A metadata fabric that provides efficient data analysis and data driven decisions, is of great value to an enterprise which utilises IOT generated data. The metadata fabric presents data and analytics, together in a business consumable format and interface. The major types of metadata are maps, derivations and complex events. An enterprise’s first concern is the ability to create and store the metadata in a business friendly interface, which will also enable its exploration, its usage in data analysis and will accept updates with changing business trends. The metadata fabric needs to adapt itself to situations, where data values can change over time or appear in a fragmented manner or even encrypted, at times. The information, regarding the analyses performed by previous personnel needs to be a part of the metadata layer, available for successive employees. When users and systems are not able to search through, or update metadata, the metadata fabric is probably broken, and so it is, when data exists in disconnected islands. Read more at:

http://www.cio.com/article/2939114/data-analytics/the-grand-unified-theory-of-metadata-governance.html

Big Data is paving the way to the emergence of Smart Data. Huge, differentiated and big volumes of Big Data practically needs smart data for its everyday working because they facilitate -
• Unstructured and structured data aggregation and analytics
• Simplified and accelerated data modeling
• Access and data governance
Big Data is inexorably transforming into smart data. It is the preferred technology used in the diverse application of Big Data including the Internet of Things, Cognitive Computing, Semantic Graph Databases, Data Lakes and Artificial Intelligence.
The nature of Smart Data represents an insurgence in the logic applied to data driven processes. Big Data is important in Data Management as it has the ability to implement action from real-time analytical insight and consolidate all of one’s data in the process. Applications such as Internet of Things automate processes that would otherwise take too long. In context of Smart Data’s ability to increase the utility of Data Lakes is its ability to help clarify the sort of role-based access that is a pillar of proper Data Governance. Smart Data Modeling is preferred in analytics because there is a degree of flexibility and agility in the modeling required for Smart Data that exceeds non-Semantic Data. Along with its advantages for analytics, application development, data integration and Big Data Governance, Smart Data’s reconfiguration of transactional data will establish the fact that Big Data is surely evolving into Smart Data.

Read more at: http://www.dataversity.net/the-evolution-of-big-data-to-smart-data/

With the advent of Internet of Things (IoT), businesses can now avail additional ways of communicating with their customers. Sensors can be embedded in household appliances allowing consumers to control those which simplify household chores. Doctors nowadays can use apps to link patient statistics and provide personalized advice. Thus healthcare market makes ample use of IoT to provide better services. Security concern should be kept in mind as organizations are now deploying IoT in their enterprise. Organizations should also encourage application developers to maintain a certain standard to prevent poor codes from being deployed. Cloud technology, big data and analytics, mobile computing and social networking along with IoT makes a complete package. Read more at: https://channels.theinnovationenterprise.com/articles/deploying-iot-in-the-enterprise

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/

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/

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/

In 2012, analytics was applied in the presidential election of USA. They employed a strong analytics team who used a strong analytics database and prediction was predicted with R and SPSS. The presidential team made a mega file to find potential voters, get their attention, predict specific appeals to persuade people etc. Consumer Data determined people who funded for the campaign as fundraising was an important aspect of the victor. Read more about this article by Euan Hunter (Global Delegate Sales Executive) at:  https://channels.theinnovationenterprise.com/articles/taking-political-control-with-digital-analytics

According to the H.O. Maycotte (Austin Tech), Success in real time means collecting, assessing and acting on real-time customer data.” Ability to collect data and respond towards that data separates successful brands from the crowd. Steps to make real-time data and real-time response part of your operations, especially in marketing and customer service:
• Collect and consider real-time customer data in context.
• Look for context beyond the customer.
• Give mobile and social the central roles they deserve.
• Act fast.
• Anticipate your customer’s next move.
Read more at: http://www.forbes.com/sites/homaycotte/2015/06/16/5-ways-improve-customer-service-with-real-time-data-and-real-time-responses/

