It depends on the enterprises' ability to find value in IOT generated data and come up with metadata fabric that enables efficient data analysis. Metadata fabric works in sync with IOT to benefit the enterprise. It can be viewed as the storehouse of business intellectual property. It makes data driven decisions a lot easier. Metadata fabric transforms unified interpretation of data and analytics into layman format and interface easily understood by businesses. IOT ecosystem often throws challenges as the number of unique data sets collected is smaller than the number of applications that need it. Introduction of an intermediate curation layer can solve this governance problem. The aim of the governance system should be to reduce onboarding cost with increasing chances of success. Optimization should also check for homogeneity in the collected data. To know more, please follow:

http://www.dataversity.net/data-governance-the-metadata-fabric-and-the-internet-of-things/

Big Data may be relatively new but data has been a hot topic of discussion for over a 100 years. Interesting to note, the challenges in fetching and understanding it remain fundamentally the same. We are told that this is the era of Big Data but its roots go back to 1914. Willard Brinton wrote the first book on communicating data, Graphic Methods for Presenting Facts. He had stated that those who possessed cutting-edge data technologies would be the pioneers in their field. Processing and visual representation of data as information would allow us to give just the required information and facts to allow those who aren't proficient in data, actionable insights into their problems. In 100 years more, we would hopefully understand analytics and effective data visualization. Read more at: http://www.computerworld.com/article/2942728/big-data/big-data-the-100-year-old-buzzword.html

Although most modern enterprises run on data, it becomes useless if we cannot derive intelligence from it. Data includes all the information that is available including raw facts about companies and individuals. Intelligence is when we look at the data and try to find a relation to better interpret challenges and opportunities in the market. Insight with data and intelligence deals in adjusting strategies in response to the market. To develop predictive models about the market, we need intelligence and insight alongside data. Raw data is often mistaken for insights. For smarter decisions, internal and external data sets should be used as starting points and then create intelligence to draw sensible insights. To know more:  http://www.smartdatacollective.com/ray-renteria/327508/data-does-not-equal-intelligence-predictive-analytics-enterprise

Industries are flooded with data and this data can be used to extract a lot of money if only we knew how to use it. With the rapid advancement of technology, we have high-speed networks and improved algorithms for analytics. Various industries including healthcare and financial services are basically resting on terabytes of valuable data and information. Six best methods for monetizing data are as follows:

1. Building up trust and neutrality among the customers.
2. Anonymization and aggregation of data is required.
3. To acquire permission of utilizing the data that is not owned by the company.
4. To consider spinning out the data monetization unit.
5. Provision of data analytics in real time.
6. Furnishing customers with self-service capabilities.

In order to know more about data monetization, please follow the link: http://www.cio.com/article/2941291/big-data/memo-to-cios-cash-in-on-that-data.html

Cloud has changed the dynamics of data analytics. It is believed that in the coming five years, organizations will be spending more on cloud based big data and analytics solutions than on the on premise solutions. Dave Wang, senior product marketing manager at Databricks, lists down 5 major reasons why data analytics in cloud is becoming a top priority for organizations trying to build a competitive edge through analytics. To know which among them has an upper hand and why, follow the link http://insidebigdata.com/2015/05/08/5-reasons-data-analytics-in-the-cloud-will-take-center-stage-in-2015/

Organizations are now facing a change in the way they get their business intelligence services. Initially it was the IT department that provided business intelligence services, with all powers vested upon them. With business intelligence tools becoming increasingly accessible and users having knowledge of data and processes, IT departments are no more the only source of business intelligence services making self-service business intelligence the next step. The market for self-service business intelligence analytics tools is growing pretty fast. With these tools, business users can access pooled data from various sources, leverage those and produce business insights. Self-service analytics comes with several advantages. With self-service business intelligence, data has lower turnaround times leading to increase in decision-making processes and productivity. Read more at: https://channels.theinnovationenterprise.com/articles/the-drive-towards-self-service-business-intelligence

Media companies are taking advantage of advanced analytics and technology in order to increase their sales. Hence it allows advertisers to foresee what ROI they might anticipate from an investment in different channels and how this basically generates revenue. Advertisers are also interested about the exact efforts that they need to pursue to improve the results.

Daniel Kehrar (VP Marketing, MarketShare) in his article on Forbes indicated the five rationales which led media companies to move towards performance-based selling and they are as follows-                       

* Advertisers are under greater security.

* Brands are growing accustomed to analytics.

* The technology to accurately and cost-effectively predict advertiser outcomes is here.

* Advertisers want more.

* Better data and analysis brings greater credibility.

