Competitive Advantages of Big Data

Volume: Enterprises are awash with ever-growing data of all types, easily amassing terabytes-even petabyte of information.

•          Turn 12 terabytes of Tweets created each day into improved product sentiment analysis
•          Convert 350 billion annual meter readings to better predict power consumption

Velocity: Sometimes 2 minutes is too late. For time-sensitive processes such as catching fraud, big data must be used as it streams into your enterprise in order to maximize its value.

•          Scrutinize 5 million trade events created each day to identify potential fraud
•          Analyze 500 million daily call detail records in real-time to predict customer churn faster

Variety: Big data is any type of data - structured and unstructured data such as text, sensor data, audio, video, click streams, log files and more. New insights are found when analyzing these data types together.

•          Monitor 100’s of live video feeds from surveillance cameras to target points of interest
•          Exploit the 80% data growth in images, video and documents to improve customer satisfaction

Veracity: 1 in 3 business leaders don’t trust the information they use to make decisions. How can you act upon information if you don’t trust it? Establishing trust in big data presents a huge challenge as the variety and number of sources grows.

Competitive Advantages of Big Data

Although Hadoop initially has been used by large web companies such as Google, Yahoo, Facebook for applications such as search engines, but its potential is much, much more. This report details what Hadoop is (and isn’t), brings into focus the key technologies and their interplay, provides a perspectives on different players, who’s doing what to productize them and how they fit into the ecosystem. It profiles the growing number of companies — from startups like MapR to Cloudera, the present leader in the space leveraging Hadoop plus 52 others – both announced and in stealth, to the strategies being adopted by Relational Database/Data Warehousing/ Business Intelligence/Data Integration incumbents like Oracle, IBM, Microsoft, SAP, Teradata, SAS, Microstrategy etc. to embrace the emerging technologies while new Big Data Infrastructure entrants the likes of EMC, NetApp, Cisco, Dell, Fujitsu, HP, Adobe and scores of others planning new products to address this space. . It charts out the SWOT analysis of both leaders and new suppliers as well as their competitive positioning and strategies. 

The report outlines how the market opportunities in Big Data are crystallizing to pick up serious steam while taking into account the challenges still hindering widespread adoption and where potential users can expect the market to go. It presents a 5 year market forecast 2010-15, market shares of leaders, likely M& A scenarios and examines go-to-market plans of leaders to provide big data solutions for several vertical industries such as financial services, healthcare and media. Finally it provides recommendation for vendors, channel players, end users and investors for timely play to leverage the opportunities presented by the emerging big data markets

To capitalize on the Big Data trend, a new breed of Big Data technologies (such as Hadoop and others) many companies have emerged which are leveraging new parallelized processing, commodity hardware, open source software and tools to capture and analyze these new data sets and provide a price/performance that is 10 times better than existing Database/Data Warehousing/Business Intelligence Systems. While most people would think of Google, Facebook as Media companies. In reality they are a myriad of other high growth internet oriented Big Data companies because the reality is that their businesses have been created due to their ability to effectively harness Big Data to their business advantage (e.g. Big Table from Google)

The report outlines how the market opportunities in Big Data are crystallizing to pick up serious steam while taking into account the challenges still hindering widespread adoption and where potential users can expect the market to go. It presents a 5 year market forecast 2010-15, market shares of leaders, likely M& A scenarios and examines go-to-market plans of leaders to provide big data solutions for several vertical industries such as financial services, healthcare and media. Finally it provides recommendation for vendors, channel players, end users and investors for timely play to leverage the opportunities presented by the emerging big data markets

To capitalize on the Big Data trend, a new breed of Big Data technologies (such as Hadoop and others) many companies have emerged which are leveraging new parallelized processing, commodity hardware, open source software and tools to capture and analyze these new data sets and provide a price/performance that is 10 times better than existing Database/Data Warehousing/Business Intelligence Systems. While most people would think of Google, Facebook as Media companies. In reality they are a myriad of other high growth internet oriented Big Data companies because the reality is that their businesses have been created due to their ability to effectively

With the explosion in the use of the Internet, vast majority of data growth is coming in the form of data sets that are not well suited for traditional relational database vendors like Oracle. Not only is the data too unstructured and/or too voluminous for a traditional RDBMS, the software and hardware costs required to crunch through these new data sets using traditional RDBMS technology are prohibitive. To solve this, new companies have emerged that through using new Big Data technologies, are leveraging commodity hardware and open source software - from data capture, operational integration, advanced analytics to visualization.

