Big Data applications

1. Fraud detection

For businesses whose operations involve any type of claims or transaction processing, fraud detection is one of the most compelling Big Data application examples. Historically, fraud detection on the fly has proven an elusive goal. In most cases, fraud is discovered long after the fact, at which point the damage has been done and all that's left is to minimize the harm and adjust policies to prevent it from happening again. Big Data platforms that can analyze claims and transactions in real time, identifying large-scale patterns across many transactions or detecting anomalous behavior from an individual user, can change the fraud detection game.

2. IT log analytics

IT solutions and IT departments generate an enormous quantity of logs and trace data. In the absence of a Big Data solution, much of this data must go unexamined: organizations simply don't have the manpower or resource to churn through all that information by hand, let alone in real time. With a Big Data solution in place, however, those logs and trace data can be put to good use. Within this list of Big Data application examples, IT log analytics is the most broadly applicable. Any organization with a large IT department will benefit from the ability to quickly identify large-scale patterns to help in diagnosing and preventing problems. Similarly, any organization with a large IT department will appreciate the ability to identify incremental performance optimization opportunities.

3. Call center analytics

Now we turn to the customer-facing Big Data application examples, of which call center analytics are particularly powerful. What's going on in a customer's call center is often a great barometer and influencer of market sentiment, but without a Big Data solution, much of the insight that a call center can provide will be overlooked or discovered too late. Big Data solutions can help identify recurring problems or customer and staff behavior patterns on the fly not only by making sense of time/quality resolution metrics, but also by capturing and processing call content itself.

4. Social media analysis

Of the customer-facing Big Data application examples we could discuss, analysis of social media activity is one of the most important. Everyone and their mothers are on social media these days, whether they're "liking" company pages on Facebook or tweeting complaints about products on Twitter. A Big Data solution built to harvest and analyze social media activity, like IBM's Cognos Consumer Insights, a point solution running on IBM's BigInsights Big Data platform, can make sense of the chatter. Social media can provide real-time insights into how the market is responding to products and campaigns. With those insights, companies can adjust their pricing, promotion, and campaign placement on the fly for optimal results.

Multimedia Advantages and Disadvantages

MULTIMEDIA SYSTEM

System means the combination of different components which work in coordination with each other in a certain manner so as to serve some specific purpose. Multimedia is the technology which can disseminate two or more different media like text, audio, video, etc in a single time frame. Thus multimedia system means the proper combination of different multimedia components which work together to create, edit, store and disseminate the multimedia components. Almost all of today’s multimedia systems take advantage of power and ease of computer systems. Without computers, today’s sophisticated multimedia cannot be thought of. So, multimedia system could be more accurately defined as the combination of computer hardware and software designed so as to create, store and disseminate the multimedia content.

The multimedia computer generally contains the following components.

1) Computer

2) Multimedia Software

3) Sound card

4) CD-Drive

5) Micro-Phone

6) CD-ROM Disk

7) Multimedia speakers/Head-Phones

8) Digital Camera and many other components.

All of some of the above mentioned components work together to make up the multimedia system which can capture, digitize, compress, decompress, retrieve the multimedia component (multimedia data) and show it into the output devices like Computer Monitor, Multimedia Projectors, etc. Literally speaking, all the components that could be used in the multimedia system are impossible to list out. The above mentioned are only some of the important components of multimedia computer system.

ADVANTAGES OF MULTIMEDIA

We have already mentioned various advantages of using multimedia. We can still specifically list out following points that explains the advantages of the multimedia.

1. It can be used to help students and teacher to teach as well as learn the given topics easily.

2. It can be used to spread the knowledge easily all over the world wide in the cheap cost.

3. It is easy to take the multimedia files from one to other places as it can be stored in the cheap and light storage devices like CD-ROM.

4. It can be used for any subject and for anyone.

5. It can be used in Television, Films Industries and for personal entertainments.

6. It is highly used to realize the concept of Open University, Distance Education System, and Video Conferencing.

7. It is also used in Internet to make up the interactive web-page contents.

8. We can give the everlasting impression to the intended audiences on a specific topic by the use of multimedia.

9. Colored pictures, Motion pictures and other graphics could be shown in monitors and other big screens so that many people could view it and make out the impression about it.

