Finding suitable talent for your workplace is a difficult process. It is much more difficult to retain talent, if the workplace conditions are not suitable. Most of the organizations use annual feedback system to know about the employees. But the better way is to have a continuous check on the performance and behavior of employees. This helps in getting a better picture. Big Data helps in identifying the relation between engagement and retention. Cloud based solutions provide access to all the data at one place and this can be used for further decisions by HR. Data can be collected, measured, analysed at one place. And accordingly solutions can be thought of to eliminate the problem. Organizations who use big data solutions have observed a better turnover. Read more at: http://www.cio.com/article/3023311/careers-staffing/how-big-data-can-drive-employee-engagement.html

In today's world Data Scientist is the most- desired workers in IT industries. Since the demand for such skills is high, this has become a desired job for the potential candidates as well. So here are some skills which will increase your chances of hiring, if you want to become a Data scientist - SQL, Hadoop, Python, Java, and R. Not only these, but MapReduce, Hive and Pig skills which will increase your desirability as a data scientist. So, gear yourself up and start working and learning these skills to become a data scientist. Read more at: https://adtmag.com/articles/2016/01/08/data-science-skills.aspx

Security is an important aspect in any organization. In case of a security breach, company suffers a loss of trust in addition to money. For security, organizations are storing terabytes of data and with big data analytics security has become a nimble and deterrent strategy. It rapidly spots a suspicious pattern. In enterprise security, a small anomaly can be of great importance. Security breach occurs via an unimportant channel in a long period of time. If the organization has right tools, then only it can detect a hacker’s actions in time. With big data analytics data at rest and real time activity can be monitored with ease and organizations can be safeguarded in better way. Read more at: http://www.forbes.com/sites/centurylink/2015/11/23/improving-data-security-through-big-data-analytics-2/

If we talk about the recent past, machine learning was only used by a few who understood the algorithms and had access to very huge amount of data on which to employ it. But with the evolution of big data technology becoming a commodity and algorithms easier to use, machine learning has moved out of the hands of the few to the hands of the citizen developers and regular users. Four key steps taken in 2015 for the development of the machine learning are:-

# learning became easier to use.

# Everyone and their brother released a machine learning library or toolkit. 

#Big data to feed machine learning also became cheaper and easier.

#The label “machine learning” was applied to way too many items.

Read more at: http://www.infoworld.com/article/3017250/application-development/4-great-leaps-machine-learning-made-in-2015.html

Datafication is a process that captures almost every aspect of the world as data. With datafication, interactions can be quantified easily with the help of big data analytics. Data is generated for almost everything ranging from temperature readings to social media. This data is useful for analyzing patterns and making predictions about the future. These predictions not only improve business efficiency and finances, but also boosts customer experience and improve relations with the customer. Big data's tools' ability to extricate insights from the collected data have led to a new information revolution of sorts. Universal applicability is another exciting opportunity in this field. The future of retail commerce seems brighter now as datafication is here to stay. Read more at:http://insights.mastercard.com/2015/07/06/datafication-the-new-buzzword-for-business/

Primary activities of any oil and gas company generate large amount of data. This is developing heightened demand for big data and services in this sector. The market ofr such solutions is expected to grow by $3.99billion to $5.41billion in 2020.  Big data analytics could help in reducing time lag, and improving drilling parameters in identifying any traces in seismic signatures derived from geological and operational data. Employing data analytics can also help detect any abnormalities in drilling and could save millions in labor and equipment costs. Recovery and production can be enhanced by applied analytics seismic and drilling data. One of the major challenges faced by large companies is lack of skilled labor which can be solved with knowledge management using big data. Recent Paris Agreement has created unprecedented opportunity for countries for decreasing temperature less than two degrees celcius above pre-industrial time which encourages oil companies to improve operational performance and efficiency. Read more at: http://www.technavio.com/blog/how-oil-and-gas-using-big-data-better-operations

Large companies are collecting big data every minute. The need for new data base analyst and system programmers is increasing having specialised skills and in depth knowledge. Their roles include 1) management of databases and their recovery readily measured and monitored against disaster. 2) Implementation of self-realising and self-tuning processes. 3)SQL and database performance tuning. 4) Database logs and error management. Many of these roles are unnecessary due to high speed of big data analyst solution. Similarly, database performance tuning has become redundant as it lacks internal performance tuning resulting of no need of specialist in new enterprise environment. The task of specialist involves, 1) System and network performance tuning along with software installation and migration. 2) They be the leading technical specialist for critical application assisting data architecture changes. 3) Set the performance benchmark for prospective vendor tools. This infers that less and less specialist will be required in the coming phases when the role of generalist can be easily done by supplementary staff. Read more at:  http://it.toolbox.com/blogs/database-administration/big-data-technologists-transition-to-customerfacing-roles-the-current-team-70675

