SigmaWay Blog

Earlier, the concept of cloud security was obscure and hence corporate executives and IT professionals used to tremble even at its very mention. The reason behind it was that only few could protect the data. Since data is vulnerable to hacking therefore any minor negligence could put the enterprise at risk. However, things changed in 2015. Businesses need not have to be concerned about cloud computing security due to the following reasons:

• Introduction of new and effective methods to safeguard data through company suites, backups/redundancy and virtualization.
• One needs to be extremely cautious while choosing a vendor and it is highly recommended to visit their site in person in order to scan their system.
• Finally, with the advent of advanced technologies companies started using cloud computing without any worries.

Read more at: https://channels.theinnovationenterprise.com/articles/why-businesses-shouldn-t-worry-about-cloud-computing-security

Just like the earlier days, even now we view healthcare solutions in two-dimensional ways – patient-doctor relationship or technology revolutionizing patient’s health and wellness. The problem gets more acute with invention of new technology solutions. But if we don’t intervene to gather the data and use them, there’s no use of new technologies such as wearable devices. Thus real collaboration is required where every member connected to the patient acting as a crew member can steer the patient towards possible better health. Everyone working collaboratively can harness the power of technology and big data, to deliver a 360 degree-solution built around the patient. Implementing the approach of multi-stakeholder interaction of social media, can make true social healthcare realize its potential. Read more at:  http://www.socialmediatoday.com/social-business/paul-tunnah/2015-07-09/effective-healthcare-requires-social-approach

The concept of work/life balance is absurd to some in a reviving economy. People who are passionate about their work seem to perceive it as an essential part of their lives and hence they attain great success, although it is seen that those people are also simultaneously dedicated to their families and to the duties outside work. Hence achieving a balance between work and life is of utmost importance but nowadays it has become increasingly difficult. The following points needs to be considered in attainment of such kind of balance and they are as follows:

1. Make time to think about it.

2. Seek energy, not balance.

3. Take advantage of flexibility.

4. Make choices ahead of time.

5. Communicate your boundaries.

6. Manage your mind-set.

Read more at: http://www.business2community.com/human-resources/the-worklife-balance-myth-01268346

Banks lose money with small business loans because of high operating costs cannot make up profit from the net interest spread. Components determining costs of a $100,000 loan are origination, underwriting, loan review, operations, monitoring and collections, and compliance. Locking in a loss and reversing a loss are vital strategies, to reinvent internal cost structures. Management can effectively turn around the situation by:
1. Evaluating the profitability on more holistic basis
2. Increase cross sales
3. Process improvement, by simplifying processes.
4. Outsource or partner on lending, by allowing alternative financiers help reduce cost and increase productivity.
To know more: https://www.bai.org/bankingstrategies/Strategy/Small-Business-Banking/Making-Small-Business-Loans-Profitably?cclass=pointOfView

Choosing the right Hadoop distribution can be a tricky process. There are 4 basic categories that businesses should look at for specific qualifying criteria.
1. Performance
Hadoop is widely chosen as a data platform due to its high performance achieved by replacing the stock MapReduce by Apache Spark. However not all operations need such superior hardware and a business must choose its hardware on basis of the operations it hopes to perform.
2. Dependability
When looking for a distribution, dependability is a significant but rare feature. Only few implementations in Hadoop can guarantee a system availability of 99.999%. Look for a distribution that provides Self-Healing, No Downtime Upon Failure, Tolerance of Multiple Failure, 100% Commodity Hardware, No Additional Hardware Requirements, Ease of Use, Data Protection and Disaster Recovery.
3. Manageability
Look for a distribution that has intuitive administrative tools that assist in management, troubleshooting, job placement and monitoring.
4. Data Access
Gathering and storing data is just the beginning of the process. What really matters is that the stored data must me easily accessible for further processing. Look for a distribution that provides
• Full access to the Hadoop file system API
• Full POSIX read/write/update access to files
• Direct developer control over key resources
• Secure, enterprise grade search
• Comprehensive data access tooling
Hopefully these four specification along with your criterions will enable you to choose the best Hadoop distribution for you.