10. Multimedia systems are generally very interactive so it is interesting to use.

DISADVANTAGES OF MULTIMEDIA

In comparison to the enormous advantages we have very little disadvantages of multimedia. These disadvantages could be listed out as:

1. It is expensive to produce multimedia contents.

2. It is expensive to set up the multimedia systems.

3. It needs well trained manpower to create and use it.

4. Multimedia files are too large so, it is time consuming to transfer across the Internet and Intranet.

Hadoop Streaming

Hadoop streaming is a utility that comes with the Hadoop distribution. This utility allows you to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer.

HOW STREAMING WORKS:In the above example, both the mapper and the reducer are python scripts that read the input from standard input and emit the output to standard output. The utility will create a Map/Reduce job, submit the job to an appropriate cluster, and monitor the progress of the job until it completes.

When a script is specified for mappers, each mapper task will launch the script as a separate process when the mapper is initialized. As the mapper task runs, it converts its inputs into lines and feed the lines to the standard input (STDIN) of the process. In the meantime, the mapper collects the line-oriented outputs from the standard output (STDOUT) of the process and converts each line into a key/value pair, which is collected as the output of the mapper. By default, the prefix of a line up to the first tab character is the key and the rest of the line (excluding the tab character) will be the value. If there is no tab character in the line, then the entire line is considered as the key and the value is null. However, this can be customized, as per one need.

When a script is specified for reducers, each reducer task will launch the script as a separate process, then the reducer is initialized. As the reducer task runs, it converts its input key/values pairs into lines and feeds the lines to the standard input (STDIN) of the process. In the meantime, the reducer collects the line-oriented outputs from the standard output (STDOUT) of the process, converts each line into a key/value pair, which is collected as the output of the reducer. By default, the prefix of a line up to the first tab character is the key and the rest of the line (excluding the tab character) is the value. However, this can be customized as per specific requirements.

The future of BIG Data

Prescriptive analytics can be seen as the future of Big Data. If we see descriptive analytics as the foundation of Business Intelligence and we see predictive analytics as the basis of Big Data, than we can state that prescriptive analytics will be the future of Big Data. Earlier, I already explained the difference between these three types of analytics, but let’s have a small recap: descriptive analytics means looking at historic data, ranging from 1 minute ago to years ago. It can be compared as looking in the rear mirror while driving. Predictive analytics means using all that data to make a prediction about where to go; it is the navigation that tells you how to drive and when you will arrive. Prescriptive analytics is the self-driving car, that knows exactly what the best route is based on infinite data points and calculations. Not surprisingly, Google’s self-driving car makes extensive use of prescriptive analytics.

Prescriptive analytics uses the latest technologies such as machine learning and artificial intelligence to understand what the impact is of future decisions and uses those scenarios to determine the best outcome. With prescriptive analytics it becomes possible to understand and grasp future opportunities or mitigate future risks as predictions are continuously updated with new data that comes in. Prescriptive analytics basically offers organizations a crystal ball. Prescriptive analytics will become really powerful when it has developed into a stage where decision makers can predict the future and make prescriptions to improve that predicted future, without the needs for Big Data scientists.

Although prescriptive analytics is really still in its infancy, we see more and more use cases being developed. Also several Big Data startups focus especially on prescriptive analytics. The most well know is Ayata. They use patented software to predict what is going to happen, when it is going to happen and why it is going to happen. They focus primarily on the oil and gas industry, but there are more use cases of prescriptive analytics. Prescriptive analytics is used in scenarios where there are too many variables, options, constraints and data sets. Without technology it is too complex for humans to efficiently evaluate those scenarios. Also when experimenting in real-life is too risky or expensive, prescriptive analytics can come to rescue. Let’s have a look at three of the possible use cases:

Big Data for the Enterprise

With Big Data databases, enterprises can save money, grow revenue, and achieve many other business objectives, in any vertical.

