Faced with challenges of bureaucratic vices and realities, government agencies often let things slip through the cracks, including fraud. It is disheartening to learn that huge losses are incurred by many public facing agencies due to fraud and such losses are regarded as expected operating costs. Yet, no measures are being employed. Thankfully, investments are being made in predictive analytics tools by agencies and some progress has been achieved in detecting preventing and prosecuting fraud. However, to tackle crimes effectively the tools need to be comprehensive, flexible and affordable. For example, dynamic case management solutions can be applied to tackle the mammoth challenges faced by the agencies. Read more: http://gcn.com/articles/2015/06/10/fraud-control.aspx

Companies can use data scientists to prepare data sets, business analysts to develop models using both statistical and machine learning algorithms, application developers can be used to deploy and manage predictive analytics life-cycles, and tools. There are many vendors in the categories of customer analytics, cross-selling, smarter logistics, e-commerce etc. Open source software community is driving predictive analytics into the mainstream. Many Business Intelligence platforms also offer “some predictive analytics capabilities."  Rapid Miner’s predictive analytics platform can also be integrated into the cloud. Read more about this article at: http://www.cmswire.com/cms/big-data/3-vendors-lead-the-wave-for-big-data-predictive-analytics-028684.php?mkt_tok=3RkMMJWWfF9wsRomrfCcI63Em2iQPJWpsrB0B/DC18kX3RUnJb6Wfkz6htBZF5s8TM3DVlJGXqlI4UEKTLE%3D 

From Software and Platform as a service to data as a service, the trend has always been to evolve to the requirements of the current generation. The next step is mixing them all together and mass upscaling of the data in the system to provide Big Data solutions as a service (or BDaaS). Multiple businesses in the past have sprung up and are offering cloud based Big Data solutions. Big Data refers to the large, mostly unstructured information created and stored, and the analysis and use of this data is called Big Data Analytics. BDaaS is a term used to describe the outsourcing of Big Data functions to the cloud. It includes the supply of data, providing the analytical tools and the actual analysis of the data. BDaaS can also include consulting and advisory services. Read more at : http://www.forbes.com/sites/bernardmarr/2015/04/27/big-data-as-a-service-is-next-big-thing/

Analytics is being widely used in many areas and sports is no exclusion. Data analytics has successfully scripted victories for many teams, already. Data analytics can be applied to any sport be it tennis, baseball or cricket. But, sports analytics is a completely new concept in India. Sports analytics is the amalgamation of sports and information technology. Sports teams are now hiring data analysts who feed data in their algorithms, which then process the data fed and perform numerical calculation to figure out the strategy for the next match. The analyst then informs the coach and the team players about the prescriptive strategies. Read more at: http://www.cio.in/feature/big-data-is-here-to-have-a-good-innings

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

Getting around in big cities without facing traffic jams is almost impossible. After the businesses, it's the city planners and transportation experts now who are resorting to Big Data when making improvements to city transportation. Commuters have certain patterns they like to keep to while travelling. By analyzing these actions and the factors responsible for them, transportation experts understand why certain routes and modes of transportation are preferred over others. Gathering call data records help provide access to data about travelers, which data scientists can use to decode the transport pattern of travelers. Countries like Australia and Brazil have already implemented Big Data to upgrade public transportation to keep up with the current demand of metropolitan cities. Read more at: http://channels.theinnovationenterprise.com/articles/big-data-s-impact-on-public-transportation

Mobile applications are an integral and inseparable part of modern data analytics. Mobile applications not only influences consumer`s decision to make purchases, but also allows companies to get insights into consumption pattern and market demand. Enterprises seek immediate-data from a wide range of sources, to fuel business processes by means of big data and analytics and this is provided by different types of mobile applications. It plays an important role, particularly in the field of global supply chain management and predictive analytics. On the other hand mobile applications are also being fuelled by data analytics, so that it cannot only collect the data but also analyze it and process it simultaneously to provide instantaneous results in real time. Read more about this article at: http://www.forbes.com/sites/benkerschberg/2014/12/17/how-big-data-business-intelligence-and-analytics-are-fueling-mobile-application-development/ 

With significant increase in online sales and more customers opting to purchase through mobile devices, high street retailers are facing difficult times. No wonder it is time to elevate shopping experience to a whole new level. Though Retails Analytics is not a new concept, the approach faces challenges in brick-and-mortar stores. Investing in real-time Wi-Fi analytics, where modern in-store analytics technologies can integrate with existing in-store Wi-Fi infrastructure to capture precise and real-time customer behaviour in stores, can be a solution. Creating an inclusive shopping experience that effectively combines the online and offline experience is the need of the hour. Read More at: http://www.fourthsource.com/mobile/future-analytics-retail-19148

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 Analytics has started to make significant strides in less explored areas like improving manufacturing performance. No wonder the theme of ARC European Forum, 2015 was intended to help develop some clarity over emerging related concepts such as Industrie 4.0 and the Industrial Internet of Things (IIoT). Several use cases about how "applying these concepts can deliver tangible benefits in real-world industrial production across a broad cross-section of industrial sectors" were presented. Read More at: http://www.automationworld.com/industrial-internet-things/big-data-analytics-improve-manufacturing-performance

