Instead of cracking your head on the entire data available it is of utmost importance to target the data which actually matters. Here are 5 ways to do so:-

1. First, find out the KPI (key performance indicator) of interest.

2. Drain out the noise that is creating useless buzz. This includes activities like likes and retweets graphs.

3. Filter the data patterns that make sense and avoid irrelevant or accidental patterns.

4. Use these data patterns to generate meaningful conclusions.

5. Avoid redundancies by sharing information. This is a key point in the field of analytics.

To read more, follow: http://marketingland.com/blinded-by-data-181971

An extra push in today’s world is a must to withstand your product from the rest of the products available in the market. Marketing is the only sector in a firm that earn revenues for the firm, so any tricks that can provide that extra edge to your marketing is always an add on. Especially, today in a generation of social media, something as relevant as Buzz Analytics can be used to take your business to another level. Buzz Analytics make the use of free and abundant data available on websites to give you the positive or negative sentiments of customers which can always be incorporated while developing a product. Apart from this, Buzz Analytics helps you to keep a note of your competitor’s strength and weakness by analyzing your competitor’s offering. To know more, follow: http://www.mckinsey.com/business-functions/operations/our-insights/using-buzz-analytics-to-gain-a-product-and-marketing-edge

Predictive analysis is now getting more popular as most B2B companies are using it to expand their businesses. But what is important here is to target the right people/accounts. The best way to do this to look at the CRM (customer relationship management) but there is a dearth of optimal databases. To expand their databases, companies are coming up with new marketing ideas, prompting people to view their websites, generating leads and opportunities. But this method may be tedious and costly. Thus we can say that predictive analysis should be used to identify appropriate a/c targets as well as increase the no. of contracts from cost effective marketing programs. To read more:

http://marketingland.com/predictive-data-abm-move-account-lists-account-contacts-181446

According to Nasscom (National Association of Software and Services Companies) the expected rise in the Indian business analytics sector is 8-fold (from $2 billion to about $16 billion). It targets to make India among the top three big data analytics market in the world. To achieve this target Nasscom is partnering with its members to build a multi prolonged approach that encompasses skill development, thought leadership, products and platforms. India is an emerging hub for analytics solutions across the globe. The witnessed rapid growth is due to increased demand for cloud based and predictive analysis solutions by industries like BFSI, retail, telecom and healthcare. Even the requirement of manpower in this sector would increase magnificently in the next 5 years.

It is the rapid advancement of artificial intelligence and deep learning algorithms which enabled the development of machines which can do tasks that requires deep expertise and skills. Read more at: http://economictimes.indiatimes.com/tech/ites/big-data-analytics-to-reach-16-billion-industry-by-2025-nasscom/articleshow/52885509.cms

In the task of predictive analysis, predicting the unknown itself is a challenging problem. Moreover, the entry of an unknown variable in the equation makes the task all the more troublesome. Summary-level data are generally inaccurate and lack deep insights, because of which sometimes such unknown variables manage to creep in. Buyer life cycles generally vary in length in spite of which analysts generally tend to work with smaller cycles, which is dangerous because sometimes important marketing decisions are taken based on flawed information. B2Bs are also depending on real-time insights and are scrapping linear prediction models. It is noticed that, combining Big Data with traditional CRM information is also not sufficient because data science involves lot of research and experimentation. Hence we can conclude that predictive analysis derives its success from data governance and collection. Read more at: http://www.marketingprofs.com/opinions/2016/30118/predictive-analytics-has-a-scaling-problem-and-bad-data-is-to-blame

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

Machine Learning algorithms are classified as –
1) Supervised Machine Learning Algorithms
2) Unsupervised Machine Learning Algorithms
3) Reinforcement Machine Learning Algorithms

Top 10 Machine Learning Algorithms --

1)    Naïve Bayes Classifier Algorithm
2)    K Means Clustering Algorithm
3)    Support Vector Machine Algorithm
4)    Apriori Algorithm
5)    Linear Regression
6)    Logistic Regression
7)    Artificial Neural Networks
8)    Random Forests
9)    Decision Trees
10)  Nearest Neighbours

