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
“Analytics have emerged as a must-have for CIOs today, especially as they struggle with putting a strategy around applying Analytics to their Big Data for competitive advantage,” said Andy Wild, president of OpTier. “We commend EMA for recognizing the evolution of APM into Analytics and the critical role it will play going forward. OpTier is on the same page with its Contextual Big Data that is disrupting the traditional Analytics market.”
See on www.marketwatch.com
Companies are realizing analytics are actually at the center of their company, whereas before analytics was just at the edge. According to Bruno Aziza of SiSense, that’s having an impact.
How does a company that doesn’t have an analytics culture in place move forward in developing one?
There are six cultural stages:-
1. Increased Visibility – see the context but understand the detail
2. Move Beyond Gut Feel – Understand the data, its details and apply judgment to be able to react to information faster.
3. Plan for Success – Here is what we can say, based on what we know, what we think we should be able to achieve, informs strategy.
4. Execute on Strategy – Align strategy to knowledge and understanding of the world from a complete standpoint, and the ability to adjust based on success or failure on certain actions.
5. Power to Compete – When you have visibility, able to understand the full context and define a strategy that that can be executed on, and understand the levers, that you can truly then stretch your organization to a point where you are able to truly compete in your market and adjacent markets.
6. Culture of Performance, which is more of the North Star rather than a place where you end up.
See on sloanreview.mit.edu
Fred Balboni, Global Leader, Business Analytics and Optimization, speaking to the need of infusing analytics throughout the organization and how IT and LOB executives are changing partnership models to bring this to reality. In the panel discussion, JP Morgan Chase shared how they are using analytics to mine information from the “new” customer who is banking via mobile channels and Thompson Reuters described the role that analytics plays in their customer centricity by creating upsell and cross-sell opportunities externally and greatly reducing the cost of ownership internally.