SigmaWay Blog

In today's world data is not only growing at a very fast rate but it is also being accessed and processed at unprecedented rate. So now, businesses have to focus on the velocity of Big Data, acting on it with precision for real-time results. This is where Fast Data comes in. Fast Data can provide real-time insights from events as and when they take place and help to make a decision at that place and point in time. Fast Data is complementary to Big Data for managing large quantities of real-time data. There are many examples of why Fast Data is becoming increasingly important. In a telecom industry, a Fast Data approach can help telecoms manage resources more effectively. In the financial services industry, Fast Data is using event correlation to contextualize available financial data. In retail industry, customer service centers are using Fast Data for click-stream analysis and customer experience management. Healthcare is another area where Fast Data holds huge potential. Read more at:

http://www.informationweek.in/informationweek/perspective/296992/speeding-business-transformation-fast

Research Company Gartner suggests that there will be 4.4 million big data jobs available in the next two years. Everything is moving towards data: big data, mobile datasets.  Creating and implementing an analytics program requires four steps: 1) Defining your metrics and developing a plan 2) Collecting the data 3) Developing reporting features and capabilities 4) Ongoing analysis and implementation. Understanding each of these core components enables a company to make the right investments at the right time. Corporate data culture is a spectrum that can often be classified as follows: 1)No or limited data 2)Basic data 3)Deeper data that's soiled or controlled 4)Democratic data access. The idea is that your strategy should take the following points into account: 1) Data collection and reporting. 2) Analyzing the data, articulating the implications for business. Useful data tracking comes down to evaluating: 1) who is coming to your site, and what are those people doing once they get there. 2) What channels are driving buying customers? 3)Who is converting 4)What conversions are deepening relationships 5)What conversions are driving revenue 6)Who is buying multiple times 7)What's your lifetime customer value 8)What are your churn rates. Any solid analytics plan will take your business model into account and develop a set of metrics that maps to your unique needs and buying funnel. Read more at:

http://www.forbes.com/sites/jaysondemers/2014/02/10/2014-is-the-year-of-digital-marketing-analytics-what-it-means-for-your-company/

Insurers are today trawling big data about customers’ finances and other sources. The development shows how access to big data is revolutionizing the insurance industry and companies are accessing a wide range of information on everything from financial probity to shopping habits of the customers to determine the risk premium for individual customer. One official of AA insurance company said that the winners in the insurance market will be the ones that have got the data insights that others don’t have. It could be supermarkets, banks or social media companies. However, ultimately the big data trend means some policyholders will pay more than others not for the climate change only but also for insurers to know more about the risks posed by particular properties. But, as with the sensitive data some companies also go far beyond what customers would expect them to do with it, customers should be given the option to opt in to such analysis than allowing them to opt out. Read more at:http://analytics.theiegroup.com/article/53c7a5c03723a8014f0000cd/Insurers-Trawl-Big-Data-For-Clues

Successful companies in 2014 are expected to take Big Data Analytics and utilize it for growth and competitive advantage. Experts discussed how to use data strategically for a strong analytics culture at the webinar "Fine-tune your analytics strategy for 2014". According to Stephen Sharpe, Director of Global Strategic Analytics of Johnson & Johnson, companies need to start working on a cloud based system to integrate and broader integration and training across the various sectors to share best practices. According to Larry Seligman, VP, Advanced Consumer Analytics, Inter-Continental Hotels group, companies need a new definition of data integration and they need to learn how to value projects and programs and quantify the things which were previously not quantifiable. According to Mazhar Hussain, Leader Big Data Practices, HP, gives insight on the importance of analytics as a service. Mid-sized and small customers will take great advantage of analytics as a service which is going to take up big stream and it is important to have the right kind of strategy implemented in the company. Read more at:

http://www.modernanalyst.com/Resources/News/tabid/177/ID/2915/6-things-to-focus-on-in-2014-analytics-strategy.aspx

