New leaders when inherit a group that is not enough hard working should take some time to listen and appreciate whatever the present situation is rather than imposing new norms to make their own mark. After assessing the situation, if the managers find the need for quick and fundamental change then they should address the problem systematically. The key to become an effective leader is to first get feedback from trusted sources. One should consult with his HR to ensure new standard doesn’t conflict with company policy. You can have a conversation with your peer managers and boss before addressing workgroup. While discussing business case the leader should be open to all ideas and shouldn’t come across as a resentful person. If any problem of transgression or insubordination arises, calmly and deliberately confront the behavior and impose appropriate consequences. Effective communication can raise the bar of your new team. Read more at https://hbr.org/2017/06/what-to-do-when-you-inherit-a-team-that-isnt-working-hard-enough

In today's dynamic marketplace, it has become necessary for businesses to be able to use data to identify challenges and meet them. So, to establish a data-driven culture that empowers employees with skills to use data for accurate decision making is important. Ways in which this transformation can be done are by establishing a clear vision by imparting knowledge about the use of data, ensuring easy and secure access to data, keeping the data organized, clean and up-to-date, creating agile multi-disciplinary teams, i.e. forming teams which have at least one member who's well experienced in data analytics, and lastly by developing reward mechanisms by sharing data successes to inspire others. Thus, analyzing the data in the right way and cultivating a data-driven culture is necessary to make accurate decisions. Read more at: http://www.datasciencecentral.com/profiles/blogs/5-ways-businesses-can-cultivate-a-data-driven-culture

Association with a brand or building relationships is all about how a consumer feels when he is having shopping experience. Emotion AI can be used to find out consumers emotions while shopping and interacting with them. Use of AI in retail stores has received positive responses worldwide. Emotion AI would be more consumer friendly and would add the much-needed personalization to better understand the customers. Opinion mining would help brands to grow and build trust. With the help of traditional methods of consumer feedback, emotion data can provide an overall understanding of consumers. Read more at: https://readwrite.com/2017/07/04/retailers-emotion-ai-online-store-dl1/

The last decade was marked by the invasion of new data sources such as online clickstreams. Cloud-based analytics made low cost acquiring of massive amount of computing power at a short period of time possible. It is now common with the small companies as well. Many companies are using Hadoop-based data lakes to store a large amount of data till it is structured and analyzed. Hive and Python are mostly used for scripting, Spark for streaming data and R for statistics have gained popularity. Analytics has been integrated with the production applications. Thus, Analytic technologies have evolved over the years as more powerful and less expensive. Read more at https://hbr.org/2017/06/how-analytics-has-changed-in-the-last-10-years-and-how-its-stayed-the-same

Three principles of data science are: (i) the system built should perform well on future data sets and not just the current data set. Conclusions made on the basis of the current scenario are not always true for future cases, (ii) feature extraction is important, i.e. specifically finding the information that is required, by finding the correct elements, (iii) understanding and developing the correct model is the most important task. These are a few principle experiences which are not stated anywhere. Read more at: http://www.datasciencecentral.com/profiles/blogs/three-things-about-data-science-you-won-t-find-in-the-books

Is big data serving these businesses or is it just obscuring the decision-making procedure? Collective with analytics, Big data has numerous applications and is cast-off to find responses to glitches in a variation of businesses, it can benefit them comprehend customer behavior and get the most out of business procedures. But like so numerous things that complete good in concept, it’s not precisely working out for numerous organizations. 36% say that it has caused data overload and has made procedure of decision making poorer. Combination of the info with classy analytics tools can benefit organizations to turn rare and formless data into planned insight to get ahead of the struggle.  Since more business operators need to be able to take this data and produce intellect around it, demand is increasing for analytics tools. Read more at: http://www.hadoop360.datasciencecentral.com/blog/is-big-data-harm-or-good