Predictive marketing has taken hold of enterprises today. Marketers are now making use of predictive analytics and this has given birth to predictive marketing. Predictive marketing is the application of algorithms and machine learning tools to Big Data in order to help marketers direct their efforts in the most profitable direction. Using predictive analytics tools, marketers can gauge future sales and thus formulate appropriate marketing strategies to help boost sales. Predictive analytics can be used for segmenting customers, deciphering the pattern in their behavior and thus offering them the appropriate deals. Marketing and sales are the areas where predictive analytics is most used. Read more at: http://www.cio.com/article/2934274/why-marketers-are-betting-big-on-predictive-analytics.html

Predictive Analytics is making its entry into the toolbox of small companies. Predictive Analysis is a three-part formula- analysis of past performance of situation under consideration, understanding the present and applying the past to present to predict the future. For small businesses, it can be applied for customer retention, target marketing, demand forecasting, and overall marketing. However, it is crucial to ascertain the purpose for using Predictive Analysis, as the technology may not necessarily be right for every company or for every situation in the small business sector. Read more at: http://www.smallbusinesscomputing.com/News/Marketing/can-predictive-analytics-help-your-small-business.html

The role of risk management has become more pronounced than before and companies are resorting to predictive analytics along with business insights to visualize and manage risk. Predictive analytics has gained immense popularity in the area of risk management due to its ability to identify and predict vulnerabilities, fraud, security breaches and the quality of control systems and governance, as pointed out by Rita Sallam, a research Vice President and analyst. Several firms are utilizing the advanced techniques for data extraction to manage risks. Post data gathering and visualization, firms are able to identify risks and mitigate them. Firms engaging in risk modelling produce impressive returns. Read more at: http://channels.theinnovationenterprise.com/articles/risk-visualisation-and-predictive-analytics-in-risk-management

Inspite of the fact that Big Data analytics is found everywhere, the willingness of the firms to invest and to act on analytics insight is extremely slow. This is decision latency, where the companies have all the required data and knowledge but lacks the speed to make decisions on major issues. In today's world, data becomes less relevant with each passing minute. Traditional analytics tools face challenges in handling this data because they are designed to store and process the data but not to analyze this data from moment to moment. Three capabilities crucial for event processing systems to deliver ROI, are as follows :

1. High availability
2. User empowerment
3.  Empowerment of developers in order to built better distributed system with custom UIs (User Interface).

Read more at http://www.tibco.com/blog/2015/06/09/decision-latency-solving-the-big-data-analytics-oversight/

In modern world, there has been an increase in communication channels and hence this customer-centric era presents both challenges and opportunities for businesses. Companies must have the skill to connect to the data sources relating to customer experience. Hence, nowadays the big data challenge has gained more importance. In case of customer experience management, the data needs to be combined with unstructured customer feedback data and this is important in order to have a complete picture of customer experience. Data governance plays a crucial role here. One big challenge of customer data is that they don't know which data is more relevant in the first place. Data governance creates the base for the common understanding of the customer across the business.

To read more about customer data governance, please follow the link :

http://www.computerweekly.com/blogs/Data-Matters/2015/05/the-growing-importance-of-data-governance.html

Human intelligence is found in every organization. It is difficult to derive meaningful information from unstructured data and hence it's a big mistake not to use it in decision making.  Content Intelligence is the combination of technology and information science which allows machines to model, interpret, analyze and visualize human intelligence within an organization. It is used to generate new revenue streams, gain operational efficiencies, increase customer satisfaction, rise in productivity and avoiding costly networks. The rising pressure on enterprises increases costs and riskiness when content intelligence isn't available. It makes unstructured information self-describing and hence allows content-based information to be described in a similar way as structured data.For further details on content intelligence, please follow the link :  http://www.dataversity.net/what-is-content-intelligence/

A firm needs to build a firm base that gradually can destroy others. The amount of information produced by social media platforms is an excellent way to measure a business’s both real and potential value. 56% of marketers named the ‘inability to tie social media to business outcomes’ as the central challenge to measuring ROI from social media. Gauging customer demand prior to launch using social media helps minimize risk, especially for entrants. Social media data can be correlated with an organization’s KPIs to know their impact upon one another indicating its usefulness. Allowing big data analytics capabilities to the social media platform helps firms to cross-reference social data to other data streams from their business. Read more about this article at: http://channels.theinnovationenterprise.com/articles/social-media-analytics

Data mining results can be used by organizations to prevent losses ahead of time. Organizations usually use indications like inventory levels or employee turnover, which occur later in the business cycle to gauge shrinkage cost but by that time the losses start to incur and there is nothing much that can be done. According to a report by PricewaterhouseCoopers, if companies gather and analyse data, they are able to get better insights into the business and use the early warning indicators to stop losses from happening by making informed, strategic decisions, thereby saving time, money and effort. Read more at: http://www.tibco.com/blog/2015/06/03/strategic-data-analytics-to-help-reduce-shrinkage/

Now big data will help diagnose climate change. Shalene Gupta from Fortune talks about five big data projects that are on verge of changing the way we look at the problem of climate change. From tracking deforestation in Amazon, graphing animal life in African Savannah, tracking illegal forest fires to telling people how much energy they consume compared to their neighbors, we have it all in these 5 big data projects-

• Google Earth Engine

• Madingley Model form Microsoft

• Data.gov’s climate

• Global Forest Watch

• Opower

For more interesting insights follow the link http://fortune.com/2015/02/14/big-data-climate-change/