Integrated into the data

If we have designed a business model that relates us the number of queue management visits, the number of deals and the number of https://queuemanagementlist.wordpress.com/2014/10/16/this-changing-environment/ sales, and we do not have the number of visits, we have to modify the processes and applications to collect this information.

If sales decrease, should be attributed to it has decreased the number of customers interested in our cars or to our effectiveness has http://queuemanagement.over-blog.com/2014/10/explained-that-organizations.html declined. Decided the information sources we check off the quality of the data.

Consequently, it is necessary to ensure that data quality is high. If there are errors in the data warehouse, they will spread throughout http://queuemanagement.webnode.com/ the entire organization and are very difficult to locate. They can cause erroneous decisions affecting the results of the organization are taken.

The costs of the quality of the data is not correct can become very high. Users perceive that they have sufficient data quality quickly http://queuemanagement.tumblr.com/post/100138428944/forcing-them-to-be-advanced Business Intelligence project. In one of the forecasts is affirms that Gartner54: Assume that the data quality is good can be a fatal error in the draft Business Intelligence.

Normally, when a data warehouse is built most organizations focus on the identification data they need to analyze, the extracted and http://queuemanagementfree.soup.io/post/473152583/Throughout-the-book loaded into queue management the data warehouse.

Generally not think the quality of the data, allowing errors to be loaded to the data warehouse. It should therefore be a control or set http://www.queuemanagement.portfoliobox.me/depending-on-the-level of controls in the project that locate errors in the data and not allow the burden of the same set.

The checks are to be carried out manually or automated, taking into account different levels of detail and varying periods of time, http://queuemanagementassociates.blog.com/2014/10/16/to-the-appropriate-information/ ensuring that the loaded data match those of the original data sources;for example, checking that the total sales or number of daily orders to coincide with the information loaded in the data warehouse.

Less frequently, check that aggregates a queue management period match between the two environments, the data warehouse and the http://systemqueuemanagement.bravesites.com/ original data sources. In some cases errors are caused by failures in transaction systems are detected, which should result in improvement projects in them.

Many of these cases are due to which users can enter data without any control. Whenever possible, it is recommended that users http://lilaalvarez.kazeo.com/to-which-they-belong,a5173652.html choose between different values rather than introduce them freely.

Not a good option to correct the ETL process and not modify the technical origins queue management applications. This alternative http://queuemanagementprocess.jigsy.com/ is much faster initially but more expensive in the long term.

Errors can also occur, for example, in the ETL process or warehouse. In the co GRAFI adjunto56 the http://www.havinganefficientqueue.sitew.in/#Home.A process in which the control points are identified is as follows: in the load, audit and reconciliation, and users of Business Intelligence.

Data warehouse is typically

To describe the various components let's start first with the sources of information, we will continue with the other components and http://www.studyabroad.com/members/ismaelweber/default.aspx by visualization tools. Follow this order to describing the different queue management components that make up the solution,.

But this will not be the order will continue in a real project. In a real project first we define what the objectives and scope of the https://getsatisfaction.com/people/teribowers solution are, what we wish to analyze business models.

This information is much easier to take the necessary decisions in each of the components. In each of the sections of this chapter use http://themeforest.net/user/murielmcgee the previous co GRAFI noting the part we are describing; explain the component.

References to other products on the market, as well as references from the providers described in Note6 of Chapter técnica3 Many http://sonyapadilla.newgrounds.com/ factors contribute to the complexity of queue management loading information into a data warehouse.

One of them is the number of different sources of information that we loaded information. Moreover, the number of sources varies http://www.friendster.com/profiles/206462223 from one organization to another: in large corporations talking about an average de8 databases.

And in some cases can reach 50 different Access databases requires different skills and knowledge of different SQL syntax. If the http://www.hotelchatter.com/user/carlrobinson number of databases that we have access to is high, it can cause both definitions as codified cations in different environments are different.

Which will add to our project difficulty; therefore, a key issue will be to know the model of transaction information and significance http://www.hi5.com/rodneychavez queue management of each of its elements.

The definition of the various components of our information system is not always consistent across different applications that are not http://community.good.is/members/violacarpenter integrated. If applications are developed normally,.

They are not sufficiently documented to be interpreted correctly. In most cases they are applications that have been modified over http://www.carepages.com/users/7777922/profile time by different programmers, and usually have not been updated.

The information loaded into a queue management structured, one that can be stored in tables: in most http://www.tagged.com/celiamathis cases it is numerical information.

Increasingly, technology allows us to work with unstructured information, and it is expected that this type of information is https://www.zillow.com/profile/stevenbassi/ increasingly important. In unstructured information we have: cos electronic mail, letters, reports, videos, etc.

A survey indicated that 60% of the directors of Information Systems and Technology believes that the semi-structured information is http://www.idealist.org/sherrybrady critical to improve operations and to create new opportunities negocio52.

