SigmaWay Blog

In modern world, analytics plays a significant role and therefore various organizations develop cohesive analytics for customer satisfaction and thereby leading to enhancement of sales. According to some retailers, customer experience drives revenue while analytics focuses on an organization's commitment in terms of value to the customers.

 Audi representative Aaron Cohen (the company's general manager of strategy and analytics) explained the process of optimizing customer experience and hence the importance of data blending. Companies are shifting their focus to freemium services, open source platforms for valuable insights. In this context, Monobina Saha (senior data engineer at Sears) highlighted the significance of R-programming among data scientists.

Read more at: http://www.cmswire.com/analytics/how-are-you-capitalizing-on-customer-analytics/

Just like other areas of economy, marketing, too, has been impacted significantly by the growth of data analytics. Although the importance of analytics has been recognized in marketing, yet it is observed that in most cases the tools are not being used efficiently. For this, aligning data with the goals of the company needs to be done. Secondly, the smooth transition from big data to useful data and determination of the relevant metrics and adoption of those metrics are required. And last but not the least, hiring of the right people with analytical and creative minds to properly use the data is imperative. Read more: http://www.forbes.com/sites/johnrampton/2015/06/21/how-can-cmos-make-sense-of-big-data/

Industries are growing techno-savvy and understanding that being able to gather, identify, and comprehend patterns in data is critical for their long-term success. We are finally entering an age of data-powered farming that work on open data sets of weather and yields, IoT, drones and smart agricultural machinery. Such digital tools are becoming as important as traditional tools in the agricultural sector. IoT sensors are also helping companies predict and manage risk for automobile policies in real-time. Governments and industry are cooperating to share data on natural catastrophes in the hope to predict them. Drones are even being used to inspect and manage energy sites like turbines and rigs. Governments too will start using Big Data for evidence-based policy making. Read more at: http://yourstory.com/2015/06/big-data-unleashing-value-for-startups/

Healthcare Analytics Market has been predicted to have tremendous potential over time with low cost and improved population health. Survey report gives an increase in analytics adoption rate though many still don't use predictive modelling techniques. Data integration is required to derive analytical value which comes as a big challenge. Data Breeches and Data Security is a lesser factor than the former one since enterprises take their own data. But this factor works when external data is taken into account. People with a unique combination of statistics training, technology skills, and data knowledge are also required in large number. Also, infrastructure improvement for analytics must be done according as many healthcare decision makers. Thus analytics is getting much priority in healthcare in recent times. Read more about this article at:  http://www.cio.com/article/2904270/healthcare/healthcare-analytics-4-things-impacting-the-adoption-rate.html

Business decisions in modern times are being taken on the basis of appropriate data analysis and projections, which are generated by crunching the data. But business leaders need to stop and think once about the quality of the data; the most important factor because all the forecasts and predictions are done in the light of the data. So here are some points which should be kept in mind before starting data processing. They are: • The source of the data, i.e. survey/ public domain data etc. should be known correctly.

• Purpose of data collection: objective of data collection should be clear.

• Data collection method should not have any sampling bias.

• The nature and scope of the data should be clear. Read more at: Business decisions in modern times are being taken on the basis of appropriate data analysis and projections, which are generated by crunching the data. But business leaders need to stop and think once about the quality of the data; the most important factor because all the forecasts and predictions are done in the light of the data. So here are some points which should be kept in mind before starting data processing. They are: • The source of the data, i.e. survey/ public domain data etc. should be known correctly.

• Purpose of data collection: objective of data collection should be clear.

Data analytics is no longer limited to the field of business and market research but it has spread across the financial sector as well. Stock market investors are using data driven insights and predictions to make crucial investment decisions. By observing the market conditions and various metrics related to it, the data scientists are being able to come up with accurate inferences about the market, and the data analysis is becoming increasingly precise and data intensive.  This is due to the availability of financial data and decreasing cost of information technology. The growth of analytics in this sector is likely to continue and is going to revolutionize the way financial trading is done. To know more read: http://www.huffingtonpost.com/irene-aldridge/why-big-data-matters-in-f_b_7553438.html?ir=India&adsSiteOverride=in

We know how big data has helped organizations achieve big success in almost every field. Today, when everyone is in a race to establish a competitive edge over the other, businesses want their data and analytics to be real time or near real time so that they can act quickly. Mary Shacklett, president of Transworld Data, elaborates for us four ways real time analytics lead to competitive advantages. They are:

