Big Data And Analytics

INFOGRAPHIC: The Physical Size of Big Data

noreply@blogger.com (Anonymous) — Thu, 16 May 2013 19:04:00 +0000

Today’s organizations are dealing with an astronomical volume of information. Is your business capable of transforming all this “big” data into “this-actually-makes-my-business-better” data? When there’s enough of it piling up to reach the moon, it’s no easy task. In this infographic, Domo breaks down a report from Stanford University to see just how big “big data” really is.

How to tackle the 4V’s with Big Data

noreply@blogger.com (Anonymous) — Tue, 14 May 2013 16:34:00 +0000

Each day, millions of bytes of data are created owing to the transactions carried on the internet. Every day, the Ecommerce industry rises and companies are using newer technologies to make the websites more user-friendly. The upcoming generation believes in plastic money, and the transactional data in the Ecommerce industry rises tremendously. To support the transactions, the companies need associated processes, which cater the demands of the customers and ensure the smooth functioning of the company. Hence, a tremendous amount of data happens to be generated on a daily basis in any company.

Based on private research, it is found that different data produced needs to be managed, and the Data Management system needs to follow certain criteria. Big Data is the latest ground-breaking invention to tackle huge amounts of data. The following criteria is required to evaluate any Database management system and Big Data efficiently meets it.

1st V: Volume
Enterprises are growing with a huge amount of data. The integration of social media platforms has poured in ‘Tweets’ and ‘Updates’ worth a few Terabytes of data. People post reviews of products on blogs and other media, and eventually everything ends up coming in the company. Even service and utility companies like Power companies have to capture meter readings from a number of people and the subscription list rises every day. To tackle this huge amount of data, Big Data has proposed the revolutionary ‘No-SQL database’. This technology stores data as it comes without losing any of it. It offers the minimum delay, unlike its Relational counterparts.
2nd V: Velocity
Every now and then reports are required, and the executives require information to create them. Due to different departments in the company, the executives query different sections of database and information needs to be retrieved speedily. Big Data employs No-SQL technology like ‘Hadoop’, which does not store data in tables. The data gets stored in clusters and based on requirements you can retrieve the data from the cluster and meet your requirements. As entire database need not be scanned, the velocity of the system is considerably higher.

3rd V: Variety
Different sections of the company have different processes and each have different attributes. Sometimes, data needs to be passed and maintained across different departments. Big Data stores data in clusters and is not defined by Entity-Relationship parameters. Hence, it allows the storage of complex data like multimedia without any trouble. Thus, it is known to manage a variety of data.

4th V: Veracity
The management class does not trust information, if it is just from a single source. Big Data can aggregate data from diverse sources and improves the credibility of information. Thus, it promotes the veracity of data in an effective way.

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Big Data Analytics in action in the Telco industry.

noreply@blogger.com (Anonymous) — Mon, 06 May 2013 23:42:00 +0000

Source: http://www-01.ibm.com/software/data/bigdata/

Deepak Rangarao, IBM Client Technical Specialist, takes us through a demo showing Big Data Analytics in action in the Telco industry. IBM's Big Data platform is at the heart of the solution that includes a real time dashboard and data mining from Netezza, InfoSphere Streams processing and scoring CDR's, BigInsights and ad-hoc analysis using BigSheets.

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Competitive Advantages of Big Data

noreply@blogger.com (Anonymous) — Mon, 06 May 2013 22:25:00 +0000

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.

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

noreply@blogger.com (Anonymous) — Mon, 06 May 2013 21:53:00 +0000

Big Data & High-Performance-Analytics from SAS Deutschland

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Experience capital gains using Low Latency Trading infrastructure

noreply@blogger.com (Anonymous) — Mon, 29 Apr 2013 23:49:00 +0000

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

noreply@blogger.com (Anonymous) — Mon, 29 Apr 2013 23:41:00 +0000

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

noreply@blogger.com (Anonymous) — Thu, 25 Apr 2013 23:28:00 +0000

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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Big Data Analytics – the next human leap

noreply@blogger.com (Anonymous) — Wed, 24 Apr 2013 23:28:00 +0000

Since the past few centuries, there have been initiatives taken to come up with methods to predict human behavior. The current initiative is an effort to understand the world using ‘Big Data.’ Efforts have been made to assess behavior based on models developed on human nature. The basic understanding of big data involves gathering huge amount of data, observe the patterns emerging from it and estimate how things or people in this case will act out in the future.