For more information visit:
http://www.smartdatacollective.com/davemendle/324791/four-considerations-when-choosing-hadoop-distribution

Big Data is a resource that helps organizations to understand and take control or corrective action to address problem such as customer centric service delivery, by accurately joining big data with Master Data Management (MDM). MDM helps to control the momentum of incoming data and tools that applied in MDM helps to verify facts about customers, vendors and products. Data definitions and rules previously established by the corporation are reflected in the MDM. Service Oriented Architecture (SOA), due to its agile nature, plays a key role in the corporation achieving its vision.
There is a symbiotic relationship between MDM and SOA. SOA provides the filtering needed for a steady stream of consistent data, which is an important aspect of data mining as the quality of data derived determines the accuracy of predictions.
Thus MDM is a valuable tool as it has the capacity to store and analyze large volumes of data to discover new patterns which leads to an increase in profitability.

For more information visit:
http://www.finextra.com/blogs/fullblog.aspx?blogid=11073

Forex, in terms of daily trading volume, is the largest market on the planet and should not be surprising that data mining has a lot to do with determining success in the Forex market. Algorithmic Trading is the automated execution of a trading algorithm. In our case, the algorithm is derived from data mining. Once the algorithm is in place, all it requires is some programming codes to allow it to run the algorithm. The most important part of this puzzle is the data used to set up the algorithm-the scope for extensive data mining.
Many open source platforms are available for this purpose and one can choose the desired one according to his/her preference of speed and accuracy with there being an obvious trade-off between the two, given the vast amount of data available to us.

For more information visit:
https://www.mql5.com/en/blogs/post/640795

In today's world where manufacturing has turned into a truly global industry spanning over multiple time zones with multiple languages and cultures, language can prove to be a stumbling block for communication and hence success. The expression “a picture is worth a thousand words” has never had more practical meaning than today. Visuals can actually help us understand complex problems faster than just texts or numbers.
Let us look at what goes in the making of a great visual:
• Easy to Setup: In a learn environment where stress is imposed on minimizing waste it is of prime importance that the time and resources needed to setup, a visual is minimized.
• Easy to See: Even before we get down to the task of analyzing the graphs what must be kept in mind is that the graphs should be visible. And by visibility we also broadly mean that the visuals should be placed at strategically at the “right place”.
• Easy to Maintain: Information posted via a visual must be constantly updated and it would be more cost efficient if every consequent update does not render the previous version as unusable.
• Easy to Use: The visuals should be designed in such a way that everyone is able to use the information displayed- so that no special training is needed to read the information shown.
• Easy to Understand: Visual symbols can work great for communication only if the information displayed is readily usable for analysis. If further processing of data has to be done before they can be used, it greatly diminishes its functionality.

For more information visit:
http://www.apriso.com/blog/2015/07/the-5-es-great-lean-visuals-have-in-common/

A certain cross-country survey revealed that the Internet of Things (IoT) was first adopted by the Healthcare Industry. Organizations that adopted the IoT, believed that they could meet business challenges like patient engagement, loyalty, safety and satisfaction without any delay.
According to Laura DiDio, Director of IoT Strategies Enterprise, the need for IoT is felt highly by enterprises to address and solve pragmatic business issues. Andrew Brown, SA’s Executive Director of IoT and Mobility says that IoT can also be used for cost savings, development of new services, increased competitiveness and better understanding of the business.
It can be said that the Internet of Things has great potential in the field of healthcare and with evolvement of mobile capabilities its growth rate will be escalated.
Read more at: http://healthitanalytics.com/news/healthcare-industry-is-an-early-internet-of-things-adopter

Changing attitude of the young generation along with urbanization in India is driving the expansion of digital marketing in the Asia hugely. Noticing such changes, digital ad spending is also increasing gradually. Increasing use of mobile is enhancing marketing by helping product research work better.  Smartphone has penetrated regions in Asia Pacific like never before. This increases the usage of mobile app which indirectly increases mobile internet ad spending as marketers are benefiting. Western marketers are also looking to move into this region as they can have better social measurement. They focus on the impact of social on key business objectives. Digital marketing increases in countries like India, China that grow into true economic powerhouses with increasing ROI. Read more about it by Stuart Found (Global Digital Events Manager) at: https://channels.theinnovationenterprise.com/articles/how-is-digital-marketing-expanding-in-asia

