As the field of Data Science is growing, the confusion regarding the skills needed to be a data scientist is also increasing. Most of us think data science skills range from computer science and statistics, to machine learning and strong communication. But, the top data science skills list includes data analysis at the top, followed by others like R, Python and machine learning. As per recruiter lists, R, Python, SQL, SAS and Hadoop are appreciated. To know more about data science skills, follow the article written by Daniel Levine (Content Marketer for RJMetrics) at: http://www.smartdatacollective.com/daniellevine/366486/top-20-data-science-skills

Need for Chief Data Officer (CDO) in boardrooms has increased now days. Importance of CDO is same as that of big data in companies, as a person in charge of data team. There has been increase in number of companies who are employing CDOs. At present 43% of executives reported that their firm had appointed CDO, while two years back it was only about 19%. During financial crises in 2008 many companies lost their significant amount of money after their data had been shown completely wrong. That gives rise to Chief Data Officer, a person that can be held responsible for validity of data. He can protect businesses from loss due to crises and also to help appease law makers who were going to require change within reporting and storage of data.  Read more at:

https://channels.theinnovationenterprise.com/articles/the-evolution-in-the-cdo-role

A U.S. regional bank while reducing its staff and technology cost wanted to see how it could maintain its collection rate. A consultancy based in Chicago and Bangalore analyzed the actions of a U.S. regional bank such as calls, mailers and IVRs and concluded that the bank was overspending.  The bank was thus able to reduce costs by a million $. Analytical companies are now investing in platforms and products to fill the void in analytic stack. To know more: 

http://www.forbes.com/sites/tomgroenfeldt/2015/07/08/bank-reduces-debt-collection-costs-through-big-data-analytics/

Big Data is perceived to be a high tech thing that allows us to gain insights and solve problems like never before. Big Data is processed using brand new computers possessing huge processing power. To utilize data to its full potential, constantly updating systems is necessary. Contrary to our belief that data is a new age concept, data has been in use since a long time as is evident from Willard Brinton’s book Graphic Methods for Presenting Facts published in 1914. Most of the techniques discussed in the book are relevant even today. The only difference between then and now is that the size and availability of data has increased manifold thanks to our growing digital footprint. Big Data is becoming bigger with time but the relevance and use of data remains unchanged. Read more at:https://channels.theinnovationenterprise.com/articles/big-data-its-not-new

The introduction of Hadoop has changed the outlook of businesses. It is a software system that has made processing large sets of data located on distributed servers an easy task. Hadoop is mainly used for analytical purposes boosting business performances and customer relationship management. It has worked wonders for small businesses as it has incorporated concepts of big data analytics. Data is much more accessible now and the cost of procurement has been drastically reduced. Some convenient features in Hadoop has made it much more attractive for small businesses. Hadoop has made effective use of big data possible to optimize businesses. With its value increasing day by day, more and more companies from various sectors are going in for Hadoop. Read more at: http://www.dataversity.net/an-introduction-to-hadoop-for-small-businesses/

Data science is the most dynamic and multifaceted industry providing useful and interesting insights with the data. Recently due to more than exponential growth of data and digital revolution, it is becoming increasingly difficult for organizations to use this data efficiently. Sifting data has become cumbersome and getting to the right data has become a top priority. Thus most organizations are formulating strategies to use this data wisely without any hassles. From operational procedures to recruiting employees, right data can direct to right ways of doing it. Thus CEOs are showing more interest in this digitized environment. In short an overhaul in usage of data driven techniques are taking shape to have a leading edge. To  read more: http://www.predictiveanalyticsworld.com/patimes/plotting-your-data-science-strategy-0618152/

Data science with number of practical uses is becoming an indispensable tool for all businesses. Its pace has reached an exponential level in recent times due to many advancements. But many experts are now shifting the gears into next level, predictive analytics, to save the future of this invaluable science, but getting this accepted industry wide is going to be a rough ride. Getting a smooth change from data science to predictive analytics will need an industry wide trust. It also needs the barrier of welcoming the new entrant eliminated by showing the effectiveness of this new technology which will create a helping environment for employees and employers alike. But to many hardcore fans of data science it is tough time leaving it to accept a new technology, only the returns and competitive edge this provides will make the shift more pleasant. Read more at: 

https://icrunchdatanews.com/3-keys-smooth-migration-data-science-predictive-analytics/

