Advertisement is one of the major ways through which businesses can attract customers. A lot of money and time is invested in order to create ads. However, these days a helping hand has come for rescue and is successfully able to attract customers by presenting customized ads. Machine Learning Algorithms, Artificial Intelligence and Deep Learning have come into play. With the help of these technologies, customized ads can be created based on the current searches done by customer. For example, you recently searched for “affordable mobile phones”. These learning algorithms tracks it down and soon starts displaying mobile phones ads presented by various companies. Other than that, Data Mining also plays an important role in this. Among various data that is available on world wide web, data mining algorithms browser and stores valuable data. 

Read more about this at https://www.techworm.net/2018/06/how-machine-learning-algorithms-help-businesses-target-their-ads.html

In this world of competition, companies need to take advantage of available data and take a look about what might happen in future. Predictive Analysis is one such branch of Data Analytics that aims to make predictions about future outcomes using various algorithms and other data analytics tools. Methods like data mining, big data, machine learning are back bone of Predictive Analysis and organizations are able to decode patterns and relations which helps them to detect risk and opportunity. Financial Services, Law Enforcements, Automotive, Healthcare are few fields which have already adapted this technology. 

To know more visit: https://www-cio-com.cdn.ampproject.org/c/s/www.cio.com/article/3273114/predictive-analytics/what-is-predictive-analytics-transforming-data-into-future-insights.amp.html

Some of the pros for executing data driven marketing efficiently are:-

1. Joint collaboration with IT department and marketing team is the key to achieve efficiency.

2. Hiring an industry analyst, professor or data scientist to review the data before publishing is necessary to check accuracy.

3. Planning the data collect and analysis before starting the project will help immensely.

4. It is important to focus on what the data means and what are its implications.

5. In order to increase exposures create strong relations with media and analyst (to find out what kind of data suits best for them) before publishing the data.

6. Company should decide whether they want to invest in product growth or data marketing.

7. If the company doesn't have data they don’t need to invest in costly data gathering procedures. Inexpensive tools are easily available.

To know more, read article by James A. Martin on the link : http://www.cio.com/article/3052442/marketing/how-to-do-data-driven-marketing-right.html?page=2

E-Commerce firms very well understand that data is a wealth. The data can provide very crucial and meaningful insights, if used properly. Data Mining is one such way. It is a field of computer science that discovers the patterns in large data sets. This is done using the methods of Artificial Intelligence, Machine Learning, Statistics and Database Systems. Algorithms are made to act on the data set. This often reveals "game changing" results for the industry. Data Mining can be either done manually or can be automated. E-Commerce Firms use this to get to know more about customer's emotions, preferences, and other factors that impact their purchasing style. So they excel in offering a personalized experience through this. It doesn't stay limited to customers. Data Mining is done to keep an eye on the competitors. Read more about it in the article written by Arie Shpanya (contributor to Econsultancy)  at: https://econsultancy.com/blog/67360-why-data-mining-is-the-future-of-online-retailing/

Information plays a vital role in sales. Sales analytics is the application of analytic techniques such as data mining, which helps in sales. It tells in which direction you are going, and also help in finding prospects, target the sales efforts, nurture leads and eventually close the sale. Analytics requires data, rather data from many different sources, and CRM system should be a repository of all your organization's information on customers. Read more at- http://it.toolbox.com/blogs/insidecrm/grow-your-sales-with-analytics-70578

Data mining has taken a new form termed as a Dark Web. Law enforcements have made use of dark web to analyze data on websites and concluded that in 2010 over 100,000 sites contained extremist and terrorists' content. Today, dark web has become popular in finding and shutting down websites that can be used to facilitate criminal activity. Big data is also making use of such technique to collect data to better understand criminal behavior. Dark web has made it easier to keep an eye on illegal activities. Making use of dark web can stop these criminal activities which would ultimately results in favor of people. Read more at:https://channels.theinnovationenterprise.com/articles/mining-in-the-dark

In our daily life we came across new technologies and making our life simpler. Today people are enjoying benefits of internet in form of online shopping, easily sending emails and transactions with friends online. Data is collected on the bases of these activities and improvements are made according to people’s preferences and tastes. Data mining is the key to collect big data and most of the businesses are dealing with these data to provide good services to consumers. Both of the large and small organizations are using big data. Earlier big data was providing benefits to retail and sales industry but now it also giving advantages to learning and development industries. Big data is also beneficial for employee training and can improve performance of current employees. Transformation of training process from traditional to modern techniques there has been a significant improvement in employees’ performance. Employers can easily motivate and inspire their employees. After getting good output from employees, employer gives more importance and satisfaction to employees. So, big data help employers to better understand their employees. Big data help companies to understand people’s requirements and providing facilities according to their preference. It also came in a form of learning and development tool for businesses to improve the performance of employees. Read more at: http://www.smartdatacollective.com/briggpatten/331869/how-big-data-shaping-future-learning-and-development-industry

As the typical scenario in any data analysis includes more than one type of data source, working with large datasets, messy and unorganized data, there is a huge need of data prep required. Most data sets are relatively dirty and need to be thoroughly cleaned for the analytic result to be usable. The need to have some structure for reporting and analytical tools to grab onto resulted in a boom of data prep.

It is very imp to have the data validated in the initial stage, because if that goes wrong, then everything downstream of that becomes very problematic. Thus we need to have the data ready for analysis and to avoid any non-value add, which is achievable by big data prep.

