SigmaWay Blog

Today, a smartphone app would tell one what to eat, how much to eat, when to visit a doctor etc. based on analysis of medical research, medical history of that person, family medical records etc. Quality of our health will be increasingly improved by the quality of data and the ability to bring it all together. The growth of big data in the health industry will only take place once privacy concerns are addressed because health data, unlike the marketing data, is lot more personal. Big data analysis is already being used to make diagnoses in some hospitals. In Canada, Toronto Hospital uses big data to detect blood infections in premature babies. It could save the American health care system $300 billion per year and the European public sector €250 billion, according to a 2011 report. Doctors today are using Watson, IBM's supercomputer, to keep up with health research and to leverage the latest breakthroughs. Big data analytics also could be used to follow epidemic outbreaks. For example, Big Data enabled doctors and scientists to learn so much about the Severe Acute Respiratory Syndrome (SARS), and how quickly it spread, within weeks of the World Health Organization's initial warnings. In such case social networks and mobile data are used to ensure the delivery of real-time information. Read more at:http://analytics.theiegroup.com/article/53b6b4c43723a83b82000035/Big-Data-Could-Help-Your-Health-If-You-Let-It

Generally people think that the role of a data scientist is just to examine the relationships between diverse sets of data as well as the disparate systems, processes and locations which store them. But the role is actually mature across certain sectors like retail. With the help of this, Amazon, for e.g., is able to analyze the behavior across multiple accounts, and knows exactly when and why to push a certain product to a customer. But the case is somewhat different in financial services where the role is not properly organized. Though Big Data analytics is used across the retail banking industry from fraud and sanctions management to improving account management processes, analysis of Big Data provides the potential for banks to create new income streams and the sector as a whole is benefitted when it comes to deriving value from vast quantities of information. Thus financial services, in spite of having people with good skills to do modeling and statistical analysis, need people who are able to spot key trends and focuses on looking for the relationships between data across disparate sources. Once these two skills are combined, the financial sector will start to see the rise of data scientists in it like other industries. Read more at:http://www.banktech.com/business-intelligence/piecing-together-the-data-scientist-puzz/240168604

According to the Business Technology Innovation Research, analytics is the topmost priority. Three key core areas comprise 2014 analytics research agenda. The first consists of a definite focus on business analytics and methods like discovery and exploratory. The people and process aspects of the research include how governance and controls are being implemented along with these discoveries. The exploratory analytics space comprises business intelligence. Value indexes, mobile business intelligence and business intelligence will provide deep explanations and ranking of vendors in these categories. The area of second agenda is big data and predictive analytics. The first research on this topic will be released as benchmark research on big data analytics which explains vendors of software that helps organizations do real-time analytics against vast data. The third focus area includes information simplification and cloud-based business analytics including business intelligence. Thus, Analytics as a business discipline is getting more importance as we move forward in the 21st century. Read more at: 

http://tonycosentino.ventanaresearch.com/2014/01/23/business-analytics-in-2014-trends-and-possibilities/

Manufacturing industry, in the past 20 years, have been able to reduce the quantity of wastage and the variability in the production process and improve their product quality after implementing Lean and Six Sigma programs. However, extreme variations are found in certain processing environments. Thus manufacturers need a better approach that would remove such flaws and advanced analytics helps in this way.  In manufacturing, managers use advanced analytics to identify patterns of data, relationships among discrete process steps and inputs and then optimize the factors that greatly affect the yields. Advanced analytics also helps to increase yield. Manufacturers that want to use advanced analytics to improve yield, consider how much data the company has at its disposal, as their first step. Some companies have too little data to be statistically meaningful and the challenge for these companies lies in taking a long-term focus and investing in systems and practices to collect more data. Advanced analytics and big data forms a critical tool to realize improvements in yield. Process complexity, process variability, and capacity restraints are present in such manufacturing environment. Read more at:  http://www.mckinsey.com/insights/operations/how_big_data_can_improve_manufacturing

Some strategies haven't succeeded dealing with supply-chain management. The reason is the cost of hiring expert workers. According to researchers the union of data science, predictive analytics and big data likely to alter the way in which supply chain managers lead and supply chains function. They named this as DPB. Companies have used datasets to plan ideas to meet customer demand. But now they combine external data to better estimate future risks .two points to judge analytic skills: 1) Data science and domain expertise are not mutually exclusive: Analytical skills are important for data scientists who focus on Supply Chain Management (SCM).2) that doesn't mean theory doesn't apply: Strong theoretical knowledge is essential in SCM. Use of suitable theory to build models before operating predictive analytics is key to justifying a circulation of false positives. The three links in supply chain: manufacturers, retailers, supply management, shipping management and human capital. Read more at: 

http://sloanreview.mit.edu/article/are-predictive-analytics-transforming-your-supply-chain/