Over last quarter-century, there has been a steady increase in the features available for different application of analytics industry. At one level all the features help organisation get the most value possible from their information assets. All different technologies are designed for different user types. Due to merging of needs of different user, if we want to leverage all the features, we typically need several servers and interfaces. SAP cloud analytics integrates all technologies into a fast blazing platform which we don’t even need to install. With SAP HANA, we can easily cleanse, integrate combination of data sources and provide insightful budget forecasting. We can also analyse combination of structured and unstructured data and augment data exploration with sophisticated geographical mapping with the ultimate vision of Big Data Discovery. Based on SAP HANA’s HTAP capabilities, its all possible on real operational data with instant sophisticated of data for analysis. Read more at:- http://timoelliott.com/blog/2015/10/reinventing-the-analytics-experience-sideways.html

Marketing is always evolving. This year we dealt with Big Data, we understood the term empowered consumer, and embraced the idea of cross-channel marketing. While these will continue, marketers also want to know what next year will bring.  Larisa Bedgood (Director of Marketing for DataMentor), writes in her article about the 5 trends that we see making a big impact in 2016. Read more at: http://www.business2community.com/marketing/top-5-marketing-trends-will-rule-2016-01375322#tSFR5pvWDXMkbL2v.97

The relationship between structured and unstructured data in real time can be analyzed by turning the structured and unstructured data (wide data) into fast data. Origin of a problem can be found by wide data. Quicker decision and analysis can be made by fast data. With the boom of Internet of Things (IoT), new challenges relating to the structured and unstructured data may arise. By turning wide data into fast data we will be able to retrieve massive amounts of data from various sources and then use them to identify and potentially assess their behavior in real-time.
Unstructured data can also be used to meet real-time data demands. When wide data is turned to fast data it opens doors to various opportunities.
Read more at: http://www.tibco.com/blog/2015/07/17/big-datas-companion-wide-data/

Big Data appears to be the solution of every problem, especially when it comes to IT. But, one thing we often forget is that the big repository of data is useful only when one is able to generate actionable insights from it. Otherwise, it’s just another stack of data. With organizations becoming increasingly dependent on technology for every aspect of business operations, IT enterprises are finding themselves locked with big data with no useful insights. Raja Mukerji, president of ExtraHop, in an article in Inside Big data talks about IT operation analytics as the most viable solution to this problem and how it has emerged as a framework for a horizontal approach to Big Data in IT. He believes that ITOA will help release useful insights form IT Big data and the horizontal, silo buster approach will transform IT from a mere support system into a strategic business driver. For more on this piece follow the link http://insidebigdata.com/2015/07/18/taking-a-horizontal-approach-to-big-data-for-better-it-and-business-outcomes/

Big data and internet of thing (IOT) have made it easier for companies to collect new data. This will help companies to have comprehensive view of consumer’s buying and viewing behavior which in turn is used to make competitive business intelligence. Today businesses are struggling to face high bills of Internet Service Providers (ISP) as they already have too much data to cope with. With huge investment, industries have spent time in finding solutions for crunching large data within limited time. There is huge cost involved in storing big data. But Software Defined Storage (SDS) gave storage the operational efficiency as other big data aspects. This provides flexible and scalable architecture to empower the business to meet big data fluctuation demand.  Read more at:https://channels.theinnovationenterprise.com/articles/big-data-and-the-communications-data-bill

Big data and financial services have played their role together to boost the company's profits. Big data is providing advantages to financial services in three ways. Firstly, big data is keeping financial companies ahead to develop more creative and innovative ways of using big data to predict future, which companies are going to earn profits and which type products will be demanded in future. Nowadays, financial companies can easily monitor and respond to changes that exist in financial markets. Secondly, banks are also using big data to become more customer focused. This can be done by tapping the reams of unstructured data like lifestyle information, social media activity, and customer feedback and support requests. Thirdly, big data can be used in fraud detection. Read more at:http://www.smartdatacollective.com/bernardmarr/335942/3-ways-big-data-changing-financial-institutions-forever

Marketing is heading towards Big Data and analytics. Marketers are experiencing increased complexity in both their departments as well as daily pursuits. Data complexity is increased with campaigns on social media combined with other data sources from sales and finance. So companies are now resorting to marketing analytics for data aggregation, analysis and making data-driven decisions. Before going ahead with marketing analytics, companies should identify their key objectives based on a definite analytics platform which is divided into milestones in sync with the existing framework of the company. Once this is done, one can start the analytics journey and hold key stakeholders responsible for results all of which are based on the company’s goals and vision. Data techniques to be used should be decided solely on the basis of the company’s objective. It is important to focus on the correct segment of data within the entire data set. This way one can increase the chance of gaining actionable insights on data and thus pushing the company towards growth. Read more at:https://channels.theinnovationenterprise.com/articles/marketing-analytics-do-you-have-a-framework