Is Big Data trouble for the Existing Database Order?

The economics inherent in using open source big data software running on commodity hardware will drive companies to consider these next generation systems when implementing new systems for new non transactional workloads. This is despite of being faced with the concomitant cost of developing expertise in the emerging alternatives. To illustrate this cost discrepancy in achieving high-performance solutions being provided today by traditional database vendors vs. the price points solutions put together using open source software and commodity hardware by the new generation big data vendors, three separate use cases illustrate the savings that are easily achieved as shown in the diagram as follows: (For details see chapter 3 of this report)

If RDBMS Could Handle Big Data Volumes, Why Bother? 

the economics inherent in using open source big data software running on commodity hardware will drive companies to consider these next generation systems when implementing new systems for new non transactional workloads, despite being faced with the cost of developing expertise in the emerging alternatives.

Leveraging the Competitive Advantages of Big Data

Much as Hadoop initially has been used by large web companies such as Google, Yahoo, Facebook for applications such as search engines, but its unrealized potential is huge.

Venture Investments Accelerated for Big Data 

Hadoop isn’t the only thing going in big data, but it’s driving the bus at this point and it seems to have a Midas touch: everything that touches it turns to gold. Hadoop-based startups have raised $104.5 million since May 2011. This is over and above $159.7 million raised since 2009 till May 2011 when Cloudera closed its first round. These totals don’t include the recent or unattributed rounds for Odiago, high investment for Yahoo spinoff Hortonworks (now adopted by Microsoft). In addition the NoSQL in their focus on unstructured data, also have announced just more than $90 million in funding overall. Not counted in the above numbers are: - Opera solutions $84M (Silverlake, JGE, Accel, KKR, Tola Capital) - Infineta $15M (Rembrandt Venture Partners, Alloy Ventures, North Bridge Venture Partners) - Xignite $10M (StarVest Partners, Spring Mountain Capital) - Platfora $5.7M (Andreessen Horowitz, In-Q-Tel) Never mind, the many analytic database vendors that play in the big data arena that have been acquired over the past couple years for billions in aggregate.

The Big Data 2011 Industry Report (updated in Feb 2012) brings into focus the key Database and Big Data technologies and their interplay, provides a perspectives on different players, who’s doing what to productize them and how they fit into the ecosystem. It profiles the growing number of companies — from startups like MapR to Cloudera, the present leader in the space leveraging Hadoop plus 104 others in full detail – both announced and in stealth, to the strategies being adopted by Relational Database/Data Warehousing/ Business Intelligence/Data Integration incumbents like Oracle, IBM, Microsoft, SAP, Teradata, SAS, Microstrategy etc. to embrace the emerging technologies. It outlines how new Big Data Infrastructure entrants the likes of EMC, NetApp, Cisco, Dell, Fujitsu, HP, Adobe and scores of others are planning new products to address this space. 

It charts out the SWOT analysis of leaders and new suppliers as well as their competitive positioning and strategies. The report lists the various operational business intelligence challenges companies face and provides guidance as to how enterprise IT leaders can harness the deeper insights provided by Big Data to manage complex operational problems and empower their management in knowledgeable decision-making to get the competitive advantage for their companies. 

For IT, the report lays out the rapidly evolving big data technology ecosystem - different big data technologies from Hadoop, Distributed File Systems, emerging NoSQL derivatives for implementation in private and hybrid cloud-based environments, Storage Infrastructure Requirements to Store, Access, Secure, Prepare for analytics and visualization of data while manipulating it rapidly to derive business intelligence online, to run businesses smartly. 

