SigmaWay Blog

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

In the recent years, advertising has become more accurate and particularly with the support of better information. This expansion in accuracy is due to the development of computerized advertising. 5 reasons how data science could be the advertising wave of the future are: (i) it is a requirement for marketers, it gives advertisers the capacity to set up client profiles and design coordinated strategies, (ii) portable applications can give knowledge about client’s conduct on the web and their interests, (iii) it helps in streamlining and enunciation of the client travel, (iv) it helps in increasing brand income and also increases advertising spending plans, (v) cloud capabilities helps advertisers run investigations on an assortment of data. Read more at: http://www.datasciencecentral.com/profiles/blogs/5-reasons-why-data-science-could-be-the-advertising-wave-of-the

The logistics industry in India is growing rapidly because of the growth in retail, e-commerce and manufacturing sectors. Though it has been underperforming over the years, it has received importance recently and is expected to reach a worth of $2 billion by 2019. Lack of facilities, technology and manpower have been reasons for the loss in the perishables sector. Better infrastructure, better storage, better network and warehouse facilities are the way forward. The sector is currently using IT solutions and the hub-and-spoke model for freight movement. It provides end-to-end supply chains to the consumers and being a key player in the economy, it is expected to grow by 10-15% annually in years to come.Read more at: http://www.iamwire.com/2017/04/indian-logistics-industry-scenario/151331  

Database vendor choice is one of the factors affecting the cost of provision of data. Even when licensing pricing is unpredictable we need database pricing to be low and stable. The pricing model can be per storage/ per server or user based where users need to create their separate profiles. Another type of models is in which cost structure have an influence over architecture where customers are not able to get all the expected cloud features such as independently scaling computing and storage. Computing and storage should be priced independently. This works well in case of the seasonality of data where one only has to pay for what he uses. Read more at https://gigaom.com/2017/05/08/know-your-embedded-database-cost/

Location data can be mingled with technology to better manage traffic issues, infrastructure as well as planning city development. CARTO a SaaS platform creates mapping tools that help governments and citizens take smart decisions. For example, in SanDiego, visualizations with the help of data assisted citizens to track the progress of their street repairs. In Finland, reindeers could be detected from reasonable radius which could avert their deaths by car. Similarly, location intelligence could be used for tree plantation in appropriate habitats. Businesses can decide on their investment strategies based on locations and planners could do predictive intelligence in handling traffic. Overall citizens should be a part of development. Read more at https://readwrite.com/2017/07/05/location-intelligence-key-citizen-centric-smart-cities-cl1/

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/