Digital twins refer to computerized companions of physical assets that can be used for various purposes. Digital twins use data from sensors installed on physical objects to represent their near real-time status, working condition or position. With digital twin, optimized machine design and smooth operation is ensured. It also helps predict precisely when maintenance will be required by a particular asset based on unique conditions. A few advantages are: 1) understands how change in manufacturing process can impact cost, 2) checks the current operating status, 3) can perform proper maintenance procedures. Digital twin with advanced analytical tools and machine learning will improve predictive analysis. Read more at: http://www.datasciencecentral.com/profiles/blogs/what-are-digital-twins

According to the author,ten important steps to enterprise strategy alignment are as follows:                                                                                                                  

1)Several aspects like current results,loyalty,etc are evaluated by enterprise assessment.                                                                                         

2)Every enterprise should focus on their mission,vision and values.                  

3)Go-to-market strategy like target customer,value proposition and others.                                                                                                                   

4)Business Model Design like Metrics.                                                           

5)Revenue Plan like Customer Goals.                                                                   

6)Execution Plan and Improvement.                                                                     

7)Financial Budget involves financing capital,asset,cash flow.                            

8)Alignment of the systems and processes.                                                                 

9)Rollout,i.e. Distribution, Communication.                                                           

10)Monitoring -This includes process review,system review and strategy review. Read more at: http://www.businessmapping.com/blog/enterprise-strategy-alignment/

In the age of automation ,many hotels are using the data analytics to understand the free amenities that consumers like the most. The proper use of analytics will help them to please both the new and old visitors. In order to execute the hotel's amenity analytics, the following methodologies are used:                                          

1]To tap into the hotel's reservation system to find data                              

2] To incorporate this data with survey data from consumers. This helps the hotel management process easier by making the digital computation of the value of free amenities and by providing better analysis of the data available and making space for new reliable and valuable data.

Read more at: http://www.techrepublic.com/article/how-analytics-help-hotels-gain-customers-loyalty/

GST-Network (GSTN) is trying to provide hi-tech security and analytics center to give protect the data under the Goods and Services Tax (GST) for the cyber threats. GSTN will be appointing security companies by August to build up Security Management and Analytics Centre (SMAC). SMAC will implement data analytics to protect the GST system from cyber attacks and will provide a better and protected environment for GST ecosystem. Read more at:http://cio.economictimes.indiatimes.com/news/digital-security/paranoid-about-security-setting-up-analytics-centre-gstn-ceo/59092397

India is planning to observe the pollution level in the ocean with the level of automation and based on the observation it will try to take measure to reduce the pollution level in the ocean. As the tourism industry along the coasts are flourishing in a large scale in India, the pollution is also increasing . This project will help to predict a threshold value of dumping waste into the ocean by forecasting the pollution. Different time scales will be taken into account to observe the significant changes occurring in the coastal waters and to estimate how different parameters are like along the coastline of India. Read more at: http://economictimes.indiatimes.com/news/science/india-plans-automated-ocean-pollution-observation/articleshow/58640371.cms

A huge amount of digital data is getting piled up every day and to deal with that the technology recruiters are valuing the skills in data visualization, data science, machine learning and data analysis the most. These skills in data analysis help the companies to give more insight about the data and help to predict a better future. With the courses on data science people are now showing immense interests in machine learning and data visualization tools. Professionals are willing to upskill to keep pace with the automation. Read more at: http://economictimes.indiatimes.com/jobs/techies-reskill-to-log-on-to-big-data-deluge/articleshow/58103804.cms

Many big companies are trying to adopt sophisticated artificial intelligence and other advanced technologies without practicing basic data analysis. But this will affect the companies adversely. Without basic automation and strategic vision of solving basic problems using basic data analytics, companies cannot use the AI and advanced technologies to reach the correct results. The different ways of AI are not yet fully discovered by the companies .AI has the full. potential to reinvent business but before that proper automated and structured analytics are needed to be prepared to nourish the advantages of AI. Read more at: https://hbr.org/2017/06/if-your-company-isnt-good-at-analytics-its-not-ready-for-ai:

Data mining is the practice of examining large pre-existing databases in order to generate new information or to predict future trends. It is useful because: 1) it helps in identifying the multi-dimensional patterns of large data, 2) establishing relationships, 3) putting together predictive models, 4) customer data can be mined to acquire new customers, retain customers, cross-sell to existing customers, 5) helps financial sector companies build fraud detection models and risk mitigation models, etc. Skills required for data mining are statistical knowledge, artificial intelligence/machine learning, and competency in Python, R, and SQL; also along with these technical skills a data miner should have business knowledge and other soft skills. Read more at: http://www.datasciencecentral.com/profiles/blogs/data-mining-what-why-when

GDPR gives customers the right to control their personal data, to modify, restrict or withdraw consent, and transfer data. GDPR helps in reinforcement of customer’s trust and reduce regulations. With unified data governance, businesses will comply easily with law regulations that redefine client personal data usage. While without it they will be at a risk of potential data breaches and loss of client trust. With GDPR, companies are required to have technologies and data and analytics solutions which help them manage their data to provide a positive customer experience across channels. In future, these organizations will be able to secure a competitive advantage by gaining client consent to use personal data and improve the customer experience. GDPR will help in increasing awareness of the value of personal data. Read more at: http://www.datasciencecentral.com/profiles/blogs/gdpr-a-change-in-the-making