According to Narendra Mulani (senior managing director of Accenture Analytics), to survive in this competitive world organization need analytics to analyze opportunities and threats to their business. But now the question arises how to use analytics into their organization. Most of the organizations get stuck in applying analytics. So, the author discussed about the steps to simplify their analytics strategy and generate real outcomes. Some of them are:

• Accelerate the data.
• Delegate the work to your analytics technologies.
• Recognize that each path to data insight is unique.
Ways to delegate the work to your analytics technologies are: Next-Gen Business Intelligence (BI) and data visualization; data discovery; analytics applications; machine learning and cognitive computing. Read more at: https://hbr.org/2015/06/simplify-your-analytics-strategy

With the rising importance of big data, organizations are now collecting and storing data, but many don't put it to good use. Retailers can leverage customer data to make personalized recommendations about offers and promotions thus providing a shopping experience customized to individuals. Analyzing customer data can help companies to identify customer preferences for products and the prices they are willing to pay. Customer data can be used to identify the most relevant users to ask for feedback, create new products or services and provide better customer services. By analyzing customer data, companies can identify patterns of behavior of customer data and thus formulate targeted marketing strategies, improve organizational effectiveness and reduce risk and fraud. Read more at: http://www.cio.in/feature/8-ways-to-make-the-most-out-of-your-customer-data

A recent study has revealed that there is a growing gap between the amount of data that firms are gathering and their staff's ability to analyze it. A major difficulty that the companies are facing is that as the value of data analytics is being recognized the competition for capable analysts is also increasing. Employees are more attracted to companies with a pre-established analytics framework than to newly established ones. To mitigate this problem several companies are outsourcing their analytics work. But having data analysts internally is beneficial as it allows for easier transformation of analytical insights into business actions. Companies should partner with higher education institutes to initiate analytics courses to meet their needs. Firms should also recruit multi-talented analysts and also offer training to existing staff. Read more at: http://channels.theinnovationenterprise.com/articles/the-analytics-skills-gap

Companies now are emphasizing on converting data into action as quickly as possible. This is being done by unifying the databases used for operational applications with those used by analysts. This leads to continuous up gradation of models and adjustment of business functions as things are happening. Information is essential in a number of areas, targeted marketing being one of them. Due to the advent of online shopping and real-time processing of data, retailers are now being able to sell their goods at unadvertised low prices, without loss of profits, to customers who leave their website without purchasing the goods in their cart. In these cases, data handling and extracting data to deliver real-time actionable analytics is a challenge. Read more at: http://channels.theinnovationenterprise.com/articles/driving-real-time-decision-making-with-business-analytics

For start-ups and SMEs, Open Source (OS) software will be the most attractive option because it’s free and also there is no vendor lock in. So there are no contracts to break. The biggest advantage of OS software is that is gives us the option of operating, sharing, and modifying the software as we choose. Collaborators are able to access the software from around the world to share ideas and add features that could improve it. Its drawbacks involves low costs, which gets further complicated by a lack of support with OS software. Lyndsay notes four questions that needs to be answered in Wise’s book, Using Open Source Platforms for Business Intelligence: Avoid Pitfalls and Maximize ROI. Read more about this article at: http://channels.theinnovationenterprise.com/articles/is-open-source-analytics-software-the-right-option 

Big Data and analytics have a huge role to play in the future of healthcare. Data will play a major role in deciding how healthcare companies, practitioners and technologies interact with their patients. Big Data ensures efficiency and accuracy in predicting treatments. Using data, doctors can make more accurate predictions based on the specifics of the patient rather than using a general rule for treatment. Major companies are venturing into healthcare by formulating pre-defined algorithms; making it easier to cure complex medical conditions. With the advent of Big Data in healthcare, people will have the opportunity to keep a track of their health; doctors will have a better idea of somebody’s health thus allowing them to provide health advices on the go. Read more at: http://channels.theinnovationenterprise.com/articles/big-data-has-a-huge-part-to-play-in-the-future-of-healthcare

