPUs or more memory to the database management system. The second problem could not be dealt with at all because relational databases simply cannot categorise unstructured data. so new databases like NoSQL and new processing platforms like Hadoop emerged The first businesses that had to deal with big data were the leading internet companies such as Google, Yahoo and Amazon. Google and Yahoo, for example, ran search engines which had to gather unstructured data like web pages and process them within milliseconds to produce search rankings. Worse, they had to deal with millions of concurrent users all submitting different search queries at once. So Google and Yahoo 8

9 engineers designed entirely new database platforms to deal with this type of unstructured query at lightning speed. They built everything themselves, from the physical infrastructure to the storage and processing layers. Their technique was to scale out horizontally (rather than vertically), adding more nodes to the database network. Horizontal scale out involves breaking down large databases and distributing them across multiple servers. These innovations resulted in the first distributed databases and provided the foundation for two of today s most advanced database technology standards, commonly referred to as NoSQL and Hadoop: NoSQL: a broad class of database which does not use SQL as its primary query language and is designed to handle semi-structured data (though without the level of data integrity associated with RDBMS) Hadoop and NoSQL are now used by Oracle Hadoop: a distributed database processing platform designed to store and analyse big data across several thousand nodes Together, NoSQL and Hadoop provide a framework for analysing big data in a fast and cost effective manner. Both are open source and both lower costs by storing data in smaller chunks across several servers. Source: Oracle They are able to process queries fast by sending several queries to multiple machines at the same time. Their main advantages are their low cost, high speed and high degree of fault tolerance. Their main disadvantage is they are not as accurate or complete as relational databases. Both Hadoop and NoSQL are now being embraced by the database incumbents In recent years, IBM and Oracle have acknowledged that their core RDBMS platforms are not designed to cope with big data. Together with Microsoft, EMC, Teradata and other big data industry leaders, they have incorporated emerging database technologies like NoSQL and Hadoop into their own big data platforms. There is a risk that open source database platforms may lower industry margins Whilst most relational databases were proprietary, Oracle is the market leader in databases Hadoop is open source. Some say that lowers barriers to entry and threatens the profit margins of the leading database players. The most exposed are Oracle and IBM, who own 42% and 24% of the database market respectively. But this risk may be overblown. Red Hat is a $12bn base market share by revenues, 2011 Others 12% SAP 3% enterprise software company that specialises in Oracle open source solutions. Moreover, while Hadoop 42% provides the basic infrastructure to cope with big Microsoft data, software developers still need to write the 19% business intelligence code that sits on top of it, so there is significant scope for each of the big players to differentiate themselves, despite IBM basing their big data appliances on an open source product. 24% Source: Company data, IDC, Gartner, CM Research ANALYTICS The lesson that Amazon, Google and Facebook all learnt early on in the digital age was that in order to build really fast big data engines you need all the ingredients to fit perfectly together the servers, the databases, the networks, the analytical engines and the security. That s why Google decided back in 2002 to build its big data analytical engines itself. Sometime afterwards, the leading players in big data like IBM, Oracle, HP, EMC, Teradata also came to this realisation. As the M&A chart on the following page demonstrates, each one of these industry leaders has been buying up the missing pieces in their portfolio of big data engine components. 9

10 Over the last five years, Oracle, EMC, HP, IBM, Microsoft, SAP and Teradata have collectively spent more than $45bn on buying software, security or storage companies. The bulk of this money has gone on business intelligence tools such as Netezza, Aster, Hyperion, Business Objects, SPSS and Cognos. Big data analytics is the new battleground in the technology sector. As databases become open sourced or commoditised, analytical engines will suck out most of the industry s profits. Business intelligence tools feature high in the target list for large technology companies The chart shows the transaction value (in $bn) of recent M&A deals in the big data technology space Source: CM Research SAP acquires Success Factors (Online HR software) Oracle acquires RightNow (Cloud computing) IBM acquires Algorithmics (Risk management software for Teradata acquires Aster ( analysis software) Acer acquires igware (Cloud computing) Dell acquires Force 10 Networks ( centre networking) Salesforce.com acquires Radian6 ( analysis software) Ericson acquires Telcordia (Enterprise software) CenturyLink acquires Savvis (Cloud computing) Apax acquires Epicor Software (Enterprise software) Apax acquires Activant (ERP software) GGC Software acquires Lawson software (ERP software) Verizon acquires Terremark (Cloud computing) Oracle acquires Art Technology (CRM software) Attachmate acquires Novell (Intelligent workload EMC acquires Isilon ( storage software) Misys acquires Sophis (Application software) IBM acquires Netezza ( analysis software) HP acquires 3Par ( storage) Hexagon acquires Intergraph (Mapping software) IBM acquires Sterling Commerce (B2B software) Warburg Pincus acquires IDC (Information management) SAP acquires Sybase ( analysis software) IBM acquires SPSS ( analysis software) EMC acquires Domain ( storage) Microsoft acquires llegro ( analysis software) SAP acquires Business Objects ( analysis software) Brocade Communications acquires Foundry Networks Oracle acquires BEA Systems (Enterprise applications Microsoft acquires FAST Search and Transfer (Enterprise Oracle acquires Hyperion ( analysis software) IBM acquires Cognos ( analysis software) CLOUD Both databases and analytics are moving to the cloud. This makes sense given the fact that big data can be very difficult and expensive for companies to analyse in-house. By moving to the cloud, companies can rent state-of-the-art super-computers and business intelligence tools that could previously only be afforded by the largest companies and governments. The leading big data cloud services on the market include Amazon Web Services, Salesforce.com s database.com, Microsoft s SQL Azure, IBM s SmartCloud and Oracle s base Cloud. This sector is also full of new entrants who use open source tools such as Hadoop and NoSQL to provide relatively cost effective business intelligence tools to all kinds of companies. Some of the fastest growing names include Cloudera, Greenplum (EMC), Hortonworks, Impetus, MapR, MetaScale (subsidiary of Sears Holding Corp), Mu Sigma, Nuevora, Opera Solutions, RedGiant Analytics, Scale Unlimited and Think Big Analytics. 10

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