Viktor Mayer-Schönberger and Kenneth Cukier clearly define in their book ‘Big Data’ that big data is a movement from causation to correlation. The people who happen to use big data are not psychologists or novelists, who apply the intuitive perspectives to explain the causal chain of happening of events. Many of them are analysts, statisticians and mathematicians that apply these techniques to loads of amounts of data in database to ascertain valuable information. Popular ecommerce companies like Wallmart use it for assessing the sales trend and inventory management outlook. A number of doctors in big hospitals use it for clinical trials to undertake the diagnosis of certain diseases in an anteceding manner.

Correlation is the key attribute of Big Data, which holds a great importance in developing perceptions about a certain process or service. Big Data helps in discerning meaningful correlations from meaningless ones by relying on certain causal hypothesis. Thus, Big Data Analytics helps in drafting a clear picture of what leads to what and what happens eventually.
Like beings ruled by instinct, we are discontinuous most of the times. Hence, the data collected may be ambiguous and full of outliers. Past mistakes help us to learn or mislearn. Hence, unpredictability is shelved with us and to understand about it, one needs predictive modeling – a service offered by Big Data Analytics.

Big Data has attained fame because it is able to store different types of information. The diversity allows analytics of data considering different views and dimensions. Hence, the concept is said to be broadly applied in the investment industry where huge amounts of data needs to be analyzed and patterns need to be deduced to assess the upcoming tick price for individual financial instruments (like Stocks).

Not only is Big Data Analytics limited to the finance industry, but also it finds its use in the corporate world. Decisions regarding production line and sales strategy involve studying of different reports. Big Data Analytics provides feasible reports that cover different realms of the organization and help in taking better decisions. Thus, with great potential of applications in different sectors, Big Data Analytics is said to be the next big human leap.

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3 prime needs to choose Big Data Analytics

noreply@blogger.com (Anonymous) — Wed, 24 Apr 2013 23:22:00 +0000

The rise of the dot com industry brought in a huge surge of data. The beginning of millennium brought in the intense requirement to manage huge amount of transactional data associated with ecommerce and other online businesses. Computers took over the working of departments of several industries bringing in the concept of enterprise wide data. Many forerunners came up with enterprise based solutions to tackle the terabytes of data, but the scalability was limited due the use of relational databases.

Big data is defined as collection of datasets, so vast and assorted, that a single database following relational storage mechanism is unable to store them. The traditional database management tools strike to be incompetent, when it comes to handle the complex data covering the different horizontals of the mentioned industry. To manage this special kind of data, we have Big Data Analytics.

Big Data analytics includes a process of analyzing large amounts of data with an intention to find useful patterns, usable correlations and other hidden opportunities that are not visible directly. Here are the 3 basic requirements which Big data analytics fulfills to make any business survive.

1) Effective decision making –
Big data analytics helps in scrutinizing data from different sections irrespective of category or department. This helps in finding hidden relationships which could seldom be found using analytics for typical relational databases. This assists the top management in making decisions lucrative for the business.

2) Capacity to manage industry wide data-
It is proven that all the departments of the industry are said to be inter-related and work in tandem to deliver the final product or service. Big Data allows the stratification of data with specific requirement based storage for individual departments. The IT managers find the implementation of Big Data Analytics feasible and help in providing reports aggregating diverse data sets keeping in congruence with the data relationships.

3) Use of predictive analytics in place of deterministic approach-
Traditional approach of analyzing data included querying of relational databases. This would take a considerable amount of time scrutinizing loads of datasets associated with different horizontals. Further, the portability of data would strike as an issue, when relational databases were used. Big Data Analytics employs the use of NoSQL databases like Hadoop and MapReduce. The queries put are dynamic and the nature of output is versatile and probabilistic as associated with the requirements of user. This gives the liberty to determine patterns and fill in the missing values with the use of complex algorithms available in Big Data Analytics. Thus the decision process becomes time critical with greater accuracy.

