SigmaWay Blog

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

Social media and networking sites are helping businesses and companies to grow. The social media not only helps as a platform to communicate with the customers and build up a brand reputation, but it also helps the way business is run. It also helps business enterprises to collaborate across departments, offices, countries, and with other business houses as well.  According to recent studies, social media analytics along with predictive analytics is going to be the most effective technology for business development. Its impact will be greater than internet of things and mobile payments. So embracing this aspect will be necessary for a business to survive.To know more read: http://www.cio.com/article/2937401/social-collaboration/how-collaboration-tools-can-turn-your-business-into-a-social-enterprise.html

In business, one not only has to keep a track on competitors and a close watch on the market but also keep a tab on what the customers are feeling, and how they are responding about their products in social media. Luckily with the advancement of data analytics many such tools are available and are becoming popular. One such marketing tool is social mention, which is a search engine which searches any social mention of the topic in social media there are other tools such as quora, tweetreach, etc. These platforms enable a business leader to evaluate customer sentiments and use it for business development. Read more at: http://www.huffingtonpost.com/jayson-demers/7-marketing-tools-to-find_b_7605262.html?ir=India&adsSiteOverride=in

As more and more data pours in, marketers need to keep adjusting their strategies to be updated in this dynamic ecosystem of information. Thomas Begin, vice president of strategy in Targetbase, in his special guest feature in Inside Bigdata talks about the analysis paralysis in times when many organizations are turning to consumer centric strategies. He points out the major lags in this transformation:

• Inadequate internal collaboration

• The inability to act quickly

• More focus on reporting rather than action. 

To have a look at the recommended solution to these lags follow the link http://insidebigdata.com/2015/05/05/keeping-big-data-small-to-create-engagement/

Supply chains help in harmonizing and maintaining the balance between the production and delivery of those goods to the customers. The Internet of Things (IoT) helps in optimizing the supply chains for better profits in a faster and more efficient manner. The IoT provides companies with improved market opportunities. With the help of IoT the usage of the products by the customer can be tracked and accordingly the supply chains can be used to deliver the supplies on time rather than when requested. Change in the market can be observed very quickly and thus they can respond much faster and gain benefits.
Read more at: http://www.industryweek.com/supply-chain/internet-things-iot-opportunities-smarter-supply-chains

With IoT becoming the norm rather than the exception, there is an overwhelming amount of customer data at the disposal of companies. But with huge data comes huge challenges. The challenge of combining, cleaning and shaping data before the analysis. Cari Jaquet, vice president of marketing at Paxata, in a special guest feature in Inside Bigdata talks about why data preparation solutions are important to make data more consumable. According to a recent Gartner report, it has been advised to use simple data preparation tools to first, save analysts time and second simplify the task of data preparation from diverse sources. For a deeper insight into data preparation follow the link  http://insidebigdata.com/2015/06/19/data-preparation-the-key-to-unravelling-the-big-data-opportunity/

There is huge amount of data and searching for ways to use that data can seem terrifying. Although very little of this data is useful, more and more can be extracted for useful patterns and information. This portion of the useful data is called “Big Data”. For now Big Data is a good way for any company to gain advantage but in the coming ten years use of Big Data will be inevitable. Cloud computing and things like Hadoop and NoSQL provide data analyzing tools to several businesses and entrepreneurs to help analyze the right data sets. Experts on Big Data give some useful tips on how to use this technology.
Know the problem you are trying to solve.
The better way to think about the use of Big Data is to consider it as a tool to solve challenges. Once the problem is identified the data can be used to find a solution.
Start small and grow.
It is a good idea to run a trial analysis and see if it solves the problem. If it doesn’t then you haven’t risked much and if it’s a success you will come out with useful data.
Choose the right data.
 Although it can be difficult to find data, data should be combined from different places to obtain the most useful ones.
Move Fast.
Big Data is not only about analyzing information but also acting on it in real time so that all parts of the company is moving towards a common target and can make the most out of the available data.
Read More at: http://www.forbes.com/sites/mikemontgomery/2015/05/07/small-businesses-shouldnt-fear-big-data/

Large amounts of real-time data is being daily generated by internet usage. Nowadays, devices are being interconnected and smart products are also connected with the internet. In order to use this data efficiently, organizations need to re-structure their supply-chain. The motivation is not just to use historical data in the traditional manner; but to combine data from multiple product interactions generated by both consumers and suppliers, connected via cloud portals. Supervised machine learning can search for and capitalize on the patterns and relations that they derive in the data and help in supply chain based decisions. Once implemented, they can be continuously evaluated and improved based on performance. The end aim is to accurately predict the attributes of future demand. Read more at: https://hbr.org/2015/06/inventory-management-in-the-age-of-big-data

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 fast advancement of consumer-driven technologies, new alternate ways to measure workplace risks are emerging. Technology improvements like growth of the Internet of Things (IoT) help us in assessing the working environment in real time.  The increase use of wireless communications for performance specification in the field of safety, health and industrial hygiene allow them to safely monitor multiple workers in real time and be alerted when exposure levels become significant or excessive. Another recent trend is the emergence of Big Data and the use of data mining as an analysis and management tool that will forever change the safety and health profession. With the increase in samples, the accuracy grows and statistical analysis takes decisions regarding the limit of exposures. This helps identify outliers (individuals, processes and practices that result in greater exposure levels) as well as gives confidence to the management about the accuracy of assessment of the workers. Read more at: https://ohsonline.com/Articles/2015/05/01/Technology-Megatrends.aspx?Page=1

