SigmaWay Blog

Business leaders understand the strengthg and usefulness of managing the improvement of business results through process improvement teams. There is no better approach to process improvement than the application of Six Sigma tools and methods. Moreover, the disciplines of project management and Six Sigma are substitutes but they are complementary. The similarities between project management and Six Sigma include:

• Utilization of established project management techniques to efficiently manage a project and project teams.

• Necessity of proactive project sponsors or champions to help ensure project success.

• Life cycle and phases.

• Need for a well-experienced project facilitator to lead the effort.

The year 2014 tends to be an important year where technology discovery will further build a future in which companies make data-driven decisions. The Top 5 data analytics trends that companies believe are going to rule the industry are:

• Data Visualization Goes Mainstream-Visual analytics allows business users to ask interactive questions regarding their prepared data sets which makes the whole process engaging.

• Mobile Data Marches to the Top- The top priorities for companies will be defining mobile metrics that matter, understanding mobile technology and collecting and analyzing mobile data.

• Analytics in the Cloud Grows Up- Innovations like cloud data warehouse platform from Amazon will gain importance  enabling fast and secure solution at very cheap prices.

• Predictive Analytics Takes Center Stage- the increasing demand for business users to examine data for decision making, provide the base for predictive analytics to gain significant ground in 2014.

Today companies have vast amounts of informations available to carry business moves. The General Mills and IBMs of the world now are using complicated-and expensive-Business Intelligence (BI) systems that can combine data together from a multiple operating systems and generate best reports that highlight not only what has occured and what is happening, but also what will probably happen. Can Small Business Benefit From Business Intelligence Software?

• It can help a small business compete with larger competitors or enhance market share.

• Vendors are getting experienced at making software that’s affordable.

• BI vendors are starting to educate a younger crowd. 

• The cloud now puts non-IT users in the driver’s seat. 

Master Data Management (MDM) programs often start with the analysis of the available technology and products but not the business problem they tend to solve. Three primary categories of MDM benefits are operational efficiency, better business intelligence, and regulatory compliance. The following are ten things one can do now to prepare the organization for  MDM initiative:

• Set goals and success criteria.

• Prepare a business case.

• Build a governance process.

• Choose the first subject areas to attack.

Analytics and big data are on the verge of scoring major points in sports. The following are eight ways data analytics can improve efficiency, accuracy and profitability in sports:

• Better Precision in the Strike Zone- In baseball, Pitchf/x technology from Sportvision has been set up in Baseball Stadiums to have a track on pitches.

• More Resources for Analytics Buffs- Statistic enthusiasts have a series of websites they can visit to see different aspects of specific games and plays.

• Data From Wearable Technologies- Adidas has a system called miCoach that works by attaching a wearable device to the players's jerseys to gather data.

• Live on the Field Data Collection- A Company; SportVU has six cameras in each NBA arena which gather data on the movements of the basketball 25x per second.

20-30% of the business decisions really need the use of advanced techniques like predictive analytics.   70-80% of marketing decisions can be judiciously carried with simple analytics techniques. A CMO broadly expects 3 key outcomes for his business:

• Bring more “future” customers in the most cost-effective manner.

• Convert those who come to the door into customers.

• Maintain the current customers “buying.”

Predictive Analytics need advanced skills and constant maintenance. A product manager or an operations manager equipped with the right “Data to Decisions” framework and easy access to data can optimize 80% of their daily workflow on their own, without having to depend on little and costly analytics resources. For 20% of decisions, where the potential ROI justifies the use of advanced techniques, they can work with their analytics counterpart. In summary, a smart CMO knows that a marketing team equipped with a “Data to Decisions” framework and easy access to data without the company of a data science team would emerge much better than a marketing team lacking data skills supported by a large data science team.  Read more at: 

Big data analytics has attracted the interest of the security community for its promised ability to correlate and analyze security-related data judiciously and at unprecedented level. New big data applications are beginning to become part of security management software because they can help prepare, clean and query data in incomplete, and noisy formats efficiently. Fraud detection is one of the best uses for big data analytics. One of the main results from big data technologies is that they’re supporting a large variety of industries to build affordable infrastructures for security monitoring. In particular, new big data technologies are enabling the analysis of large-scale, heterogeneous datasets at unprecedented scales and speeds. Now big data tools are improving the information available to security analysts by consolidating, correlating and contextualizing diversified data sources for longer periods of time. Big data tools are also particularly suited to become basic for advanced persistent threat (APT) detection and forensics. Hence big data is changing the landscape of security technologies for network monitoring and forensics.  read more at: 

http://www.infoq.com/articles/bigdata-analytics-for-security

Growth in social media networking with increased logging into Facebook and twitter accounts has been accounted to be about 37 % in 2013 according to trends in digital marketing, media and commerce. Though the global rate might be less the growth rate in India of social networking has been high especially in urban areas. India has recorded the second highest number of Facebook users and is likely to become the largest number of Facebook users in 2016. A survey by Internet and Mobile Association of India found that there has been a 50% increase after 2012 in Facebook use, 33 million in Twitter and 20 million on LinkedIn. Social media being the next being frontier is definitely unavoidable and has become an imperative part of a holistic marketing strategy benefiting businesses reducing costs and improving customer services. Corporate and B-schools give latest promoting strategies and marketing strategies through social media. Read more at: http://www.hindustantimes.com/technology/socialmedia-updates/india-records-highest-social-networking-growth-study/article1-1244761.aspx 

