Major sporting events like the U.S. Open are not only exciting to watch and follow, but are also a living lab for how “big data” can translate into big business. This year, the USTA is using predictive analytics and cloud computing to improve the experience for everyone: fans, tennis players, event organizers and broadcasters. USTA’s Phil Green and IBM’s Rick Singer explain how.
Really interesting video about big data and the potential growth. We believe that there is a massive opportunity for new businesses to be created to drive this market forward.
There are four (4) ancient postulates of data warehousing:
- Postulate 1 (1970s): Operational and informational environments should be separated for both business and technical reasons.
- Postulate 2 (1980s): A data warehouse is the only way to obtain a dependable, integrated view of the business.
- Postulate 3 (1980s): The data warehouse is the only possible instantiation of the full enterprise data model.
- Postulate 4 (1990s): A layered data warehouse is necessary for speedy and reliable query performance.
See: Devlin, B. “Business Integrated Insight (BI2): Reinventing enterprise information management”, (2009), www.9sight.com/resources.htm
This explication of the underlying assumptions (or postulates) helps to explain the evolution of the data warehouse architecture. It seems now that these decisions were made based on the available computing power at the time. The operational data stores were straining under the load at the time, and BI was seen as a luxury compared to the real business of making money. Now with the large computing resources of CPU, disk space, and networks, this constraint is no longer a barrier to integration of front-end and back-end business processes.
Devlin says that the explosion in the number of DW components from the mid 1990s onwards suggests that the data warehouse architecture is failing. From my perspective, this mess came about because some enterprises tried to do data warehousing on the cheap. Requirements were usually vague and the implemented solutions were ad-hoc. I think Devlin is saying that this mess was inevitable given the ancient posulates given above.
After reflecting on this mess, Devlin came up with five (5) modern postulates for highly evolved business:
- Modern business processes seamlessly combine action-taking and decision-making, and require an integrated continuum of consistent information.
- The new information architecture must be based on a comprehensive enterprise information model, spanning all types of information used in the business.
- The business information resource is best maintained as a single copy of each data item, with only the most minimal resort to transient layers or copies of specific subsets of data for specialized needs.
- An integrated, model-based and closed-loop process environment is needed to create, maintain and use both the business information an activities.
- An integrated, flexible and role-based user interface provides access to the entire business information.
What is a comprehensive enterprise information model? How is it different from a data model? Data model was mentioned in postulate #3 above. So, are we moving up the knowledge hierarchy from data to information? If so, I think the analysis is confused by the ambivalent meaning of data model—see my earlier notes at On the Logical Difference Between Model and Implementation.
Devlin goes on to propose a new architecture Business Integrated Insight (BI2)…covering all information and process:
- People Personal Action Domain
- Process Business Function Assembly
- Information Business Information Resource
See: Devlin, B. “Business Integrated Insight (BI2): Reinventing enterprise information management”, (2009), http://bit.ly/BI2_White_Paper
Devlin introduces Biz-Tech ecosystem. He does not think IT is dead despite what many analysts say. He says that IT has evolved into a Biz-Tech ecosystem which is the fully symbiotic existence and IT. This has the following three (3) characteristics:
New technology enables business possibilities;
new business opportunities drive technology advances
Silos in business and IT are obvious to Web-savvy customers;
coherence becomes mandatory
Business people need IT skills to see how to recreate the business with new technoology;
IT people need business acumen to see how to satisfy business needs in new ways with emerging technology
This view flies in the face of the idea of computing (or IT) as a commodity. IT people need to be integrated into the business as much as sales, marketing, HR, production, and design. All of these people has to come together to create a coherent product for the customer. IT people are no longer resources simply to be brought on the open market. And IT people need to stop thinking of themselves as simply Java programmers or Oracle DBAs.
He gives three (3) examples of Biz-Tech ecosystems:
- Business Intelligence reinvents Retail (cf Walmart)
- The web recreates the library (cf Wikipedia)
- Big data redefines automobile insurance—Pay as you drive
Devlin sees evolution of BI2 occurring in three (3) parts:
- Removal of layers in BI2.
- Introduction of the advanced information warehouse which has pillars rather layers. Data, metadata, and models are shared across the pillars. EDW has evolved into Core Business Data. (See slide #21)
- Data virtualisation becomes more important by enabling queries to be constructed across differing data stores.
- Dealing with new information types:
- Big data challenges our fundamental beliefs about the relationship between data and knowledge.
