Selected definitions for the term “Analytics” from literature:
Analytics is the method of logical analysis (while Analysis is the separation of a whole into its component parts).
Analytics is the discovery and communication of meaningful patterns in data.
Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain and so on).
Analytics is defined as the scientific process of transforming data into insight for making better decisions.
How do you define “Analytics”? Email us and publish your idea!
Business intelligence (BI) and analytics are central to smart city initiatives. These solutions are what puts the “smart” in smart cities and cities need to have an independent assessment of which vendors can provide the best solution for their needs. To support this effort, IDC Government Insights announced the availability of a new report, IDC MarketScape: Worldwide Smart Cities Business Analytics Software 2015 Vendor Assessment. The scope of this report is limited to business intelligence and analytic tools and performance management and analytic applications. Vendors featured include IBM, Microsoft, Oracle, Salesforce, SAP, SAS, and Tableau.
This is the short list of global Smart City business analytics software vendors. The vendors studied for this IDC MarketScape are among the few business analytics vendors that have specific offerings geared towards Smart Cities and that are addressing the most important characteristics for smart cities.
There is a tight field of leaders. There is contention in the Leaders category based primarily on capabilities.
According to Ruthbea Yesner Clarke, Research Director for IDC’s global Smart Cities practice, “The vendors studied for this IDC MarketScape are among the few business analytics vendors that have specific offerings geared towards Smart Cities and are addressing the most important characteristics for smart cities. This report will help city decision-makers understand their options more fully.”
Those important characteristics include:
– Ease and speed of analysis/ self-service
– Strength of analytics
– Flexible delivery models
– Ability to share data
– Innovation and/ or Co-innovation
According to IDC, the amount of data that is created each year is expected to grow from 4.4 zettabytes in 2013 to 44 zettabytes – or 44 trillion gigabytes – in just five years, a growth of 40% per year. (Source: EMC Digital Universe Study, research and analysis by IDC, May 2014) Much of this growth is driven by connected devices and, more specifically, mobile connected devices (RFID, smart cards, body cams, GPS). Government organizations will need to analyze data created from government systems as well as from outside government. Social media, information from mobile apps and smartphones will become more and more useful to cities as they work on managing traffic, crime, events etc.
Written by Seb Murray | Future Of Big Data | Monday 26th October 2015 00:25:00 GMT
Companies that invest in and gain value from their data will have a distinct advantage over competitors, according to research from EY, and the gap between great and good will widen as emerging technologies which enable faster, easier data analysis continue to develop.
By turning information into intelligence, managers can create market advantages, manage risk, improve controls and, ultimately, enhance operational performance and boost earnings.
It is a sizzling hot topic that every aspiring business leader must consider. Those without the pre-requisite analytical skills sought by sectors like finance and consulting will be at a disadvantage, agrees Mark Kennedy, associate professor of strategy and director of the KPMG Centre for Business Analytics at Imperial College Business School.
“It’s no longer enough to know how to use [Microsoft] Excel very well,” he says.
Big data has created fresh business models like those of Salesforce or SAP and even entire new industries, but it has bombarded its way into virtually every sector, from healthcare and hedge funds to banking and brewing.
“Data and analytics have become part of the fabric of how we do business. It’s almost instrumental,” says Matthew Guest, head of Deloitte’s digital strategy practice for EMEA.
While the ability to capture and store vast amounts of data has grown at a rate of knots, the use of technology to analyze entire data sets has been slower to take root, and there continues to be a worrying skills shortage across sectors.
“Increasingly, we see client[s] make more data-driven decisions and putting analytics at the heart of their businesses,” says Gregor McHardy, managing director and technology consulting lead for Accenture UK and Ireland. “So our teams are increasingly bringing data and analytics skills into project analysis and execution.”
Guest columnist Phil Butler is back and advocating that hotels balance tangible conservatism with feasible potential to find their competitive edge with technology
Everyone in the hospitality industry has, by now, been inundated with the hype, the potential, the trend toward empowering hoteliers with big, big, big, big DATA!
But questions for the average hotel director remain: Where is the proof? Is there any tangible evidence a universe of data has helped any hotel owner?
Surely this is this what every revenue manager on the planet wants to know! So what is the state of data analytics in the travel industry, and who is doing what?
Heads or tails?
For hoteliers, or any business for that matter, revenue and profit are what matter. This is from the supplier’s side of the business however. The service, what the customer experiences in dealing with a hotel is the driving force behind profit. Indeed, the fundamentals of the hospitality business remain unchanged; namely, guests being satisfied, willing to spend their money, and happy they did.
However logical and static an idea of selling hotel beds may be, the technologies and marketing behind selling those beds has undergone dramatic changes. The situation today is not unlike the early days of television. That said, we constantly hear digital call points like ‘the experience’, and the ‘customer journey’ parlayed at conferences and summits on the speaker circuit, just like soap suds were evangelised on early TV soap operas.
For the potential consumer of data analytics, and ‘big data’ tools, much of what’s being advertised is just that, just more marketing noise. But not all the noise is hyperbole, some stunning tools are being manufactured, albeit incrementally.
In this ‘so far’ nebulous world of hotel analytics it’s difficult for hoteliers to make heads or tails of what is going on. But what’s clear is that ‘deep customer insights’ will lead to a better guest satisfaction score, or even optimized room rates. Having said that, the hotel concierge or desk clerk is far from being empowered to provide these heightened experiences.
The new chief executive of Cisco has made the case for the networking giant as a key player in building, securing, and making sense of the nascent Internet of Things.
Cisco predicts that the 15 billion devices connected to the internet now will rise to 25 billion – or maybe even 50 billion – by 2020, as the Internet of Things gathers pace. Internet traffic will triple over the same time as a result, and Cisco expects that around 40 percent of mobile internet traffic will be machine-to-machine communication.
All of this will create big changes for how enterprises manage data, said Cisco CEO Chuck Robbins: instead of hauling all that information back to a data center, some of the analysis of that data will be distributed across the network.
Speaking at an event at Cisco’s headquarters in San Jose, California, his first major presentation since taking over as CEO, Robbins said: “You need a very intelligent network infrastructure to make that happen. So now we will have not just data centers but remote centers of data. We can’t always depend on taking the data back to the data center and acting on it because it has a shelf-life, it’s perishable, the value only exists for a short period of time.”