There has been a transformation and a creation of positive impact on the social and economic relationships across the stakeholders. Thus companies today need to treat Big Data and analytical tools as an asset. However data presents inherent challenges in adoption. Companies are grappling on how to contextualize information, and presenting the data in a lucid manner is another challenge. Without making inroads into old processes and investing in the right kind of people to tap into data's hidden potential, a company can never fully realize the scope data offers. According to several research studies, Big Data is a top business priority that can transform processes and organizations. Thus a good data miner always finds a right balance between machine and man. Data visualization is another important tool to help management and reflect how businesses and markets are changing. To know more, please follow:

http://www.informationweek.in/informationweek/perspective/297284/-challenges-opportunities

Nowadays we are witnessing an enormous data explosion which is set to continue and even accelerate. The volume of data is growing at a very high velocity and is rapidly becoming more varied, complex and less structured. As a result, the word Big Data has grown strong on the mind of every business leader who wants to extract critical insights and business benefits from data. Many organizations are planning to implement Big Data related initiatives or have got them already. However most organizations lack an articulated strategy for Big Data execution. Thus there is a strategy gap between high potential and risk about investing in Big Data initiatives. Although, the essential mix of technologies may deliver on the promise of Big Data, what leaders must choose and incorporate for interlocking the set of available data sources and technology is a specific business goal which makes the initiative unique. In response, Big Data providers have prepared a strong ground through the use of cloud computing at the core which address these issues. Organizations can analyze the feasibility and cost of investing. Termed as "Big-data-as-a-service" (BDaaS), it basically refer to services that provide analysis of enormous or complex data sets, typically over the cloud platform as a managed service. The adaptation of Big Data on above grounds precedes with Hadoop which was a major stepping stone, but it still has its own limitations, specifically for Small and Medium Businesses (SMBs) that do not have the resources to create a Hadoop infrastructure in house. Thus, on a conclusive note, It's difficult to predict which Big Data solution businesses will freeze on, but having a majority of Big Data service providers now providing a version of their platform in the cloud, it will emerge a safe bet for SMBs to venture, in wherein cloud will play a major role in building Big Data an integrated part of their business strategy. Read more at:

http://www.informationweek.in/informationweek/perspective/297257/cloud-leveling-playing-field-smbs

There is a blending of the system administrator and network administrator roles as a result of emergence of cloud computing and software defined infrastructures. Historically, the demarcation of the two, happened at the RJ-45 socket on the NIC. Anything that involved the cable, switches, routers etc. was not the concern of the sysadmins. Likewise, if the light was blinking on the NIC, anything happening inside the box was not the problem of network admins. Now, sysadmins, particularly application administrators, must now be cognizant of network technologies and operations, network admins who want to keep networks in top shape must now have an awareness of what application traffic is flowing across the network and how to design and implement networks to support those needs. Thus the roles are merging and in the near future, they both can be together known as "cloud administrators". Read more at: http:

http://www.informationweek.in/informationweek/news-analysis/297170/rise-cloud-administrator

Social media is an ever changing scenario where social media marketers are increasingly using predictive analysis to ensure longevity. Various brands are using predictive analysis technologies to trawl through social media chatter to identify upcoming trends. It will also ensure that your brand be one of the first few to take advantage of the trend and gain maximum exposure. Your social media campaigns will also be much more refined compared to those of brands that don’t use predictive analysis. With predictive analysis, your brand will be able to pick out the right news, items, etc. that could become the next big thing on social media landscape, giving you ample time to prepare. Now smart brands are realizing that predictive analysis can be used in social media marketing to understand what consumers are looking for. Predictive analysis tools ensure that brands understand consumer behavior on social media.

Read more about this article at:

http://www.simafore.com/blog/bid/205332/How-Predictive-Analytics-Can-Boost-Your-Social-Media-Campaigns

Many investors think whether or not investing in stocks is worth. Inspite of the real problems, the following common myths often arise.

 • Investing in Stocks Is Just Like Gambling.

Investors often mistake that stock represents the ownership of a company. Gambling on the contrary takes money from a loser and gives it to a winner.

• The Stock Market Is an Exclusive Club For Brokers and wealthy People.

Most brokers are notoriously inaccurate. Furthermore, the discovery of the internet has made the market much more open to the public.