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Big Data analytics – an IT requirement

noreply@blogger.com (Anonymous) — Wed, 24 Apr 2013 23:17:00 +0000

There is a persistent hype in market when ‘Big Data’ is spoken of. However, data taken into consideration is nothing new. It’s just presented in a better assorted form with higher volumes taking over. With the rise of Internet industry, the volume of data flowing in and out has become colossal. And that’s when the industry experts came up with the concept of ‘Big Data’.

Big Data is typically a dataset, which happens to be huge and defined by several attributes. Also the defined dataset is not specific to a certain horizontal of industry. It traverses around different levels in the industry and gathers attributes which make it highly impossible to store and analyze for a typical Relational Database Management System. To fill the gaps, technical fore runners have come up with NoSQL databases like Hadoop to store this massive amount of data. However, data stored cannot produce results without applying analytics to it. So, Big Data Analytics is said to be the big news in the market.

Big Data analytics helps in addressing the 3 basic needs of any data – Volume, variety and velocity. The NoSQL databases have an efficient architecture much advanced over typical RDBMS, which provides solutions for storing this huge amount of data. Variety is a concern when the data obtained is from different sections of industry. The datasets encompass different factors that are independent and defined as per the sections of industry. Hence, there is a requirement to propose stratified solution to manage this diverse data. Big Data Analytics provide the feasible solution over the same. And when velocity is concerned, the highly stratified system works independently for every industry section, thus able to grant higher access speeds along with capacity to handle multiuser needs.

With the rise of internet and social media platforms, unstructured data sources have been commonly used. It has been reported that 84% of individuals are analyzing and processing unstructured data which hails from weblogs, social media, email, photos and videos. This data is sometimes in batch or sometimes real time. IT managers face the need to source out these both categories of data equally. However with time, the need to manage real time data is increasing. The prevalent IT infrastructure cannot handle the rising needs without turning cost sensitive. Hence, IT managers feel the need to choose Big Data Analytics.

Analytics of typical databases involved monotonous querying while NoSQL databases like Hadoop do not employ standard SQL querying techniques, allowing versatile queries to be used to gain probabilistic results. This gives an edge for Big Data Analytics to be used.

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4 best ways to increase company’s value using Big Data

noreply@blogger.com (Anonymous) — Mon, 22 Apr 2013 22:21:00 +0000

A decade back, companies have been engaging in collecting data from daily transactions and storing them in databases. The prime aim was to keep track of records or make some necessary forecasts. However, with the internet revolution, the amounts of data and its sources have risen to a drastic level. For instance, Weblogs are available to collect history of individual customer interactions. Similarly, marketing people constantly gather information about what people of their endorsed brands. Though it sounds conceptually easy, an entire array of new processes, technology and governing mechanisms are needed, which are together called as Big Data.

Following are the 4 broadly implemented strategies to increase value of company using Big Data-

1> Management of performance

Performance management typically involves proper understanding of the meaning of ‘Big Data’ in databases of company. This is done by using predetermined queries and multi-dimensional analysis. Data used for analysis is transactional in nature. A number of business intelligence tools are available under big data analytics for providing different types of reports and graphs. Thus, the performance of individual executive can be assessed and necessary training can be provided to improve their skill sets.

2> Exploration of Data

Data exploration implies heavy use of statistics to experiment over data and discover valuable insights. This helps in answering many unanswered questions of managers and grants them a competitive edge. For instance, Cluster Analysis is used to group customers based on purchase history in order to determine attributes, which may not have been visible to Analysts before. This aids the business in attracting prospect clients and thus sees a positive enhancement in the sales.

3> Social Analytics

This happens to be the unexplored section of data which has gained importance recently. Data is collected from popular social media platforms like Facebook, Twitter, etc in context to awareness, engagement and spoken words reach. Based on this social metrics, managers are able to draw conclusions about the success of digital marketing and advertising campaigns. Big Data Analytics report in this realm also helps in unraveling the unexploited opportunities which can be focused upon.

4> Science of Decision making

Decision science specifically involves processing of non-transactional data involving customer reviews, ideas and product feedbacks. This unstructured information is best sorted using the Big Data Analytics and suitable experiments can be carried over it. Thus, provisions are provided to source and poll ideas from various sources and draw decisions based on them. Thus, Big Data Analytics is a true benefactor of Decision making process.

