SigmaWay Blog

Business intelligence provides you a greater customer insight and predictability on a product life cycle. But such digitalization has also some disputes as well. Most people around the world are tech savvy and surfing for a new product with better features. Analytics is growing at a fast pace and is creating a backlog. Day to day new product launch is providing zero visibility on upcoming product life cycle. Removing backlogs to smart productivity as per customer's likability is the new trend of business analytics. So there are challenges like cost, security and infrastructure, availability of skilled professionals. So overcoming challenges in BI and complete leverage on it is the new mantra for business empowerment.

To read, follow: http://www.business-standard.com/article/management/the-challenges-of-business-intelligence-116041000615_1.html

In this digital world at every touch point huge amount of data is being created in seconds that tells us about the needs & wants of consumer. The capability to extract valuable insights from the huge data poses a critical challenge for any modern organization. It has become an more & more important to efficiently & effectively ( both in terms time & labor) to  conduct data mining and extract valuable information out of big data to meet the strategic objective of an organization. In this paper MAS (multiple agent system) concept is being introduced to extract value data from CRM aspect. It also gives us a 3 point model to extract out potential valuable data to manage CRM optimally.Read full article at =_blankhttp://www.csroc.org.tw/journal/JOC26_4/JOC26-4-7.pdf

This paper gives us a holistic understanding of two main topics – big data analytic & supply chain management. In this highly competitive world, every organization is looking forward to a competitive advantage in the form either cost effectiveness or better utilization of resources. To achieve this goal organizations are using a data driven supply chain to integrate the entire processes. This enables organization to stream line supply chain while mitigating the risk in an uncertain business environment. Big data analytic also helps an organization to extract important trends & information that can be used to make the systems more efficient & robust.

To read the full article click:- http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2755828

Retailers and most of the brick and mortar shops store their customer's data to understand their buying and transaction behavior. Some obscure data point that may help them become more knowledgeable about their customers. However, hidden from these in-store shopper analytics are all of the transactions their customers make outside their stores with competitors. In recent past, there are many switching of preference to online one. But this up growing online marketing basically picking the flaws of customers' real time analysis in case of brick and mortar shops. So it's high time to either integrate or dig into flawless marketing analytics.

To read, follow: http://www.forbes.com/sites/forbesinsights/2016/04/27/the-hidden-danger-in-brick-and-mortar-customer-files-amazon-prime-members/#69beabd83681

Big data analytics is becoming self-explanatory day by day to even a lame person who has lesser knowledge in statistics, analytics and data science. It is a positive sign with respect to current business purpose because we need real time analysis and that can be only possible if data interpretation and visualization becomes more feasible. There are different software like R, SAS and many other online service providers like google analytics, Piwik helps to create better data visualization and interpretation of customer and business data. Even our very own Microsoft excel has some in built data interpretation and visualization programs. But one program, called Quill, takes the trend a step further, producing text-based reports that explain the data clearly and concisely.  Think of it as an executive summary created by a computer to explain a set of data at the click of a button. So it’s time for personalize big data analytics.

to read, follow: http://www.forbes.com/sites/bernardmarr/2016/04/27/will-we-soon-no-longer-need-data-scientists/#540a99bf55f3

We can now analyze real estate data with the help of Business Intelligence or BI. BI tools are also beneficial for sellers and brokers. These tools can track prior industry performance statistics, such as sales in a given area, which in turn help sellers set asking prices that are actually competitive. In short, they are making marketing easier. For more read the article written by Sean Mallon (blogger) : http://www.smartdatacollective.com/seanmallonbizdaquk/402264/how-business-intelligence-making-real-estate-easier

Embedded Analytics mean adding features associated with BI software such as dashboard reporting, data visualization and analytics tools to existing applications. This can for the most part be accomplished in two ways: o In-house development o Purchasing and embedding out-of-the-box software. To know more about embedded analytics, follow the article written by Eran Levy (Content Manager, Sisense) : http://www.smartdatacollective.com/eran-levy/400533/does-future-lie-embedded-bi

Oil is the backbone of the worldwide economy. But the procedures to transform crude, raw petroleum into a refined item is costly. Application of analytics in the oil industry minimizes hazards and augment benefit. With analytical information, the refiner can better predict processing issues and actualize financially savvy moderation systems. With the recent advances in data and analytics, it is possible to minimize risk and maximize profit. There are several analytical tool which is helping the oil industry. They are: Unrefined fingerprinting - a diagnostic gadget measuring properties of the rough, coupled with refined predictive analytics. It offers quick reaction, on location analysis of unrefined oils and other hydrocarbon liquids. Crude fingerprinting - tests and recognizes unrefined crude before it goes into the refining process. 