To know more: https://www.dezyre.com/article/top-10-machine-learning-algorithms/202

Thomas C. Redman (Ph.D),  in one of his article, 'Data quality should be everyone's job', published by Harvard Business Publishing, mentioned that correction of errors in the data is an expensive and time consuming process. Moreover, at times even after correction of the data, some flaws remain which leads to bad credibility of the firm and angry customers. However, if companies welcome provocateurs - individuals concerned with addressing data proactively with the help of their teams, departments, and companies, then errors can be prevented at their source itself. Most of the data revolutionists while exploring with their work found out that to eliminate the root cause of the error and prevent future error, was the best way to ensure high quality data. No matter how innovative an idea is, a Provocateur is a must for the first step in a company dealing with data. So people concerned with data should take up this role actively, which will lead not only in the creation of innovative ideas but will also ensure high quality data output. Read more at: https://hbr.org/2016/05/data-quality-should-be-everyones-job#

Partner loyalty means that each partner serves different populations, yet they have the ability to work together to provide more value to consumers. So, building brand loyalty with partners is becoming important for businesses. This article explores some tools to build brand loyalty together with CRM. They are - Contact Management, Lead Generation, Collaboration, and Opportunity Management. Read more at: http://it.toolbox.com/blogs/insidecrm/using-crm-to-build-brand-loyalty-with-partners-73560

According to a new industry study, demand for embedded analytics is increasing. The study has found a growing number of users who wants analytics integrated with applications. The study also suggests that the trend toward embedded tools is being driven by the view that an application's value is tied to the data and the analytical tools available to an application. The goals of embedded analytics include moving beyond the traditional business intelligence approach of extracting insights data and differentiating platforms in a market being flooded with analytic approaches. Read more at: http://www.datanami.com/2016/04/18/analytics-increasingly-seen-embedded-apps/

Both the IT giants, Google and Apple have revamped their business model which ensures that you get more money for each downloaded app by the consumers. According to reports, both Google and Apple have been internally testing out the viability of this process. For iOS developers, their share will be only paid after a consumer has been subscribed for more than a year. Google on the other hand, is flexible and lenient enough to release the money in the direction of Android developers right away. Android is the most popular mobile platform in developing countries, primarily due to its availability of affordable devices. While iOS have kept its sanctity with stringent app certification norms, Android has flourished with its open-source approach that comes with its own caveats. Read more at: http://www.tgdaily.com/mobile/160751-app-developers-can-expect-more-money-from-google-and-apple

While developing data strategies in the future, organizations must consider these new emerging trends: 1) Data's center of gravity is moving more to the cloud day by day, which means one should be looking to keep all their data tools for processing to analytics in the cloud. 2) Hybrid data technologies are critical to the cloud system and hybrid data systems like SQL Server, MySQL, etc. is predicted to become the norm by 2018. 3) With new tools for data analysis being innovated, businesses need to connect to many data sources that span across databases, Hadoop ecosystems, and web applications. Read more at: http://data-informed.com/how-to-capitalize-on-the-data-landscape-of-tomorrow/

Nowadays, mobile customer relationship management (CRM) is becoming important. According to a research, it was found that two-thirds of people in America own a mobile device and 65% of smartphone users check their phones 15 minutes after waking up and mobile e-commerce is projected to constitute 24.4% of total e-commerce revenue by 2017. Mobile CRM helps the sales department to become more efficient with internal reporting and new customer acquisition. It is also gaining popularity because of its administrative benefits, and allow sales departments to get real-time access to data. Read more at: http://it.toolbox.com/blogs/insidecrm/effective-mobile-crm-strategies-that-increase-sales-73429

Big data storage is not a tough task to current organizational context. There could be n number of big data on different aspects of a company, but storing will not serve the purpose. Analysis and implementation are the most necessary part in any business. So that means it requires specialized personnel and a processing tools and techniques which can leverage these data into predictive modelling. But in the current context only 23% of total industrial scenario can have that resource to successfully implement. Most of the cases the organizational legacy system takes too long, time to process these data and becomes non contextual in the business sector.

