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Showing posts with label technology. Show all posts
Showing posts with label technology. Show all posts

Wednesday, May 22, 2013

Cool BI: Emerging Trends and Innovations in BI



Cindi2



Cindi Howson, founder of BI Scorecard

It’s hard to be innovative when your BI team is deluged with fixes, fighting fires, and basic data requests.  Yet to move from reactive, report-focused development to break through BI demands innovative BI teams and technologies.
In this keynote, Cindi Howson, founder of BI Scorecard and author ofSuccessful Business Intelligence: Secrets to Making BI a Killer App, highlights:
  • Being proactive when there’s no time or budget for innovation
  • Evangelizing BI in a culture resistant to change
  • Prioritizing innovations that will provide the biggest value
  • The trends most disruptive to BI including mobile, social, visual data discovery, big data, and cloud.
Check out this ketnote presentation at the Gurus Of BI (GOBI) conference on June 1oth: www.gurusofbi.no

Tuesday, August 28, 2012

Nordic Choice Business Intelligence

Here is a video where shows the job that me and BI collegues from Nordic Choice Hotels have done last 2 years.
Also we collaborated with Platon (www.platon.net) in UX and Dashboard Designing of our Business Intelligence Solution.



A Visionary Choice - Nordic Choice Hotels Business Intelligence vision from Platon on Vimeo.

Wednesday, June 20, 2012

Time to Invest: Predicting What’s Next for Technology in Hospitality

Time to Invest: Predicting What’s Next for Technology in Hospitality


3/1/2012

Douglas C. Rice


One of the biggest challenges for any technology executive is predicting the landscape of toolsets and IT infrastructure that will be available in the future. If you make the right choice, today’s investments may last for 10 or even 20 years. In contrast, the wrong choice could force you to replace core elements of your systems strategy in half that time, or less.

The hospitality industry is largely a consumer of these building blocks, which include such things as network protocols (think TCP/IP), materials (silicon, copper, fiber), data protocols (SQL, ODBC), operating systems (Linux, Windows, iOS, Android), and presentation and messaging protocols (HTML, SOAP). These are not developed for hospitality; rather they serve a wide variety of consumer and business needs. The building blocks just referenced are familiar names in the industry now, but how can you determine what the building blocks of the future will be?
One of the best lessons I learned from a wise person many years ago was that if you want to predict the future, find out where the big money is being invested. In technology, this means learning where the industry giants are investing their billions in research and development (R&D) – companies like Microsoft, Intel, Apple®, Cisco, Google, Oracle, IBM and AT&T, to name just a few. When many of them invest in the same building blocks, you can count on those building blocks becoming mainstream and supportable for many years to come. If you were watching these barometers, you foresaw the end of the mainframe era. You also saw the Internet coming years before the dot-com boom began, and you anticipated the mobile app revolution.



A Window to the Future

One of the great privileges I enjoy from the vantage of running on of the hotel technology industry’s largest trade associations is frequent opportunities to see the world from the vantage point of many different industry technology leaders, including those that focus on hospitality, as well as those serving the broader technology space. HTNG’s regular face-to-face meetings of industry technology leaders offer great insights into where technology industry leaders expect to go in coming years, and how hospitality technology providers view those trends. These insights provide clues as to which investments will be future proof and which will be risks.

