Wave analytics platform is one of the key product sets of Salesforce. When Salesforce first launched the Sales Wave analytics app it had an inbuilt packaged application Wave platform. A platform seems to be quite challenging in terms of its requirements, implementation, price of entry etc. So the company doesn’t sell only wave platform but also the analytic app and it has brought a radiant result. The app can be implemented very quickly and it has reasonable price. The company has significantly matured the tooling and product to render dashboard and make the platform easier for business analysts. But the main challenge is getting the right data and taking advantage of machine intelligence to automate elements of the analysis. Salesforce has introduced a self-service data prep console in the Wave platform. It focused on user specific cases. To capture the enterprise analytics, market vendors must do the part of building an analytics application. For more read : http://diginomica.com/2016/03/08/salesforce-wave-and-the-future-of-enterprise-analytics/

An analytical model takes time to capture data from environment to give a representative distribution. Machine-learning technologies will analyze the data. They try to determine an appropriate distribution. Statistics, analytics, and machine learning are some tools that will help resolve security problems faster, and with fewer resources. This will influence the next wave of automated and predictive defenses. If it is implemented properly, the security benefits of big data analytics will be huge. For more read the article written by Celeste Fralick (Partner Perspectives): http://www.darkreading.com/partner-perspectives/intel/knowledge-gap-series-the-myths-of-analytics-/a/d-id/1324602?

401(k) is a retirement savings plan sponsored by an employer. It is found that retirement planning and wealth management firms are upgrading themselves with client segmentation. Nowadays, segmentation and predictive analytics projects are important to organizations. Segmentation is an important as it help firms continue to add resources. Firms are also trying to progress with the help of big data. For more read the article written by John Sullivan : http://401kspecialistmag.com/segmentation-critical-fully-understand-clients-across-channels-march-2016-boston-new-research-cerulli-associates-global-analytics-firm-finds-firms-approaching-client-segmentat/

Prescriptive analytics is considered the third wave of the Big Data revolution after descriptive analytics and predictive analytics. Prescriptive analytics add a third layer of technology by evaluating possible actions in response to the data and produce a desired outcome. Combination of predictive and prescriptive analytics can be helpful for organizations which wants to do more in less time. Combining prescriptive and predictive analytics add more features into the equation, such as the responsiveness of a prospect to your outreach. Effective prescriptive analytics deliver more transparency and control. For more read: http://insidebigdata.com/2016/03/03/how-prescriptive-analytics-puts-complex-decision-making-on-rails/

Nowadays, computers solve crimes, drive cars, cure sickness and accurately predict political races. But the problem is, it's not enough to just store, access and process data. Machine learning and artificial intelligence algorithms are divided into two factors: huge quantities of timely and accurate data. There are four typical methods to be used to acquire data. They are -1. Required self-declared data,2. Self-declared or observed data 3. Volunteered self-declared data, 4. Crowdsourced observed data. For more read the article link written by David Elkington: http://techcrunch.com/2016/03/01/getting-predictive-about-politics-and-everything-else/