For more read the article written by Tom Stanley

(Chief Technology Officer, distributed power and water GE Power): http://www.environmentalleader.com/2016/04/04/how-data-analytics-is-changing-the-oil-gas-industry/#ixzz45s52g6qt

Analytics is gaining the highest popularity day by day in the business context. Reason is very simple. This provides a real time analysis of mind share and salience. Google is trying to venture in this section and finally it positioned themselves into this arena unlike other companies. Their model is mostly web specific because most companies are tracking their customers via web activities, no of hits, activity index and frequency. Not only that it helps to you understand social presence and real time web mining. For getting all these bunch of information you need to log in in google analytics. Such real time analysis not only provide you traffic notification but also provide user cookies which is closer to site relevance. Even they will provide detailed geographic, browsing info and language specific information which helps to u understand your customers need more specifically and accurately.  

To read, follow: http://www.business2community.com/business-intelligence/getting-started-setting-google-analytics-01519623#FEKZ3JA1kj8FBQ8H.97

NASA is working on big data analytics to facilitate their mission mars more productive and informative. They are using big data analytics to understand the environment of the mars. That is they are using modern technology to collect and analyze environmental data and based on that it will give a predefined signal to the Curiosity rover currently operating in Mars. These data points help to generate a more specific road map for upcoming 2020 mars mission for NASA. One of The expert in NASA JPL laboratory says these huge analytic modelling defines an easier way to resolve the problem. 

To read, follow: http://www.forbes.com/sites/bernardmarr/2016/04/14/amazing-big-data-at-nasa-real-time-analytics-150-million-miles-from-earth/#1cbda1b371dd

Social media is providing endless opportunity to grow our popularity as individuals. But, for business enterprise the best thing is to divulge itself in social media networks. But social presence should have some purpose. We post a lot of daily stuffs in our social wall. In one way that is good because you are one step closer to your distant audiences. We often follow social platforms like twitter, Facebook to get more exposure to what is happening. Social platforms like YouTube, Pinterest provide extensive peripheral knowledge. So in one word these platforms, providing our enormous facility in the learning process, business expansion and lifestyle development, but there is a point when social media becomes an addiction. So, it is high time that we define our position in social media. 

to read, follow: http://www.irishexaminer.com/lifestyle/features/do-we-share-too-much-on-social-media-389859.html

Graphs are not just nodes or links. They are powerful data structures anyone can use to represent complex dependencies in their data. Graph applications are used in various places ranging from cancer research to large-scale cyber threat detection to collaborative filtering recommendation systems. In the world of data-intensive analytics, memory bandwidth is the primary performance restrictor. Because graph algorithms display non-locality and data-dependent parallelism. When you crisscross a large group, you are constantly asking for from main memory. For these problems, GPUs provides superior bandwidth to memory and can deliver significant speedups over CPUs. GPUs are very fast for graph processing and analytics, where memory bandwidth is a problem. The memory bandwidth of GPUs provides a new way to speed up data-intensive analytics and graph analytics. For more read the article written by Brad Bebee ( CEO, Blazegraph) : https://devblogs.nvidia.com/parallelforall/gpus-graph-predictive-analytics/

Computer systems are quite similar to living organisms. The living part of the system is not so much the hardware or even the software it is the data. We seek to build computer systems based on ourselves and take this as models. Non computer science based behavior patterns and structures provides a language for discussing the patterns, behaviors and structures of systems. An artificial neural network is inspired by the biological nervous systems, such as the brain. It is composed of a large number of highly interconnected neurons to solve specific problems. An Artificial Neural Network or ANN is configured for pattern recognition or data classification. For more read the article written by Bruce Robbins (CEO, Xcipi) : http://www.smartdatacollective.com/bruce-robbins/397616/data-alive-through-deep-learning-and-intelligence