The Cloud

Despite that no one really even agrees on the meaning of the word, there is no question that the cloud is by far the biggest area of investment. Microsoft, Amazon, Force.com, Apple, and now even networking companies like Cisco are placing huge bets on moving complexity and cost up the wire, away from the user and into data centers where they can benefit from scalability, shared support resources and load balancing. You can argue that much of the money being spent is on marketing hype rather than technology, and there is undoubtedly some truth to that point of view. But these companies would not spend money on marketing if they didn’t expect sales, which means they expect to deliver product. We are only in the early days of the cloud revolution currently, but the amount of money these companies are spending ensures that it will catch on.
One of the most important aspects of this, from a hospitality perspective, is the development of cloud service brokerages. In an industry where the dozens of different systems controlling a hotel must be mashed together from different parties – at a minimum this includes the building owner, the management company and the franchisor – if services are going to be cloud based, there must be cloud-level interoperability. Brokerage services can be thought of as cloud-based middleware that ensure robust and reliable communication between cloud-based systems operating in different clouds. They are what will enable a cloud-based CRS running in the Force.com cloud, for example, to easily connect with a cloud-based PMS running on Microsoft Azure. They can also allow a hotel company to engage a single vendor to manage the aggregation, integration, customization and governance of cloud services. Intel is one of many companies making big investments in the cloud services brokerage arena, which by definition are independent of any single cloud services provider.
This is good news for hospitality. It holds the promise of relieving the hotel owner of responsibility for managing the operation and integration of premise-based systems, with associated costs for deployment, equipment and maintenance performed by on-site or locally based staff. There is a healthy debate as to which technology services must remain premise based to avoid major problems in the event of network outages. But the number of hotel technologies that are proving to be robust in cloud deployments – at least in parts of the world with good Internet access – are growing every year as obstacles are overcome. There is a distinct possibility that every aspect of hotel technology except for end-user devices and portions of the network infrastructure may ultimately move to the cloud.



Mobility

Turning from infrastructure to hardware, it’s hard to deny that the big money is moving to mobility. Apple may have been the first of the megacompanies to figure this out –arguably, they became a megacompany by doing so – but other giants like Samsung, Microsoft and Amazon have also been leading the charge. Tablets have not yet fully replaced PCs in business travel, but the gap is narrowing rapidly. Indeed, the form factor of notebooks is getting smaller as that of tablets gets larger. We are fast approaching the day when the difference between the two is the presence or absence of a paper-thin keyboard.

For hospitality, this creates both opportunity and challenge. Mobility gives us the ability to communicate with our guests and staff in real time. This capability can be used to both define new service models and revenue streams, and to improve existing ones. Today’s challenge is that mobility requires massive investment in wireless infrastructure and bandwidth. (More about that challenge in the third trend.)

The key takeaway for hospitality is that when you invest in user interfaces, it will typically be wise to design for mobile devices first, rather than for PCs. Certainly this is true for applications that face guests, but also for staff-facing applications where the staff is or could benefit from being mobile. This includes a large proportion of front-of-house, back-of-house and guest-facing hospitality applications.

Don’t bet on a particular operating system, the leadership in this area will change based on competitive dynamics outside the control of anyone in hospitality. Multiplatform toolsets such as HTML5 are widely supported and are becoming de facto standards for deployment of applications across multiple platforms. While not yet perfected in all environments, it’s the clear winner in overall investment by mobile operating system and device manufacturers.



Cellular Offload

Mobile devices create the need for massive bandwidth. iBAHN collects extensive data on these trends, which it has generously shared with the industry, and the data is downright scary: bandwidth requirements are roughly doubling every year, with mobile devices leading the way.

Many hotels have shortchanged the investment in upgrading bandwidth and supporting Wi-Fi infrastructure, believing that the migration of mobile devices to 4G/LTE cellular technologies will solve the problem by ultimately reducing or eliminating Wi-Fi. But a look at where the megacarriers are investing proves this assumption completely false.

Carriers such as AT&T, Verizon and Sprint realized in 2007 to 2008 that the data tsunami was coming, and there was simply not enough cellular radio spectrum for them to outrun it, even given future advances in cellular technology through LTE and beyond. Carriers such as these and their counterparts in other countries know they cannot satisfy the demand for mobile data with cellular technologies, at least not in densely populated areas. Their strategies for satisfying the need are based on moving cellular traffic to terrestrial networks – meaning Wi-Fi. Virtually all major carriers in developed countries are aggressively investing in what they call offload, meaning they are building out or gaining access to Wi-Fi networks, and enabling their devices to roam onto these networks automatically. If you have an AT&T smartphone and leave wireless enabled, and walk into a Starbucks, McDonalds, American Airlines Club room or Hilton-branded U.S. hotel, you have probably already experienced this. These few examples exemplify the economics: in congested areas, it is far cheaper for a cellular carrier to build or fund a Wi-Fi network, than to install an additional cell tower and/or buy additional spectrum.