To read, follow: http://www.cio.com/article/3075423/it-strategy/it-wants-but-struggles-to-operationalize-big-data.html

Social presence and cyber security issues have a deeper correlation regarding security breaching. Even a normal post can harm any damage by sending a phishing mail. You can be at home, work, on your phone, with a tablet or on a public computer or borrowed device. Your phone, which doubles as a virtual office, is the easiest channel for data to leak out. Data leaks can happen in a harmless post about relocating for work when that news is still confidential to your company. It can happen if you hit "paste" with text from a work-related email in your clipboard and post it into the wrong window on your screen and it can also happen if your smartphone auto-corrects something you spelled wrong in a social media post to some project code name that you just used in an email. So an unimportant activity can create a huge impact.

To read, follow: https://www.entrepreneur.com/article/272459

Any organization is hugely dependent on data. Either these data are raw in nature or coders has full authority to cluster and filter them. But the process of critical analysis of such data is not simple. So highly skilled data scientists are required, but unfortunately these job demands high specialization and its platform is not yet developed.

Read more at: http://www.pcworld.com/article/3067957/how-the-skills-shortage-is-transforming-big-data.html

Currently one of the state of the art algorithms in Machine Learning is Gradient Boosting Machine (GBM). GBM can be used for regression, based on decision trees as prediction models. In GBMs, the learning procedure consecutively fits new models to provide a more accurate estimate of the response variable. The principle idea behind this algorithm is to construct the new base-learners to be maximally correlated with the negative gradient of the loss function, associated with the whole ensemble. The loss functions applied can be arbitrary, but to give a better perception, if the error function is the classic squared-error loss, the learning procedure would result in consecutive error-fitting. Read more at: http://www.dataminingblog.com/

NASA has revealed that it is going to fund for futuristic space technologies. There are eight projects which are part of the Innovative Advanced Concepts Program. There are various phases and aspects of the project. Out of the many revolutionary things in this project is that, engineers are developing an aircraft which can stay aloft for a long time. If this project is a success, it will be a great leap for mankind. Check out the article to know about the other parts of the project: http://www.dailymail.co.uk/sciencetech/article-3589538/From-Magnetoshells-humans-Mars-growable-habitats-Nasa-reveals-funding-futuristic-space-technology.html

As a result of increased e-commerce in B2B and the general availability of data on the Internet, B2B pricing and product information is easier to find. Buyers have more information than before to have relevant and convenient product and pricing. To meet these assumptions, B2B companies are leveraging advanced analytics. Analytics can help companies to customize buying experiences through commerce channels. It also provides sales reps about what customers are likely to purchase and what prices make sense to quote in the context of the deal. Read more at : http://data-informed.com/how-advanced-analytics-is-changing-b2b-buyer-expectations/

Precision analytics is enabling e-commerce dealers to deliver goods fast after getting an order. They study buying patterns and prompt the buyer even before they decide to click. India's largest mobile wallet, Paytm is also trying to strengthen its presence in e-commerce. E-commerce company Amazon also use predictive analysis to study customer buying trends. Read more at: http://www.business-standard.com/article/companies/precision-tool-to-get-speed-on-e-commerce-profit-road-116051600057_1.html#

Big data & analytics bring together open source technologies. That works together to accelerate the data pipeline. They are : Spark - for large-scale data processing

Mesos -  for cluster resource management

Akka - for data-heavy applications

Cassandra - for storage engine and 

Kafka - for event processing

To gain the full value of data, there is a need to analyze it in real time. Read more at:  http://www.cio.com/article/3068672/analytics/igniting-faster-analytics-with-the-smack-stack.html