While some industries are beginning to see the transformational capacity of big data and predictive analytics, these methods haven't quite panned out for supply-chain managers. The reason is that the largest obstacles happen to be the cost of hiring experienced employees. Researchers Matthew Waller and Stanley Fawcett write in a paper that the convergence of predictive analytics and big data has the capacity to change the way in which supply-chains managers lead. The goal is to increase the understanding of how to utilize big data efficiently and develop a new breed of supply chain leaders that are experienced in using data and analytics judiciously. A recent Wall Street Journal article quoting a survey by The Economist points out that while most companies see the value in using predictive analytics and big data to eliminate increasingly complex issues within their supply chains, they still perceive the cost of deployment as too high.Read more at: 

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

Over the past two years, one of the seminal issues regarding Big Data was storage, especially with respect to the exponential growth and size of unstructured data that did not fit into databases. Today, however, the competitive landscape is very different. Proper storage is merely a pre-condition to finding the real jewels in Big Data-turning data from massive streams into knowledge, and thereby actionable intelligence in real time as events unfold. The following five steps are imperative to master Big Data and drive business growth:

1. Infer, Infer, Infer- Inferences transform data into knowledge, which results in greater process transparency and improvements.

2. Empower a C-Level Data and Predictive Analytics Champion. - With big data analytics changing rapidly and straining information structures, corporations and governments need “executive horsepower” behind its data initiatives.

3. Assess And Modify Your Supply Chain In A Multidimensional Global Context. - Analysis of supply chain will ultimately include relationships with parties such as customers, manufacturer, etc. 

4. Give Your Data Time-Critical Situational Awareness. - Analytics help a business line identify potential points of improvement.

Sales and marketing platforms can easily be combined to generate a closed-loop approach to marketing analytics whereas business analytics understands the structure of the past to estimate what might happen in the future. Through marketing analytics, marketers can easily measure all aspects of their marketing efforts. Business Analytics can measure everything from warehouse efficiency and manufacturing. Business Analytics typically incorporate high level data from each department to gain knowledge of how the organization works. What makes marketing analytics different from other business analytics, is its concentration on real market output. Marketing analytics go beyond measuring strictly online performance and provide representatives from sales, customer service and senior business management with real market feedback that helps guide decisions on where to invest and how to prioritize. Thus marketing and business analytics are really a two way street - without marketing data business analytics wouldn't tell the whole tale, and vice versa. Read more at: 

http://blog.hubspot.com/insiders/marketing-analytics-vs-business-analytics

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There are many parts to a customer service strategy. The benefits of social media is overlooked. In today's world, an existence on social media is as important as having a toll-free phone number. Here are just a few of the reasons why. The benefits of two-way brand engagement. Customers like it when commerce shares information, instructions, thoughts, and humor across social media.  Social media an addition of contact data: clients remain for  reply to their comments or distress fast. Pinch complaints in the sprout with ease: ignoring  social media won't discontinue clients from talking on Twitter or Facebook.  Spot trends as -- or before -- they occur: Responsive brands pay close notice to what consumers are discussing about in order to mark movement before it's too late. have additional admission to customers: Social media conduit shouldn't be measured an publicity prospect. . Read more at:

http://www.socialmediatoday.com/content/how-social-media-impacts-customer-service

 Amazon CloudWatch Logs is a service that  helps us to watch, accumulate, and access our system, application, and practice log files from Amazon EC2 instances. e.g., we can monitor request logs for specific literal terms  or calculate the number of occurrences of a literal term at a particular location in log data (such as "404" status codes in an Apache access log). When the term we are penetrating for is found, CloudWatch Logs reports the data to an Amazon CloudWatch metric that we specify. We can then recover the linked log data from CloudWatch Logs if, for example, the count of "NullReferenceExceptions" exceeds its normal range. Following are the steps to arrange Amazon CloudWatch Logs with AWS OpsWorks. Step 1: Create Recipes. Step 2: Extend the AWS IAM Role.Step 3: Create an OpsWorks Stack. Step 4: Add a Layer. Step 5: Add an Instance. Step 6: View your Logs. Read more at: 

http://blogs.aws.amazon.com/application-management/post/TxTX72HFKVS9W9/Using-Amazon-CloudWatch-Logs-with-AWS-OpsWorks

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Data analysts want data in their data warehouse. When transaction-optimized databases were used for warehousing analysts would replicate data from manufacture systems into the data warehouse at the cost of change throughput. Analytics data warehouses are usually loaded numerous times each day, but often clients want to revive data every hour, every minute. This post outlines best practices for using Amazon Redshift for micro-batch loading and is planned for data architects, data modelers, and DBAs. Any data model can be laden via micro-batching, but data prepared as a time series is often ideal for this type of loading. To make the COPY command as capable as probable, ask it to do as little as promising . Read more at:

http://blogs.aws.amazon.com/bigdata/post/Tx2ANLN1PGELDJU/Best-Practices-for-Micro-Batch-Loading-on-Amazon-Redshift

Many healthcare executives consider that, due to the insightful nature of remedial care, patients don’t desire to apply digital services apart from  a few exact circumstances; conclusion makers frequently quote data that indicate to reasonably small use of digital healthcare services. The consequences of their survey disclose something quite unlike. The cause patients are reluctant to accept digital healthcare is primarily since accessible services don’t meet their wants or because they are of deprived quality. All the countries in the survey, more than 75 percent of like to use digital healthcare services, till those services meet their requirements and supply the level of superiority they anticipate . Of course, no digital channels will keep on to be significant and essential, so digital channels will have to be fixed through multichannel idea. Read more at: 

http://www.mckinsey.com/insights/health_systems_and_services/healthcares_digital_future