Mention database and what all things will come to your mind? Data management, storage, tables, RDBMS… But this trend is changing. The reason for the change is big data. Data has become so big that conventional methods and tools are not enough to manage this data. So what to do? We need some database technologies which can deal with big data. Hadoop is the most popular technology which can tackle big data. It is a data-centric platform which is highly scalable and used to run applications in parallel. Another alternative to RDBMS is the use of NoSQL platforms like MongoDB. Read the full article here: http://www.infoworld.com/article/3003647/database/how-big-data-is-changing-the-database-landscape-for-good.html

90% of the world's data has been generated over the last two years, according to the reports. With this increasing amount of data it is necessary for the organizations to upgrade the data centres. In order to cope up with the emerging technologies, data modernization is very important. Those still stuck to the old ways are at a loss. Data Oganization done in a proper way gives way to crucial insights. This doesn't mean only adding more servers, but finding a solution that takes care of economic conditions, technical aspects, people and the processes involved. Cloud based platforms are of great help and with increased automation of technology, data centers can reduce the repetition of the mundane tasks. Read more at: http://www.information-age.com/technology/data-centre-and-it-infrastructure/123460849/why-all-industries-should-consider-modernising-their-data-centres

Failures in the functioning of equipment are inevitable in any kind of industry. The repair and recovery time often leads to big financial losses each year. But we have machine learning and predictive analytics as a solution. The machine learning models are trained to learn the ideal functioning of the machinery. Then this functioning is compared with how the machines are working at present. So if even a minor change occurs somewhere, it doesn't go unnoticed. Then, with the help of predictive analytics the loss that can take place in the near future is predicted. This adds to the huge advantage of the firms. Learn more about this in the article written by Mike Reed (manager of analytical services for Avantis PRiSM software) at: http://www.intelligentutility.com/article/16/02/saving-money-and-man-hours-machine-learning

Machine Learning is getting easier and accessible because of the computing power becoming affordable. Moreover, big enterprises are making their algorithm open source. This is because data is the food. More data an algorithm gets, the better it becomes. But from step 1, making algorithms, feeding data in humans play a significant role. Sometimes there are outliers which the algorithms cannot interpret. Here human intervention is necessary. They manually check such pieces. But when these are fed into algorithms, they make them robust by identifying outliers. Thus, human intervention is both necessary for accuracy and training. Read more at: http://insidebigdata.com/2016/01/11/human-in-the-loop-is-the-future-of-machine-learning/

The support for Windows server 2003 has been ceased so a shift to windows server 2012 is required. It is not only necessity, but good investment of time and finance as well. There are three main steps which are required to keep in mind to avoid any pitfalls in journey -

1.       Review and evaluate equipment: Servers running 2003 should be able to run 2012 but only if the hardware was already legacy equipped at the time of server 2003 installation. Details need to be considered in depth.

2.       Check current server and hosting environment: Apart from calculating financial benefits of the shift, one must also reassess hosting environment to determine how much server population should remain in house.

3.       Plan for migrations of workloads: The major advantage of upgrading is ability to access the enhanced features present on windows server 2012 which will help one on enabling migration, consolidation and improvement of overall server operation

Read more at: http://www.itworld.com/native?prx_t=6gACAyjwFAPUsMA

This is the era of e-commerce. Everything you desire is just a click away. People can shop, read reviews, compare prices of the products and take better-informed decisions sitting in their homes. Sounds like online shopping is a blessing. But then why retailers like Disney, Under Armour and Nike have said that they don’t focus on online sales anymore? Today, retailers are focused on creating a friendly customer experience that crosses channel borders. Leading brands understand that they need to be leaders in both online and offline means. The changes in marketing trend are due to things like artificial intelligence, 3D printing and advance analytics. Merging the two modes is the solution. So to attract more and more customers, we need their engagement in both online and offline modes which will eventually result in better sales. Read more here: http://www.computerworld.com/article/3024262/retail-it/for-retailers-e-commerce-alone-wont-make-the-sale.html