Cryptology plays a crucial role in cyber security. There could be use of sign language and different cypher code to encrypt the sensitive data. The decipher mechanism helps to decode the message. In ancient times, Julius Caesar used to send his secret messages to his army in cypher code. Later this process is followed by Germans during the Second World War. In modern organizational context, cyber security is the most important parameter. So, to protect organizational data they can adopt cryptology to encrypt their data and hide them from the normal visibility level. The reader can go through the article written by Mark Ward (Technology correspondent, BBC News) and understand more about cypher process and the levels of challenges at:

http://www.bbc.com/news/technology-34312697

The challenges faced by the insurance industry are same as other companies. Insurance industry involves generating, storing, and making large & complex sets of data to create efficiencies and improving their bottom line. In a recent survey, 54% of 48 U.S. and P/C insurance executives said they use predictive modelling for underwriting/risk selection, and that usage is expected to grow by 40% over the next two years. Predictive modeling helps in claims triage, underwriting appetite and strategy, market-share analysis, and litigation propensity. Predictive analytics can boost companies' profitability by: 1. Developing a clear analytics roadmap across business units. 

2. Monitoring their outputs against what is happening to avoid the situation where underwriters push back on predictive models.

3. Developing an enterprise-wide model monitoring program to ensure models are recent and recalibrated on a consistent basis. 

4. Looking outside the industry to see how other organizations measure ROI.

For more read the article written by Loren Trimble and Michael Kim(Contributors) : http://ww2.cfo.com/risk-management/2016/03/predictive-modeling-can-make-insurers-better-risk-managers/

The need for wearing body cameras for police personnel has increased. Police departments are also taking the help of specialized data mining solutions to predict and prevent misconduct. The problem with this, is that, it leads to officers being treated differently based on actions they are yet to take, and might never take at all. Predictive analytics is playing an important part in modern policing and in ending crimes which are less serious than police misconduct. For more read the article written by Graham Templeton ( Writer ): http://www.extremetech.com/extreme/224560-new-analytics-can-predict-and-possibly-prevent-police-misconduct

Nowadays, new solutions are based on IoT, analytics, and business intelligence and we are surrounded by more applications of big data, analytics, sensors, and business intelligence. According to Mary E. Shacklett (president of Transworld Data) writes in her article about some examples of behind-the-scenes technology that is redefining the world. Some of them are - 1: Weather and political prediction systems for global supply chain and financial market impacts

2: Merchandising

3: Police suspect identification

4: Robots for cleaning your floors and your carpets

5: Medical patient demographics

To understand the crime pattern, data are used by police from the past. The objective is to predict acts of terrorism based on the large amount of surveillance data. The technology will collect data on ordinary citizens, including information on their jobs, hobbies, consumption habits, and other behaviors and will flag unusual behavior that could signal a potential terrorist attack. Police in both Los Angeles and Manchester ran trials using a computer algorithm to predict where crime would most likely to take place. Their aim was to find patterns in criminal behavior by analyzing crime data to prevent crime through predictive policing. There is a need for research into machine learning and other artificial intelligence technologies to identify human faces in surveillance video. The police can analyze large amounts of crime data, i.e. 'big data'. Enterprises are now using big data technologies to detect and fight cyber threats. Big data analytics and shared threat intelligence can increase the organization's effectiveness in security measures. For more read: http://www.cxotoday.com/story/how-big-data-can-be-used-to-predict-crime-patterns/

The Internet of Things (IoT) generates semi-structured or unstructured data in real time. Organizations take advantage of cloud because big data can be best managed in the cloud. By utilizing fog computing, organizations can decrease time to action; reduce costs, infrastructure and bandwidth; and can get greater access to data. The advantages of the decentralized method of fog computing and IoT analytics cover both the organization and the end user. One of the benefits of centralization is to focus and understand the data location and the accessibility. The decentralized method is associated with flexibility and agility. This tends to describe the data management trends and applications. Read more at the article written by Jelani Harper (blogger) : http://data-informed.com/the-internet-of-things-and-the-necessity-of-fog-computing/

The development of mobile, IoT, and the cloud has increased the need of analytics to solve challenges in the customer, product, operations, and marketing domains. The established companies need to restructure their business and technology to increase their sales. Organizations need to involve cross-functional teams to establish data governance. Analytics 1.0 was data warehousing and business intelligence; Analytics 2.0 was big data, Hadoop, and NoSQL. Now in the era of Analytics 3.0, when tools make decisions and measure the impact. For more read the article written by chandramohan Kannusamy (Technical Architect) : http://data-informed.com/analytics-3-0-and-data-driven-transformation/