Big data prep uses a combination of machine learning algorithm to automate most of the work that goes in sanitizing data. Read more here- http://www.datanami.com/2015/06/22/why-big-data-prep-is-booming/

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

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

Data mining results can be used by organizations to prevent losses ahead of time. Organizations usually use indications like inventory levels or employee turnover, which occur later in the business cycle to gauge shrinkage cost but by that time the losses start to incur and there is nothing much that can be done. According to a report by PricewaterhouseCoopers, if companies gather and analyse data, they are able to get better insights into the business and use the early warning indicators to stop losses from happening by making informed, strategic decisions, thereby saving time, money and effort. Read more at: http://www.tibco.com/blog/2015/06/03/strategic-data-analytics-to-help-reduce-shrinkage/

Data mining sounds like a monotonous activity on a pile of information, requiring little oversight. It is however, in the words of Professor Uwe Aickelin, University of Nottingham, "A discipline that blurs the lines between artificial intelligence, machine learning, statistics and other cutting-edge disciplines to unearth the golden nuggets that lurk within data." He explains how data mining is the effort to extract valuable information from unstructured or 'messy' data. Statistics fails to recognize patterns and it is here where Evolutionary Computation and Machine Learning is required. Industries have begun to understand the need to make sense of the large amounts of data out there and Data Mining is more important than ever now. Read more at: http://www.gizmodo.com.au/2015/05/why-data-mining-is-so-important/

Flipkart , an e-commerce co., has decided to focus on social media, mobile interface and data mining to boost its position in the online retail market.  Data mining and technology improvements are going to take up a major role in the coming days. Up gradation in infrastructures and data centres are essential to maintain its position in the market.  The company also expects to achieve $8 billion dollar sales thus utilizing every resource at its disposal.  To know more, please follow:

http://articles.economictimes.indiatimes.com/2015-04-13/news/61102918_1_mukesh-bansal-binny-bansal-billion-day

Companies are collecting datasets to have a competitor advantage in the market. But what exactly are they doing with the collected data? And how are they dealing with the ever increasing data that is inrushing their servers or storage units? 

To answer any of above question, we need to know about a process called Data Mining. Data Mining is a process used to analyze raw information to try and find useful patterns and trends in it. Basically a data miner’s job is to make some sense out of the huge pile of data that is available. There are a lot many techniques available to do this. If you want to know more about data mining and these techniques go through: http://www.businessnewsdaily.com/5947-data-mining.html

• Apache Hadoop- as software vendors are targeting the distributed storage and processing architecture, need for Hadoop is increasing 
• Apache Spark-The rapid rise of the in-memory stack is being extended as a faster and simpler alternative to MapReduce-style analytics.
• NoSQL-Databases like MongoDB and Couchbase are taking over jobs previously handled by monolithic SQL databases like Oracle and IBMDB2.
• Machine Learning and Data Mining- Big data pros who can harness machine learning technology to build and train predictive analytic apps are in high demand Statistical and Quantitative Analysis-Add in expertise with a statistical tool like R, SAS, Matlab, SPSS, or Stata is of high demand in today’s world. 
• SQL- SQL is still in demand for the next-generation of Hadoop-scale data warehouses. 
• Data Visualization-It has become most important in the job market. 
• Progamming Languages-Knowing programming languages like Java, C, Python, or Scala could give you the edge over other candidates. Read more at: 

http://www.datanami.com/2015/01/07/9-must-skills-land-top-big-data-jobs-2015/

Predictive analytics is a “business game changer” that will separate the winners from the losers, according to Forrester. The better a company is at predicting what will happen in the future, the better positioned they are to do something about it. While data scientists will do the heaviest analytic-related lifting at big enterprises, the improvements that have been made to predictive analytic (also called advanced analytic) applications enables regular business people and developers to partake of the predictive bounty. With so many companies coming into the foray of analytics services, today the users have more options to choose from keeping the cost-benefit & need-value trade-offs in mind.  RapidMiner offers a “rock solid” enterprise solution with more than 1,500 methods that address all stages of the analytics lifecycle and has among the tightest integration with the cloud, Forrester says. There are also other options like SAS, SPSS, KNIME, sap, oracle to name a few.

To read more, visit:

http://www.datanami.com/2015/04/07/predictive-analytics-now-in-reach-of-the-average-enterprise-forrester-says/

According to Manish Godha (Founder & CEO, Advaiya Solutions), social media seems to be the most attractive media through which a business can interact with an audience. Individuals allow an exceptional view into their thought processes, by leaving traces of their activities - personal, social and professional - on the internet. Social analytics attempts to draw relevant understanding and inferences from them which could be used by different divisions of various brands. It is very difficult to ensure that the analysis is relevant and reliable. The most common use of social media analytics is to mine customer sentiment in order to support marketing and customer service activities. It is important that the models correspond to the business context and the intervening mechanics are accounted for. Read more at: http://www.business-standard.com/article/management/social-listening-for-market-intelligence-114092100688_1.html

Billions of dollars are lost by gamblers every year along the Vegas Strip, but some casino operators are taking strides to soften the blow of serious gambling losses and leveraging big data to keep customers coming back, according to one executive.  "They could win a lot or they lose a lot or they could have something in the middle. So we do try to make sure that people don't have really unfortunate visits," said Caesars Entertainment Chairman and CEO Gary Loveman on Big Data Download.  Caesars and other casino operators offer loyalty programs. As gamblers spend, companies gather data on those spending trends. Customers also receive tailored incentives for gambling and spending. 

"We give you very tangible and immediate benefits for doing so. So we give you meals, and hotel rooms and limousines and show tickets. You share with us information on what you've been doing, what sorts of transactions you've made," said Loveman, whose company is the biggest U.S. casino operator.

Caesars in particular employs about 200 data experts at its Flamingo Hotel alone. They scour through data on the types of games customers have played, what hotel they've stayed at and where they've been dining. So the next time when you visit a casino, expect a suddenly friendlier slot machine after you are on a losing streak.

Read the complete report here:  http://www.cnbc.com/id/101027330