• Build new applications: Big data might allow a company to collect billions of real-time data points on its products, resources, or customers – and then repackage that data instantaneously to optimize customer experience or resource utilization. For example, a major US city is using MongoDB to cut crime and improve municipal services by collecting and analyzing geospatial data in real-time from over 30 different departments.
• Improve the effectiveness and lower the cost of existing applications: Big data technologies can replace highly-customized, expensive legacy systems with a standard solution that runs on commodity hardware. And because many big data technologies are open source, they can be implemented far more cheaply than proprietary technologies. For example, by migrating its reference data management application to MongoDB, a Tier 1 bank dramatically reduced the license and hardware costs associated with the proprietary relational database it previously ran, while also bringing its application into better compliance with regulatory requirements.
• Realize new sources of competitive advantage: Big data can help businesses act more nimbly, allowing them to adapt to changes faster than their competitors. For example, MongoDB allowed one of the largest Human Capital Management (HCM) solution providers to rapidly build mobile applications that integrated data from a wide variety of disparate sources.
• Increase customer loyalty: Increasing the amount of data shared within the organization – and the speed with which it is updated – allows businesses and other organizations to more rapidly and accurately respond to customer demand. For example, a top 5 global insurance provider, MetLife,used MongoDB to quickly consolidate customer information from over 70 different sources and provide it in a single, rapidly-updated view.

Few Things about Data science

Data science was developed to handle the flood of big data engulfing the world. A blend of statisticians, computer scientists and creative thinkers, data scientists have the:

• Skills to collect, process and extract value from giant and diverse data sets
• Imagination to understand, visualize and communicate their findings to non-data scientists
• Ability to create data-driven solutions that boost profits, reduce costs and even help save the world

Data scientists work in every industry – from the Defense Department to Internet start-ups to financial institutions – and tackle big data projects on every level.
One of the most popular jobs of the 21st century is only getting better. A McKinsey report estimates that by 2018, the U.S. could face a shortage of 140,000 to 190,000 data scientists and 1.5 million managers and analysts who understand how to use big data to make decisions. Do you have to invest in a master’s degree? Not necessarily. Plenty of successful data scientists launched their careers with a B.S. and a hefty handful of skills. However, as data problems become more complex, many big companies are demanding advanced degrees. Some of the industries employing data scientists include:

• Pharmaceuticals
• Computer Software
• Internet
• Research
• IT and Services
• Biotechnology

They may pay you good money for your efforts. Silicon Valley salaries for entry-level data scientists can reach six figures (see this article).

Schools Offering Master’s Programs in Data Science

Ready to make the leap? In our annotated list of great schools, you’ll find information on each program’s:

• Location and length
• Full-time, part-time and online options
• Concentrations and majors
• Courses, internships and real-world opportunities

Look for quality. Universities are scrambling to offer new degrees, but there’s no guarantee they’ll be worth the investment. Research the background of the program and professors. Search for solid partnerships with businesses and research institutions. Ask about job placement figures. You deserve a practical qualification.

Data Science Industries

Focusing on a particular industry? We’ve created a reference for past, present and future to help you prepare for what’s ahead. On each industry page, you’ll discover:

• A condensed history of data science within the field
• Current opportunities and developments
• Examples of vendors and big data projects
• Data-related risks and regulations

We don’t discuss many typical day-to-day tasks or job titles primarily because these vary widely among employers. Indeed, the first requirement on many job listings isflexibility.

Data Science Blog

Need more guidance? Grab a cup of coffee and browse our data science blog. We’ve included articles such as:

• Data Scientist Foundations
• Open Source Tools for Big Data Analysis
• New Technologies in Data Science

They’re all designed to provide you with a practical knowledge base to help launch your career.