 For further details on this study, please follow the link: http://www.forbes.com/sites/forbesinsights/2015/06/25/4-ways-analytics-are-helping-media-companies-sell-outcomes-over-eyeballs/

The combination of new software technology, industrial data science and visualized information is acting as a game changer in the aviation industry. Analysts monitor flight data every second on the ground, for variations and immediately alert the pilot of any anomalies. Real-time data monitoring also helps in reducing fuel consumption apart from providing safety and security. Tim Leonard of southwest Airlines while talking to business insider said that the predictive analytics tools help prevent disasters they make pilots, mechanics, and employees more aware. It also helps provide frequent and precise data on current weather conditions leading to better scheduling and minimising delays. Read more at: http://www.businessinsider.in/Why-every-flight-you-take-is-obsessively-monitored/articleshow/47694902.cms

In the face of global competition, the business world needs to adapt new technologies and forgo past financial traditions. Following Derek Klobucher (Moderator of SAP Business Trends)'s article, most software were designed in the 70s, and 80s so they are slowly losing relevance in the modern era as the technology landscape is vastly changing. The way forward is adapting the next generation business platform. SAP is working on solutions to integrate instant real time insight, offer updated user interface and user experience which saves on transaction times by 30 to 40 percent, and be non-disruptive. Month end closing is a traditional; however modern times dictate fast, accurate and secure data analytics. To know more http://www.news-sap.com/how-real-time-analytics-will-kill-a-financial-tradition-sapinsider-2015/

Text data is the best answer in understanding customer feedback. It is best for answering the why and what questions. Text Analytics is becoming mainstream as interest is pouring in. Sources include call center notes, email messages, social media etc. Following Fern Halper's TDWI article, 5 fundamentals include: 1. Text analytics involves analyzing unstructured data to extract relevant information.
2. Extracting various information in the form of keywords, entities, concepts and sentiments
3. Considering taxonomy, while dealing with specific vocabularies.
4. Integrating text data with traditional data, depends on the kind of data
5. Text data is not as accurate as statistical techniques
To know more http://tdwi.org/Articles/2015/06/23/5-Text-Analytics-Fundamentals.aspx?Page=2

Like other conservative industries, chocolate industry is also interested in applying smart marketing techniques for their business. Companies need to be more scientific in their marketing ways and by now it's proven that future growth of the chocolate industry also depends on data-driven consumer marketing. Vosges Haut-chocolat company used the method of predictive analytics solution to reach its buyers. The difficulty in this task was to figure out how price sensitive were the consumers. With newly discovered software's and AgilOne predictive marketing cloud, Vosges tried to target all its customers who had left shopping carts and also found all different reasons for their - not purchasing the chocolates. Predictive analytics made it possible for the company to provide heavy discounts to the people segmented into proper user-groups and increase their sales so that consumers who would never buy any chocolates ever could be differentiated from the rest.

Read more at: http://www.smartdatacollective.com/socialmktgfella/326221/even-chocolate-needs-smart-data

Just as sourcing ingredients is essential to the quality of a great dish, the quality of labels determines the accuracy of a supervised or semi-supervised machine learning (ML) solution. What is a label?
Data labeling involves taking unclassified data and augmenting each piece of that data with some sort of meaningful ‘tag’, ‘label’, or ‘class’ that is somehow informative or desirable to know. Assigning a label is a judgment task to be performed by an analyst depending upon the kind of variables he/she wants to work with and is an essential part of the ML process.
How to ask Questions?
All tasks involving labels involves, at some point, asking a question to a human being for collecting the data. The questions should be relevant, clear and precise in addition to being plain and understandable and one for which answering does not involve much effort for the individual.
How to debug tasks?
The collected data may sometimes show discrepancies due to:
• Data: Certain factors which can cause bias in the workers have to be eliminated.
• Workers: We have to detect the expected errors arising due to human involvements, say, an error arising due to spammers rather than people making genuine mistakes.
• Tasks: If the problem still persists then there might be a problem with our initial assumptions. Time to rethink from the start.
How to assess Work Quality?
Specific domain based algorithms exist for every point during the project that determine the ongoing quality of work.
Labels are essential and cutting on effort in this regards is most likely to lead you to erroneous results.

For more information visit:
http://blogs.technet.com/b/machinelearning/archive/2015/06/23/label-quality-for-machine-learning.aspx

As the amount of data being generated daily is increasing, sales teams are now using analytics to boost sales. Several factors should be kept in mind while doing this like choosing the right set of data. The sales team may be lead astray when wrong data sets are chosen leading to drawing wrong conclusions. Choosing the right metric and making correct assumption is also crucial. Data on compensation can also provide insights into how sales can affect individuals or a group. Using this data and applying predictive analytics, one can easily forecast future performance and accordingly plan sales process. Read more at:http://www.cmswire.com/digital-marketing/dont-let-data-blind-your-sales-team/

A company’s success depends largely on its staff. So, hiring the best talent and motivating them is a challenge being faced by the HR professionals. A data-driven HR department can make use of analytics to accomplish several tasks like identifying the factors that influence performance of employees, retention rates and using statistics to understand how salary is related to performance. When compared to other departments in an organization, the HR department lags behind when it comes to using analytics due to lack of analytical skills required to convert data into insights. With the advent of cloud technology analytics solution can be generated at a fester rate. Several cloud based companies are now providing in-built analytics solution in their software. Read more at: https://channels.theinnovationenterprise.com/articles/hr-analytics-in-2015