For the Operational IT Management, the report delineates the operational issues businesses companies still encounter today in using legacy RDBMS systems despite their embracing fast access in-memory and solid state storage technologies. It details how IT is harnessing the emergent Big Data to manage massive amounts of data and new techniques such as parallelization and virtualization to solve complex problems in order to empower businesses with knowledgeable decision-making. 

Further, the report outlines how the market opportunities in Big Data are crystallizing and picking up serious steam and to understand the challenges still hindering widespread adoption and where potential users can expect the market to go. It presents a 5 year market forecast 2010-15, market shares of leaders, likely M& A scenarios and examines go-to-market plans of leaders to provide big data solutions for several vertical industries such as financial services, healthcare and media. Finally it provides recommendation for vendors, channel players, end users and investors for timely play to leverage the opportunities presented by the emerging big data markets.

Companies Analyzed

This service reviews the strategies, market positioning, and future direction of several providers in Big Data, including:

Alteryx, Attensity, Attivio, CloudIQ, Cloudera, Concurrent, Cray, DDN, Datameer, Dell, Digital Reasoning, EMC, GridGain, HP, HStreaming, Hadapt, Hortonworks, IBM, Informatica, Jaspersoft, KXEN, Karmasphere, Kitenga, MapR, Microsoft, Mu Sigma, NetApp, Objectivity, Opera Solutions, Oracle, ParAccel, Pentaho, Pervasive, Platfora, Progress Software, RainStor, Revolution Analytics, SAP, SAS, SGI, Splunk, SyncSort, TIBCO, Talend, Teradata, TideMark, Tresata, Versant, and Zettaset.

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Big Data & High-Performance-Analytics from SAS Deutschland

Big Data & High-Performance-Analytics from SAS Deutschland

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Experience capital gains using Low Latency Trading infrastructure

Capital Markets live and work on just one thing – Speed. Speed is seen in terms of speculation, execution and analysis. However, in the world of capital markets, the tick price of the financial instruments changes rapidly. In such scenarios, sometimes there is a delay between execution of orders and change of tick price. This delay is termed as Latency.

It’s high time since capital markets have shifted into the electronic domain and employ the use of computers and networks. Electronic Trading has taken place of traditional ring trading and is now governed by the control of exchanges. However, most of the times, the stock prices seem to change in a fraction of milliseconds and a time gap is introduced before the updated prices are visible to the users. To tackle this delay, exchanges and firms are constantly looking to implement a low latency trading infrastructure.

Latency introduced in the realm of capital markets is of two types. First type involves the delay between the actual price and price shown on the screen. The second type of delay involves the difference between the price at which order is placed and what it gets executed. Market participants and capital firms are constantly striving to reduce the latency in order to gain a competitive edge.

Many of the leading IT firms came up with a solution to inhibit or reduce latency significantly. They have introduced an infrastructure of systems, which comprises of high speed network connections and fast trading platforms. The collaboration of both these entities is believed to decrease the problem of latency. The infrastructure developed is called Low Latency Trading Infrastructure and is actively deployed on leading stock exchanges around the globe.

The first component comprises of network elements for connectivity. High speed routers and reliable internet connections are used. Further, to tackle issues of disconnection, leased lines are used. Internet Service Providers (ISP) provides data lines from servers having the least downtime. Thus, any change in the stock price will be immediately given to the trading software.

The second component consists of high speed trading platforms. The systems work on real time basis and instantly show responses displayed. They also facilitate instant order placements and the turnaround time taken to place an order from the user terminal to the exchange is dramatically reduced. Apart from this, all the tick prices are displayed on the client’s terminal on real time basis. So, instant change in price is quickly visible on the trader’s screen.

Low Latency trading platform has been implemented by New York Stock Exchange in association with popular investment and capital trading firms. It has immensely improved the scope of trading and helped individuals attain profitable returns.

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10 reasons to choose Big Data Analytics

Big Data is a recent concept that has struck the IT industry in a hard and fast way. It is seen that all kinds of new technologies are first adopted by larger enterprises and then considered by midsized companies. With time, the amount of data to be processed is going to increase drastically. In order to mitigate the risk of managing loads of data, Big Data Analytics is said to be a feasible solution.

