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. 

• Before investing in BI tools, one must know what answers they are looking for.

• Forget about using BI software if one don’t have access to best operational data.

• BI is increasingly going mobile. 

 Read more at:

http://www.inc.com/articles/201109/business-intelligence-software-for-small-business.html

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.

• Carry a data inventory.

• Recognize executive sponsorship.

• Identify business champions.

• Set up an educational forum.

• Educate yourself about products and approaches.

• Develop a business model of the data to be handled.

Read more about this article at: 

http://esj.com/Articles/2008/06/25/Ten-Tips-for-Master-Data-Management-Success.aspx?Page=1

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.

• Predictive Insight into Fan Preferences- Analytics can advance the sports fans' experience as teams.

• Career Opportunities for the Blended Sports Fan and Numbers Whiz- Bryan Colangelo, former president of the Toronto Raptors, says "There are mountains of opportunity in analytics now"

• Influence Coaching Decisions- Data analysts could help to transfer the most requisite data sets to coaches for better outcomes on the field.  Read more at:

http://www.cio.com/article/2377954/data-management/8-ways-big-data-and-analytics-will-change-sports.html

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: 

http://www.forbes.com/sites/piyankajain/2013/05/26/the-8020-rule-of-analytics-every-cmo-should-know/

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

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

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.

5.   Rely On a Core Platform That Creates Derivative Intelligence and Knowledge in Real Time -statistical inferences can turn data into actionable intelligence that supports reasoned decisions. Read more at: 

http://www.forbes.com/sites/benkerschberg/2014/01/03/five-steps-to-master-big-data-and-predictive-analytics-in-2014/

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

When one starts a new project that involves analyzing his company's data especially when the data is stored across functional areas, that person is in trouble. The data lake model helps in this case. To get access to data doesn't require an integration effort, because data is already there in the lake and one can apply MapReduce and other algorithms to use it. In the lake some data are unstructured or not structured by us for a given project. To construct a data lake one needs to learn some of the Hadoop stack such as Sqoop, Oozie and Flume. Next a data scientist should be found who understands Hadoop as well as business and the company’s business data in particular. Then one should start with basic cases and use simple and familiar tools like Tableau to make nice charts, graphics, and reports demonstrating that he can do something useful with the data. Next security up front should be considered, as well as who can access what data. Use of core Hadoop platform is beneficial. Apart from this one should keep in mind that lake security may have business unit implications and one should not have a lot of mini lakes i.e. data ponds that are separate and not equal. Read more at:http://www.infoworld.com/d/application-development/how-create-data-lake-fun-and-profit-246874?page=0,0

Nowadays a new set of tools is developed to help business users visualize data and interact with it in real time. One may think of mobile reporting as a good device for business but it is not good enough indeed and we need analytics tools too. Mobile analytics can be quite different from traditional business intelligence, in fact, and may even change the way you work with data comfortably behind the desk. Three basic aspects of mobile analytics are- For any application, the touch interface makes the difference between mobile and desktop environments. It encourages exploration. It is possible to make interactive areas large enough to touch through a mobile device. Mobile analytics also demands responsive interface design and the analysis must work across many devices. A responsive interface supports many form factors and one’s BI platform if designed for mobile ought to handle these scenarios generically. Mobile analytics also helps in speedy discovery and facilitates an interactive experience which is touch-enabled. These practices not only enable, but encourage exploration.

Read more at:http://www.infoworld.com/t/business-intelligence/getting-your-hands-data-mobile-analytics-246331?page=0,0

One can use the big data, available in hand, in a right or a wrong way. Here is the list of top 10 worst big data practices which one should try to avoid. First, though MongoDB has an aggregation platform, it is not good as an analytical system and thus should not be used as big data platform. Second, RDBMS schema is used as files by many which should be avoided too. Third, creating a series of data points. Fourth, failing to develop use cases. Fifth, over-dependence on Hive should be reduced as the whole point of big data is to expand beyond what one could do with one technology. Sixth, it's not right to treat HBase like an RDBMS. Seventh, trying to install Hadoop and all its moving parts on 100 nodes by hands is also a worst practice. Eighth, one should also avoid RAID/LVM/SAN/VM-ing one's data nodes. Ninth, instead of treating HDFS as just a file system one needs to think about how one is going to secure all of this and for whom. Finally, everyone is free but each one should have a plan. Read more at:http://analytics.theiegroup.com/article/53c925453723a81857000073/The-10-Worst-Big-Data-Practices-

