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Answer Upon - Data Warehousing - Tom's Ten Data Tips
A distinguishing factor of a hosted PBX system from conventional systems is the lack of any onsite equipments. In a hosted system, the functionalities of a standard PBX system are provided as a service through a dedicated connection. This kind of PBX system is therefore affordable. Any small businesses can get a sophisticated phone system with all the features found in expensive PBX systems, but at a fraction of the cost.There are service providers who claim to provide hosted PBX systems. However, many of these systems turn out to be PBX systems implemented through software. This kind of phone system cannot be termed as hosted, since they function in the premises itself. The only difference lies in the absence of any hardware elements. All the functionalities are implemented through software. This kind of PBX system is technically a premise based system, and not hosted.Software PBX system are implemented using a dedicated PC or server machine. All though it may contain all the functionalities of a conventional phone system, it will not contain the advanced features of a hosted system such as remote extensions, which is
6. Which Data Should Be loaded In The Data Warehouse?
The data that enter the DWH ultimately determine its place in the organization. A "let's load all data, to be safe"-attitude is a sure fire way to derail your DWH project. Choices as to what should and should not be included need to be made early on, to keep the project manageable. After proven success of the delivered, deployed, and profitably exploited DWH, there always will be funding somewhere to include previously ignored interfaces. Given the anticipated lifecycle of the DWH, it makes perfect sense to consciously exclude certain sources. The choice as to what data to include needs to be driven by business con
Machine quilting is becoming more and more popular by the day. Long gone are the days when you would sit down with a quilting frame and manually hand sew it until you are satisfied that it is well designed and will stand the test of time. If you still do use that method then you should really try machine quilting for size. If you do not like it then it is your choice, but you owe it to yourself to try out the technology that may just save you a lot of time and make it more enjoyable as a pastime. Some quilters love machine quilting, especially those that are new to the hobby, but some prefer to stick to the traditional methods of quilting instead. The choice is yours!Hand-Sewing Vs. Machine SewingYou have to be creative if you want to create works of art via quilting. There are various patterns that you can follow but it is much more satisfying to come up with your own designs and create a style for yourself. Hand sewn quilts are symbolic of the effort that individuals are prepared to put into their handiwork and if you make your quilts that way then you should be proud of yourself. However, you may well have to
How large the Data Warehouse market will grow nobody knows yet. But for sure it is still growing fast, and currently is estimated at 4,5 billion dollar per year (IDC).
1. Why Do Data Warehouse Projects Run Into Scope Creep?
To quote Bill Inmon (guru and author of several great books on Data Warehousing) "Traditional projects start with requirements and end with data. Data Warehousing projects start with data and end with requirements." As soon as the project gets under way, users will find new applications, and with it will come new requests for data. Interestingly, these projects often are justified by moving Q&R work away from the 'data people'. What we've seen is that the first thing that happens as soon as the project delivers is that more requests for special queries are submitted to these same 'data people'. This may appear to undermine the initial business case but actually signals the onset of value creation from the DWH project.
2. Star Schema Versus Entity Relation Model?
There has been enormous debate in the community about the merits of different data models. At the risk of over simplifying: ER models tend to have better performance (processing time) for the end user, and are often perceived as "easier" to understand by end users. Drawbacks are that ER models require more disk space, and, because of the intrinsic redundancy in the data, have consistency problems from a maintenance perspective. Having said this, the practice seems to be that often some combination of the two is unavoidable in the practical setting, despite preferences (ER or Star) of the chief architects. Overall, Star models seem to have gained the most ground.
3. The Importance of a Data Warehouse Business Case
Much has been written about the business case for a Data Warehouse. What goes in to a good business case? IT savings are ubiquitous in DWH business cases. The important point is to not limit this to 'pure' savings, but to connect to primary business processes as much as possible. As an example, faster turnaround cycles for list selections are fine (when quantified in hourly rates), but it is even better if the revenue from more customer acquisitions that follow from these selections can be tied in. Not only will the relation to revenue growth rather than savings make for a more balanced business case, more important is the intrinsic business buy-in that results from a direct connection to the company bottom line. These days, changes in legislation (in particular Sarbanes-Oxley) play a major role in justifying business cases. This may be either through a higher company valuation for its transparent information gathering, or, less sleepless night for the CEO, which is of course priceless...