Big Data analytic has the ability to improve business strategy leading to the identification, provide unprecedented insights and utilize untapped profit sources. The top Big Data trends that emerged in 2013 are Open Source, Convergence and Cloud Data. Companies have to synthesize analytic results across domains and work to produce holistic insights in order to achieve the maximum benefits. A variety of new and complex sources of data, extracting tools and new data mining have been introduced specifically designed to handle disparate sources of data. These systems can interact intelligently; minimize data movement and share data and/or analytic results between components. Overall analysis efficiency has been given a boost and producing better returns. Big Data analytic is based on the open source revolution and the adoption rate of open source technologies. Companies are increasing their overall spend on cloud infrastructure. The intersection of analytic and cloud is creating new value and means for Big Data exploitation. Read more at: 

The desire to be mobile and make a mark is not new to us. Today we use GPS for wherever we go and communicate on a variety of devices. Big data is the phenomenon which has helped in generating and sharing information. The difference between data in ancient times and now is that before, only humans created and collected data whereas now with the rise of sensors and other technology that creates and collects data. However, the big thing about big data is the self-organization i.e. without human intervention and awareness, data is organizing itself.  But, this leads to a big question that-Are we playing with fire? With big data revolutionizing, there comes a new responsibility, because the purpose of managing data is not to predict the future but to shape it which is a huge responsibility. However, revolution hasn't stopped. Changes took place slowly in the evolutionary manner. Using technology that provides insight into data, today's business leaders have a unique opportunity to make thoughtful decisions that will have long-lasting impacts. But along with all this, disruptive changes are happening in every industry around the world which increasingly making us concern about whether today's leaders rise to the challenge of shaping the future in a responsible way or not. Read more at:http://analytics.theiegroup.com/article/53c3f6413723a87216000156/Shaping-The-Future-With-Big-Data-Are-We-Playing-With-Fire

Cloud, social, mobile and big data technology have been primarily viewed as the drivers behind Customer Relationship Management (CRM). According to a Gartner report, they are now being joined by an emerging driver- the Internet of Things (IoT). According to Joanne M. Correia “CRM will be the heart of digital initiatives in the coming years”. In the report Gartner views that ioT, where sensors connect devices to the internet, create new services previously never considered. Gartner forecasts CRM market to reach $ 23.9 billion this year, with cloud revenue accounting for 49 percent. IoT joins cloud, social, mobile and big data to spur a critical need for more operational CRM, according to Correia. According to an article in Information Week, Gartner predicts that in 2020, there will be 7.3 billion smart phones, tablets and PCs in use- but about 26 billion IoT devices. IoT is driving CRM investments, because the use of cloud- connected intelligent devices is creating new business opportunities for marketing, sales and support executives. Read more at:

http://www.cio.com/article/2453781/big-data/big-data-as-a-driver-for-crm-investments.html

Big data contains consumer information including transaction data, demographic information, buying patterns, CRM data etc., collected across multiple platforms. The data gives an insight to the customers' preferences of support options, desired communication mode, future buying patterns, impactful promotions, etc. Big data provides better customer service in several ways. Using predictive analysis tools organizations can now predict a customers' next move also. Big data using these tools helps the organization to predict customers' present and future preferences, drive real-time decisions, increase customer retention and increase profitability. More than 77 percent marketers agree that individualized messages and offers are more effective than mass messages and offers, which can drive engagement, boost sales and increase conversions. Usage of Omni channel marketing strategy increases client retention rates and bring superior financial results. Using data to create a cross-platform customer engagement strategy ensures highest customer service. A multichannel shopping experience shapes a brand's story generating revenue and customer loyalty. In two years smartphone users will be more than basic phone users, mobile service increases at the rate of 7 percent annually. Thus best customer experience is delivered through mobile channels for high performance organizations. Unable to ignore the potentiality of social media big brands register tens of thousands of social media interactions every day. There are wide range of options available. Communication through online communities reduces call center costs. Read more at:
http://it.toolbox.com/blogs/insidecrm/5-ways-big-data-can-enhance-customer-service-62054

Importance of Big data and Talent Analytics is a much discussed topic for the last couple of years. One just has to know how to use talent analytics as it can do a whole lot of difficult task like quantifying different behaviors, skills, intelligence, and mindsets of a HR. Talent analytics uses data in management decisions like talent acquisition, retention, placement, promotion, compensation, and succession planning. By analyzing the skills and attributes of high performers in the present, it enables organizations to build a template for future hires. Advanced software algorithms can identify talent and match it to an organization's needs. Some intangible aspects like social skills, flexibility, emotional intelligence, initiative, attitudes are now measurable- thanks to talent analytics. Along with these, new mobile apps also make talent searches a matter of anytime and anywhere. However, as it's still growing, Gartner predicts that the market for Big Data and analytics will generate $3.7 trillion in products and services and generate 4.4 million new jobs by 2015. Read more at:http://analytics.theiegroup.com/article/53b2b9c13723a81923000046/Talent-Analytics-A-Crystal-Ball-For-Your-Workforce