Software testing market trends

1. Testing in agile: Today, most of the organizations requirement fast and responsive Quality assurance testing services integrated with agile development methods. Today, Agile adoption stands at the highest priority for most of the industries today, agile test approach being a part of it.
2. Flexible pricing models:  Today, there cannot be “one size fits all” pricing model for all types of services. The pricing models not only vary according to the services, test use cases but also on the region and the clients. The models can vary from pay per use, use case based, outcome based, time and material etc.
3. Crowdsourcing:  This model is driven by specialized testing services , crowdsourcing is gaining traction in the market due to more of localization and less need for specific knowledge. Hence there is low cost involved which makes ti easily adaptable.
4. Service virtualization and Testing is about continuous Quality:  “Testing tools that capture, model, and simulate application behavior to test the functional and nonfunctional behavior of applications in a simulated production environment” – Forrester
5. Service virtualization is similar to devops model and hence leads to faster development and testing of complex application scenarios. It is best suited for complex environments in order to reduce obstacles to testing, simulating production environments and decoupling integration dependencies.  
6. Cloud to be the driving force in testing market:  In order to deal with the upcoming IT infrastructure issues with booming technologies, testing on cloud or testing cloud can provide on demand testing services which enables handling of projects that are critical in nature and require faster time to market.
7. Cloud testing incorporates automated testing tools and covers beyond functional testing.
8. Mobility defining the next generation testing requirements:
9. With the proliferation of mobile devices there is a need to ensure that the quality of testing has to be standardized. With the boom in mobile industry, the expectation is that there will be a hike in the mobile testing tools in the near future. Mobile testing services can include mobile application testing, mobile device testing and enterprise mobility and systems integration testing services. Mobile application testing services can include  test planning and advisory, test scope definition, test case development, test case execution, regression testing, system testing, end to end testing, user acceptance testing, automation testing etc.  Mobile testing services are going to change the landscape of testing in the near future.
10. Automation and Standardization: With the increase in focus on faster time to market and shorter delivery time, the practice will incline towards creating standard tools and accelerators that shorten project timelines and reduce the risk factor.  Also, with the increased demand of more for less among CIOs, automation and reusability is emerging as the hot buttons of testing services.

Software Businesses in Future

1. Data crunching. The era of big data is just getting started, with many firms eager to tap vast new databases to gather more info on their customers, their competitors, and even themselves. The challenge isn't just crunching numbers; it's making sense of them, and gaining useful insights that can be translated into a business edge. Marketing and market research are two growing fields where the use of data is exploding.

2. Counseling and therapy. There's now widespread recognition that mental health is as important as physical health, which is likely to increase demand for professionals in this field. The BLS expects the need for marriage and family therapists, as one example, to grow 41 percent by 2020.

3. Scientific research. New technology will continue to generate breakthroughs in medicine, manufacturing, transportation, and many other fields, which means there will be strong demand for workers schooled in biology, chemistry, math, and engineering. Some areas that show particular promise: biotechnology and biomedicine, nanotechnology, robotics, and 3D printing, which allows the manufacture of physical products from a digital data file.

4. Computer engineering. A lot of software development is done overseas these days, but the need for high-level computer experts able to tie systems together is still strong. In finance and investing, for instance, high-speed computing is increasingly a prime competitive advantage. And most big companies will need networks that are faster, more seamless, and more secure.

5. Veterinarians. Pets are more popular than ever, and some of them get medical care that's practically fit for a human. The BLS expects the need for vets to rise 36 percent by 2020.

6. Environmental and conservation science. Making better use of the planet's resources will be essential as population growth strains existing infrastructure. Green energy, despite some political controversy, still seems likely to boom. Developers need more efficient ways to heat and cool buildings. And dealing with global warming may require new technology not even on the drawing board yet.

7. Some healthcare fields. It's well-known that the aging of the baby boomers will require more caregivers in many specialties. Some healthcare jobs tend to be low-paying, with a lot of workers flocking to what are supposed to be "recession-proof" fields. And the need to lower overall healthcare costs could pinch some doctors, hospital workers, and diagnosticians. But demand should be strong for nurses, optometrists, audiologists, dentists, physical therapists, and some doctor specialists.

8. Management. The boss earns a lot for good reason: His job isn't as easy as it might seem. Effective management in the future will require basic business knowledge plus the ability to oversee operations in many locations and countries, and some technical know-how. Anybody who can improve a unit's performance while lowering costs should rise quickly. The BLS and IBISWorld also expect growing demand for some support fields such as human relations, benefits administration, and event planning.

9. Finance. The movement and management of money is technically complex, and integral to most companies. Plus, nontraditional investing firms such as hedge funds and private-equity firms are likely to grow as the traditional banking sector complies with new regulations and reins in risk-taking. That means there will be more need for finance experts. There may even be a shortage as students once interested in finance veer into other fields, turned off by the 2008 financial crisis and the vilification of banks.

10. Entrepreneurship. It's often overlooked, but the need for innovators running their own businesses could be more important than ever in 2020. Forecasters expect strong growth in traditional businesses such as used-car dealers, hair and nail salons, pet grooming, and office services, which means anybody able to come up with better, cheaper ways to serve customers will reap a windfall. Technology startups will no doubt keep changing the way consumers work and live. And nobody really knows what the next iPad, Twitter, or Pinterest will be—except, perhaps, some entrepreneur who's dreaming about it right now.