Amalgamation of data, technology and marketing helps the manufacturing industry acquire new opportunities for earning profits. Data obtained from the customers are stored in various different systems like inventory, billing etc. And normally the manufacturers have to gather information from a common source thus having no competitive advantage in the market.
Industrial manufacturers are now gradually shifting from traditional marketing methods to data-driven ones so as to gain more prospects. Solutions such as Data-as-a-Service (DaaS) are influencing the Big Data ecosystem increasingly. It mines the appropriate data required from the Big Data sets. Patented Web Mining is also an effective and efficient way to find new prospects. Social media also actively helps in promoting the manufacturers.  Thus industrial manufacturers can increase their market share to a great extent by shifting to data-driven strategies.
Read more at: http://www.smartdatacollective.com/lbedgood/327457/how-manufacturers-can-use-big-data-acquire-new-customers

Many business have been extracting the benefits that come from applying big data as years go by. But there are still a few start-ups left who haven’t yet reaped its benefits. It’s crucial to know that now is the correct time to get into big data as it has considerably improved in the past few years. The reasons why big data should be employed now are:

• It keeps the data secure and all the sensitive data stay well protected in the system.

• It opens up brand new revenue sources and gives us a clear perception about the market which is very valuable.

• Big data provides an advantage of supplying better data which helps compete against others in the market when trying to innovate.

• It provides better visuals as well as it is very easy to set up big data in the business of any size.

• It helps the business expand and can efficiently cut down on maintenance costs.

• With big data employment, it’s easier to analyse the risk and create better improved products in the market.

• Lastly, big data improves company-customer relationships helping the business in every possible way.

Read more at: http://www.smartdatacollective.com/rick-delgado/323981/10-reasons-why-now-time-get-big-data

In order to achieve accuracy of understanding customer’s experience the company executives have to come up with varied surveys and several tricky questions. We can gain a more empathetic experience of customers experience with the appropriate amalgamation of observations, sensors, data and designs. Being aware of the users experience is very critical for the technology design.
There are certain emotions which cannot be explained by words. Sensors can pick up these stress signals, combine them with the available textual data and thus identify the emotional triggers of a customer’s experience.
The comprehensive data on the experience of the user can be gathered by observing the way user interacts with the product or application. It exactly determines how the design is performing and whether it needs improvement or not.
Read more at: http://www.informationweek.com/it-life/can-data-teach-us-empathy/a/d-id/1321022?

Manufacturers, logistics companies, pharmaceutical companies and other energy companies have been using supercomputers to push the limits of research and discovery and answer questions that were not possible using other means. Companies are using cloud to solve problems which were once solved by supercomputers. Cloud computing has encircled high performance computing or HPC and supercomputers along with cloud solutions continue to advance. Companies in the auto industry, airlines, energy are willing to work with supercomputers to solve their big questions. Along with wide range of hardware, optimised software’s run efficiently on supercomputers. Such computing also helps companies to improve their state of art. The six different reasons which encourage companies to use supercomputers are that they have amazing computational capacity, the datasets fit properly into the memory, the interconnect is analytical, advanced modelling power, expansion, and speeds discovery.

Read more at: http://www.informationweek.com/it-life/supercomputers-unleash-big-datas-power/d/d-id/1321013

Big Data’s prospects are tremendous and will keep growing but only if we can keep up with it. Bangalore now monitors traffic in real time. It is only then that Data Analytics steps in, helping take calculated decisions for the strategic spots for sign boards and road markings. Thereby reducing congestion and allowing safer travel. We see how crucial the hardware, software, networking and data center are in the analysis of such data. Analytical data mining seems to be the majority of the effort now. Smarter data centers, networking practices and rapid storage technologies will be the start of a cycle of innovation that may be a few decade long. Ironically enough, in-depth analytics generates data that has greater need for computation and large storage systems that will allow rapid testing and deployment. Read more at: http://www.firstpost.com/business/big-data-paradox-2323460.html

Imagine your treadmill turning into a wonder machine which knows your fitness goals, takes inputs from you every morning and then suggests the best workout for you. Seems too good to be true. But not that dreamy for Sunil Koduri CEO of Zsolutionz, who believes that fuzzy logic will lie at the helm of realizing this dream. In simple words, fuzzy logic deals with approximate rather than exact reasoning and is most useful where computers are required to make decisions like human. Mr. Koduri, in his special guest feature in Inside Big data talks about how fuzzy logic technology can combine with the huge data generated by wearables to provide personalized fitness experience. No stopping here, all this data from wearables, health history information, and cloud data can be combined to create a huge ecosystem which is user centric to enhance user experience and also keep the healthcare providers in loop. To read the original piece of article, follow the link http://insidebigdata.com/2015/01/12/fuzzy-logic-key-connected-health/