See on techcrunch.com
Do you think that you’re working with “Big Data”? or is it “Small Data”? If you’re asking ad hoc questions of your data, you’ll probably need something that supports “query-response” performance or, in other words, “near real-time”. We’re not talking about batch analytics, but more interactive / iterative analytics. Think NoSQL, or “near real-time Hadoop” with technologies like Impala. Here’s my view of Big versus Small with ad hoc analytics in either case.
See on jameskaskade.com
The key themes:
Be experimental and change ready – be proactive: analytics are changing fast, so you will have toRethink your information – information is not just a byproduct, it’s an assetBroaden analytical architectures – it’s about much more than traditional structured transaction dataMarket analytics internally – communication is key
See on blogs.sap.com
In summary, as analytics becomes pervasive organizations need to spur next phase of analytics-driven innovation. The ability to learn best practices from across domains and industries, making the leap from intra to inter and trans-firewall analytics and blending discovery-driven analytics along with problem-driven methods will play a key role in separating the leaders and the laggards. Making this successful will require developing the right mindset before investing in the datasets, skillsets and toolsets.
See on www.livemint.com
The business case for Big Data analytics is rather easy to make for most health care providers, and you can do so on many levels. However, the path to deployment of these systems is costly and complex. Many providers just don’t have the budget or the time to deploy these systems.
But as time passes, that business case will be too compelling to ignore. Patients will demand it, and I suspect the government will as well. It’s time to get to work.
See on www.healthdatamanagement.com
Mu Sigma DIPP™ framework understands dynamic nature of business and curtail solutions according to such dynamism. Analytics is more than a purely statistical, technical or processing exercise. Mu Sigma believe that analytics is a tool for decision sciences and should help answer the following questions:
What happened or is happening in the business? (Descriptive analytics – D)Why did it happen? (Inquisitive analytics – I)What is likely to happen based on historical information? (Predictive analytics – P)What action should be taken? (Prescriptive analytics – P)
See on www.mu-sigma.com