Massive amount of climate data waits to be  interpreted by the press, public agencies, and general public and the challenge lies in finding analytics software that is easy to use, and produces understandable results, and can handle the volumes of big data available. However, to get a handle on the sort of governmental data on climate and resources that often resides in generic comma-delimited files, Circle of Blue, who specializes in reports on the global competition for water, food, energy in a changing climate, uses QlikView Business Discovery Platform. Such visual analysis software helps users to view, explore, and interpret the data with little technical training. Circle of Blue hopes to make the public more informed about the vulnerability of water supplies in the era of climate change by merging technology with on-the-ground reporting and online networks. With the use of Qlik View platform, it becomes easier for them to deliver data to a wide range of people. QlikView dashboards work with the large data sets to produce sophisticated, engaging, and state-of-the-art graphics. The data can be scaled to compare local information with national and global trends and that information, in turn, can help in formming public policy discussions.

Read more at:http://tdwi.org/Articles/2014/07/08/Massive-Climate-Data-Awaits-Analysis.aspx?Page=1

A Survey, based on the responses from 111 data scientists in US, found that Hadoop is too slow according to 76% of data scientists as they believe that the open source software framework requires too much effort to program and isn't fast enough to keep up with big data demands. On the other hand almost 91% of the survey respondents claim that they are performing complex analysis of data on the basis of which 39% of overall respondents say that their job is getting tougher. However, Big Data is becoming highly important for all enterprises. According to a research commissioned by Dell and conducted by Competitive Edge Research, a big section of midmarket companies with 2,000 to 5,000 employees are embracing the rise of big data and almost 80% percent of the midmarket thinks they need to better analyze their data, as they believe big data initiatives provide a significant boost to company decision making. Read more at:http://analytics.theiegroup.com/article/53baa9d23723a81e1300007b/Survey-Finds-Hadoop-Is-Too-Slow-Big-Data-Is-Too-Complex

The healthcare analytics market is expected to grow at a CAGR of more than 25% over the forecast period 2014-2019. Increasing healthcare IT adoption, centralized healthcare mandates across the globe, emerging fields of predictive, prescriptive analysis and venture capital are the factors driving the market growth. Digitization of world commerce, the emergence of Big Data and increase in the number of advanced technologies are other growth providing factors. Factors hampering the growth of the healthcare analytics market include lack of skilled labor with analytical skills, data securing and patient data privacy. North America holds the largest share of healthcare analytics market driven by US centralized healthcare mandates such as Meaningful Use and The Patient Protection & Affordable Care Act (PPACA). These initiatives assist to improve the acceptance of Electronic Health Records and Healthcare Information Exchange, thus improving the usage of analytics to influence the generated data. Read more at:

http://www.fortmilltimes.com/2014/07/21/3616557/research-and-markets-healthcare.html

Big data has a vital application in healthcare industry. Social media will increase communication between providers, patients and communities. This will not only work to globalize and democratize healthcare, but it is also a potentially important source of big data.  It will impact how these players engage with the healthcare ecosystem, especially when globalization, external data, regionalization, mobility and social networking are involved. The problem in healthcare isn't the lack of data but the lack of information that can be used to support decision-making, planning and strategy. In healthcare, big data challenges are compounded by the fragmentation and dispersion of data among the various stakeholders, including payers, data vendors, standards organizations, providers, labs, ancillary vendors, financial institutions and regulatory agencies. The entire healthcare system can realize benefits from democratizing big data access. Big data can make decision support simpler, faster and ultimately more accurate because rational decisions are based on higher volumes of data that are more current and relevant. Read more at:

Internet forecaster Mary Meeker in her latest report includes that trading of sensors for phones, tablets, etc., have increased by 32 percent up to 8 billion in 2013. And mobile data traffic is rising at an annual rate of 81 percent. With the change from desktop to all things mobile, high-thinking marketers should be looking for ways to make use of all the untapped signals available to them. Marketers can carry the opportunity to discard a huge amount of deadweight from their activities by:

• Sourcing new leads.

• Leveraging predictive lead scoring.

• Continuously personalizing.

The digital universe of data is growing largely and expected to reach 13 zettabytes by 2016. While out of 34 percent of the useful created data only 1 percent is being analyzed today. Today, companies must be thinking about gathering data and partnering with experts that have the machine learning capacity needed to enable truly data-driven marketing decisions.

  Read more at: 

http://www.destinationcrm.com/Articles/Web-Exclusives/Viewpoints/The-Impact-of-the-Mobile-Boom-on-Data-Driven-Marketing-97796.aspx