This is good news for hotels, because it means that cellular companies have an economic reason to help fund hotel Wi-Fi networks. In New York and San Francisco, where cellular coverage is saturated, some carriers have gone so far as to offer free Wi-Fi networks to certain hotels, because it was the least expensive option for them to satisfy the needs of their customers. Hotels in less congested areas won’t get free Wi-Fi networks anytime soon, but many hotels can now find, at a minimum, willing investment partners to help offset the cost of a Wi-Fi network in return for the ability to route cellular traffic through it. In remote areas, the cellular network may be sufficient to meet consumer needs. Urban and suburban hotels are well positioned to benefit from this trend, but will need to forge appropriate alliances with carriers to do so. Over time, carriers expect roaming models to develop, enabling phones from different carriers to offload to a single Wi-Fi network, with payments to the provider of that network based on traffic volumes or other factors.

There are many risky bets in technology, but a few safe ones. When you are making decisions on investments, strive to determine the major trends, and then invest in solutions that align with those trends. If you aren’t looking at the cloud, expecting the user interface to migrate to mobile devices, or thinking about how your hotel can benefit from carrier investments in Wi-Fi, you’re probably missing the boat.



Douglas C. Rice is the executive vice president and CEO of Hotel Technology Next Generation.







www.htng.org

The future of Technology

After some serious posts it is time to laugh a bit. Parody for Apple technology can make your day:

Thursday, April 5, 2012

Big Data, the amazing thing

Big data is a term applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target currently ranging from a few dozen terabytes to many petabytes of data in a single data set.

In a 2001 research report[15] and related conference presentations, then META Group (now Gartner) analyst, Doug Laney, defined data growth challenges (and opportunities) as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in/out), and variety (range of data types, sources). Gartner continues to use this model for describing big data.






Whether through blogs, twitter, or technical articles, you’ve probably heard about Big Data, and a recognition that organizations need to look beyond the traditional databases to achieve the most cost effective storage and processing of extremely large data sets, unstructured data, and/or data that comes in too fast. As the prevalence and importance of such data increases, many organizations are looking at how to leverage technologies such as those in the Apache Hadoop ecosystem. Recognizing one size doesn’t fit all, we began detailing our approach to Big Data at the PASS Summit last October. Microsoft’s goal for Big Data is to provide insights to all users from structured or unstructured data of any size. While very scalable, accommodating, and powerful, most Big Data solutions based on Hadoop require highly trained staff to deploy and manage. In addition, the benefits are limited to few highly technical users who are as comfortable programming their requirements as they are using advanced statistical techniques to extract value. For those of us who have been around the BI industry for a few years, this may sound similar to the early 90s where the benefits of our field were limited to a few within the corporation through the Executive Information Systems.

Analysis on Hadoop for Everyone

Microsoft entered the Business Intelligence industry to enable orders of magnitude more users to make better decisions from applications they use every day. This was the motivation behind being the first DBMS vendor to include an OLAP engine with the release of SQL Server 7.0 OLAP Services that enabled Excel users to ask business questions at the speed of thought. It remained the motivation behind PowerPivot in SQL Server 2008 R2, a self-service BI offering that allowed end users to build their own solutions without dependence on IT, as well as provided IT insights on how data was being consumed within the organization. And, with the release of Power View in SQL Server 2012, that goal will bring the power of rich interactive exploration directly in the hands of every user within an organization.
Enabling end users to merge data stored in a Hadoop deployment with data from other systems or with their own personal data is a natural next step. In fact, we also introduced Hive ODBC driver, currently in Community Technology Preview, at the PASS Summit in October. This driver allows connectivity to Apache Hive, which in turn facilitates querying and managing large datasets residing in distributed storage by exposing them as a data warehouse.