TCP/IP PROTOCOL

TCP (Transmission Control Protocol) and IP (Internet Protocol) are two different procedures that are often linked together. The linking of several protocols is common since the functions of different protocols can be complementary so that together they carry out some complete task. The combination of several protocols to carry out a particular task is often called a "stack" because it has layers of operations. In fact, the term "TCP/IP" is normally used to refer to a whole suite of protocols, each with different functions. This suite of protocols is what carries out the basic operations of the Web. TCP/IP is also used on many local area networks. The details of how the Web works are beyond the scope of this article but I will briefly describe some of the basics of this very important group of protocols. More details can be found in the references in the last section.

When information is sent over the Internet, it is generally broken up into smaller pieces or "packets". The use of packets facilitates speedy transmission since different parts of a message can be sent by different routes and then reassembled at the destination. It is also a safety measure to minimize the chances of losing information in the transmission process. TCP is the means for creating the packets, putting them back together in the correct order at the end, and checking to make sure that no packets got lost in transmission. If necessary, TCP will request that a packet be resent.

Internet Protocol (IP) is the method used to route information to the proper address. Every computer on the Internet has to have its own unique address known as the IP address. Every packet sent will contain an IP address showing where it is supposed to go. A packet may go through a number of computer routers before arriving at its final destination and IP controls the process of getting everything to the designated computer. Note that IP does not make physical connections between computers but relies on TCP for this function. IP is also used in conjunction with other protocols that create connections.

Data Analysis and data mining

Data analysis and data mining are a subset of business intelligence (BI), which also incorporates data warehousing, database management systems, and Online Analytical Processing (OLAP). 

The technologies are frequently used in customer relationship management (CRM) to analyze patterns and query customer databases. Large quantities of data are searched and analyzed to discover useful patterns or relationships, which are then used to predict future behavior.

Some estimates indicate that the amount of new information doubles every three years. To deal with the mountains of data, the information is stored in a repository of data gathered from various sources, including corporate databases, summarized information from internal systems, and data from external sources. Properly designed and implemented, and regularly updated, these repositories, called data warehouses, allow managers at all levels to extract and examine information about their company, such as its products, operations, and customers' buying habits.

With a central repository to keep the massive amounts of data, organizations need tools that can help them extract the most useful information from the data. A data warehouse can bring together data in a single format, supplemented by metadata through use of a set of input mechanisms known as extraction, transformation, and loading (ETL) tools. These and other BI tools enable organizations to quickly make knowledgeable business decisions based on good information analysis from the data.

Analysis of the data includes simple query and reporting functions, statistical analysis, more complex multidimensional analysis, and data mining (also known as knowledge discovery in databases, or KDD). Online analytical processing (OLAP) is most often associated with multidimensional analysis, which requires powerful data manipulation and computational capabilities.

With the increasing data being produced each year, BI has become a hot topic. The increasing focus on BI has caused a number of large organizations have begun to increase their presence in the space, leading to a consolidation around some of the largest software vendors in the world. Among the notable purchases in the BI market were Oracle's purchase of Hyperion Solutions; Open Text's acquisition of Hummingbird; IBM's buy of Cognos; and SAP's acquisition of Business Objects.

Definition

The purpose of gathering corporate information together in a single structure, typically an organization's data warehouse, is to facilitate analysis so that information that has been collected from a variety of different business activities may be used to enhance the understanding of underlying trends in their business. Analysis of the data can include simple query and reporting functions, statistical analysis, more complex multidimensional analysis, and data mining. OLAP, one of the fastest growing areas, is most often associated with multidimensional analysis. According to The BI Verdict (formerly The OLAP Report), the definition of the characteristics of an OLAP application is "fast analysis of shared multidimensional information.

Data warehouses are usually separate from production systems, as the production data is added to the data warehouse at intervals that vary, according to business needs and system constraints. Raw production data must be cleaned and qualified, so it often differs from the operational data from which it was extracted. The cleaning process may actually change field names and data characters in the data record to make the revised record compatible with the warehouse data rule set. This is the province of ETL.