Today big data is able to change the way real-estate professionals, buyers, sellers and even banks think about transactions involving property. Companies, promoting services that plug consumers into big data real estate information which gives a better education and insight.There are five key ways through which big data is changing the game of real estate business. They are- 1. Big data helps to democratize data for the real-estate customer. Companies such as Zillow combine big data with real estate and offer services like- Mining census information, the results of consumer surveys, listings of homes for sale and rent etc. 2. Big data is not only providing new information to consumers but also new ways of looking at developments and community planning. From that gathered information, real-estate developers can learn what kinds of spaces work best in terms of tenant health, energy efficiency and other points. 3. Institutions like banks are also able to plug into big data resources. 4. As an expert real estate advisor knows recent sales, incentives, and inside secrets to getting the best deal, buyers should have to be careful and should start property searching with the help of big data sites. 5. Finally, Big data let the professionals know what visitors are doing when looking for real estate online and they adjust their paid and organic efforts based on this data daily. Read more at:http://analytics.theiegroup.com/article/53be79af3723a84f1000003f/5-Ways-Big-Data-Is-Changing-Real-Estate

Many companies are discovering that big data can also be used for security by offering a broader view of risk and vulnerabilities. In today's complex network environments, Advanced Persistent Threats (APTs) and other cyber threats can be wiped out by leveraging intelligence from data providers. Six ways for using big data to wipe out cyber threats include DNS feed which can provide lists of newly registered domains, domains commonly used for spamming and newly created domains, which can be incorporated into black and white lists, C2 systems which provide black lists of IP addresses and domains, threat intelligence which may be used to help determine if an address is safe or not, network traffic logs help to log all of the networks traffic  or even just parts of it, honeypots can be effective in identifying malware targeting a particular network and finally data quality is important to call attention to the data feed itself. By utilizing big data organizations can create more robust threat and risk detection programs. Read more at

http://www.informationweek.in/informationweek/news-analysis/296971/tips-wipe-cyberthreats/page/2

 A McKinsey study last year stated that companies using Big Data and data analytics effectively show high productivity rates and profitability. But implementation of data analytics is challenged by lack of skilled manpower. Recently many B-schools are stepping up and have emphasized on advanced analytics techniques such as 'Clustering', however very few institutes teach Big Data technologies like 'Hadoop'. The hiring companies which look for data analytics include digital companies, ad agencies, and IT companies and so on. Internationally a vast number of management schools including New York University, University of Texas at Austin, Georgia Tech have had Analytics certificate programs for the last five years or so.Read more at

http://www.business-standard.com/article/management/learning-the-abc-of-big-data-114062900615_1.html

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/

Big data, the huge amount of structured and unstructured information will slow down most traditional approaches to data management. Cloud providers such as Amazon Web Services are contributing powerful, profitable approaches to analyze big data. Analytics can make organizations "self-healing" or "self-optimizing."The AWS services directory has a mixture of SQL and NoSQL database technology. Amazon DynamoDB is a managed NoSQL database service has a single-digit millisecond latency that makes it a fine fit for big data project where fast line with the data is a must.  The first difficulty need to think in big data analytics is data amalgamation. Amazon Relational Database Service (RDS) is a relational database that can sort in the AWS cloud. Amazon Redshift  (Oracle), which is a petabyte-scale database intended for big data analytics and data warehousing. Finally, Amazon Elastic MapReduce is a Hadoop file system structure on Amazon Elastic Compute Cloud decrease queries. Read more at: 

http://searchaws.techtarget.com/tip/Whats-what-in-AWS-big-data-analytics?asrc=EM_ERU_30984098&utm_medium=EM&utm_source=ERU&utm_campaign=20140630_ERU%20Transmission%20for%2006/30/2014%20(UserUniverse:%20932694)_myka-reports@techtarget.com&src=5265609

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