Today, fast and accurate decisions are critical for an organization's success. But there is a risk of incorrect decisions if we rely on approximate sciences such as intuition and judgment of individual decision makers. The value of data can be realized only once a coherent ‘data strategy’ is established. A data strategy requires an organization to embed and integrate data analytics into the process of decision-making. The organization must seek and utilize data based insights that are most fruitful. Big Data demands a tighter integration of business functions and better mechanisms for integration. Evidently, various teams shall work together to understand and exploit cross-functional data. Identifying key data gaps and taking collective decisions for data gathering will ensure that specifically data with potentially useful information is collected. Read more at: http://www.bobsguide.com/guide/news/2015/Jul/13/big-data-small-data-and-fast-data-using-data-to-drive-better-decisions.html

 Through a randomized double-blind controlled trial, we can guarantee predictive analysis though these trials sometimes fail. So in real-world the chances of true predictive analytics is virtually none as randomized double-blind control trials are also highly suspected. Thus workforce analysts should start using correlational analytics by first accepting that predictive analytics probably doesn't exist in most organizational settings and that we need to do with correlational analytics. Then they can assess the risk of investing large sums based on analytics which are correlational rather than predictive. Next always run a small pilot or two before spending big money on implementing the results of "predictive analytics" as we now know it is just correlational analytics. Read more about it by Max Blumberg (Workforce analytics and automation consultant) at:  https://www.linkedin.com/pulse/dont-waste-money-predictive-analytics-doesnt-exist-hr-max-blumberg?trk=hp-feed-article-title-like

The atmosphere in an organization has a huge impact on the creativity and productivity of its employees. The kind of work culture you choose as an entrepreneur or businessman will determine whether your employees are loving the job or diminishing their efficiency under the immense pressure presumably being put on them. The importance of creativity and innovation is crucial for a company to grow out of the box and possibly become the next billion dollar company. In order to know more about the approaches to work, and their relevance and impact, click on the following link: 

http://www.callcentertimes.com/Articles/tabid/59/ctl/NewsArticle/mid/407/CategoryID/1/NewsID/1010/Default.aspx

The best of form of marketing is through your customers! If they're happy, you'll find your products selling, but if customers aren't happy, you'll find yourself in a bit of a tough spot. Success of a brand not only depends on how awesome the product is, but also on customer satisfaction. Keeping customers happy is more than a prerequisite in today's world for the success of a brand. Right from respecting your customers to investing in improving customer loyalty, each impacts the performance of the company; this is reflected directly on the sales! Follow the below link to find out ways to keep your customer happy: 

http://www.listenupespanol.com/News-Resources/bid/92851/4-ways-to-keep-your-customers-smiling

Annual marketing data benchmark report reveals that around 84% of marketing database are non-functional, 64% of records do not include a phone number and only 35% of emails were identified to be functional. Undoubtedly, this will take the profits on a downward flight. Larisa Bedgood in her post on Datamentors tells us 4 ways we can bring the data to shape and extract higher revenues.

• Begin with Data Assessment

• Establish Business Processes to ensure data quality

• Establish an ROI to fund new marketing database initiatives

• Implement a data management solution 

For more on this click the link http://www.datamentors.com/blog/dont-lose-revenue-bad-data-4-tips-whip-your-marketing-data-shape

SME’s have a large number of advantages in using big data technology and it is found to play an important role for them recently. Firstly it is important to understand that what big data is. Big Data is defined as a popular term to describe exponential growth and availability of data, both structured and unstructured and is important for businesses. It is a series of combined data lakes used for more accurate analyses. SME’s should move to Big Data storage solutions because storing the new data generated each day is becoming expensive for the companies and its increasing their costs. When SME’s start using such combined datasets, they will have enough data to analyse and improve their decision making. Interest in big data has reached new heights for SME’s as they take their chances of capturing more information from everywhere possible. SME’s can also use big data to attract new customers, tap new markets and reduce costs. Some more ways how big data technology can help SME’s prosper are by using big data tools which increase their ability to use predictive analytics for decision making. It also helps SME’s move unused data into different less expensive places. Hence SME’s should definitely be looking forward to such big data technology for improved performance.