Tuesday, March 20, 2012

Mike Walsh, futurist

In our VK2012 was invited Mike Walsh and he had a keynote about technology future. He instisted that the future of the World is DATA and claimed the most important profession of the future will be Data Scientist. Let the future begin and let say I am a Data Scientist.

Wednesday, June 22, 2011

Predictive Analytics vs Data Mining

Technology Cycle:
Data warehousing is a mature technology, with approximately 70 percent of Forrester Research survey respondents indicating they have one in production. Data mining has endured significant consolidation of products since 2000, in spite of initial high-profile success stories, and has sought shelter in encapsulating its algorithms in the recommendation engines of marketing and campaign management software. Statistical inference has been transformed into predictive modelling. As we shall see, the emerging trend in predictive analytics has been enabled by the convergence of a variety of factors.

Technology Hierarchy:
In the technology hierarchy, data warehousing is generally considered an architecture for data management. Of course, when implemented, a data warehouse is a database providing information about (among many other things) what customers are buying or using which products or services and when and where are they doing so. Data mining is a process for knowledge discovery, primarily relying on generalizations of the "law of large numbers" and the principles of statistics applied to them. Predictive analytics emerges as an application that both builds on and delimits these two predecessor technologies, exploiting large volumes of data and forward-looking inference engines, by definition, providing predictions about diverse domains.

Methods:
The method of data warehousing is structured query language (SQL) and its various extensions. Data mining employs the "law of large numbers" and the principles of statistics and probability that address the issues around decision making in uncertainty. Predictive analytics carries forward the work of the two predecessor domains. Though not a silver bullet, better algorithms in operations research, risk minimization and parallel processing, when combined with hardware improvements and the lessons of usability testing, have resulted in successful new predictive applications emerging in the market. (Again, see Figure 1 on predictive analytics enabling technologies.) Widely diverging domains such as the behaviour of consumers, stocks and bonds, and fraud detection have been attacked with significant success by predictive analytics on a progressively incremental scale and scope. The work of the past decade in building the data warehouse and especially of its closely related techniques, particularly parallel processing, are key enabling factors. Statistical processing has been useful in data preparation, model construction and model validation. However, it is only with predictive analytics that the inference and knowledge are actually encoded into the model that, in turn, is encapsulated in a business application.

Definition
This results in the following definition of predictive analytics: Methods of directed and undirected knowledge discovery, relying on statistical algorithms, neural networks and optimization research to prescribe (recommend) and predict (future) actions based on discovering, verifying and applying patterns in data to predict the behavior of customers, products, services, market dynamics and other critical business transactions. In general, tools in predictive analytics employ methods to identify and relate independent and dependent variables - the independent variable being "responsible for" the dependent one and the way in which the variables "relate," providing a pattern and a model for the behavior of the downstream variables.

In data warehousing, the analyst asks a question of the data set with a predefined set of conditions and qualifications, and a known output structure. The traditional data cube addresses: What customers are buying or using which product or service and when and where are they doing so? Typically, the question is represented in a piece of SQL against a relational database. The business insight needed to craft the question to be answered by the data warehouse remains hidden in a black box - the analyst's head. Data mining gives us tools with which to engage in question formulation based primarily on the "law of large numbers" of classic statistics. Predictive analytics have introduced decision trees, neural networks and other pattern-matching algorithms constrained by data percolation. It is true that in doing so, technologies such as neural networks have themselves become a black box. However, neural networks and related technologies have enabled significant progress in automating, formulating and answering questions not previously envisioned. In science, such a practice is called "hypothesis formation," where the hypothesis is treated as a question to be defined, validated and refuted or confirmed by the data.