To avoid the complexity of natural language for computer programs, a proposed approach is suggested which simplifies the language into sequences of predefined keywords. The approach is: 1) the relevant proposed keywords should be obtained by applying patterns and rules in the text. 2) New keywords must be defined and the corresponding idea must be explained by those. 3) The statistics of the new keywords and patterns should be systematically collected. 4) Statistical analysis or data mining techniques will help in detecting sub-optimal situations. 5) Progressively this will lead to the generation of an optimal set of keywords and patterns and the final knowledge encoded will be easy to mine using simple programs. Read more at: http://www.datasciencecentral.com/profiles/blogs/feasibility-of-a-personal-knowledge-management-system-based-on

Banking is that sector of an economy which is very sensitive to changes in worldwide economics events, in a negative or positive way. International banks infuse the required capital and technology into a developing country’s financial system apart from risk sharing. But after 2007 subprime crisis international banks have received some criticism. Therefore some questions regarding trust issue, supervision of these banks by developing nations,  reduced access to SME’s and households and impact of digital technology on international banks have to be answered. Read more at https://blogs.worldbank.org/allaboutfinance/globalization-and-banking 

Earlier VR was within the realm of tech companies and start-ups but nowadays universities are adopting the same technology to teach students and conduct research on the subject. Academicians are engaged in finding the impact of this technology in our lives. VR has made classroom experience memorable with the availability of 360-degree cameras and cheap VR headsets. VR could change the behavior of society as well. Heat maps can be used to analyze the time spent looking at different areas in VR landscape. It is a boon in the field of psychology and journalism. While the students can choose their own experiences through VR field trips, professors have an effective way of teaching. Read more at https://readwrite.com/2017/07/06/universities-defining-future-virtual-reality-dl1/

Technologies changed roles at home, from the kitchen phone to smartphone, tablets etc. The latest edition is IoT. Here, smart means safety in terms of health and security. We will be able to control various electronic devices like AC, Fridge, Fan, TV etc. directly with our smartphones and also able to know the safety status of those. We can take care of our loved ones when we will be outside of our homes. The time of the smart home is coming to us soon. Read more at: https://www.entrepreneur.com/article/295464

To the huge size and structure of the healthcare industry in India, technology can add immense value to it. Technologies can make differences in the various problem areas. Technologies like telemedicine can increase the reach of doctors to these rural areas to increase the accessibilities of doctors. A startup which is providing e-consultations through message, voice or video call do provide a convenient option at a lower cost for affordability issue. We can aware people by giving them the information about symptom, diagnosis and treatments of various critical diseases through mobile. We can also make people aware about the price and quality of medicines. Read more at:https://www.entrepreneur.com/article/296157

India’s retail market is predicted to double from $600 billion in 2015 to $1 trillion by 2020 as per IBEF. The growth in e-commerce influences development in the retail sector. Faster internet speed, reliable telecom operators, increased adoption of online services plays a key role in gaining that tremendous growth. In upcoming years, we will witness advanced technologies and innovations to revolutionize the customer’s shopping experience and collect more online sales.AI based chatbots will be the future of customer service. As consumers are gaining digital prominence mobile wallet will be crucial for cashless transaction. Read more at:https://www.entrepreneur.com/article/293462

Bots are the update of apps and websites. Bots will improve the way we use mobiles and computers. Bots will make it easier and faster to get things done. Bots also solves the download problem caused by apps. For developers and businesses, bots dramatically reduce the cost, time of developing and marketing their services. Read more at: https://www.entrepreneur.com/article/278001

Artificial Intelligence is one of the most important part of our daily life from simple features like automatic image tagging to prediction and recommendation in business. AI will begin to learn emotion, better understand human sentiment and solve more complex problems. Soon, companies will be able to automate a large chunk of data analysis, decision making and customer service, allowing employees to tackle the most complex challenges rather than get bogged down in the details. Read more at:https://www.entrepreneur.com/article/290444

Marketing to millennials is challenging for their changing consumption patterns in various platforms, devices or content formats. AI collects smart real-time data and use those which may be personal data, brand data, historic data, etc. A chatbot can go to messenger platforms to accelerate a personalized e-commerce without human interactions. Acceptance of AI is growing in millennials for interaction with brands, online and offline. Read more at: https://www.entrepreneur.com/article/295047

In recent times, innovation of machine learning and artificial intelligence has changed the paradigm of the tech world. Some trends prominent in the development of machine learning are: (i). Big companies are developing machine learning based artificial intelligence systems and it will continue to grow, (ii). Along with data, with the development of machine learning, algorithm economy is expected to develop more, which will distinguish small business from the large ones, (iii). Interaction between machine and humans is expected to increase. In conclusion, machine learning and artificial intelligence is expected to grow but humans can’t be completely replaced right now. Read more at: http://www.datasciencecentral.com/profiles/blogs/trends-shaping-machine-learning-in-2017

Data exhaust refers to the data generated as trails or information byproducts resulting from all digital or online activities. These consist of storable choices, actions and preference information that are generated for every process or transaction done digitally. This is mainly produced by manufacturers and retailers. A lot of data are collected by manufacturers, which they don’t need, but it’s needed by suppliers or for quality control. Data exhaust is also a revenue opportunity; the data which is not needed by the manufactures can be sold to the suppliers or a chain of suppliers. Read more at: http://www.datasciencecentral.com/profiles/blogs/what-is-data-exhaust-and-what-can-you-do-with-it