Big data, legacy data, operational data and streaming data - these are all impacting the ever-increasing volumes, velocity and variety of data. - Deepak Belur (In4Group). Data and analytics have become essential to formulate critical business decisions. The big data storage technology is capable of analyzing massive amounts of data. But, analyzing a big data rarely gives you a full picture. To analyze full picture, a company need to analyze “unstructured data” which is present in mainframe servers. This data can be analyzed by using performance analytics. Enterprises need to combine relational and non-relational data, open system and mainframe, to retrieve the combined information via a simple, single query or request. And they need agile data architecture. Timeliness, accuracy, usability and accessibility of data are critical factors in the successful decision making process. Read more at: http://www.itweb.co.za/index.php?option=com_content&view=article&id=143791

Data, guided by technology, always provides precision to a marketer’s intuition. While technologists favour conservative decision-making on the basis of comparative testing, marketers prefer taking risks and walking the extra mile. Now-a-days, as a company's profitability depends on its digital marketing capability, to a large extent, customer data and analytics, mostly decides a company’s success rate. On the other hand, technologists also facilitate in isolating the redundant tools and programmes in the system, thereby cleaning the system and reducing unnecessary expenditure. The right marketing technology makes the decision-making process less complicated thus proving once more, the importance of the relationship between marketing and technology.
Read more at: http://www.cio.com/article/2933479/marketing/how-to-strike-a-balance-between-marketing-and-tech.html

Sigmaway uses data analytics technologies to deliver business insights from your marketing data. Contact us at contact@gosigmaway.com for a demo.

Don't ignore your supply chain risks if you love your profits, that's the advice Keith Peterson, president and CEO of Halo, is giving in his article. He talks about:

• Various supply chain risks
• The department they affect
• Its financial impacts
• Finally! Solution. 

Solution to all the problems these days is in analytics. Right software and analytics programme will increase your profit generating abilities to multiple folds. Intrigued? Follow the link at:http://smartdatacollective.com/keith-peterson/322401/solving-supply-chain-risks-infographic

Data analysis conventionally involves a series of process of collecting data from several sources and then analyzing them after some time. Normally these data come with a expiry time i.e. there is a specific short time by which these data needs to be analyzed, processed and utilized. With development in IT world we can no longer wait for data to be collected and then analyzed and processed. The need of the moment is to shift the Big Data nearer to the data source. Thus the value obtained from the data is more important than the way it is collected. Read more at: http://www.tibco.com/blog/2015/04/14/data-analytics-how-to-be-advanced-pervasive-and-invisible/

The big data has penetrated into healthcare. But merely collecting the patient records and keeping them in the form of electronic health records (EHR) is not enough. It starts with using programming software languages like R to sort through the huge database. R is an open source programing software used for statistical computation and data mining. In this case, R can be used to interpret the EHR for different purposes like side effects associate with a particular drug among a typical set of patients with similar records and could also help identify what causes the side effect. R can be customized according to the needs of the patient and the physician and thus can be helpful in providing personalized healthcare .Also, since R is open source software, it involves no cost and a large amount of documentation is available to learn the language. Read more at:http://www.healthcareitnews.com/blog/radical-potential-open-source-programming-healthcare 

Need a big data solution for your project? The 3 V's of big data will bail you out. In an article by Paul Holland (Consultant), he talks about the 3 V dimensions of big data initially discussed in 2001 by META group (now Gartner) analyst Laney Douglas in a research report. Fourteen years hence, big data challenges and opportunities have acquired 8 V dimensions. The author exclusively focuses on the 3 V's:

• Volume
• Velocity
• Variety 

Volume talks about zettabytes of data generated every second. For example, if we look at airplanes they generate 2.5 billion terabytes of data every year. Velocity is about the speed with which data moves around. Unlike earlier times, data is created in real time or near real time. Variety is basically the different types of data. Gone are the days when data used to fit neatly in tables. Today 80% of world data is unstructured (text, images, videos, voice etc.). Read more at:http://makingdatameaningful.com/2015/05/26/characteristics-of-big-data-part-one/?utm_source=rss&utm_medium=rss&utm_campaign=characteristics-of-big-data-part-one