The automobile industry can make use of data to improve vehicles, sustainability and profitability. Smart cars are the next generation of cars and data will assist in building these. Surveys have revealed that customers prefer cars with features that save time, ensure safety and sends warning alerts in case of malfunctions. In order to accomplish these needs, integration of technologies is required. Services based on car data can be introduced like sending a warning alert to replace car battery prior to its depletion. Smart infrastructure can be built by connecting roadside sensors, traffic signals and remote data centers to ensure a safe and smooth ride. Data can be analyzed to map travelling patterns by looking at how individuals behave in various road scenarios. Read more at: https://channels.theinnovationenterprise.com/articles/7719-what-smart-carmakers-can-learn-from-the-smart-phone-industry

Companies of every shape or size recently have moved to Big Data as they realized how beneficial it is for their organizations. To efficiently use the big data, companies need to collect and process it first, from multiple sources. Some data remains unused in the case of unstructured data mostly, which poses a problem later leading to clogging up data centres. Such unused data has been termed as – Dark Data which needs to be cleaned up or else it increases security risk and costs for the companies. Dark Data firstly needs to be identified. It is the information collected but not put to any specific use. Such data remains unused for years and takes up a lot of space which could have been used otherwise to store useful data. Dark Data remains unprotected for a while and poses risk of security breaches and mishaps. Dark Data is costly and dangerous but still companies keep them to make sure they don't lose any kind of records. Cleaning of Dark Data is time consuming but is important as well. One of the most effective ways of avoiding such data is by filtering the data which prevents data from piling up. Other ways to clean such data is by moving the Dark Data to different repositories using cloud computing. Dark Data can be deadly for organizations and hence it should be avoided before it poses any problem for the business.

 Read more at:  http://www.smartdatacollective.com/rick-delgado/323706/importance-cleaning-your-dark-data

User Behavior Analysis (UBA) helps in collection and analysis of huge amounts of user-specific data and access logs which can help us to identify unusual user behavior, investigating potential threats, detecting and responding to internal threats quickly, etc.
Enterprises can incorporate UBA in the existing security framework in the following ways:
1. Defining the problem – The most critical first step is to define the problem. Organizations that take time to identify their unique organizational security characteristics can determine the critical company assets and thus help UBA to safe guard their resources.
2. Identification – The next step would be to ascertain the group of users and the types of data that needs to be monitored.
3. Implementation – The third and the ultimate step is implementation. The various ways in which collected data can be used for use in security insights are:
• Visualization
• Drill-Down
• Aggregate
• Profile
UBA can be used by identifying the problem, gathering the relevant data, and establishing the required tools to gain visibility of the unseen threats in the network. Read more at: http://www.smartdatacollective.com/idan-tendler/326260/adopting-user-behavior-analytics-uba-solution

Data scientists are the ones who use sophisticated quantitative and computer science skills to both structured and analyse unstructured data as well as derive intuitions from the data and suggest actions. Data scientists can tackle with problems that are complex, huge in size and disorganized using several coding languages. To identify a data scientist following points are to be taken into consideration:

• Qualifications: Data scientists are required to have an advanced degree usually a masters or PhD in a quantitative discipline such as economics, statistics and their educational background may be diversified.

• Skills: Data scientists are efficient users of different tools used for analytics and are well versed with coding languages such as Python or Java used for writing programs, transformations etc. They are also have expert knowledge about statistical and machine learning models such as R and SAS.

• Dataset size: They usually work with datasets measured in gigabytes up to petabytes.

• Job responsibility: Data scientists are well equipped to work on every stage of analytics life cycle which also include data acquisition, transformation/cleaning, analytics to predict patterns of the datasets, prescribing actions and programming/automations to contribute to a firms data products.

The main idea behind this is that whether you are a data scientist, analytics professional or programmer you always need to be well versed with the new languages coming in the market each day just as big data has been gaining importance and keep up with the new technology.

Read more at: http://www.smartdatacollective.com/lburtch/320541/more-just-title-how-identify-data-scientist

In order to do away with bottlenecks in data science supply chains we need strategies by data scientists. Not only the productivity of the data scientists need to be improved, but also the increasing demands of data scientists. Technology solutions can be used in place of skilled professionals in this field which will present analytics to non-data scientists in familiar business terms. The models developed by the data scientists can be given to the business analysts then they can successfully get the work done without being technical.
The basic idea behind this is not to do away with data scientists but to keep them free for new challenges. Proper communication between data scientists and business analysts leads to a cultural shift which reduces the gap between these.
Read more at: http://www.forbes.com/sites/teradata/2015/07/08/sugar-spice-hadoop-and-everything-nice-a-recipe-for-more-data-scientists/