Over a decade ago, High frequency trading (HFT) used to be part of very few financial firms, but now it is an integral part of every major financial firm and is key to drive the success of these firms. Many industry experts in the field are of an opinion that big data has started entering into the financial sector at a minute level for now and it will follow the same trend like the HFT in expanding into ever major company. It will be the major decisive factor in taking many calls in near future. On the technical level, many experts feel Machine Learning (ML) will take a dominating role in areas where Statistical techniques are now used for finance and risk management. ML with its ever increasing algorithms/techniques is an ideal replacement to humans in trading scenario, though it has its own caveats. It is seen that ML and Big data is going to lead a new revolution into the field of Finance. To read more: 

http://www.automatedtrader.net/headlines/153852/gdtrm15-machine-learning-is-the-new-c%2B%2B

In the gambling industry, the house with most information wins the game. And it’s no wonder that Big Data is changing the world of gambling. Betting firms are now making extensive use of Big Data and analytics to manage their business and stay at the apex of the game. Big Data services empower gamblers by giving them more information and helping them to plan strategies more effectively. Big Data is also transforming sports gambling. With lots of data to collect and analyze, sports organizations are using it to study players and their tactics. Gamblers are now using Big Data as a way to get the odds in their favor and this has led to the growing popularity of fantasy sports betting. Gamblers are not only using Big Data for their games but also to improve their marketing efforts for example the casinos are analyzing customer data to develop personalized marketing campaign. Read more at:https://channels.theinnovationenterprise.com/articles/how-big-data-is-changing-the-gambling-world

Big data will play a major role in shaping the future of businesses. The two major areas that are going to undergo significant changes are as follows:

(i) Previously, once data had been used, it used to be deleted. But now, as predictive analytics makes use of historical data, so data needs to be stored for an indeterminate period of time. This is against the current laws and thus requires amendment. In future, well-defined laws about collection, distribution and storage of data are going to be the key to success for businesses using Big Data.

(ii) With technological advancement, data is being collected at a faster rate than before. Also new and improved technology is being used to sort it. In future, frameworks will need to be robust in nature and analytical processes should be improved in terms of speed and accuracy. Read more at: https://channels.theinnovationenterprise.com/articles/big-data-and-the-future-of-business

Big data is playing an important role in football during these days. Football is continuously making use of performance analytics and making use of available information more efficiently to achieve competitive advantage. Most of the top level European football clubs adopted structured analysis process and dedicated performance analysts. Now clubs are able to analyze their data at just one click of mouse. Today players are wearing GPS trackers during training, acceleration sensors and heart rate monitors which records their data of performance and after analyzing those data optimal steps can be taken to improve performance. Also analyzing the opponent's data can give the team a lead ahead to win the game. Today performance can be judge easily on and off the field. Selection of players can be done by analysis of performance. Finally we all are seeing a completely different game during these years. Read more at: http://www.smartdatacollective.com/bernardmarr/332906/how-big-data-and-analytics-are-changing-football

The idea of using information and data for various purposes is not new and has been there since the first web connection was developed. Computer scientist John McCarthy had developed the idea of cloud computing. The internet has grown at a very small pace since the 70's but got revived in the early 90's when first move towards cloud computing was taken. Heavy investment and development in cloud computing shows it is predicted that cloud computing is soon to go mainstream with 34.1 percent of IT sector already running applications in the cloud and 66.4 percent are looking into the cloud, planning to implement installations. Server performance tuning has become easier and finding query performance is now rapid through database analyser, all due to the cloud where our future lies. Cloud computing has become a global phenomenon and people are choosing it for solution in a hybrid manner. Soon companies have to fully turn to cloud computing as it becomes mainstream and lots of other companies shall create their own unique framework including internal cloud adoption. Read more at: http://www.smartdatacollective.com/rick-delgado/324781/foreseeable-future-cloud-computing-explained

Today marketers know how to enhance customer experience and implement new strategies to attract new customers. Big data, social media marketing and customer relations have influenced the marketing behavior. There will be no insights without data. But by managing data and analyzing it will lead to achieving marketing goals. Analysis of data should be such that it provides helpful conclusion to businesses to make strategies and reach goals. Documentation of business objectives helps business to know what type of data is required and how to manage data. Data should be integrated and structured in such a way that it is reliable to customers. Data validation is required to check the validity of data.   Read more at: http://www.smartdatacollective.com/lbedgood/332008/ultimate-beginner-s-guide-data-quality-and-business-intelligence