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/

We often struggle to extract real benefits from the large pool of data we have and volume of information is only going to swell in the future. What we need is to "learn to listen to the data".

1. Focus should not be on goal setting but on learning.


2. Many new voices- big data allows us to hear the voices of the silent. Like the customer voice and Workers voice. We can understand our team’s habits in real world by seeing their working behaviors. We can get the employees to tell us how to make the business model smarter and we can use this knowledge to design systems and incentives that make your business better.


3. Addition of sensor data enriches our understanding even further.

4. Wearables will open up whole new sources of data on human behavior and we will also hear from machines, buildings, even animals and oceans.


To read more - http://www.webanalyticsworld.net/2015/06/listen-to-the-voices-of-your-data-for-great-analytics.html

Here are a few tips on how to bridge that gap between big data and the real world. New product development is a field full of quantifiable unknowns. While working on a new concept, the decisions are based on evidence that proves that their idea outmatches the other alternatives. Identifying connections between the data on social media and online communities and the comparison of this with their competitors’ development policies then become their groundwork for smarter solutions. Visualization of data quickly converts the data is understandable forms. This speeds up the decision process. It is important to note that more data doesn’t always mean smarter data. The motive should be to capture relevant Read more at: http://www.innovationexcellence.com/blog/2015/06/29/actionable-ways-big-data-analytics-can-actually-improve-innovation/

As the number of smart wearable technology is growing, companies who have the potential to rapidly optimize their value chain are growing into a digital business through competitive advantage. Customer’s loyalty is also increasing with the changing rules of their digital lives. Their experience can be transformed by improving their understanding with analytics, enhancing top-line growth and through social media. To transform operational processes, process digitalization is must along with enabling proper staff and their performance management. Thus to transform business, every business is now digitally modified to make digital globalization. In this way, companies worldwide are adapting digital transformation with social media, mobility, big data analytics and cloud to make a better business plan than others. Read more about this article at:  https://channels.theinnovationenterprise.com/articles/digital-transformation-a-need-of-now

As the Internet evolves with the usage of Big Data and Cloud, the shift from cloud data to cloud action is eminent. This shift has large implications for consumers. In five years’ time, the number of connected devices will grow to near 25 billion. From regulating the temperature of the house using smart thermostats to using reports and dashboards for a clearer insight to make smarter decisions. There is a need of a system which we can entrust our identity with, where our identity is safe while making transactions in the background. For this, companies of types must begin to bridge the gap between the physical and digital worlds. Only then can we begin usage of the delicate frameworks in a network based on our truly unique digital identity. Read more at: http://recode.net/2015/06/29/big-data-vs-best-data-the-future-of-you-in-a-networked-world/

The quality of big data is more important than the quantity of big data. Three types of data a company should be stressing on:
1. Social data- Marketers are now focused on estimating the number of followers of the company website, and its products now that there are ample opportunities to share the knowledge of their products in social platforms like, Facebook, LinkedIn and Pinterest.
2. On-line IDs- Data has taken marketing to a whole new level. Data can be collected and used to personalize messages and send offers through mediums most preferred by consumers. This is often done by connecting off-line data with on-line identities.
3. hard-to-find data- Enterprises produce large quantities of data everyday as a by-product of their day-to-day business transactions. Many companies, realizing the benefits of monetizing these data assets are making it available to other firms and the fact that the data has already been mined and sourced, adds value to the whole procedure.
Read more at:

http://www.business2community.com/big-data/3-big-data-types-will-increase-roi-01257959

Big data is the new buzzword. It has become one of the imperative equipment required to conquer growth across industries. Here is how you can master the art by following thee 5 rules, coined by Rishi Sikka, Advocate Healthcare.

#1 Clue lies in the past: to be able to predict future, it is necessary to dig deeper into the past data and EHRs.

#2 Diversify risk through strategic distribution of resources.

#3 Impactability- healthcare organisations should focus where and to which extent do they impact.

#4 Take population Health as a predictive exercise, not academic.

#5 Minimize costs, work to provide best healthcare.