Decision making is powerful when data is coupled with insight. A survey revealed that 46% of CFOs admitted to making important decisions based solely on their gut, out of which 15% blamed poor decision making on inaccessibility of internal data. Only 23% make the effort to perform data mining for decision making. In order to make way in the competitive world, a combination of both is required for survival. According to Duncan Watts, a Microsoft researcher, ignoring data while making predictions can lead to huge losses. Both insight and data are equally important to earn better revenues. Read more at: http://www.cmswire.com/analytics/many-managers-mistrust-data-infographic/

Big data is completely transforming the way we look at business. Analytics is being used to foster growth in every industry. The entertainment industry uses predictive analysis to forecast the box office collections, gauging the success of the movie before they even start shooting it. Production houses can use big data to plan strategic release dates and to retain consumers by analysing patterns derived from the data. By collecting data from the social media sites, they can formulate plans based on consumer sentiments as to what kind of music, casting, genre, are in trend thereby increasing the ROI and giving improved entertainment to the end user. Read more at: http://analyticsweek.com/how-big-data-is-changing-the-entertainment-industry/

Study of human beings is more complex than machines and so is the study of their data. It is important to make accurate decisions which decide life and death matters and thus require error free and credible data. Therefore apart from the 3Vs- volume, variety and velocity; healthcare data should also have veracity. As much as it is required, it is difficult since there are issues in the healthcare data - is the information furnished about the patient, drug prescription, disease correct? Also since the data is huge and frequency is also high.it is difficult to clean the data, thus magnifying the credibility issue. To know more, read the following journal at: http://www.hissjournal.com/content/2/1/3

Predictive analytics helps police officers in America and the UK solve crimes. To reduce crime rates, there is an increase in budgeting, planning, installing cost which aren't considerable to the proportionate reduction. Thus more money spending would help improve public spending. New equipment will also assist public safety. Likewise, gradually predictive analysis will also be used to solve terrorism. PredPol is one such systems to target robberies, crime etc. Obviously, like others it too has backdrops as it involves mass data collection. But the advantage that has occurred and might occur because of it are much important to focus on than its backdrops. Read more about this article at: https://channels.theinnovationenterprise.com/articles/how-predictive-analytics-is-revolutionising-public-safety

Next time while you are trying to log in and the "remember your password?" window pops up, think before hitting yes. Password managers help users sync their usernames and password so they can choose strong passwords which are hard to guess, thereby reducing the risk of hacking. But if the password manager fails, putting all the passwords in one place would be regretted since now all the accounts run a risk of security breach. Password manager stores passwords either on the user's machine or uses cloud-based security. Both could turn unsafe as the former is vulnerable to malware and in the latter case the password is sent to remote servers. To know more, read: http://www.theguardian.com/technology/2015/jun/17/do-we-really-want-to-keep-all-our-digital-eggs-in-one-basket

Predictive analysis helps in analyzing the choice and behavior of the viewers. This in turn helps in finding the target audience as well as optimizes revenue. Combination of system dynamics and agent based modeling can be used to predict the profits from the shows or movies. Proper techniques along with real time analysis can assess the content precisely and accurately.
Social media analytics or sentiment analytics allows the industry to understand the perception of the audience and the critical reviews that they give.
“Bag-of-words opinion analysis” approach can help in evaluating the tweets from various social media sites. Cost efficiencies can also be achieved with proper efficient use of data analytics.
Read more at: http://sites.tcs.com/blogs/agile-business/analytics-entertainment-industry

Location analysis is a very important part of Big Data classification in automobile segment. Big data analysis is used in every stage of production and marketing of automobile industry.
Product Design – Data is collected from customers who would help them to make newer improved models.
Supply Chain – With the help of Big data analysis manufacturers can now analyze different suppliers based on various aspects and reach a decision.
Marketing – Helps in identification of the right kind of customers.
Aftermarket – Helps In improving logistic and sourcing processes.
Financing – It enhances the understanding regarding the market of each model.
Read more at: http://www.smartdatacollective.com/anandsmartdata/325598/big-data-automobiles-use-cases-today-and-opportunities-future

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

Through predictive algorithms, text analytics in Big Data can discover a wide range of happening things about a business. On the positive side, text analytics can be fed a lot of unstructured text. Customer experience management, brand monitoring, compliance and brand management can be done with the help of text analytics. This was of great use during the US election as it found out reasons behind interested voters.  Though on the negative side, text analytics tool get confused for words which has a dual meaning in the same culture or for different cultures. Hence, it should be made such that it can stand up successful for different sentiments too which many are trying to establish now. Read more about this article by Elliot Pannaman (International Events Director) at: https://channels.theinnovationenterprise.com/articles/maximizing-business-performance-with-text-analytics

The Great Recession was the general economic decline observed in world markets around the end of the first decade of the 21st century. The exact scale and timing of the recession is debated and varied from country to country. The most probable cause behind this recession is the use of bad data. Generally, retailers lose their revenue because of out-of-stock issues which is due to the use of incorrect and outdated data. Hence bad data is destructive. It results in losses because of dwindling customer satisfaction and incurs additional expenses in order to correct the faulty data. Big data can affect both small and large enterprises but companies can be immunized against bad data by being active and using the correct tools.

Read more at: http://www.business2community.com/big-data/bad-data-a-21st-century-business-epidemic-01252370