The term Big Data has become popular recently and is used to define exponential data development and availability, both structured and unstructured. Big data is crucial to businesses since more data implies precise analyses which results in efficient decision making, reduced risk and cost reduction. Big Data is used for more complex datasets and refers to application of predictive analytics and other advanced application tools to derive the results and seldom to the size of datasets. The three V’s of Big Data are namely: Volume, Velocity, and Variety. Apart from increasing varieties and velocities of data, data flows are very challenging. Variability, complexity are some other factors of Big Data. Data flows in from different sources and it’s difficult to connect them which has become a huge task these days. Big Data has become necessary for companies which helps them stay updated with the new technologies. Processing the Big Data smartly and accurately is worth a lot of income for the companies. Big Data demands amazing technologies to efficiently process such datasets in given deadlines. There were companies who spend a lot of money on data analytics and other management systems to increase their efficiency. Recently Big Data has increased the need for information organizations. Big Data also helps to grow and continue increasing the number of servers used which only adds to expenses of the company.

Read more at: http://www.smartdatacollective.com/anandsrivastava1988/328749/what-big-data-and-how-useful-it

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

To master social media, learning Social Media Management Software, we can schedule posts at different social media channels to know if someone is complaining or mentioning about me. Adding new features masters us in learning the software better. Automation is everything in social media marketing as we get more time to manage other aspects. But it is a pitfall as we need to respond to changing needs of the clients. Using analytical tools, we can track our followers within each network. Making it interactive and fun with free stuff, no one can stop from being famous with more followers. Do good, feel good – make that a moto. Taking healthy risks be creative enough and check that through SWOT analysis. Read more at: http://www.socialmediatoday.com/marketing/sarah-snow/2015-07-10/how-master-social-marketing-quickly-and-effectively

In the present economic landscape, operating costs are critical in deciding competitive advantage. To explain cost gaps, size, sales channel, product mix and geography comes into the picture. Management decisions play a key feature in operating cost in the following roles:
1. Business complexity an important player, as large product portfolios and multiple brands produce higher cost on average. Higher complexity leads to inadequate priced contracts.
2. Operating models show that optimized setup reduces costs. Workload backlogs lead to customer dissatisfaction and deteriorate financial performance.
3. IT system landscape is a root cause to failing economies of scale.
4. Performance management helps reducing costs and improve company’s culture
To know more:  http://www.mckinsey.com/insights/financial_services/what_drives_insurance_operating_costs

Make way, its drone landing on your terrace with your favorite pizza. That’s how your favorite pizza outlet might advertise in a few years down the line. IoT based drones for pizza delivery is going to be the buzz for both pizza vendors and consumers. You get your pizza faster, it’s not soggy and sizzling hot direct from oven to your home. Scott Andreson, Director of consulting Services at CGI, in a post on Cloud Tweaks give us fascinating insights about this new possibility, the challenges which are likely to occur and of course there will be a few solutions too.  All you pizza lovers and technology aficionados hit the link to know more http://cloudtweaks.com/2015/06/hot-emerging-trends-the-pizza-delivery-drone/

Predictive analytics tools and techniques are becoming a rage nowadays because of their immense application in a variety of sectors. Collecting data and predicting future trends have been so easy. More and more business are vying for these tools as they help with predictive modelling of customer behavior. Even bigger potentials are lurking in the near future and organizations are investing in the development of these software keeping that in mind. Companies which have still not adopted these software are falling behind in competition with other firms. Thus there has been a surge in the usage of predictive analytics as every firm is craving for more information. Read more at: http://www.dataversity.net/how-your-business-can-benefit-from-predictive-analytics/

Self-service analytics softwares are being used by businesses now and it has given rise to a new need, data governance plans. These new softwares provide a host of benefits. These have made accessing and analyzing data a lot easier coupled with the ability to feed bad data back into enterprises. A competition has been brewing between software vendors regarding improved data governance. Companies are launching business intelligence products that stress data governance. Such softwares can be built around the company's pre-existing enterprise data infrastructure bringing multiple data sources into a common data warehouse accessible by the entire organization. Read more at: http://www.dataversity.net/adding-data-governance-tools-to-self-service-analytics/

A startling revelation has been made by a group of math and computer experts that has been hard to digest. Leaving behind data consumption giants like you tube, twitter in social media and astronomy, particle physics in science the field that has emerged as a leader is genomics.  Genomics generate the most electronic bytes per year relative to other fields. It has been a challenge to capture, store and interpret the genome encoded biological information. Data generation has increased at a fast pace with the growth of biological sciences. Leading edge data generation and processing technologies are needed to tackle the present situation be it in the field of biological sciences or social media or high tech science. To know more, please follow: http://www.dataversity.net/wheres-the-biggest-big-data-to-be-found-genomics/

The Energy industry is set to benefit largely from the usage of big data and analytics. Efficient use of information and improved analytics will help meet the difficult economic and environmental goals, bringing energy into the next century. For this we must update our energy infrastructure with the necessary software for collection and analysis of the required data. The potential of big data is such that it can help all customers support energy savings along with providing an environmental upside. Big data once employed correctly can improve operations for the benefit of businesses, customers and our environment. While the energy industry’s use of big data is future-focused, real progress is being made and these investments have improved operational efficiency and reduced the cost of energy all while helping protect the environment. Read more at: http://thehill.com/blogs/congress-blog/energy-environment/247559-big-data-means-big-energy-savings