A recent survey by The Economist Intelligence Unit found that those who were polled believe that customer service and marketing is expected to gain most from analytics, but customer service is unprepared to deal with big data. Proper analytics of the brand will enable business houses to understand how their business is performing, whether they need any improvement or not.  An October 2013 Gartner report, Market Trends: Leveraging Analytics in Vertical Industries, identified four major categories of analytics: Descriptive, Diagnostic, Prescriptive, Predictive. Three ways in which analytics is transforming customer service: Analytics is encouraging organizations to break down barriers

Analytics is changing perceptions of important measurements

Analytics is allowing brands to get ahead

To know more about the three ways in which analytics is transforming customer service, go to:https://blog.zopim.com/2013/11/20/3-ways-analytics-transforming-customer-service/

Cloud computing includes using a network of remote servers hosted on the internet as opposed to a local server. This helps schools and educational systems to manage their content in a simpler way and it helps to cut IT cost. In fact, it is said that within one year, cloud computing in K-12 schools is expected to consume a quarter of the entire IT budget; four years from now, that figure will grow to 35 percent. Benefits of cloud to students and educators:

Storage: The Cloud allows its users to store almost all types of content and data including music, documents, eBooks, applications, photos, and much more

Accessibility: Any data stored in the Cloud can easily be accessed from almost any device including mobile devices such as phones or tablets.

Collaboration: Because the Cloud allows multiple users to work on and edit documents at the same time, it enables effortless sharing and transmission of ideas.

Resource and Time Conscious: With the availability of content online, it is no longer necessary for teachers to spend time and resources printing or copying lengthy documents or lesson plans.

Assignments: Cloud allows teachers to post assignments online. Students are able to access these assignments, complete them, and save them in a folder to be reviewed later.

See more at:http://www.pearsonschoolsystems.com/blog/?p=1507#sthash.Mmt9pOFt.dpbs

In-order to perform better and earn more profits a company should take help of data analysis and CRM analytics to find correlations, patterns, and find out trends that will serve up the type of information to tailor customer experience. According to an article by Marianne Cotter at CRMSearch. According to Forrester analyst Kerry Bodine “Despite its economic power, customer experience remains the most misunderstood element of corporate strategy today,” In a soon to be published book called “Outside In,” Forrester Research argues that customer experience is a fundamental business driver. Five reasons to integrate big data analytics to CRM are:

• Better customer understanding

• Better understanding of the customer-facing operations

• Decision support

• Predictive Modelling

• Benchmarking

To know more about the five reasons to integrate big data analytics to CRM, go to:http://spotfire.tibco.com/blog/?p=12660

While CRM applications officially gather terabytes of helpful client data for organizations, significant deeper insights are also en route because of a creating new pattern of predictive analytics capabilities being integrated into CRM. The huge draw is that organizations will have the capacity to utilize existing CRM information to tremendously enhance basic one-on-one associations with clients. An alternate key profit is that it will help organizations create extra deals when clients reach them by breaking down approaching client information progressively. 

It's the same thought with CRM that incorporates add-on or implicit predictive analytics when a potential client arrives at your company's Web webpage to make a purchase. In the event that a client is offered this item at this cost at this point, would they say they are likely to purchase it? One can make a targeted offer to a client focused around what they are looking for. The probability that they acknowledge that offer will figure out whether you can augment client maintenance, deals and benefits. 

As these sorts of predictive analytics features are presented, organizations will need to evaluate their methodologies to joining the right parts into their own particular foundations. That will take research, detailed inquiries and discussions with teams from marketing, IT and other departments, as well as market research and more. It's not something one will be able to jump into with little thought. One ought to know his objectives before he make the first strides so he can attain sufficient payback from his investments of time and resources. To read the full article visit: http://www.cio.com/article/2371968/customer-relationship-management/how-predictive-analytics-will-improve-crm.html

Is our privacy at risk?- This is the most discussed question in today's world of big data where companies like Google is able to capture data from homes and offices as well as video footage for storage anywhere in cloud, provided by Amazon Web Services. According to Danielle Hughes of Divine Capital, computers are learning to interact with one another and this is raising concerns. Though, it is true that people are living in the post-privacy world today as younger ones have no issues in sharing their personal information in social media. But, Hughes thinks, this is the beginning and in the future machines will start to teach other machines and tell back the information analytically to big companies. She also concludes that it will not lack investments in future, as for example IBM is already projecting $20 billion in revenue from big data in 2015. Read more at:http://analytics.theiegroup.com/article/53ac20613723a8031500002b/Our-Connected-World-Big-Data-Big-Profits-And-No-Privacy

