Sports analytics have become very popular now days. Predicting winners, player performance and team selection has taken a new form with the help of sports analytics. It has now become a new way of making money and building reputation in sports world. Analysts use the previous data to make predictive models and make future prediction using those models. There has been a shift from qualitative data that was traditionally used to quantitative data. Sports analytics have not been so easy in all sports. American Football, which has large number of variables that can change overtime, faces some difficulty seeking advantage of analytics. NFL teams hardly play 16 games a season implying very small sample size; it is very hard to get some pattern of data. Knowledge of the game and watching the games is equally as important as collecting data. In fact it is part of the data. Read more at:https://channels.theinnovationenterprise.com/articles/how-people-are-beating-the-bookmaker-with-sports-analytics

Cancer is a disease that had killed many people in past decades. Few years back it was not possible to treat a patient suffering from cancer. Now doctors have treatment of cancer. But it could more easier with the help of big data. Let us see how big data make it easier. 

Doctors collect data from pre and post treatment of patients. Predictive models can be formed using the extensive data that can help doctors to analyze whether a treatment is success or failure for some set of patients. There are some advancements where AI is used in diagnosis and treatment of cancer. With the help of big data analytics doctors can see which drugs are most effective in treatment. Also running analytical models doctors can find which drugs are not necessary and significant. Only necessary drugs can be used to target the specific forms of cancer. The systematic and data driven view for looking at cancer has increased the potential for its prevention. Read more at:https://channels.theinnovationenterprise.com/articles/big-data-in-cancer-revisited

In today's world most of the jobs which were performed by humans have been replaced by robots and computers leading to increase in unemployment. For example: robot waiters, robot doctors and self-driving cars. A company in China is installing 30,000 robots every year that has snatched the work that was earlier done by human beings. Today robots are becoming cheaper so there is possibility that they might take over the jobs of human beings even in low cost companies. The main cause of this threat is the invention of smart machines. There is inverse relation between smarter computers and employment. Every year computers are becoming smarter, doubling their processing power and memories.  Machines that have Artificial Intelligent (AI) and smart computers that can act own their own have adversely affected humans, leaving them unemployed. Also high skills are required to operate these smart computers and not all humans got the same. According to some economists, AI should be controlled otherwise the results could be disastrous. Read more at:http://www.smartdatacollective.com/bernardmarr/330436/ai-biggest-threats-your-job

There are a lot of open source analytics tools available that every user can easily find on websites. But to make appropriate use of such tools each company requires equally compatible skills. So companies are required to invest heavily to develop such skills and also on collecting data. It has been found that companies are spending on an average of $7.4m on data initiatives in 2015. Free platforms also took huge investment such as Hadoop, making more efficient systems. The increasing data size led to increase in expenditure, and is likely to increase in future. Upgrade is required in currently installed systems to process huge data in less time. On the other hand huge investment is required to meet the heavy salaries of data scientists, which is approximately $118,000m. As the supply of qualified data scientists increase this amount will fall, which decreases the overall amount of employee compensation. There is a strong evidence that spending on data initiatives has been increasing at a surprising rate and is likely to continue for next 2 years which ensures a boom for the big data companies.  Read more at:https://channels.theinnovationenterprise.com/articles/big-data-spend-is-increasing

In today’s world volume of data has been so large. This problem can be tackled through Hadoop-based Data Lake. A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. There are some strengths, weakness, opportunity and treats.

Strengths:

A Hadoop-based data Lake is a low cost operation, as it is open source software and can be processed on low cost system. Hadoop has a capability of storing and processing all types of data, whether structured or unstructured, which incurs only a small proportion of cost of our currently traditional systems.

Weaknesses:

There can be a lot of confusion due to large volume, variety and velocity of big data. Although Hadoop-based Data Lake is open source and helped to meet the increasing demands of vendor’s products, it had not received complete success yet. There is a lot of security problem with this system. Open source community and vendors tried to eradicate such problem by supporting security and privacy requirement of the organization.

Opportunity:

There is opportunity to find unknown data. In spite of the existing data agents can go through the data lake to have better answers, or they could even get the answers to the questions that were not able to find. Advanced analytics also changed the view. For example descriptive analytics provided better visuals of the situation. Advanced analytics like- prescriptive, predictive and diagnostic, helped to analyze big data. A Hadoop-based data lake provided such opportunity.

Threats:

There is a significant cost and time involved to adopt these new technologies. It can affect the people, processes and the culture of the organization. Also there had been a lack of skills required to operate these advanced techniques which hangs on adoption of Hadoop. 

Conclusion:

So, all we can conclude that with the presence of some weakness and threats there are some strengths and opportunities to explore. An organization can enjoy the benefits provided by data lake depending on its requirements. Read more at:http://www.smartdatacollective.com/tamaradull/324901/data-lake-more-balanced-perspective

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

There had been a misconception that discovery analytics is hacking. Every discovery process involves going through some data, analyzing it, making some conclusion. So why would some people judge it as Hacking? Highly prioritized business problems that occur in mind led to discovery process. After having some discussion the major component which addresses the problem is analytics. Analytics brings up the new ideas that could be solution to the business problems. One can have analytics as the strategic component of his business; just the requirement is that he has to invest in analytics like other components say core products and services that his company provides. Discovery analytics cannot be considered as hacking than any other research and development activities are. But truly speaking both of them are the same. The products sold in the market have to go through some background processes that are invisible to buyers. Huge investment, many trials and experiments are required to get a finished product. Discovery analytics are much similar to that. Not all attempts will give appropriate results, but if choosing the right doors it will result in high level of strategic value. Most of the people are familiar with R&D because it is viewed as rational, scientific, disciplined approach to developing new ideas and products. An analytics R&D function can be built just by letting few number of human and technology resources to address only a few critical business problems. When you get positive results, apply it to more number of problems. If every thing goes in right direction then we will get a stable and well functioning analytics research and development function. Read more at:http://www.smartdatacollective.com/billfranks/329559/discovery-analytics-it-s-not-hacking-it-s-rd

Evidences showed that recruitment have not been so easy during past years. Recruiting new employee requires a huge investment for most companies, specifically in managerial or professional role, which absorbs large proportion of company’s revenues. In the presence of big data, planning and strategies can improve recruitment process. US financial services company used big data analytics to examine the performance of employees. What they observed was that top performers were mostly those who had higher education. Office equipment manufacturer Xerox used analytics to study the performance and profile of candidates recruiting for its call centers which reduced the staff turnover of 20%. Big data is attracting employees and with the help of some tools such as Cornerstone and TalentBin (tools to crunch data) made it easier to find candidate for the right position. Online recruitment services are also enjoying the benefits of big data. Say for example it made easier to look at what are the areas of interest, average spending, experience, location of service. So, it is easier to plan ahead and prioritize. Read more at:

http://www.smartdatacollective.com/bernardmarr/327498/how-big-data-changing-recruitment-forever