4. Why Do Data Warehouse Projects 'Never' Go Wrong?
Actually, Data Warehouse projects do sometimes fail. But, they fail so rarely, that it is actually very hard to believe... Especially after having talked to so many disgruntled end-users. And there are many ways a Data Warehouse project can go wrong. Delivering on time, data administration issues, and unavoidable data quality issues in feeding systems. Corporate politics (see Tip 7) are probably the best explanation for this phenomenon of near 100% success rates on DWH projects. In my experience, the reason why a failure or 'semi-failure' can go unnoticed is either because senior management is not aware, or, let's say "unmotivated" to talk about misspending of company funds. As a result, not enough is learned. Maybe we as consultants have a stake in this as well, as this assures the industry plenty of ongoing business... J
5. What is Different About Warehousing Web Data?
Kimball & Merz (2000): "Although this clickstream data in many cases is raw and unvarnished, it has the potential of providing unprecedented detail about every gesture made by every human being using the Web medium". The subatomic nature of clickstream data poses unique challenges. There are fewer built in feedback mechanisms to ensure data quality, compared to other data streams. The relation between user mouse clicks and server log records is not as tight as in "traditional" transaction processing due to technical issues like proxy servers and caching. Because of these differences, IT people need to adapt to the web process flow, rather than having the process adapt to IT needs as is common for most other DWH interfaces.
6. Which Data Should Be loaded In The Data Warehouse?
The data that enter the DWH ultimately determine its place in the organization. A "let's load all data, to be safe"-attitude is a sure fire way to derail your DWH project. Choices as to what should and should not be included need to be made early on, to keep the project manageable. After proven success of the delivered, deployed, and profitably exploited DWH, there always will be funding somewhere to include previously ignored interfaces. Given the anticipated lifecycle of the DWH, it makes perfect sense to consciously exclude certain sources. The choice as to what data to include needs to be driven by business cons
Marshall Field’s, the trendsetting, always fashionable icon of customer service in retailing, is about to become history in downtown Chicago.Macy’s, its owner, is renaming the store after itself.With the closing of Field’s another bright chapter in the history of customer service is also coming to an end.Field’s was known for carrying special merchandise, for being a place where patrons could meet for lunch, and for marketing savvy.It was so embedded into the popular lore that Chicagoans made Marshall Field, its founder, an icon of accomplishment, and a symbol of business success.My father used to tell the story about how he was accepted to a prestigious military college, but his dad wasn’t keen on the idea of his going.Grandpa reduced his concern about his son’s future to a pithy question:“Would you rather be a Field Marshal or a Marshall Field?”Dad got the point, and went on to have an interesting career in business, telling that story with a smile and just a tinge of nostalgia.Marshall Field was asked what to do in handling a certain patron at his store and he bristled
2. Star Schema Versus Entity Relation Model?
There has been enormous debate in the community about the merits of different data models. At the risk of over simplifying: ER models tend to have better performance (processing time) for the end user, and are often perceived as "easier" to understand by end users. Drawbacks are that ER models require more disk space, and, because of the intrinsic redundancy in the data, have consistency problems from a maintenance perspective. Having said this, the practice seems to be that often some combination of the two is unavoidable in the practical setting, despite preferences (ER or Star) of the chief architects. Overall, Star models seem to have gained the most ground.
3. The Importance of a Data Warehouse Business Case
Much has been written about the business case for a Data Warehouse. What goes in to a good business case? IT savings are ubiquitous in DWH business cases. The important point is to not limit this to 'pure' savings, but to connect to primary business processes as much as possible. As an example, faster turnaround cycles for list selections are fine (when quantified in hourly rates), but it is even better if the revenue from more customer acquisitions that follow from these selections can be tied in. Not only will the relation to revenue growth rather than savings make for a more balanced business case, more important is the intrinsic business buy-in that results from a direct connection to the company bottom line. These days, changes in legislation (in particular Sarbanes-Oxley) play a major role in justifying business cases. This may be either through a higher company valuation for its transparent information gathering, or, less sleepless night for the CEO, which is of course priceless...
4. Why Do Data Warehouse Projects 'Never' Go Wrong?
Actually, Data Warehouse projects do sometimes fail. But, they fail so rarely, that it is actually very hard to believe... Especially after having talked to so many disgruntled end-users. And there are many ways a Data Warehouse project can go wrong. Delivering on time, data administration issues, and unavoidable data quality issues in feeding systems. Corporate politics (see Tip 7) are probably the best explanation for this phenomenon of near 100% success rates on DWH projects. In my experience, the reason why a failure or 'semi-failure' can go unnoticed is either because senior management is not aware, or, let's say "unmotivated" to talk about misspending of company funds. As a result, not enough is learned. Maybe we as consultants have a stake in this as well, as this assures the industry plenty of ongoing business... J
5. What is Different About Warehousing Web Data?
Kimball & Merz (2000): "Although this clickstream data in many cases is raw and unvarnished, it has the potential of providing unprecedented detail about every gesture made by every human being using the Web medium". The subatomic nature of clickstream data poses unique challenges. There are fewer built in feedback mechanisms to ensure data quality, compared to other data streams. The relation between user mouse clicks and server log records is not as tight as in "traditional" transaction processing due to technical issues like proxy servers and caching. Because of these differences, IT people need to adapt to the web process flow, rather than having the process adapt to IT needs as is common for most other DWH interfaces.