In the present scenario, as the layoffs are going on in IT industry, economists predicted that Indian market can absorb it in the next 2 quarters. But the recruitment will rise more for senior-level and CXO compared to entry and middle-level hiring because of disruption by automation. Companies should effectively and continuously measure the quality of their hires with productivity evaluation, feedback from hiring managers, employee retention. Also, to retain the employees, employer branding is necessary. This cannot be achieved overnight, and takes a sustained effort from all existing employees to create. For this, of course, the right sort of environment needs to be created at the workplace. It will make it easier to attract the best people. Read more at: https://www.entrepreneur.com/article/295600

How will Artificial Intelligence facilitate the healthcare sector? This is the question that needs to be answered. The greatest returns to investments are likely to come from medical imaging and diagnostics and drug recovery. According to Accenture, virtual nursing and robot-operated surgery can be efficient and cost effective. Fraud detection and error in dosage detection can be performed by AI. Thus, healthcare sector can grow at a tremendous pace using AI. Read more at http://fortune.com/2017/06/19/healthcare-artificial-intelligence-accenture/

Interactive data analysis is very important so as to avoid making wrong mindless conclusions about given data. Firstly, it saves one from wrongly reporting values which are not statistically possible. IDA is also required for the development of new technology. Data analytics is also useful in fields like biomedical research. But IDA and data analysis workflows are not fully appreciated by decision makers, although implementing workflows when they are not matured enough can have negative effects. For developing rigorous tools IDA is very important, it is needed to assure that the process is performing well and as expected. Read more at: https://simplystatistics.org/2017/04/03/interactive-data-analysis/

The first and foremost usefulness of artificial intelligence lies in the fact that it helps companies to predict the outcome of a conference call which is profitable for the company. AI is mainly based on machine learning and it helps in solving problems using past experience, known variables and outcomes, thus replacing the jobs that were previously performed by humans. But in case of company outlook this cannot be used as there are no past experiences to depend on, also risks and opportunities maybe overlooked. AI can also be useful when there is ‘bionic fusion’ which is facilitated by meta-vision. Meta-Vision helps in navigating market forces and assessing the competition to monitoring public relations media engineering. Thus, it can be seen that with the help of AI and meta-vision, predictions can be made about quarterly earnings calls, earned media, etc. Read more at: http://www.datasciencecentral.com/profiles/blogs/artificial-intelligence-in-enterprise-meta-vision-improves

A logical data warehouse is a data management architecture for analytics, which combines the strengths of traditional repository warehouses with alternative data management and access strategy. It provides an abstraction and integration layer that hides details from users of the data, thus allowing the users to easily access data in traditional data warehouses. With the growth of data volume, data will be multi-structured and won’t fit into the existing databases, thus leading to the formation of many data stores which won’t allow getting full value out of the data. Whereas, the logical data warehouse allows one to access and govern these different data stores as if they were a single logical data store, thus making it easy. Read more at: http://www.datavirtualizationblog.com/logical-data-warehouses-matter/

To analyze and harness the data properly, businesses are now using a technique called IoT that will that will leverage the data internally as well externally. Organizations are using IoT data technology for profit maximization, for efficiency and also for decision making. Cloud computing help business by IoT method. Therefore, business intelligence must be brought up with continues method. Read more at:
https://industrialinternetnow.com/deciding-factor-utilize-iot-data-analytics-business-intelligence/

The intellectual strength that is overpassing humans and releasing more like a virus. The power of Machine is getting stronger than can human possesses or possess to think. Several areas where prediction is just a matter of seconds for machines are creating more influence than humans esp. in the field of Medicine. Many universities are allowing their work to adapt with AI learning by different nodes. On the other side, the search engine Google also making plans enrolling AI developments. Read more at: "target_blank>https://www.engadget.com/2017/05/31/ai-is-already-beating-us-at-our-own-game/

With the stress towards financial inclusion by the Indian Government and adoption of online banking and digital payments, a greater need to manage huge data arises which increase revenue and bank’s customers’. Data analytics can manage greater systematic risks. Also, effective engagement with customers is possible with technologies like chatbots. Innovations would attract new customers in a dynamic and competitive environment. Profiling as well as looking at the life cycle of each customer would create opportunities for more avenues of revenue and thereby minimizing risk of defaults. Getting a market overview by means of data analytics would help banks in deciding on their business strategies. Read more at: http://analyticsindiamag.com/data-analytics-expedite-financial-inclusion/