Before you choose any kind of analytics, here are the top 10 reasons you should consider

1) Enterprise wide real-time indexing of data from any machine

Big Data uses NoSQL databases like Hadoop. Hadoop uses file indexing instead of data indexing carried in typical relational databases. Thus, it is able to handle diverse data comprising of different sources. Here, data is stored as it comes with definition left to analysis stage.

2) Easy search and analysis of data

Data – real time or historical needs to be recalled quickly as possible for the sake of analysis. Big Data provides search tools for calling unstructured data using text search. Thus, faster responses can be provided.

3) Automatic knowledge discovery from data

Data mining and predictive analytics technologies discover the data to propose statistical models. Based on these models, continuous evaluation is automatically carried to find valuable insights.

4) Real time data monitoring-

Big Data provides continuous monitoring and processing of coming data. Based on any anomalies, real time alerts are passed out. This is essential for every business to flourish.

5) Powerful ad hoc query processing

Based on the requirements of management, different reports are needed, and ad hoc queries need to be passed. Big Data provides ad hoc analysis and report generation and aids in the short term decision making.

6) Ability to build custom dashboards and views

It is very difficult for having custom views for unstructured data. Big Data overcomes this problem by providing custom views. One can write custom Hive statements and get the necessary views.

7) Efficient data scaling

Big data analytics uses unstructured data storage, and data is stored as it comes without any problems of indexing.

8) Provide Role based security         

Data in Big Data Analytics is diverse and comprises of different segments. At this level, security regarding access of data becomes a crucial concern. Big Data provides Access control lists to maintain confidentiality of data stored.

9) Flexible deployment

When new technologies are adopted, a considerable time is spent to learn and experiment it. Big Data comes with a smooth learning curve utilizing minimum implementation time and faster deployment.

10) Portable interfacing

Big Data is versatile and can be interfaced with any programming API. Thus, different programs can be written based on capabilities of the developer.

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5 ways in which Big Data adds value to your business

Data is an integrated part of every business which runs in the global economy. Companies happen to churn out a massive amount of data consisting of trillion of bytes of transactional history associated with process including customers, suppliers and operations staff. The age of internet has introduced millions of bytes of networking data and huge amount of resultant digital ‘exhaust’ data. And not to forget the world of multimedia where data is the heart of all activities. ‘Big Data’ refers to such huge datasets whose size expands beyond the capabilities of the typical database capturing tools. Besides, the enormity associated is due to dynamic nature of data that changes with time. To manage such huge terabytes of data, we need an efficient architecture like ‘Big Data Analytics’.

The implementation of Big Data has been seen encompassing many industries in the economy. Different industries have different processes highly reliant on transactions and predictability. To assist this requirement, big data plays a vital and viable role.  Big data helps in increasing the value of the organization. Here are the 5 prime ways in which this happens.

1) Creates transparency
It is highly essential to provide transparency to the clients regarding the transactions carried over the system. Making the big data accessible to the relevant stakeholders in a timely manner increases the value of the organization. For instance, in public sector, making the necessary data available on demand across the departments will acutely reduce the search and processing time.

2) Enables opportunity to exploit needs, variability and performance
As extensive amount of transactional data gets stored in the digital form, organizations are able to collect comprehensive and accurate performance data on real time basis. Data captured may range from inventory levels to attendance of personnel. Big Data enables to set up controlled experiments which can ascertain variability, velocity and performance issues. Thus by identifying root causes one can enhance performance of processes.

3) Segmentation of users as per requirements
Big data allows implementation of highly specific segments and develop customized solutions to meet their requirements. This practice was prevalent in the risk and marketing department. By introducing it in other sections, companies are able to develop and provide customer centric services.

4) Replacing human participation in decision making with algorithms
The complex analytics of data helps in improving the decision making process, minimize the risks and disclose valuable insights or facts which would not be visible normally. This will help in maintaining the inventory levels and maximize sales when associated to the retail industry.

5) Innovating new strategies and models for businesses
Big Data enables the addition of new products and services to the existing line of products and services. Thus, it helps in exploring the unseen opportunities and aids in analysis of customer demands associated with making the product better.

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