Traditional methods of procurement of inputs like person hours, capacity and other components are ineffective and costly for the clients. According to output-based pricing model output is pre-determined by the vendor and he’s accountable for achieving that level. There has been reduction in checkout time as analytics solutions for optimization are available to the vendors. Vendors face the overall risk of an outcome and at the same time have a share in profitability. In India, due to unawareness of analytics, companies have not yet adopted the model. Companies in India are unwilling to buy analytics services. A more output based approach can cost-friendly. Read more at:http://analyticsindiamag.com/outcome-driven-pricing-model-analytics-really-work/

Optimization has a range of applications. It is best applied when you're deciding among many alternatives. One of the areas where optimization can have a significant impact is planning. To answer these planning questions, businesses face a series of hurdles. First, they need to assemble the data to get a true picture of their business. Second, evaluate options by exploring trade-offs and asking, "what if" questions. Lastly, identifying the best path forward. Businesses can link data, from across their organization in an environment they know and evaluate possible planning scenarios. Organizations can capture more value in the marketplace by improving operations, save money by managing resources more effectively, and mitigate risk by gaining insight into how decisions can impact their business. The power of optimization, however, isn't in finding a path, it's in finding the right path. Read more at: : http://www.ibmbigdatahub.com/blog/what-optimization-and-how-it-improves-planning-outcomes

Presently companies have started shifting or planning to shift on cloud and updating there entire data center with modern infrastructure solutions. They are optimizing their entire ecosystem to support mobility and new kinds of end-point technologies. To stay updated on today's market, need is to design an environment that's ready for virtualization and cloud. Organizations are running into application migration challenges.  Some apps can't be virtualized. Some are simple and can't be moved into a cloud ecosystem. Some apps have critical legacy dependencies and some apps are challenged by compliance and security measures. So here's the good news: new technologies are enabling a very granular understanding of applications and their unique requirements. Let's look at some ways that can work with challenging applications and some application migration options. # Understanding the Application DNA # What to Do With Mainframes? # Working Around Dependencies # Getting Creative with Challenge Apps # Ripping Off the Band-Aid. To read more, follow: http://www.datacenterknowledge.com/archives/2016/02/23/application-migration-know-your-options/

E-Commerce firms very well understand that data is a wealth. The data can provide very crucial and meaningful insights, if used properly. Data Mining is one such way. It is a field of computer science that discovers the patterns in large data sets. This is done using the methods of Artificial Intelligence, Machine Learning, Statistics and Database Systems. Algorithms are made to act on the data set. This often reveals "game changing" results for the industry. Data Mining can be either done manually or can be automated. E-Commerce Firms use this to get to know more about customer's emotions, preferences, and other factors that impact their purchasing style. So they excel in offering a personalized experience through this. It doesn't stay limited to customers. Data Mining is done to keep an eye on the competitors. Read more about it in the article written by Arie Shpanya (contributor to Econsultancy)  at: https://econsultancy.com/blog/67360-why-data-mining-is-the-future-of-online-retailing/

Predictive models are used to predict future outcomes on the basis of data collected from past. Many organizations take their crucial decisions based on the foundations laid by the predictive models. But what if the model goes wrong? "BOOM"- crash of a significant part of the strategy! Though each predictive model has some scope of error. There are chances that the input variables considered for the model were not appropriate. But we need to find out what kind of error and to what extent it is acceptable. There is a need to work on the foundation of the predictive models to prevent failures.  Read more about it in the article written by John Bates(Senior Product Manager for Data Science & Predictive Marketing Solutions) at: http://blogs.adobe.com/digitalmarketing/analytics/what-to-do-when-your-predictive-marketing-is-wrong/

It's all about customer experience - make it better using Data Driven Marketing! It's important to treat them like true customers by taking them through personalized life cycles. Normally, customers are expected to feed the website with lots of data regarding their preferences and personal details. So there is a lot of data in the hand of an owner. But the value lies in effectively scaling data and analytics to enrich their journey. Thus, planning the marketing strategies by keeping in focus the results of this data analytics. The article written by Kevin Lindsay digs deeper into the power of Data Driven Marketing at: http://blogs.adobe.com/digitalmarketing/personalization/strength-in-numbers-best-practices-for-data-driven-marketing/

Data analytics, especially the predictive ones, enable companies to obtain forward-looking insights they wouldn't otherwise have been privy to -- such as forthcoming changes in customer behavior and buying patterns, or the formation of new market segments as-of-yet unserved by the industry. The problem is that while you typically can't achieve the competitive edge you're looking for without analytics, you can't achieve it when you over-analyze, either which becomes a balancing act. Optimization is certainly a critical part of what companies should seek to derive from analytics, so too is innovation. Analytics becomes exclusively about finding ways to cut costs whereas also about finding ways to be cutting edge. It becomes about protecting today's existing revenue stream and should also be about finding tomorrow's new one. Thus, focusing too much on optimization can have the unintended effect of making organizations afraid to take on any risk. To know more, please read this interesting article by Joanna Schloss at: http://www.cmswire.com/cms/analytics/why-companies-cant-afford-to-go-overboard-with-analytics-029179.php