2014 is expected to be the year of cheaper analytics. Along with the announcements of new features from some SaaS providers like Gainsight, gShift and pricier systems from Adobe, IBM, Oracle etc., a firm Rival IQ has recently released the beta version of a new SEO analytics features which is now included in all the company's service tiers and its service includes analytics for websites and social media. It might also provide an easier approach for marketers who are now technically backward. About 1,000 companies are now the users of digital marketing/analytics. One of the officials of HP's cloud computing division has started using Rival IQ and said that they are trying to provide enough data across all the different areas at a very cheap rate so that one can easily grab charts, and see reports from one place and then export everything to PowerPoint, CSV or PDFs. The landscape features of this SEO analytics also allow users to see what their competitors are doing in social and SEO and can be used to research best practices for a whole area like e-commerce. Read more at:http://www.cmswire.com/cms/customer-experience/a-cheaper-alternative-to-marketing-analytics-025687.php

Most shopping websites, regardless as to whether they are auction based like eBay or are just one sprawling marketplace like Amazon, tend to prominently feature a list of recommended products on their homepages. These lists are the results of their product recommendation engine.

They work by taking into user preferences or your preferences for that matter and then correlate it with the products and services available on the site. Needless to say, product recommendation engines naturally enjoy access to the entire product and service database of the website. Information is filtered with your preferences in mind and, afterwards, the product search engine comes up with a list of products and services that it considers likely to appeal to you.

Predictions made by product recommendation engines are not only based on the description of the product and service but also on whatever information it can obtain from your own social environment and previous web history. It first gains access to a pool of users and collects data based on their behavior online, their activities, and their preferences. All the information collected is then filtered and submitted to a platform which categorizes them into products that a group of users may like or dislike. When you visit the site, the first thing it will do is to determine which group of users you belong to. From there, it will provide recommendations on the assumption that your tastes are similar to users it had studied in the past. To read more: http://www.aboutdm.com/2013/01/product-recommendation-by-amazon.html

Big Data is about large volumes of unstructured data along with rapid analysis with insights being noted within seconds. Big Data allows narrower customer segments and help in tailoring precise products and services which will then allow for companies to develop the next-generation products and services. The fact is that Big Data requires more capacity, highly efficient accessibility. It would require scale-out or clustered storage systems - such as scale-out NAS (Network Attached Storage) which can scale out to meet capacity and uses systems which are distributed across many storage devices and can handle billions of files without degradation of performance. Big Data using Hadoop stack has been gaining acceptance widely. Also, organizations which create and store more transactional data in digital form can collect more accurate and detailed performance information on everything. RAID-based storage systems have huge storage capacity but not necessarily what Big Data requires and RAID based systems cannot protect data from loss. Thus, most IT organizations incur additional costs which use RAID for Big Data storage as they need to copy it two or three times to protect it from loss. Read more at:http://www.informationweek.in/informationweek/perspective/296730/environments-object-storage

Apart from external threats that disrupt law and order, public security officers in India also face challenges within an organization. Lack of interoperability and exchange of information leads to weaker response time and fewer positive outcomes. Technology can help to overcome these problems with better integration and management of operational process and data. A single integrated information stores the data from various sources on people, objects, events etc. which in turn leads to efficient resource management. However, law enforcement agencies in India lack a robust IT infrastructure which can help them deal with threats to public security and safety. Ability to capture and mine huge amount of data from diverse sources actually leads to success of a big data project. To analyze and make fast yet accurate decisions, all intelligence sources, law enforcement and other public security agencies gather a large amount of data and employ specialized analysts to make sense of raw intelligence gathered from different sources. Using Big Data, intelligence agencies can analyze and make sense of this huge volume of structured and unstructured data, in real-time speed and it enable agencies to identify relationships and helps to detect, avert & resolve crimes. Read more at:http://www.informationweek.in/informationweek/interviews/296242/help-enhancing-indias-national-security.

Big data gives governments tools to discover more effective and innovative ideas on how to decrease poverty across the world. Proper application of big data and technology advancement can improve the circumstance of people currently impoverished. Before, collecting reliable data from all corners of a country can pose as a hindrance in the complete elimination of poverty but now this problem is solved by the advances in mobile technology. Reportedly, more than 85% people in the world have access to mobile phones, even if some of them are the poorest people which lead to greater data collection on the poor and availability of big data acts as a catalyst to help the poor people prosper. Data collected from cell phone call data records and an individual's bill payment history banks and other financial institutions can effectively evaluate a person's ability to pay back loans and determine their risk attitude. This helps the institutions to give credit to the required people through micro financing. Read more at:

