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/

Nowadays, daily sales are not enough to ensure longevity of a business. Markets are beginning to realize the necessity of predicting future sales. They are tapping into the business trends and applying machine learning algorithms on big data to allow businesses to maximize their efforts in the most profitable areas. By knowing ahead of time, what a customer will buy, profits can be maximized and businesses can be tailored accordingly. Predictive Analytics allows us to segment customers, find patterns in their behavior and take preemptive measures to reduce churn. Analytics can be used to identify correlations and also to find causation. It can allow us to predict an individual's purchase behavior once we understand the causation underlying a pattern. Read more at: http://www.computerworld.com/article/2934086/business-intelligence/marketers-are-betting-big-on-predictive-analytics.html

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

Even in this multichannel world, customers still prefer to call in and hence voice channel is critical to a business. These days’ businesses are coming to a standstill when it comes to listening to conversations that flow through their voice channel as they are stuck on sampling and high cost manual monitoring. A recent survey showed that it is important to link online marketing to offline sales.
Mining for the truths and hidden meanings from these conversations is crucial. Previously automatic data mining of the voice channel was unattainable. But now with conversation analysis it is possible. Being in the century of customers, the ability for the company representatives to understand the customer, be empathic and deliver fast solution is essential.
Thanks to conversational analysis or text mining, it is now possible to extract data from conversations, to automate and process this data in real-time and at scale. Thus listening to and analyzing conversations helps the representatives to build better relationships and in turn provide customers with the experience they expect.
Sigmaway provides text mining solutions for businesses. Contact us at contact@gosigmaway.com for a proof of concept.
Read more at: http://smartdatacollective.com/calljourney/304151/are-you-listening-your-conversations

Fascinating, the first word that comes to the mind. Drones and big data are bonding pretty well these days and can be a successful recipe for innovations. 

Jonathan Buckley, senior vice president of marketing at Qubole, gives us a glimpse of this promising combo but not without the challenges. 

Agriculture, disaster relief and anti-poaching are some area where the combination has worked wonders. Needless to say more such wonders are in pipeline. But, yes there is a ‘butt’ of challenges attached here. The data gathered by drones is immense, it will require real time big data analytics and ad-hoc analysis to improve. But, one can feel optimistic about the future.

Have a look at  http://smartdatacollective.com/jonathanbuckley/316611/growing-relationship-between-drones-and-big-data

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

Internet of Things (IoT) helps in transfer of data over a network without human interaction and this is one of the major sources of Big Data. Tom Davenport, independent senior advisor to Deloitte Analytics agrees that even though the concept of IoT implies that the most important feature of sensors is their connectedness but in order to analyze the data generated and use it for efficient action, analytics is required. This has been referred as the “Analytics of Thing” by him. Compiling data from devices and comparing them across time for achieving best results is one of the principal advantages. Utilizing data to optimize processes, identifying maintenance problems, diagnose anomalies and identifying reason for variations are other things that can be included in the Analytics of Things. Davenport suggests that companies that have to work with fast paced data and have relatively less experience with sensor analytics should invest in The Analytics of Things. Read more at: http://www.tibco.com/blog/2015/05/20/moving-from-the-internet-of-things-to-the-analytics-of-things/

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 success mantra for a perfect digital strategy is to deliver the right message (RM) to right person (RP) at the right time (RT). But we are often overwhelmed by popular trends and forget to look beyond them. Here is an inventory of strategies one can follow to find an optimal solution (might not be the trendiest one). Avinash Kaushik (author) in his blog Occam’s Razor talks about the 5 myths of digital marketing and analytic:

• Programmatic Platforms are a panacea.
• A data first strategy is a winning formula.
• All we need is Facebook, forget our websites.
• The web is dead. Mobile web is dead. Apps are the past present and future.
• Cookies! Cookies are all we need.

For more insight into this piece don’t hesitate to follow http://www.kaushik.net/avinash/digital-marketing-analytics-deadly-myths-de-mythified/