Most businesses need a combination of multiple marketing techniques to exhaust the full potential and make it more productive. Though internet usage is increasing each day, but offline marketing channels still involve lots of customers. Best marketing strategies merge both offline and online marketing. This might seem a difficult task, but machine learning models make it easier. These lessons from the data field which channel, in what way, and at what time will be most effective for the particular set of customers. This is called intelligent messaging. Thus leading to maximize success of marketing. A significant increase in the success rate has been observed in the marketing strategies using these techniques. Learn more about it in the article written by Brendan O'Kane (managing director and chief executive officer of OtherLevels) at: http://www.itproportal.com/2016/01/04/intelligent-messaging-big-data-machine-learning-a-powerful-combination-for-multi-channel-marks/

There is a big gap between what attackers do and what preventions defenders do to prevent it. The general idea is to build bigger and better blacklists for all the threats known or calculated using better threat intelligence.  We always hunt for easy ways, trying to seek out automation of security infrastructure. But these won't' suffice. The reason is because all the defenses are static and accessible to all. All it takes a hacker is to write a script to bypass these security measures. So a possible solution might be RITA, which stands for Real Intelligence Threat Analysis. Its SANS's free new framework that will help in hunting attackers by extending the traditional signature analysis. Read more at: http://www.darkreading.com/vulnerabilities---threats/introducing-rita-for-real-intelligence-threat-analysis/a/d-id/1323244

The evolution of Machine Learning (ML) is affected by the approach of the tech giants towards it. Open Source Platforms and the data sources also have an important impact on the ML models. Tech giants have realized the importance of ML, and this is becoming the new normal for them. They are now focusing on providing ML models as a Service. These are built for the common usage, not just for the data scientists. Most of the softwares being used for ML are open sources, thus affecting the market of other softwares making sources. Tools like Apache Spark are going to dominate the market. Read more about it on: http://www.infoworld.com/article/3017251/data-science/what-machine-learning-will-gain-in-2016.html

Machine Learning (ML) will have a big impact on economics as it helps in taking properly calculated decisions. The ML methods can be modified to make them fit for using in econometrics. These reduce the risk to failure. Data is the key to success for this. Based on the previous records, a model can be trained. Economists are being increasingly interested in technology. ML can help in clarifying the real situation from what is being assumed. Many technical firms are hiring economists. The mix of proper proportion of technology with the knowledge of economists will definitely lead to great results. Read more at: http://www.forbes.com/sites/quora/2016/01/27/what-will-the-impact-of-machine-learning-be-on-economics/#5bccf597ad5e

In the Healthcare industry, what we really need? Better healthcare results to improve our lives. Healthcare industry generally takes decision using data- like case histories. Well the good news is, now we have lots and lots of data. This is the era of big data, where millions of bytes of data are generated every second. And the bad news is we can't keep up with this fast rate of generation of data. Big data is unstructured which makes it difficult to hunt down relevant helpful information. Using data science with health care we can predict epidemics, advance cures and can provide better, safer and more pleasant experience for patients. Read full article here - http://www.cio.com/article/3001216/analytics/4-big-reasons-why-healthcare-needs-data-science.html

No, remote access should not mean less control. It admins need to examine the problem as quickly as possible but conflicting to user expectations, it's not possible always. Remote access tools are the solution to these problems. Tools like GoToMyPC, LogMeIn, TeamViewer are the quite popular which provides remote access, control and support. But apart from this, remote IT administration is also needed i.e., capability to diagnose the system even before connecting to the server. Microsoft provides such ‘manual’ remote control tools. Goverlan is also one such tool which provides remote troubleshooting. Read more here: http://www.infoworld.com/article/3021938/systems-management/it-admins-remote-doesnt-have-to-mean-less-control.html

A lot is investment is made in the digital marketing campaign. Before planning the campaigns, it is important to correctly determine whether to use Rule Based or Algorithmic Attribution. First, we have to analyze the data in hand properly. If the data is coming from different platforms and needs to be combined or analyzed at one platform, then Rule Based Attribution can't be even thought of, go for the latter. Rule Based Attribution uses the last touch as the final touch, so sometimes it can be misleading. If the journey to the destination has been maneuvered by the smart moves at each step, then we can't rely on the Rule- Based approach. But if the algorithmic approach was that easy, everyone could have adopted it. Read more about the pros and cons of each in the article written by Matt Scharf (Senior Manager at Display Media Operations & Analytics at Adobe Systems) at: http://blogs.adobe.com/digitalmarketing/analytics/rules-based-vs-algorithmic-attribution/