6. Which Data Should Be loaded In The Data Warehouse?
The data that enter the DWH ultimately determine its place in the organization. A "let's load all data, to be safe"-attitude is a sure fire way to derail your DWH project. Choices as to what should and should not be included need to be made early on, to keep the project manageable. After proven success of the delivered, deployed, and profitably exploited DWH, there always will be funding somewhere to include previously ignored interfaces. Given the anticipated lifecycle of the DWH, it makes perfect sense to consciously exclude certain sources. The choice as to what data to include needs to be driven by business con
Every great corporation we see today started as a business idea. It must have started as a small business and developed into a large-scale business over time and effort. Having this in mind, success of these small businesses should be taken very seriously in order to have a virile and sustainable economic growth in any nation like ours.For example in Africa, Nigeria has about 35% return on investment, which is the highest in the world today, with this, there is room for small businesses to thrive and survive beyond 5 years of establishment. The government has also seen the importance of small business success that they came up with the idea that banks and financial institutions should set aside a certain percentage of their profit after tax (about 10%)to serve as loans and grants to entrepreneurs even without collateral or interests.i also know that this obtains in other economies of the world where small scale business owners are given every support needed to thrive.7 secrets entrepreneurs need to know and do to have a successful small business are:Set goals and be focusedSetting of goals is very important bec
4. Why Do Data Warehouse Projects 'Never' Go Wrong?
Actually, Data Warehouse projects do sometimes fail. But, they fail so rarely, that it is actually very hard to believe... Especially after having talked to so many disgruntled end-users. And there are many ways a Data Warehouse project can go wrong. Delivering on time, data administration issues, and unavoidable data quality issues in feeding systems. Corporate politics (see Tip 7) are probably the best explanation for this phenomenon of near 100% success rates on DWH projects. In my experience, the reason why a failure or 'semi-failure' can go unnoticed is either because senior management is not aware, or, let's say "unmotivated" to talk about misspending of company funds. As a result, not enough is learned. Maybe we as consultants have a stake in this as well, as this assures the industry plenty of ongoing business... J
5. What is Different About Warehousing Web Data?
Kimball & Merz (2000): "Although this clickstream data in many cases is raw and unvarnished, it has the potential of providing unprecedented detail about every gesture made by every human being using the Web medium". The subatomic nature of clickstream data poses unique challenges. There are fewer built in feedback mechanisms to ensure data quality, compared to other data streams. The relation between user mouse clicks and server log records is not as tight as in "traditional" transaction processing due to technical issues like proxy servers and caching. Because of these differences, IT people need to adapt to the web process flow, rather than having the process adapt to IT needs as is common for most other DWH interfaces.
6. Which Data Should Be loaded In The Data Warehouse?
The data that enter the DWH ultimately determine its place in the organization. A "let's load all data, to be safe"-attitude is a sure fire way to derail your DWH project. Choices as to what should and should not be included need to be made early on, to keep the project manageable. After proven success of the delivered, deployed, and profitably exploited DWH, there always will be funding somewhere to include previously ignored interfaces. Given the anticipated lifecycle of the DWH, it makes perfect sense to consciously exclude certain sources. The choice as to what data to include needs to be driven by business con
Medical billing of parental nutrition claims is not an easy task. There are a lot of calculations that need to be done and a lot of things that need to be accounted for such as the actual product being dispensed, calories per day and so on. Computer programs make the job a little easier when billing through electronic media and NSF 3.01 specifications. In this installment we'll be covering the GP0 record, picking up with field number 15.GP0 field 15, position 63, is the ambulatory indicator. This field tells the carrier whether or not the patient is ambulatory. There are only two valid responses for this field. If the patient is ambulatory, an A is entered. If the patient is not ambulatory, an N is entered. The field cannot be left blank or the claim will be denied.GP0 field 16, position 64, is the other forms of nutrient indicator. This field tells the carrier if the patient is receiving other forms of nutrition other than what this CMN is prescribed for. This is important because in some cases, if the patient is receiving other treatments, this claim may not be 100% payable. As it is, with Medicare, most cl
5. What is Different About Warehousing Web Data?
Kimball & Merz (2000): "Although this clickstream data in many cases is raw and unvarnished, it has the potential of providing unprecedented detail about every gesture made by every human being using the Web medium". The subatomic nature of clickstream data poses unique challenges. There are fewer built in feedback mechanisms to ensure data quality, compared to other data streams. The relation between user mouse clicks and server log records is not as tight as in "traditional" transaction processing due to technical issues like proxy servers and caching. Because of these differences, IT people need to adapt to the web process flow, rather than having the process adapt to IT needs as is common for most other DWH interfaces.