A data warehouse also contains metadata (structure and sources of the raw data, essentially, data about data), the data model, rules for data aggregation, replication, distribution and exception handling, and any other information necessary to map the data warehouse, its inputs, and its outputs. As the complexity of data analysis grows, so does the amount of data being stored and analyzed; ever more powerful and faster analysis tools and hardware platforms are required to maintain the data warehouse.

A successful data warehousing strategy requires a powerful, fast, and easy way to develop useful information from raw data. Data analysis and data mining tools use quantitative analysis, cluster analysis, pattern recognition, correlation discovery, and associations to analyze data with little or no IT intervention. The resulting information is then presented to the user in an understandable form, processes collectively known as BI. Managers can choose between several types of analysis tools, including queries and reports, managed query environments, and OLAP and its variants (ROLAP, MOLAP, and HOLAP). These are supported by data mining, which develops patterns that may be used for later analysis, and completes the BI process.

Facts about Android

Android was not founded by Google

The Android operating system was founded by Andy Rubin, Chris White, Nick Sears and Rich Miner under the umbrella Android Inc. It was established in October 2003. Android was later acquired by Google who had been backing the company all along. The deal was struck in August 2005 at a price of $50 million. Google marketed the platform to handset makers and carriers on the promise of providing a flexible, upgradable system.

Google launched Android in 2007

Google officially launched the operating system in November 2007. The operating system had initially been developed for cameras but Google saw a potential in the usage of the OS on smartphones and worked on a lot more features that you can see now. So Google saw it as potential opportunity to establish its impact on market by making it a platform for newer version of Smartphone.

Android was initially developed only for digital cameras

Android operating system was developed as a platform for digital cameras.  But Google later changed its focus to smart phones as it saw its potential. So Google decided to go on and adopt Android OS for smartphones which has created a revolution in smartphones as it was widely accepted and easily accessible.

HTC Dream – First Android Smartphone

The first publicly used phone to run Android was HTC Dream. The HTC Dream was first released in October 2008.  The device used the Linux-based Android operating system by Google. It used Android version 1.0 and was upgradable till 1.6. The Android operating system on the device was criticized for lack of functionality and software in comparison to certain established platforms like Nokia’s proprietary Symbian OS but was still considered to be innovative.

Android has more than a billion users

Google’s Vice President for Android, Sundar Pichai, announced that the Android operating system has powered hundreds of millions of mobile devices in more than 190 countries around the world. It’s the largest installed base of any mobile platform and growing fast—every day another million user’s power up their Android devices for the first time and start looking for apps, games, and other digital content.

Android Version Names

Apart from Android 1.0 and 1.1, all other Android versions have been named after sweet treats or desserts. These codenames are chosen alphabetically, and have thus far all been dessert items. Some codenames are associated with more than one version number, while others are limited to only a specific one. The reason for this inconsistency is not currently known. The naming typically appears to correspond to changes in the developer API levels, but this is not always true (example: 3.0 and 3.1 are both “Honeycomb” but they have different API levels).  

Open marketplace for distributing your apps

With Google being a member of Open Handset Alliance (OHA), Android has given users or interested party right access to modify the source code of the operating system.  Android was built from the ground-up to enable developers to create compelling mobile applications that take full advantage of the specs that a handset has to offer. It can be liberally extended to incorporate new cutting edge technologies as they emerge. The platform will continue to evolve as the developer community works together to build innovative mobile applications. This has allowed users and smartphone manufacturer’s great flexibility in adding features to the operating system. This also enables OEM’s to develop their skinned versions.

Android is open source

Google offers Android operating system to smartphone manufacturers without payment for its license. This is one of the major advantages of Android which had attracted users as a platform to innovate more new apps based on it.

Google gains from Android

Despite offering the Android software open sourced to smartphone manufacturers, Google will likely achieve its aim of becoming the mobile advertising king through Android devices. Google makes its biggest revenue from advertising and this will pay off in the big way with the users from PCs to smartphones and tablets. It must be noted that Android dominates both categories.

Why we need web analytics?

I think we all know analytics is important.  That’s probably why it gets installed in the first place.  Website analytics tracks visitor behavior on your website so you can see how many prospects visited your website and then which pages they visited.  Think of analytics like your report card.  When you log in and view the historical data, you’ll see whether your online marketing is passing or failing.