• Buying Preferences

• Network Intrusion detection and forensics

• Logistics Orchestrations

• Predictive maintenance

The healthcare sector is now exposed to data breaches as it has adopted the online mechanism, for maintain health records or say for remote patient monitoring. Data breaches are not only expensive in terms of costs incurred by the healthcare provider but also in terms of patients' lives. Data analytics helps in interpreting unstructured data, which may help to prevent not only data breaches but also (say, medical prescription) frauds.  Big data analytics allows organizations to amalgamate and analyze their data irrespective of the source, type, size or format and finally understand the patterns needed to address fraudulent activities. Again, by updating, pharmacies, doctors, and hospitals on data insights, concerning substance history information of patients, it is possible to take better prescribing decisions and identify potential prescription drug abuse. Read more at: http://venturebeat.com/2015/03/31/big-data-analytics-can-prevent-health-care-fraud-heres-how/

Sigmaway consultants have worked with clients in benefits solutions workspace providing analytics on healthcare plans. For more details visit http://www.sigmaway.us/

Smartphones are fantastic examples of devices whose success largely depends on the performance it delivers. Big data researchers are working towards developing the network, computing and cloud architecture required to sustain their projects. The sheer volume of data generated by us doubles every two years and there is an urgency to make storage devices manageable and affordable. SSA (Solid State Array) at the hardware level and SDS (Software-Defined Storage] at the software level are becoming quickly affordable. With the evolution of in-device memory, a variety of data will now come into light that may revolutionize the applications of Big Data in the mobile industry. In-memory databases will allow real-time analytics and awareness of the ‘live’ transaction data – rather than delayed ‘fact-based’ analysis of old data. Read more at: http://www.forbes.com/sites/howardbaldwin/2015/06/22/flash-ways-to-make-big-data-run-faster/

The future of analytics seems to be brighter with IoT data, it's near real speed analysis and complex event processing systems. But, where exactly is IoT analytics going. Michael Hummel, co-founder and CTO of ParStream, talks future of IoT analytics. Daniel Gutierrez has summarized his talks in an article on Inside BigData highlighting the 4 predictions Michael made:

• Big data, fast data and more analytics

• Horizontal integration and vertical application 

• Decentralization

• Integration of advanced analytics and Machine learning

Planning before working out is the most basic idea. A planner successfully merges both aesthetics and practical planning. But a strong design comes from the collected data. Combining user and sales data, Ford F-150 has been designed to reduce weight and increase sustainability which are made keeping in mind both environmental and business sense. Though A/B testing was a popular method of creating effective marketing choices due to its manufacturing constraints, companies nowadays use data gathered by observing public interest to create new products or make changes to earlier products. With increasingly complex algorithms, architectural design is now getting more accurate. With faster computing power, various tests can be done to establish strength and quality. Read more about this article by Chris Towers (Big Data Divisional Head) at:  https://channels.theinnovationenterprise.com/articles/designed-by-data

Rapidly changing consumer demands are becoming one of the major concerns of the financial institutions. Analytics is the technology which can be adopted to address this need. With evolving customer expectations, analytics solutions, too, have adapted, but the question under consideration is whether the banks are prepared to adopt and benefit from the next-generation analytics solution. As viewed by Thomas H. Davenport, evolution of analytics is a three-stage process of which Analytics version 3 is the latest generation which merges qualities of previous technologies and focuses on prescriptive analytics. Based on the data collected, Analytics 3.0 will enable the financial institutions can create tailor made services for customers. However, to fully reap the benefits, the institutions have to make sure that the right technologies and practices are in place. Read More at: http://internationalbanker.com/banking/are-banks-ready-for-next-generation-analytics/

Responsible for the largest and most complicated science experiment, the LHC, CERN has tried to uncover the secrets of the universe. Its major experiments are all in the domain of Big Data. How? Well the LHC single handed generates 30 Petabytes yearly. They use effective data warehousing and Big Data Analytics tools to make sense of such large amounts of raw data. Crunching all the data collected from the 600 million particle collisions per second requires enormous processing power. Distributed computing gives them access to enough processing power and storage capacity otherwise far too costly to build into one data center. The data can be accessed at greater speed by researchers wherever they are in the world now. Read more at:https://www.linkedin.com/pulse/big-data-uncovering-secrets-our-universe-cern-bernard-marr

The film Moneyball based on a 2003 book, talks about the Oakland Athletics baseball team whose focus is on the team's analytical, evidence-based approach to improve the baseball team despite its poor revenue system. This film made the plot for professional sport associations to spend big on analytics. Analyzing data points captured by video cameras around the stadium uncover patterns that coaches and players might miss. Data can also be collected by wearable devices to help prevent injury by measuring its likelihood. So, companies need to follow get up and move up to the major leagues of Big Data. But predictive analytics can decrease efficiency as supporting analytics platform records, analyzes and visualizes trends. Read more about this article at: http://www.tibco.com/blog/2015/05/26/how-big-data-analytics-are-transforming-sports/