Read more at: http://www.smartdatacollective.com/bruce-robbins/329520/big-data-smes

The automobile industry can make use of data to improve vehicles, sustainability and profitability. Smart cars are the next generation of cars and data will assist in building these. Surveys have revealed that customers prefer cars with features that save time, ensure safety and sends warning alerts in case of malfunctions. In order to accomplish these needs, integration of technologies is required. Services based on car data can be introduced like sending a warning alert to replace car battery prior to its depletion. Smart infrastructure can be built by connecting roadside sensors, traffic signals and remote data centers to ensure a safe and smooth ride. Data can be analyzed to map travelling patterns by looking at how individuals behave in various road scenarios. Read more at: https://channels.theinnovationenterprise.com/articles/7719-what-smart-carmakers-can-learn-from-the-smart-phone-industry

Companies of every shape or size recently have moved to Big Data as they realized how beneficial it is for their organizations. To efficiently use the big data, companies need to collect and process it first, from multiple sources. Some data remains unused in the case of unstructured data mostly, which poses a problem later leading to clogging up data centres. Such unused data has been termed as – Dark Data which needs to be cleaned up or else it increases security risk and costs for the companies. Dark Data firstly needs to be identified. It is the information collected but not put to any specific use. Such data remains unused for years and takes up a lot of space which could have been used otherwise to store useful data. Dark Data remains unprotected for a while and poses risk of security breaches and mishaps. Dark Data is costly and dangerous but still companies keep them to make sure they don't lose any kind of records. Cleaning of Dark Data is time consuming but is important as well. One of the most effective ways of avoiding such data is by filtering the data which prevents data from piling up. Other ways to clean such data is by moving the Dark Data to different repositories using cloud computing. Dark Data can be deadly for organizations and hence it should be avoided before it poses any problem for the business.

 Read more at:  http://www.smartdatacollective.com/rick-delgado/323706/importance-cleaning-your-dark-data

User Behavior Analysis (UBA) helps in collection and analysis of huge amounts of user-specific data and access logs which can help us to identify unusual user behavior, investigating potential threats, detecting and responding to internal threats quickly, etc.
Enterprises can incorporate UBA in the existing security framework in the following ways:
1. Defining the problem – The most critical first step is to define the problem. Organizations that take time to identify their unique organizational security characteristics can determine the critical company assets and thus help UBA to safe guard their resources.
2. Identification – The next step would be to ascertain the group of users and the types of data that needs to be monitored.
3. Implementation – The third and the ultimate step is implementation. The various ways in which collected data can be used for use in security insights are:
• Visualization
• Drill-Down
• Aggregate
• Profile
UBA can be used by identifying the problem, gathering the relevant data, and establishing the required tools to gain visibility of the unseen threats in the network. Read more at: http://www.smartdatacollective.com/idan-tendler/326260/adopting-user-behavior-analytics-uba-solution

Data scientists are the ones who use sophisticated quantitative and computer science skills to both structured and analyse unstructured data as well as derive intuitions from the data and suggest actions. Data scientists can tackle with problems that are complex, huge in size and disorganized using several coding languages. To identify a data scientist following points are to be taken into consideration:

• Qualifications: Data scientists are required to have an advanced degree usually a masters or PhD in a quantitative discipline such as economics, statistics and their educational background may be diversified.

• Skills: Data scientists are efficient users of different tools used for analytics and are well versed with coding languages such as Python or Java used for writing programs, transformations etc. They are also have expert knowledge about statistical and machine learning models such as R and SAS.

• Dataset size: They usually work with datasets measured in gigabytes up to petabytes.

• Job responsibility: Data scientists are well equipped to work on every stage of analytics life cycle which also include data acquisition, transformation/cleaning, analytics to predict patterns of the datasets, prescribing actions and programming/automations to contribute to a firms data products.

The main idea behind this is that whether you are a data scientist, analytics professional or programmer you always need to be well versed with the new languages coming in the market each day just as big data has been gaining importance and keep up with the new technology.

Read more at: http://www.smartdatacollective.com/lburtch/320541/more-just-title-how-identify-data-scientist