6. Which Data Should Be loaded In The Data Warehouse?
The data that enter the DWH ultimately determine its place in the organization. A "let's load all data, to be safe"-attitude is a sure fire way to derail your DWH project. Choices as to what should and should not be included need to be made early on, to keep the project manageable. After proven success of the delivered, deployed, and profitably exploited DWH, there always will be funding somewhere to include previously ignored interfaces. Given the anticipated lifecycle of the DWH, it makes perfect sense to consciously exclude certain sources. The choice as to what data to include needs to be driven by business con
Winning customers and keeping customers – the Sales & Marketing Channel will help you develop those critical skills. Fine tune your sales lead generation with an advertising message or direct marketing pitch that positions your product brand management right in your customers' sweet spot. Keep them coming back for more with smart customer relationship management, help desks and call centers, and customer loyalty programs. "Find help on every facet of customer retention and acquisition, from consumer research through media planning and buying to point-of-purchase displays." The Sales & Marketing Channel connects you with the best advice and services in these critical areas: Promotional products and promotional apparel. Promote your brand with mugs, mouse pads, phone cards, tee shirts or wherever else your imagination might take your logo. Email lists. Find targeted, opt-in email lists, manage your email lists effectively, and track your email lead generation.Customer service. Run a call center, install customer support software, offer an online help desk – just be sure to keep your customers happy!Help with online
6. Which Data Should Be loaded In The Data Warehouse?
The data that enter the DWH ultimately determine its place in the organization. A "let's load all data, to be safe"-attitude is a sure fire way to derail your DWH project. Choices as to what should and should not be included need to be made early on, to keep the project manageable. After proven success of the delivered, deployed, and profitably exploited DWH, there always will be funding somewhere to include previously ignored interfaces. Given the anticipated lifecycle of the DWH, it makes perfect sense to consciously exclude certain sources. The choice as to what data to include needs to be driven by business considerations, and in particular reference to the company bottom line. If it can't be shown how data will be put to use profitably, they stay out! See also tip #3.
7. Data Warehousing & Company Politics
Data Warehouses have an impact on the company bottom line. Hence, they are likely candidates for turf battles, and are also at risk of becoming "small change" in budget allocation negotiations. None of these considerations benefit corporate long term goals. Managing a DWH project is hard enough as it is, and budget issues shouldn't make it any harder than it already is. Because DWH investments are in the present and revenues lie in the future, it is even more important to secure funding through a sound business case and buy-in from the appropriate (high) management level. See also Tip #3. Access to data means power, and talking about power is one of the greatest management taboos, still around. Sensitive as they are, even budgets are more readily discussed...
8. Data Warehouse Projects Traps
Some commonly recurring 'roadblocks' on the path to timely delivery of a Data Warehouse project:
- ETL processes have eaten up so much time (and still need "babysitters"), that little if any time is left to develop applications needed to exploit the DWH
- Some data are needed, but turn out not to be unavailable, or not in a timely fashion
- Maintenance required for tuning, indexing, and backup and recovery is severely underestimated
- Different ways of calculating the same phenomenon lead to different results, and nobody is able to conclusively explain the difference(s)
- The data that is loaded (and recombined) turn out to contain previously unknown inconsistencies in the source systems, the 'classic' data quality issues that trip DWH projects
- Metadata were lacking, and developers spend inordinate amounts of time finding out what a field really 'means'
9. DWH Hardware and Software Go Hand in Hand
In Data Warehousing, it is not about hardware, and not about software: it is about the perfect integration of these two. Those who begin their project from either end, will pay dearly for this mistake. Reasons are:
· in terms of price/performance, new, pre-integrated hardware-software combinations are taking the lead
· from a project management perspective, you never want to be caught between vendors when a proposed solution doesn't work as expected
· database tuning and indexing is very important and a hugely complex job, necessarily left to specialists (in-house trained)
10. Performance is Key
Although I don't often find technology factors to be this important, in Data Warehouse acceptance, no other factor will be as important as performance. As size increases over time, this factor becomes even more important. There are three reasons for this:
- performance has a huge impact on the development speed (initial load is always very time consuming), and hence the overall maturity of the DWH at delivery time
- performance can make or break end-user acceptance, in particular the predictability of performance
- performance has a tremendous impact on end user productivity, the ultimate driver of the business pay-off
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