Read more at: http://www.healthcareitnews.com/news/5-tips-becoming-big-data-superhero

Big data can also be applied in the field of humanities specially history. With advancement in text analytics, historians can now go through a huge number of historical documents and discover patterns and trends from the past. These then can be used as evidence to check if theories are correct. Research students analyzed a huge number of novels using big data analytics which allowed them to leverage insight on what the semantics implied about the society. Given the nature of historical documents, it is difficult to digitalize and apply text analytics to those. Thus data analytics is being considered by historians now. Big data when applied to history will help bring information scattered over the world thus helping historians to gain insight and predict the future. Read more at: https://channels.theinnovationenterprise.com/articles/data-looking-at-the-past

Suppose, if wearable were introduced into the workforce which could monitor about the employee wearing. How would be like?  The fact is that data collected from such wearable were used at an individual level, many might not welcome the idea as it might terminate employment for unhealthy employees. Nowadays, organizations engage employees rewarding their performances. Regular feedback helps directly for employees better perform thus affecting growth of the organization. With changing demands, if employees share their information through smart-watch, organizations should reward them empowering those employees. Though many might not like the concept but none can deny the internet of things (IoT) getting into our daily lives. Meanwhile, many organizations are already on their way to adopt this technology. Read more about this article at:  https://channels.theinnovationenterprise.com/articles/7582-getting-the-best-out-of-wearables-with-your-workforce

As more and more data pours in, marketers need to keep adjusting their strategies to be updated in this dynamic ecosystem of information. Thomas Begin, vice president of strategy in Targetbase, in his special guest feature in Inside Bigdata talks about the analysis paralysis in times when many organizations are turning to consumer centric strategies. He points out the major lags in this transformation:

• Inadequate internal collaboration

• The inability to act quickly

• More focus on reporting rather than action. 

To have a look at the recommended solution to these lags follow the link http://insidebigdata.com/2015/05/05/keeping-big-data-small-to-create-engagement/

With IoT becoming the norm rather than the exception, there is an overwhelming amount of customer data at the disposal of companies. But with huge data comes huge challenges. The challenge of combining, cleaning and shaping data before the analysis. Cari Jaquet, vice president of marketing at Paxata, in a special guest feature in Inside Bigdata talks about why data preparation solutions are important to make data more consumable. According to a recent Gartner report, it has been advised to use simple data preparation tools to first, save analysts time and second simplify the task of data preparation from diverse sources. For a deeper insight into data preparation follow the link  http://insidebigdata.com/2015/06/19/data-preparation-the-key-to-unravelling-the-big-data-opportunity/

There is huge amount of data and searching for ways to use that data can seem terrifying. Although very little of this data is useful, more and more can be extracted for useful patterns and information. This portion of the useful data is called “Big Data”. For now Big Data is a good way for any company to gain advantage but in the coming ten years use of Big Data will be inevitable. Cloud computing and things like Hadoop and NoSQL provide data analyzing tools to several businesses and entrepreneurs to help analyze the right data sets. Experts on Big Data give some useful tips on how to use this technology.
Know the problem you are trying to solve.
The better way to think about the use of Big Data is to consider it as a tool to solve challenges. Once the problem is identified the data can be used to find a solution.
Start small and grow.
It is a good idea to run a trial analysis and see if it solves the problem. If it doesn’t then you haven’t risked much and if it’s a success you will come out with useful data.
Choose the right data.
 Although it can be difficult to find data, data should be combined from different places to obtain the most useful ones.
Move Fast.
Big Data is not only about analyzing information but also acting on it in real time so that all parts of the company is moving towards a common target and can make the most out of the available data.
Read More at: http://www.forbes.com/sites/mikemontgomery/2015/05/07/small-businesses-shouldnt-fear-big-data/

Large amounts of real-time data is being daily generated by internet usage. Nowadays, devices are being interconnected and smart products are also connected with the internet. In order to use this data efficiently, organizations need to re-structure their supply-chain. The motivation is not just to use historical data in the traditional manner; but to combine data from multiple product interactions generated by both consumers and suppliers, connected via cloud portals. Supervised machine learning can search for and capitalize on the patterns and relations that they derive in the data and help in supply chain based decisions. Once implemented, they can be continuously evaluated and improved based on performance. The end aim is to accurately predict the attributes of future demand. Read more at: https://hbr.org/2015/06/inventory-management-in-the-age-of-big-data