- The DIKW pyramid is no longer valid. (Date -> Information -> Knowledge -> Wisdom) (See slide #23)
- Introducing m3 – the modern meaning model (see slide #24)
- Decision making moves from individual to collaboration
- Decisions are not rational
Devlin gave the following picture of the Modern Meaning Model:
I have not absorbed this model yet, but it does appear to be sensible. Whether or not it is useful remains to be seen.
Devlin sees mobile computing as important as the producer and consumer of information, and decisive in team-based decision making (the iSight Model—see slide #29). He sees the informal interactions being recorded for future analysis.
Devlin’s conclusions are:
- Overall—simplify the BI environment
- Less layers, less copies, less ETL
- Recognise the emerging biz-tech ecosystem
- Big Data—forget the hype, but do evaluate
- Business opportunities may exist in unexpected places
- Recall that big data has very different characteristics
- Enable innovation through team working
- Collaborative decisioning vs. collaborative BI
- The emerging role of informal information
The explosion of data available today has been both a blessing and a curse to enterprises in all verticals. The ability to collect, store, mine, and analyze huge quantities of data has changed the way that companies do business, providing a competitive advantage to those companies that can best leverage their big data. According to a report by Mckinsey Global Institute, “a retailer using big data to the full could increase its operating margin by more than 60 percent.” Such an advantage is hard to ignore. Yet the increased storage and use of this data increases the complexity associated with securing that data.
As concerns around data security grow apace with the adoption of big data mentality, some companies struggle to find the balance between collecting enough data to compete and ensuring that their business is not threatened by the likelihood of a compromise. Data protection remains a vitally important element. In fact as more data is collected and stored, data protection should become a more prominent concern for enterprises.
Big data can contain many different categories of sensitive data – customer data, corporate information, and even intellectual property. The vast majority of the data is in semi-structured or unstructured format. Both the quantity and the structure of the data bring with it concerns about security and close on its heels, performance. However, performance doesn’t need to be an issue when considering theencryption of big data. Technological innovations, such as IBM’s AES-NI, can help companies have their data and use it, too.
Watch this video which describes the winning combination of HP VirtualSystem, Microsoft System Center software, and the HP IO Accelerators.
If you need to know more please contact http://www.c24.co.uk
Yet just this week Capgemini announced the findings of a report (“The Deciding Factor: Big Data & Decision Making”) which showed that, in a study of over 600 C-Level execs, 9 out of 10 leaders believe data is as fundamental to their business as people and capital.
With the amount of data being generated reaching astronomical levels (and accelerating) buzz word or not, Big Data is a problem all business leaders need a strategy for.
Ever wondered just how much information is created? Domo produced an eye-opening infographic which you might be interested in.
Cloud-based, file synchronization services like Dropbox, Sugarsync, and Google Drive have exploded over the past few years. While these platforms are compelling for consumers, they can be unsettling for organizations because of the new data protection and management ramifications they carry.
Based on Gartner’s assessment that “Huge Amounts of Proprietary and Regulated Data Are Leaking Onto NoncorporateDevices, Outside of Enterprise Controls and Audit Trails,”1 here are three conclusions that can be drawn about current state of file sharing for organizations:
- Cloud-based file synchronization services have become so popular that they threaten to scatter organizational assets.
- Organizations must offer sanctioned file synchronization services and device interoperability, or they run the risk of losing control of digital assets outside the corporate LAN.
- Today’s cloud based file synchronization services sacrifice a level of control and do not fully integrate with existing infrastructure.
Read the full white paper here to learn how organizations can take back control of their data assets.
 “How to Control File Synchronization Services and Prevent Corporate Data Leakage,” by Jay Heiser, and Lawrence Pingree, Published 31 January 2012
by David Gibson
We all recognise that shoppers have certain habits before they buy products, however what are they and how can they help us increase the conversion ratios of our companies? The following infographic highlights some interesting stats.
According to a new survey by the Economist Intelligence Unit (commissioned by IT consulting giant Capgemini), corporate executives are starting to figure out that big data matters and how to leverage it, even if they haven’t fully come around on the concept.
The surveyors questioned more than 600 C-level and other senior executives across the globe, finding that while they understand certain realities — such as the importance of valuable analysis versus sheer volume of data, and the increasing role of data to inform intuition — most respondents (55 percent) still don’t think their management teams view big data strategically enough.
- Big Data Delivers Results, But Enterprises Struggle With Skills (blogs.wsj.com)