http://www.bigdata-startups.com/5-applications-big-data-in-government/

CRM (customer relationship management) analytics comprises all programming that analyzes data about an enterprise's customers and presents it so that better and quicker business decisions can be made. CRM analytics can be considered a form of online analytical processing (OLAP) and may employ data mining. As web sites have added a new and often faster way to interact with customers, the opportunity and the need to turn data collected about customers into useful information has become generally apparent. As a result, a number of software companies have developed products that do customer data analysis. According to an article in InfoWorld CRM can provide customer segmentation groupings, profitability analysis, personalization, event monitoring, what-if scenarios and predictive modelling. One of the major challenges implicit in CRM analytics is how to integrate the analytical software with existing legacy systems as well as with other new systems. To know about CRM analytics go to: http:http://searchcrm.techtarget.com/definition/CRM-analytics

Intelligent processing of data is essential for addressing societal challenges. Data could be used to enhance the sustainability of national health care systems and to tackle environmental problems by, for example, processing energy consumption patterns to improve energy efficiency or of pollution data in traffic management. 

To make the best use of big data now and in the future, government must have the right infrastructure in place. It advocates a data force, based on the successful nudge unit, to access data from different departments and identify where savings could be made. New data science skills will be needed across government, but it is more important is ensuring that public services leaders are confident in combining big data with sound judgment.

Closely linked to government's drive to make better use of big data, is its drive to make data open. Open data, particularly that in the public sector, is often big data – for example, the census, information about healthcare or the weather – and making large government datasets open to the public drives innovation from within government and outside. See more at: http://fcw.com/Articles/2013/09/25/big-data-transform-government.aspx?Page=1

Normal 0 false false false EN-US X-NONE X-NONE

Three ways analytics can change customer service: (1)Barriers to the organizations are to be broken down as encouraged by Analytics: The multi-channel analytics is causing many brands to call their customer service technologies and processes frequently to break down data vaults and multi-channel support and reaction is centralized to avoid repetition of customer information. say of the customer, a 360-degree vision of the customer, makes analytics more convenient and insightful. (2) Changing awareness of chief Measurements by Analytics: Organizations are deviating from old methods such as rapidity to answer and quantity of calls handled per hour, and moving towards more important methods such as overall client pleasure, first call decision. (3) Allowance of analytics to   move ahead: Once the right analytical reports are collected organizations use them to improve their services and support. Using analytics an organizations can estimates what customers are wanting and expecting from their services. Read more at:

http://www.parature.com/customerservice-analytics/

Enhanced e-commerce is a trending reason nowadays for the uprising online retail shopping. It includes tracking code updates, data model changes, and new end-user reports that address many ecommerce-specific use cases. Together they help online retailers see farther and understand customer behaviors better than ever before. According to reports, online retail grew above 30% in year 2013. Digital data has played an essential role in that growth offering deep insights into online shopping behavior and letting retailers make smarter moves. But needs are rapidly increasing and retailers are requiring more sophisticated and comprehensive analysis tools to understand shoppers and improve product-level performance. We should analyze how far shoppers get in the shopping bag and understand which products are viewed most and which are frequently abandoned in cart. We should also create product lists for onsite merchandising rules, product landing pages to see which lists and products are best at driving customer engagement and analyze how internal promotions impact sales and act immediately on the results. Read more at: 

In the recent past, most companies have recognized the bottom-line impact to be gained through effective pricing. Tapping the full promise of pricing requires an infrastructure to drive real and sustained pricing performance. With such a foundation, a company can establish and strengthen pricing activities by creating deliberate decision processes, a specialized pricing organization, mechanisms that appropriately measure and reward pricing excellence, and vigorous support tools and systems.

A pricing infrastructure can be difficult and costly to create. It requires investing appropriately, empowering the right people, articulating clear targets and goals, and managing risk. Yet the benefits of realizing true pricing excellence are worthwhile: a one-percentage-point improvement in average price of goods and services leads to an 8.7 percent increase in operating profits for the typical Global 1200 company. 

Every company should have a set of pricing metrics that measure the financial and operational health of pricing across the business. These metrics may include simple data, such as the average selling price, discount, and margin for key products; operational data, including the number of pricing exceptions and win/loss percentages; and special measures to track the progress and impact of specific pricing initiatives. While the manager of a single product line may see metrics only for that, the general manager of a business unit sees those same metrics across the operation and can drill down to the level of individual products to understand the root causes of pricing performance.

Without uncovering and acting on the opportunities big data presents, many companies are leaving millions of dollars of profit on the table. The secret to increasing profit margins is to harness big data to find the best price at the product—not category—level, rather than drown in the numbers flood. To read more visit: http://www.mckinsey.com/insights/marketing_sales/using_big_data_to_make_better_pricing_decisions