For example, if you look at organic traffic over the past year and it looks more like a ski slope falling gradually from left to right, then you know your SEO is not working.   That means you’re actually losing traffic month after month despite your investments.  On the flip side, if the graph is trending steeply upward from left to right, then things are lookin’ good.  But that’s just traffic.

I’m sure you are well aware that traffic does not equal sales.  That’s why Google Analytics also has a nifty section called “Conversions” or “Goals” where you can define key actions on your website.  For example, you can set up a Conversion to measure how many prospects complete a contact form.  Or if you have an e-commerce website, then you can measure every sale and even see revenue data within Google Analytics.

There’s a ton of data and it can be sliced and diced in many different ways to help you make better decisions about your marketing.  That’s the whole point here.  Rather than invest in SEO, social media, email marketing, advertising, and pray that it’s all “working,” you can actually measure the results of each marketing channel.

What Data Should You Monitor?

The answer to this question really depends on your business. However, every business should monitor some basic metrics.

The first question you’ll want to answer is, “Where is my traffic coming from?”  Is it from SEO, paid advertising, a referral partner, social media, or some other source?  To answer that question, use the Channels report in Google Analytics.  The Channels report is within the Acquisition section on the left navigation and that report will break down all of your traffic by channel.  For many businesses this will be an eye opener.  One of my clients was shocked when she saw how much more traffic was coming in organically compared to the advertising traffic.  Sometimes your gut is wrong about where your traffic is coming from.

The second question is, “Which traffic source is driving leads and sales?“  This is much harder to answer depending on your type of business.  If you have an eCommerce website, then make sure you or your webmaster get e-commerce analytics set up properly.  That will allow you to run reports to see exactly how much revenue you’re generating from each of your marketing channels.  Go back to the Channels report and you’ll see the Conversions per Channel on the far right.

However, many businesses convert sales offline, so it’s not possible to automatically report on revenue as with an e-commerce website.  The solution is to set up Conversions, or Goals, like I mentioned above.  A Conversion can be when a prospect completes a contact form, requests a coupon, or maybe when she requests a demo.  Again, the exact conversion will depend on your sales process.  The idea is to track a key step in your sales cycle so you can then see which Channel is driving more leads and sales.

As Peter Drucker, known for inventing modern business management, said: “If you can’t measure it, you can’t improve it.”

And with that in mind, I’ll leave you with a famous Joe Paterno quote, “Today, you’re either going to get better or you’re going to get worse, but you’re not going to stay the same.  So which is it going to be?”

The Google

Google is finding more and more ways to integrate itself into nearly every aspect of our lives.

Google has the largest search engine in the world, biggest video platform (YouTube), biggest web browser (Chrome), most-used email service (Gmail,) and the largest mobile operating system (Android).

What that all means is that Google essentially knows everything about us - be that what we search for, what ads we click on, what we write about, what we watch, and what apps we like.

Google's moonshot projects - like Google Glass and driverless cars - are merely the next steps in Google's quest to become a total knowledge company. Google's driverless cars mean the company will know your driving habits and where you like to go. With Glass, people can access information in real-time without having to check a phone.

Google's recent acquisitions hint that it's about to go even further. It's moving toward knowing everything about us - not just in the online world, but also in the offline world.

Google's motto has long been "Don't be evil." By and large, it has kept its word.

But with the number of resources and data it has, envisioning what Google could do with of its army of robots, drones, and satellites can be somewhat terrifying.

Meanwhile, the National Security Association has access to all of the information we freely give Google, albeit not with Google's permission. In order to make government spying more difficult,Google has since announced that it plans to encrypt all Gmail messages while they're in transit.

Google is doing its best to assure people that their data is secure, but in reality, the U.S. government still has access to your communications on Google's servers.

"The email provider can still see the message," Seth Schoen, a senior technologist with privacy group Electronic Frontier Foundation, told Investor's Business Daily. "They're just encrypting it when it's going over the Internet, not when the message is in their own system."

The only way to achieve true security would be for Google to encrypt your email to the extent that only you could decrypt and read it. But because Google needs to see what we're talking about in order to serve up ads, it seems unlikely that Google would offer full encryption. The scary part is that there's only so much Google can do to prevent the government from requesting and accessing its data.