With increasing number of data breaches, companies should now take necessary steps to combat it. Phishing, though a fairly old method, is the most common method of attacking companies. Emails which contain phishing methods should be identified, filtered and prevented from hitting inboxes. Spam filters are already doing this task to a great extent, but creating flagging system that can filter out the necessary emails through human logging works better. Systems should be upgraded to prevent from being vulnerable to malware and malicious cyber-attacks. Misuse of system and human error should be prevented to prevent hacking. The most important way of protecting system from attacks is by educating people who use it in the best ways to protect it. Read more at: https://channels.theinnovationenterprise.com/articles/data-breaches-aren-t-changing-but-you-should

Analytics are now being deployed by sports clubs to buy new players. With the huge amount of data being generated, selling clubs are now looking for ways to increase the chance of their players being bought for more money and buying clubs are looking for ways to assess the potential of new players. Analytics comes into play here. Clubs are now using data to assess the potential of new players, with efforts being devoted to developing data programmes to accomplish this task. There are several companies who offer this kind of service, but a significant number of clubs have outdone, these companies with their hiring process by carrying out deeper analysis and using metrics. Read more at: https://channels.theinnovationenterprise.com/articles/data-in-player-transfers-in-the-premier-league

Big data can be best understood using graph theory. Several open-source graph databases have come up and are used by several organizations. These databases are becoming popular as graphs are the best way for storing and querying huge amount of data. Graphs are being used by several domains of the industry as it provides competitive advantage. Social media uses graphs to provide accurate and personal recommendations to users. It is being used by a major online shopping company to compute fast and provide localized door-to-door delivery of goods. Graph databases store data safely and make querying it fast and easy. According to Dr. Marsten, creating and analyzing graphs will lead us to answers. Read more at: https://channels.theinnovationenterprise.com/articles/7132-the-key-to-understanding-big-data-lies-with-the-graph

Companies are mostly using Big Data solutions for log management. Sam Heywood (director of the Cloudera Security Centre of Excellence of Cloudera) said that "log data, intrusion alerts and other types of security-related information is a perfect fit for Big Data systems". Unlike the traditional approaches to collect the information, Big Data offers a platform which is scalable in order to collect the data and hence also cost-effective. It also provides the tools for long-term analysis which remains unaffected by the rules-based approaches. The necessary steps taken by the companies’ leads to an increase in security because they want to make sure that they are operating Big Data system in a safe and secure environment.

Read more at:http://www.cio.com/article/2935940/data-analytics/log-management-is-leading-use-case-for-big-data.html

Big data and big data analytics has been a trend setter in recent years. Unlike the traditional cause-effect analysis, big data analytics generate predictions based on huge volumes of data, using statistical tools and advanced computing techniques. An interesting case study on the use of big-data analytics was the prediction of a flu pandemic in the United States by simply analyzing Google search data. The spread of the disease was detected before any medical organization or national agency, simply on the basis of people frequently searching Google for the symptoms and remedy of flu. Big data analytics enables us to generate reliable analyses, even in the absence of clear links or causes. To know more read:http://www.forbes.com/sites/huawei/2015/02/24/how-do-big-data-analytics-enhance-network-security/

According to David Loshin (Knowledge Integrity Inc.)," big data and analytics is helping organizations to gather and analyze data in search of valuable business information and insights that can help them improve their products and services." Cost, Simplicity and Performance are the three factors helps to lower the barrier of entry for analytics and motivate organizations to adopt a big data analytics. The organizations which are planning to integrate analytics tools into their organizations need to have a data driven culture, recognize the potential of information. Key stakeholders are aware of the benefits of big data analytics and include agility in making decisions for adopting the technology. Types of big data to analyze:

• Transaction Data
• Human-generated Data
• Mobile Data
• Machine and Sensor Data.

Big data analytics tools help you in managing all these data with a reasonable investment. Helps you in analyzing these applications:
• Customer Analytics
• Sales and Marketing Analytics
• Social Media Analytics
• Cybersecurity
• Plant and facility management
• Pipeline Management
• Supply chain and channel analytics
• Prize optimization
• Fraud Detection

Read more at: http://searchbusinessanalytics.techtarget.com/feature/How-to-determine-if-big-data-analytics-tools-are-right-for-you