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Do you know how Predictive analytics helps you to improve your business?

noreply@blogger.com (Anonymous) — Tue, 16 Apr 2013 23:49:00 +0000

Nowadays many big companies moving towards the approach of Predictive analytics, but still many of us still unknown with this term. Some of the question arrives like what is Predictive analytics? And how exactly it’s helpful to improve your business?

Now the big question is what is Predictive Analytics?

Predictive analytics encompasses a variety of techniques from statistics, modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future.

By using some of the different tools you can find out statistics and according to that you can make your future business strategies. By analyzing or outlining big data you can improve your knowledge about your business, competitors and mainly the customers. It helps you to reduce risk and make better decision which lead to better services to customers.

How to implement Predictive analytics?

To improve your business with the predictive analytics you need to consider some of the important steps.

1. Business Goal: For a successful predictive analytics project you need to be clear with your business goal. If you are clear with your business goal and implementing predictive analytics successfully it helps to increase in revenue. And for better business strategy lineup your goal should need to be clear.

2. Understanding and Preparing Data: For a successful predictive analytics data plays a key role. So you need to understand data from variety of different sources. Advanced data visualization tools help you to understand different sources data including social media, government data, and other public sources. The next important challenge in predictive analytics is data preparation. You need to be very careful while preparing data because raw data is often unsuitable for predictive analytics.

3. Creating and deploying predictive model: predictive analytics modeling tools run the analyst algorithm for best data analyst. Predictive analytics modeling tools runs to analyze data by that analysis you can get the predictive model. Once you complete with the creation of predictive model the next important steps come is evaluation of model. You need to test data set for effective predictive model. It runs continuous algorithm until they find the best predictive. And at the end the most important steps come i.e. deployment of predictive model in which the deployed model comprise the logic to implement predictive rules and provide appropriate formulas and method to get appropriate data the model exactly need to return the result.

4. Monitor effectiveness of predictive model: for a better and continuous result you need to monitor effectiveness of predictive model. To stay on top you need to continuous with the predictive analyst including business goal, understanding and preparing new data, creating and deploying new predictive model as per the new analyze data and continue with the monitor process.

So for better business strategies and increase the revenue you need to implement effective Predictive analytics.

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Big data analytics

noreply@blogger.com (Anonymous) — Tue, 16 Apr 2013 23:07:00 +0000

Big data analytics can be referred as the process of examining and analyzing a big amount of data in a way that will expose unusual information, hidden patterns and correlations. The term ‘big data’ is used to describe the exponential escalation, availability as well as use of information, both structured and unstructured. In simple words, big data analytics is creating useful information for the business by processing and analyzing the huge amount of data available.

This data can be useful to gain advantages over competitors and result in business benefits, if examined and analyzed professionally. Outcome of big data analytics can be used to prepare more effective marketing strategies, resulting in increased revenue.

Big data analytics helps organizations to take business decisions which are based on through research and accurate analysis. In big data analytics, data scientists analyze the data sources that may be left untapped by the traditional business research programs.

The big data may include anything right from the social activity reports to mobile phone call details, and web server logs to internet clickstream data. To analyze structured data, there are many software programs available in the market. However, these software programs cannot process unstructured data. Therefore, nowadays, many big data analytics environments use a new class of big data technology.

As name suggests, big data is a huge amount of data which comes from various sources. Therefore, it becomes necessary to link, match, cleanse and correlate data. In order to produce high quality information that is relevant, authentic and up-to-date, it is necessary to systematically integrate structured and unstructured data assets.

Acquiring large amount of data is just half of the story. What most important is processing and analyzing the data and utilize it in a way that will be beneficial for the business. The organization should use the data to run the organization more efficiently and make its position stronger in the market. Big data should be segregated and assessed to find the most valuable information from it.

It is important to note that the entire data will not be useful for the business. An organization should process the data through big data analytics and find out the most relevant and useful information for the business practices. In last few years, companies have understood the importance of nontraditional data sources. Over the period of time, the cost associated with storage and computing data has also lowered considerably. Therefore, more and more companies are including non-traditional yet potentially very useful data in their data banks and using it while preparing business strategies.

With the help of big data analytics, organization can develop a more thorough and impactful business strategies, which can result in enhanced productivity, a stronger position in the market and greater revenues.

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