Most companies are deriving benefits from open source software's, as numbers of programmers and developers are provided access to the source code. This has helped employees gain domain expertise through collaboration. Open source brings higher visibility and better business opportunities. The reasons for growing prominence are:
1. Intellectual property concerns no more hindering the use of open source.
2. Enhancing reach and learning of the community by diversity of thought and better technique.
3. Providing better opportunities for interoperability between competing firms.
4. Increase analytics penetration.
5. Demonstrating technical ability, where ability to think innovatively can be measured by a potential customer.
To know more: http://bridgei2i.com/blog/why-open-source-is-important-to-analytics-industry/

Big Data is nowadays drastically affecting people's lives by its various uses. 1) With the growing population, infrastructure also have to grow. Hence, Transport for London (TFL) is going through a data-driven approach to increase the network access across England's capital city. This helps in informing people about alternatives when there are closures in a route. 2) With considerable growth, the necessity to plan proper waste management has increased. Greater Manchester Waste Disposal Authority (GMWDA) in UK collects data to increase the recycling level to move more towards a sustainable age. 3) Accuracy of weather forecasting is increasing with the help of big data using complex and more accurate devices. It still isn't perfect but is on the track to get that accuracy helping outside worker plan ahead of time. Read more about this article by Chris Towers (Big Data Divisional Head) at: https://channels.theinnovationenterprise.com/articles/big-data-in-real-time

Big Data Analytics which is nowadays so useful in sports, also couldn't save England cricketers during their recent World Cup match against Bangladesh. The then coach of England, Peter Moore started believing in numbers more than the spirit of the cricketers. But a great-turnover came up over the team when they defeated New Zealand in a series of one-day match by 3-2. Both teams broke one-day records and entertained the English cricketing public like never before. This wasn't done by analysis of numbers but by abilities of cricketers. Thus, Big Data is all-time powerful but can never be more than what human spirit is. Same example goes for recruitment. Read more about this article by Matthew Reaney, Director and Founder (Big Cloud) at: https://channels.theinnovationenterprise.com/articles/big-data-and-the-human-spirit

Hadoop, for many years, was the leading open source Big Data framework but recently the newer and more advanced Spark has taken over. Spark is reported to be 100 times faster although it lacks its own distributed storage system. For this reason many projects involve installing Spark on top of Hadoop, where Spark’s advanced analytics can make use of data stored using the Hadoop Distributed File System (HDFS).
What really gives Spark the edge is speed. Spark handles most of its operations ‘in memory’- copying them from the distributed physical storage into far faster logical RAM memory. Spark’s speed of handling advanced data processing tasks such as real time stream processing and machine learning is much more than what could be achieved by Hadoop. Faster dynamic data handling gives Spark the upper hand over Hadoop.
However it must be concluded that these two frameworks are not necessarily mutually exclusive and do not perform exactly the same tasks. In fact using both of them together can actually provide better results than using either one separately.

For more information visit:
http://www.forbes.com/sites/bernardmarr/2015/06/22/spark-or-hadoop-which-is-the-best-big-data-framework/

Some of the unconventional ways in which Internet of Things (IoT) creates economic and social value are by detection of earthquakes in hospitals and alerting the administration for evacuating patients, reporting and assessing severity of car crashes and thus helping the victims to get help faster.
Certain examples that show how IoT is integral to several industries:
• The era of “Agriculture 3.0” was characterized by farmers whose decisions about what crop to plant and when and where to plant them was based on analysis of sensor data, weather data and other factors. This form of farming helps increase the yield in a cost and resource efficient manner.
• European airplane manufacturing company uses scanners In airplane assembly lines in order to send required information to robots who then finish the work accordingly.
• Internet of Things is used in Restaurant business for ensuring data-driven quality control.
With decreasing cost of connected devices, the scope of IoT will keep increasing in the future.
Read more at: http://www.smartdatacollective.com/joshua-new/329188/internet-ordinary-things-still-extraordinary

The sole motive behind existence of social media is that it helps to bring people together. Mostly people do not use social media for purchasing goods and services but for boasting about festivals. The ideal media campaigns provide the customers with the relevant and valuable information. Social media analytics helps enterprises to interact with customers and this is highly beneficial in regard to targeting content and promotional activities. In case of directly selling to the existing customers there is comparatively less pressure than selling to new customers and hence there is a sublime line to walk upon while dealing with them. However the most crucial aspect of sales in social media is building trust through engagement with customers, creation of a personal account and sharing quality content.

 To know more about customer engagement, please follow the link: https://channels.theinnovationenterprise.com/articles/what-are-the-most-effective-ways-to-engage-with-your-customers