Cloud is the new buzz word. Nowadays, more and more people are willing to shift to the cloud. There are too many paths available for this journey and each path which does appear to be good, might not result in a successful decision. So people need to consider more details than they actually thought. The first step is to categorize types of workload that one wishes to migrate. These are not the applications, but the patterns of processing the applications. Some of the patterns may be - Database-intensive; Compute intensive or; User interface- intensive. If you have, say 200 applications, then, place each application in one of these categories and you will end up dealing with 3 categories rather than 200 applications. Doing so, you will be able to select proper cloud configuration to service your applications. This approach will help you in saving time and money. Read more at: http://www.infoworld.com/article/3016559/cloud-computing/the-right-way-to-migrate-to-the-cloud.html

The fast growing field of "wearables" has brought in one amazing technology. This is being called as "disappearables". The logic behind this is to make technology modest. The wearables should capture energy from the surroundings, sense and observe the environment around. But the challenge for the wearables manufacturing companies is to produce cost efficient products. These should not be restricting the body movements or shall not have the consistent charging issues. This new technology is called disappearables because the user itself can't tell whether he is wearing the smart wear or not. Read more about this amazing technology in the article written by Ben O'Brien (Contributor) at: http://www.entrepreneur.com/article/254301

Hadoop and Apache Spark are seen as the competitors in the world of big data, but now the growing consensus is that they are better convention in together. Here is a brief look at what they do and how they are compared.  1. They do different things: Both are the big-data frameworks, but they do not serve the same purposes. Hadoop is a distributed data infrastructure. It also Indexes and keep track of that data, enabling big-data processing and analytics. On the other hand, Spark is a data processing tool. Secondly, both can be used individually, without the other. 3. Spark is faster 4. You may not need Spark's speed: Spark is fit for real-time marketing campaigns, online product recommendations, cybersecurity analytics and machine log monitoring. 5. Failure recovery: differently, but still good. Read more at: http://www.computerworld.com/article/3014516/big-data/5-things-to-know-about-hadoop-v-apache-spark.html

The Internet of Things (IoT) is the network of physical devices using embedded electronics, software, sensors and network connectivity, which helps in enabling devices to collect and exchange data. Nowadays, fitness tracking devices count heart rate and daily step-count, and connected thermostats control the temperature of your house. Now in 2016, IoT is leading to many more innovations across the board, including a new concept called the CRM of Things. The trends in the CRM of Things are - Richer CRM profiles, Preempting customer needs, Dynamic pricing strategies, improved automation, and pattern analysis. Read more at: http://it.toolbox.com/blogs/insidecrm/the-crm-of-things-71115

There is a prediction that by 2017, 33% of Fortune 100 companies will experience information crises due to lack of adequate testing measures. To prevent this, the robust testing approach must be used. Developing a well-planned data warehousing, testing process will help in minimizing such risks. And while moving to agile, we can do more testing in comparison to traditional projects. The testing being done during development in parallel aids to agile development, because development and testing can be completed in a single sprint. The roadmap for building agile data warehouse will consist of following steps-

·         Build business conceptual model

·         Grass roots Data governance

·         High-level architecture with repeatable design patterns

·         Strong testing tools

·         Robust data quality program

·          Self-managing teams.

Read the full article here http://www.cio.com/article/3012097/data-warehousing/take-testing-seriously.html

It's no more difficult to shop at a new place with Location Based Marketing. We often look for the unique and best products, when we visit a new place. A common practice is to inquire about the best deals, with the people around who are aware of the new place. But with everything becoming techy we have Location Based Marketing to rescue. It integrates mobile marketing with location based services. The motive behind this is to make mobile users aware of your presence, while they are in the areas nearby. Most relevant display ads as per user's location are sent. Their interest can be pulled by either providing some discounts or some other perks. This benefit both the user as well as the business. Read more about it in an article written by Jack Simpson (a Writer at Econsultancy) at: https://econsultancy.com/blog/67418-what-is-location-based-advertising-why-is-it-the-next-big-thing/