Big Data is paving the way to the emergence of Smart Data. Huge, differentiated and big volumes of Big Data practically needs smart data for its everyday working because they facilitate -
• Unstructured and structured data aggregation and analytics
• Simplified and accelerated data modeling
• Access and data governance
Big Data is inexorably transforming into smart data. It is the preferred technology used in the diverse application of Big Data including the Internet of Things, Cognitive Computing, Semantic Graph Databases, Data Lakes and Artificial Intelligence.
The nature of Smart Data represents an insurgence in the logic applied to data driven processes. Big Data is important in Data Management as it has the ability to implement action from real-time analytical insight and consolidate all of one’s data in the process. Applications such as Internet of Things automate processes that would otherwise take too long. In context of Smart Data’s ability to increase the utility of Data Lakes is its ability to help clarify the sort of role-based access that is a pillar of proper Data Governance. Smart Data Modeling is preferred in analytics because there is a degree of flexibility and agility in the modeling required for Smart Data that exceeds non-Semantic Data. Along with its advantages for analytics, application development, data integration and Big Data Governance, Smart Data’s reconfiguration of transactional data will establish the fact that Big Data is surely evolving into Smart Data.

Read more at: http://www.dataversity.net/the-evolution-of-big-data-to-smart-data/

With fast advancement of consumer-driven technologies, new alternate ways to measure workplace risks are emerging. Technology improvements like growth of the Internet of Things (IoT) help us in assessing the working environment in real time.  The increase use of wireless communications for performance specification in the field of safety, health and industrial hygiene allow them to safely monitor multiple workers in real time and be alerted when exposure levels become significant or excessive. Another recent trend is the emergence of Big Data and the use of data mining as an analysis and management tool that will forever change the safety and health profession. With the increase in samples, the accuracy grows and statistical analysis takes decisions regarding the limit of exposures. This helps identify outliers (individuals, processes and practices that result in greater exposure levels) as well as gives confidence to the management about the accuracy of assessment of the workers. Read more at: https://ohsonline.com/Articles/2015/05/01/Technology-Megatrends.aspx?Page=1

With the advent of Internet of Things (IoT), businesses can now avail additional ways of communicating with their customers. Sensors can be embedded in household appliances allowing consumers to control those which simplify household chores. Doctors nowadays can use apps to link patient statistics and provide personalized advice. Thus healthcare market makes ample use of IoT to provide better services. Security concern should be kept in mind as organizations are now deploying IoT in their enterprise. Organizations should also encourage application developers to maintain a certain standard to prevent poor codes from being deployed. Cloud technology, big data and analytics, mobile computing and social networking along with IoT makes a complete package. Read more at: https://channels.theinnovationenterprise.com/articles/deploying-iot-in-the-enterprise

Industries are growing techno-savvy and understanding that being able to gather, identify, and comprehend patterns in data is critical for their long-term success. We are finally entering an age of data-powered farming that work on open data sets of weather and yields, IoT, drones and smart agricultural machinery. Such digital tools are becoming as important as traditional tools in the agricultural sector. IoT sensors are also helping companies predict and manage risk for automobile policies in real-time. Governments and industry are cooperating to share data on natural catastrophes in the hope to predict them. Drones are even being used to inspect and manage energy sites like turbines and rigs. Governments too will start using Big Data for evidence-based policy making. Read more at: http://yourstory.com/2015/06/big-data-unleashing-value-for-startups/

Big data is now helping recruiters to develop a system of fair recruitment procedure which minimizes any kind of discrimination. Acquiring and retaining talent is an important factor when it comes to human capital management. Often it is seen that the talent of many applicants or employees go unnoticed due to certain forms of discrimination or bias.  Data driven procedures and predictive analytics are making remarkable impact in reducing such discrimination in organizations. Various attributes within the organization such as revenue contribution, pay gap, additional benefits received, risk of resigning etc.  are being monitored continuously and algorithms being are designed to pin point events when a hardworking employee is not appreciated. To know more read: http://www.huffingtonpost.com/anne-loehr/big-data-for-hr-can-predi_b_6905754.html?ir=India&adsSiteOverride=in

We know how big data has helped organizations achieve big success in almost every field. Today, when everyone is in a race to establish a competitive edge over the other, businesses want their data and analytics to be real time or near real time so that they can act quickly. Mary Shacklett, president of Transworld Data, elaborates for us four ways real time analytics lead to competitive advantages. They are:

• Buying Preferences

• Network Intrusion detection and forensics

• Logistics Orchestrations

• Predictive maintenance

To know what these systems had in common follow the link http://www.techrepublic.com/article/4-ways-real-time-analytics-lead-to-competitive-advantages/