R Programming is used for analyzing data as well as for the prediction of data. Using data models are made to further do analytical analysis. Models once made are then further predicted  using the function predict(). The article further explains the method of prediction using an example. Read more at: 

http://www.dummies.com/programming/r/how-to-predict-new-data-values-with-r/

Today’s generation is health conscious and use wearable devices to track their fitness levels. This constantly generates data and we should be able to draw a comparison on different datasets or data collected from various devices. Lumo BodyTech, a fitness related tech company, created a platform (Lumo Motion) to analyse such data. People can monitor, regulate their health and get accurate advice. Lumo Back and Lumo Lift tracks the posture and combined data can be used to learn about the best posture. Even though the progress is limited but with growing number of products joining the platform, data is comparable from a wider range. Read more at: https://readwrite.com/2017/06/28/iot-will-let-you-understand-your-workout-data-better-hl1/

One of the common business challenges is that the required information by business users is not provided fast enough. The most commonly used company’s data structure is an Enterprise Data Warehouse (EDW), where when information is required, a data mart is created in a separate database, this process is problematic due to the cost of maintaining multiple databases and also the integrity and consistency of the information stored is questionable. However, the delay between the time that the information is required and when it is provided is the real problem. So, the following changes must be made in order to succeed. An information system that transforms data into relevant information very quickly should be provided and all databases should be centralized from the same point. Also, real-time information should be provided and the time to market with short and agile projects should be improved. And finally, new sources of information of different types should be incorporated. Thus, the companies should consider the logical data warehouse approach. Read more at: http://www.datavirtualizationblog.com/enterprise-data-warehouse-no-longer-suffice-data-driven-world/

To prepare for the General Data Protection Regulation, companies need to find a way to establish security controls over the entire infrastructure from a single point. Without investing in new hardware or re-building existing systems, data virtualization allows companies to quickly and easily comply with data protection regulations. There are three ways in which this can be done. First rule is not to replicate the data which will lead to governance and security nightmares. Second is that data virtualization makes it possible to apply consistent levels of security across the heterogeneous data sources which contains the data. Finally, data virtualization removes the need for replication and latency of updates of customer information, thus providing users accurate information as applied in the system of record. Thus, data virtualization can help in data protection. Read more at: http://www.datavirtualizationblog.com/3-steps-data-protection-compliance-gdpr/

Data Analytics has widespread impacts on not only commercial sectors but also healthcare. For example: First is by checking on hospital activities by maintaining relevant databases can help in finding inefficiencies in service provision and the overall costs of a healthcare facility can be reduced. Second is that data analysis also helps in allocating funds efficiently thus reducing the chances of embezzlement. Third is a database of patients’ records and medical histories can be maintained, which can provide a communication medium for the patient and every other individual working on that case. Also, if a healthcare facility is operated in multiple units, analytics can help ensure consistency across all facilities and specific departments. Lastly, storing staff data and keeping a check on their performance is important. Read more at: http://www.datasciencecentral.com/profiles/blogs/data-analytics-is-transforming-healthcare-systems

One can make an AI powered Chatbot in a very short period of time. You just need to figure out what problem you are going to solve with your bot, choose which platform your bot will live on (Facebook, Slack, etc.), set up a server to run your bot from, and choose which service you will use to build your bot.

Here are some of resources to get you started.

Platform Documentation:

1.Facebook Messenger Platform, 2.Telegram, 3.Discord , 4.Slack , 5.Kik etc.

Some third party Services can be used to build Chatbots. They are:

1.Chatfuel, 2.Botsify:Botsify, 3.ChattyPeople, 4.FlowXO, 5.Boikit ,6.Beep Boop , 7.MEOKAY, 8.Wit.ai ,9.Api.ai ,10.Octane.ai etc. To know more about these, read: https://www.entrepreneur.com/article/289788