It had been believed that Greece had struggled too much to meet the criteria for Euro entry. Official figures showing Greek deficit that were used to prove accession were found incorrect in 2004, and the authority continued to submit frequently revised statistics to the EU for the next decade. Data science could play a key role in protecting Greece. In 2011, Fact and Fiction in EU Governmental Economic Data (a research paper) used Benford’s Law up to a decade of economic data from 27 EU states. Benford’s Law states that in many naturally occurring collections of numbers the small digits occurs disproportionately often as leading significant digits. Benford’s Law was also used by financial auditors as a key to their fraud detection and the researchers tried to relate it to economic data. Researchers calculated the difference between the actual value and the expected value. After some research and analysis researchers found that Greece was the country with greatest deviation and they subject it to the effectiveness of Benford’s Law in determining the irregularities and manipulations in macroeconomic data. Now the question arises could data science and Benford’s Law let Greece to evade crises by keeping it away from Euro. The answer is no because the figures shown by Greece were hesitating. In fact it was Germany and France who broke the 3% budget deficit rule. Benford’s law might have helped, but the entry of Greece in Euro in fact was a political decision not economical. According to researchers, a little focus on Benford’s Law could help in recovering from such a devastating situation. Read more at: http://www.smartdatacollective.com/timoelliott/329137/could-data-science-have-saved-greece

Data science techniques are becoming increasingly popular these days to improve business outcomes. Hadoop, already showcased the broader use of big data technologies and their impact on businesses. In this new age, the limits of machine learning are constantly being tested as innovators are trying new techniques that decrease human intervention as much as possible. Companies are ready to work with the data they have in Hadoop. Penetration of SQL on Hadoop has been a great help as they have created an environment that has made data accessible to downstream apps and learning algorithms. Machine intelligence is catching up in all spheres, data science is becoming a new trend with data scientists coming in demand. To know more, please follow:

http://www.dataversity.net/predictions-for-data-science-over-the-coming-years/

Planning before working out is the most basic idea. A planner successfully merges both aesthetics and practical planning. But a strong design comes from the collected data. Combining user and sales data, Ford F-150 has been designed to reduce weight and increase sustainability which are made keeping in mind both environmental and business sense. Though A/B testing was a popular method of creating effective marketing choices due to its manufacturing constraints, companies nowadays use data gathered by observing public interest to create new products or make changes to earlier products. With increasingly complex algorithms, architectural design is now getting more accurate. With faster computing power, various tests can be done to establish strength and quality. Read more about this article by Chris Towers (Big Data Divisional Head) at:  https://channels.theinnovationenterprise.com/articles/designed-by-data

Healthcare analytics, estimated to be over 20 billion, is growing very fast. The main reasons for the growth are: improved population health, reduced healthcare cost and patient safety. Thus, a vast majority of healthcare decision-makers have made analytics their priority. However, there are four main factors that could impact the adoption rate of analytics in healthcare. Data integration: It is the most important challenge because of the absence of unified datasets and inoperability between technologies.

Data breaches and security: This needs to be taken care off when data needs to be transferred to cloud in view of the recent healthcare data hacks.

Data sciences talent: There is likely to be stiff competition for talented data scientists with experience in healthcare and analytics.

Low current levels of analytics investment: The order of magnitude of investment in analytics may be the biggest challenge for the growth of analytics in healthcare.

Read more at:http://www.cio.com/article/2904270/healthcare/healthcare-analytics-4-things-impacting-the-adoption-rate.html

Some strategies haven't succeeded dealing with supply-chain management. The reason is the cost of hiring expert workers. According to researchers the union of data science, predictive analytics and big data likely to alter the way in which supply chain managers lead and supply chains function. They named this as DPB. Companies have used datasets to plan ideas to meet customer demand. But now they combine external data to better estimate future risks .two points to judge analytic skills: 1) Data science and domain expertise are not mutually exclusive: Analytical skills are important for data scientists who focus on Supply Chain Management (SCM).2) that doesn't mean theory doesn't apply: Strong theoretical knowledge is essential in SCM. Use of suitable theory to build models before operating predictive analytics is key to justifying a circulation of false positives. The three links in supply chain: manufacturers, retailers, supply management, shipping management and human capital. Read more at: 

http://sloanreview.mit.edu/article/are-predictive-analytics-transforming-your-supply-chain/

Human element plays an important role in the application of big data. Though data science is based upon business analytics, it is different from business analytics. Data science collects data and other information from different systems and then asks many different questions. To know more about whether it is necessary to include data scientist when a company is deploying big data solutions or not, go through the article by Daniel Kusnetzky, software engineer and product manager in Kusnetzky group.

http://www.zdnet.com/the-human-element-is-critical-in-applying-big-data-7000028983/

Guy Cuthbert, managing director at visual analytics firm Atheon Analytics, says that many of the firms selectively choose data to back up currently held views, which is just opinion based decision-making. The firms usually prefer to take decisions using untested opinions rather than data science. By applying data science, firms can deliver insights which are intrinsically more valuable than what they have today.To know more on data science, go through the article of Toby Wolpe, senior reporter at ZDNet in London:

http://www.zdnet.com/big-data-what-to-trust-data-science-or-the-bosss-sixth-sense-7000026550/