In this special guest feature, Guy Yehiav, CEO at Profitect, discusses how prescriptive anaytics holds the keys to efficiency with the least amount of risk and the fastest time to value.
Our friends over at Boston University’s Master of Science in Computer Information Systems Online Program put together the compelling infographic below that highlights why Business Intelligence (BI) is the key to competitive advantage for enterprises today.
Wal-Mart handles more than a million customer transactions each hour and imports those into databases estimated to contain more than 2.5 petabytes of data.
Radio frequency identification (RFID) systems used by retailers and others can generate 100 to 1,000 times the data of conventional bar code systems.
Facebook handles more than 250 million photo uploads and the interactions of 800 million active users with more than 900 million objects
(pages, groups, etc.) – each day.
More than 5 billion people are calling, texting, tweeting and browsing on mobile phones worldwide.
Organizations are inundated with data – terabytes and petabytes of it . To put it in context, 1 terabyte contains 2,000 hours of CD-quality music and 10 terabytes could store the entire US Library of Congress print collection . Exabytes, zettabytes and yottabytes definitely are on the horizon .
Data is pouring in from every conceivable direction: from operational and transactional systems, from scanning and facilities management systems, from inbound and outbound customer contact points, from mobile media and the Web .
According to IDC, “In 2011, the amount of information created and replicated will surpass 1 .8 zettabytes (1 .8 trillion gigabytes), growing by a factor of nine in just five years . That’s nearly as many bits of information in the digital universe as stars in the physical universe .” (Source: IDC Digital Universe Study, sponsored by EMC, June 2011 .)
The explosion of data isn’t new . It continues a trend that started in the 1970s . What has changed is the velocity of growth, the diversity of the data and the imperative to make better use of information to transform the business .
The hopeful vision of big data is that organizations will be able to harvest and harness every byte of relevant data and use it to make the best decisions . Big data technologies not only support the ability to collect large amounts, but more importantly, the ability to understand and take advantage of its full value .
Analytics-generated insights are increasingly driving successful decision-making for communication service providers (CSPs). From network enablers to business support systems, there are opportunities to utilize insights gathered from careful analysis of network data.
Big data is at the core of this opportunity. But the traditional technology view of big data is not enough. Rather than focusing on big data technology, CSPs should start from the business value they want to create and apply extensive telecom competence to understand how the relevant insights can be extracted from raw data before applying big data techniques as needed.
Many healthcare organizations currently utilize a Risk Scoring program as part of doing business. Health insurance companies, for example, calculate one Risk Score for each member to reserve for risk-adjusted payments to Medicare Advantage and Health Insurance Exchange plans. While the calculation of one Risk Score per member is effective for the distribution of risk-adjusted payments, it lacks insight into a member’s medical needs. The current methodology fails to provide a complete picture of members’ health and serves as more of a reactive than preventative tool.
Many companies are confusing adopting big data technology with creating a coherent big data strategy and in the process are creating big data debt. In this paper we present our remedy for big data debt – a data and analytics centric approach. This approach is a set of concepts and guidelines that allow us to invest in big data and get increasing long-term returns rather than spending money to get short-term payback that creates long-term debt.
Should the data warehouse be deployed on the cloud? IDC addresses this question on a regular basis. As adoption of cloud software increases, organizations of all sizes across industries and geographic regions are evaluating and assessing the opportunities and challenges of deploying software on the cloud. Data warehousing solutions are no exception to this trend.
As with any major IT initiative, cost-savings drives many data lakes initiatives. However, the value will ultimately be realized in the potential avenues it offers for business growth. The next frontier for data lakes is providing organizations with greatly enhanced analytical opportunities.
The sorts of questions that oil and gas managers
need answers to is driving the need for ever more sophisticated analysis. Moving from descriptive analytics which answer the question “What happened?” to diagnostic analytics which address “Why did it happen?”. Once companies have that information, it would be natural to want to understand “When will
it happen again?” (predictive analytics). And finally, companies will also want to find out “What should happen?” (prescriptive analytics) in certain scenarios, so that they can get repeatable results.
When used effectively, data analytics can help to save lives, improve efficiencies, reduce costs, and help government deliver better citizen services. This special GovLoop report explores how data analytics is changing Government.