SigmaWay Blog

SigmaWay Blog tries to aggregate original and third party content for the site users. It caters to articles on Process Improvement, Lean Six Sigma, Analytics, Market Intelligence, Training ,IT Services and industries which SigmaWay caters to

Artificial Intelligence in Practice

Artificial Intelligence is development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Therefore, AI can process a lot more data than human brain can, and thus can do more work accurately in less time. Along with data analysis AI can perform much more complex tasks such s determining customer preferences for products based on previous buying habits and understanding unstructured data. AI is augmenting the capabilities of human by solving such complex challenges. The importance of artificial intelligence can also be seen in healthcare industry for suggesting appropriate treatments for patients, fraud detection, financial services and retail. AI can be incorporated in business by using machine, deep and memory based learning which is AI’s core. Read more at: http://www.datasciencecentral.com/profiles/blogs/artificial-or-augmented-intelligence-talks-with-intel-s-chief

 

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The Core Banking Solutions Market

Core Banking Solution (CBS) is networking of branches, which enables customers to operate their accounts, and avail banking services from any branch of the Bank on CBS network, regardless of where he maintains his account. By regional analysis, the market is segmented into North America, Europe, Asia-Pacific and Rest of the World, with North America generating the highest market share owing to higher technology implementation. Due to its capabilities to improve efficiency and better risk management, core banking solutions is growing rapidly. On the basis of solution segment, it offers better customer support service, provides single integrated platform to all the banking channels and lower operational cost, improves efficiency in streamlining process related to customer account management. Read more at: http://www.datasciencecentral.com/profiles/blogs/core-banking-solutions-market-analytical-insights-and-foresight

 

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Uses of Virtual Data Sources

Data virtualisation can simplify data access and improve agility. Three uses of virtual data sources are: 1) Merger and Acquisition: Due to merger of data warehouses, OLTP systems are changed which causes the ETL jobs to break. This situation can be avoided by introducing virtual data sources between source OLTP systems and the data warehouse or between data warehouses and data marts, 2) Stabilizing data warehouse ETL processing, 3) Simplifying self-service data preparation for business users to access data across multiple data sources. In conclusion, virtual data sources are useful in practice because of its ability to de-couple physical data sources from ETL processing improve design and introduce governance. Read more at: http://www.datavirtualizationblog.com/overlooked-capability-data-virtualisation/

 

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Digital Twins

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

 

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Data Mining- An Overview

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

 

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General Data Protection Regulation

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

 

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Suggestion for Simplifying Natural Language for Computer Programs

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

 

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Machine Learning and Artificial Intelligence in 2017

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

 

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Data Exhaust and It’s Uses

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

 

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Data Science in Advertising

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

 

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A Data-Driven Culture in Marketplace

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

 

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Principle Experiences of Data Science

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

 

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Cyber Security: An Important Step While Digital Transformation

With the increase in cyberattack throughout the world, it has become extremely necessary to install cybersecurity which uses digital technologies to protect company networks, computers and programs from unauthorized access and subsequent damage. With the market rapidly digitalizing itself, it is necessary for them to secure it. The focus must be on safeguarding the data spread across devices and cloud. There are different techniques used by hackers to attack the company’s data source, also there are ways to combat these attacks by network defense, mobile device protection, investment in securing IoT devices etc. Artificial Intelligence is used to learn new malware behaviors and correct them. Thus, cybersecurity is a key factor for every organization while digitalizing itself. Read more at: http://www.datasciencecentral.com/profiles/blogs/cybersecurity-in-digital-age

 

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Interactive Data Analysis

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/

 

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Artificial Intelligence and Meta-Vision

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

 

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Importance of Logical Data Warehouse

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/

 

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Alternatives to Enterprise Data Warehouse Approach

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/

 

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Data Virtualization as a help to Data Protection

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/

 

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Impact of Data Analytics in Healthcare

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

 

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Understanding the Importance of Quality for Digital Publishers

Measuring the effect of quality on revenue and profits is one of the biggest challenges faced by digital publishers. It can be measured by measuring engagement of customers, i.e., their frequency of visits, how recent their last visit was, etc., which confirms the quality of the product. The next is that the publisher’s dilemma is balancing advertising revenue with audience revenue. Advertising revenue depends on quantity of audience impressions while audience revenue depends on quality of content. Also, more audience revenue requires higher quality user experience. As digital advertising revenue, particularly display ads, the need for greater audience revenue is very important. To measure success, the important data to capture are revenue by individual user. Using these data, the publisher can identify the combination of product attributes and price point, thus maximizing expected advertising revenue and audience revenue. Read more at: https://www.webanalyticsworld.net/2017/02/digital-publishers-analytics-dilemma.html#more-23565

 

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