Creative Ways to Sell your software

1. Use a reliable cloud hosting service instead of managing everything in-house. Why spend the time and money on building and maintaining your infrastructure when you can host in the cloud? "In just a few hours, a hosting provider can provision dedicated servers and cloud servers for you, and your business will be up and running with limited cost and risk," explains Emil Sayegh, the CEO and president of Codero Hosting, a provider of dedicated, managed and cloud hosting services

Other benefits of hosting your software in the cloud: "You can do all your testing and development in their data center and then deploy into production," he says. "Once in production, if the load increases or decreases you can scale your infrastructure on demand without owning a single piece of equipment, or worrying about server maintenance and upkeep."

2. Beta test. "To sell software online it's important to get feedback before investing too many resources into the development of your site or product," says Phil Sharp, senior marketing manager, UserTesting.com. "Mock up the simplest version of what you're offering, get it in front of people and get their brutally honest feedback. This will help you improve your product early and save you thousands of dollars down the road."

3. Offer a free trial. Especially if you are "a small, unknown company, you need to provide something to help potential customers see that your product is not a scam, and it will work for them," says Kelly Wilkerson, cofounder, Decipher Media, which offers desktop solutions for managing iPhone data. "Slightly over half of the 'Buy' button presses on our Web page come through the 'Register' button within our trial software, rather than our regular product page on the site. Testimonials help. Software safety badges help. But nothing helps as much as a free trial."

4. Consider a freemium model. "Another option is to offer freemium versions of your software--free access to basic features with the option to access premium features for an upgrade cost," says Michelle Nerlinger, director of Marketing at SafeNet, a data protection provider. "It's a smart up-sell path that can encourage paid licenses." Adds Ryan Connors, the marketing manager at Apptegic, a customer engagement solution provider, "By breaking down the barriers to adoption, you'll find people more eager and willing to try out your software and service."

5. Show customers you understand their pain. "Whether it's telling stories to highlight how your software makes life easier, or alleviating fearful or cautious emotions that come with a potentially substantial investment by providing valuable information, creating human connections around your product is a powerful way to stand out against the competition," says Seth Lieberman, the CEO of SnapApp, a marketing platform for creating interactive content to drive leads and engagement.

6. Include product feature/benefit comparison tables. Including "product feature and benefit comparison tables, which that show your product in the best light, is essential," says David Howard, a principle at marketing firm Consultiq. "They summarize in an easy to read format the tradeoffs that a buyer has to consider in the purchase cycle. So long as you have a competitive offering, there's no reason to be afraid to do this."5. Show customers you understand their pain. "Whether it's telling stories to highlight how your software makes life easier, or alleviating fearful or cautious emotions that come with a potentially substantial investment by providing valuable information, creating human connections around your product is a powerful way to stand out against the competition," says Seth Lieberman, the CEO of SnapApp, a marketing platform for creating interactive content to drive leads and engagement.

7. Solicit independent third-party reviews. "Anyone can tell you their products are worth buying, so it is best to have an honest third party referral," says Jennifer Borun, senior marketing director, GoingOn Networks, a developer of social collaboration and communication solutions for higher education. "Encouraging your customers to tell their story of why they selected your product and how it meets their challenge is the best way to get your message out and sell your product. You can capture these stories in case studies, an interactive online forum or blog postings."

In addition, "try to get a well-respected publication to test your solution/offering," suggests Allan Thorvaldsen, CEO,Panorama9, a cloud-based IT management platform. "This will validate your product (if it is any good) and generate traffic towards your Website for no cost."

8. Offer a money-back guarantee. "Studies have shown that a trusted and well-presented money back guarantee can actually increase sales by up to 40 percent," says Stephen Dodd, CEO ofOfficeTime.net, a developer of time tracking software. As for how long the guarantee should be for, Dodd suggests 120 days, which is what OfficeTime.net offers customers.

"A short guarantee makes it more likely the customer will take you up on it," Doddsays. "They feel a time pressure to cash in the guarantee before it's too late. With a long guarantee, the customer feels they have more time to make up their mind." Moreover, he says, "the longer the customer spends using your app, the more time they have to get to know you and fall in love with what you've created--and the less likely they will request a refund."

9. Include a phone number, in addition to an email address, on your Website. "By adding our phone number to our Website, we increased sales and not calls," says John Hurley, the cofounder of the business class file sharing service SmartFile. "Sure an email is easier to administer, but showing people you have a phone number gives potential customers a sense of comfort knowing that you are a real business."