At this stage, the key point is identified the most appropriate sources of information which will recover, we analyze queue http://www.dipity.com/elsieboyd/queue-management/ management the formats, the availability and quality of information. We will have to consider whether the information we have is that we need to feed the business models that have defined above.

At this point, many times we find that we do not have the information necessary to complete the business model that we had set, a situation that can lead us to modify our transaction applications for it.

An example to which we have referred to above is as follows: in a car dealership is very important to know how many visits we have received in relation to exposure and sales.

We generated many more

The years are broken down into quarters, quarters into months and months into days. The existence of hierarchies in dimensions https://www.fiverr.com/lionelsalazar allows us to pass the maximum detail in the different aggregation levels.

In our example we can therefore analyze the sales of an item for days, months, quarters or years. In our model there are other http://www.thestudentroom.co.uk/member.php?u=1615141 hierarchies: the family, subfamily and durable, the areas and centers, and to the families and employees.

Each of these hierarchies allows us to add or dis aggregate information. Being able to use different dimensions while we are using the https://myspace.com/lilaalvarez functionality of.

Allows us to analyze the information for different dimensions together. For example, if we want to analyze the sales of an item, yet http://www.livestrong.com/profile/brucedaniels/ wish to do so by central or month.

At this point we should highlight a fundamental concept: Each data model allows to answer a limited number of questions, and each https://id.theguardian.com/profile/myramorton model answer different questions.

In the example we used, the questions that will help us answer the model of the tickets sales are not the same that allow us to answer http://www.codeproject.com/Members/wandacook the lines of ticket sales, the difference being more significant in the latter we product-related information, while the former does not.

When you are building different models is very important that the terminology used and the definition of the terms are the same for http://www.dnnsoftware.com/activity-feed/userid/3047155 everyone. When the models are complex queue management and often build a Metadata Data Dictionary that we all attributes explicit tables, systems from which and the definition of each of these attributes.

You can also enter if the fields are recalculated or transformed and the detail of the changes that have taken place. Summarizing data http://in.linkedin.com/pub/cathy-griffin/a6/401/734 modeling, the first thing to do is to define what is the business model that we are preparing for the data to be analyzed.

Once we have the context, we must determine what we want to measure-the facts-and how we want to analyze-the dimensions of https://www.librarything.com/profile/dwightgeorge queue management analysis.

The dimensions should help us to answer the following questions: what? And when? In the case that interests us have different levels http://bbs.boingboing.net/users/brettreyes/activity of aggregation resort to hierarchies, allowing us to add the information at different levels, such as family, subfamily or product.

The level of detail at which we build our model depend on the queue management questions we want to answer from it. Usually http://www.sbnation.com/users/derekwolfe better to build the highest level of detail, as long as the number of records that we loaded into the data warehouse allows.

Each time we add lose some information that we provide the data. In this chapter we have used an example of ticket sales for a very http://www.kongregate.com/accounts/jeremywashington simple supermarket, but more often than not we find ourselves in situations such as queue management the ejemplo presented below.

As we see, the information contained here is much greater and questions. To answer them we build a model of more complex data, with more dimensions; however, the procedure that we follow is the same as we saw in chapter example.

Lines of the tickets

In this case, the dimension tables are fully normalized, reducing the space they occupy, although in some cases this difference is not https://www.zotero.org/sonyapadilla/items/itemKey/F5RKQUCQ significant. With the construction of the previous model only analyzed the tickets sales.

However, we can do the same to analyze the items sold in each of the tickets sold. The level of detail in the analysis is queue https://delicious.com/troybryan management what we call granularity.

In case you want to analyze each of the sales, the granularity is greater than if we want to analyze are selling tickets. http://www.stumbleupon.com/su/30wnN1/1OsRaZGS3:p5!EOb2Q/wandacook.virb.com/ We must decide the level of granularity needed to build a model that allows us to answer those questions.

We have formulated as to determine a level of granularity can answer some questions but not others. Let us now begin with the http://www.stumbleupon.com/su/452OVm/.7tRhPTh:p5!EOb2Q/wandacook.virb.com/are-your-electronic-documents-secure/14076305 analysis of the items sold.

The model which we break allows us to analyze the items for families and subfamilies, but also by the various manufacturers. We will https://www.diigo.com/user/rodneychavez be interested to know what the racial families, subfamilies or manufacturers total sales. This analysis is much more interesting if we can do by center, as it will allow us to understand the possible local differences.

Again, the first step in building the model star is to decide what should be the fact table. What are we analyze sales items, so we need http://www.reddit.com/r/technology/comments/2jeey6/queue_management/ the highest level queue management of detail, our fact table must have all lines of selling tickets.

We have omitted some attributes like id box as we believe that we will not provide information that allows us to improve http://www.reddit.com/r/technology/comments/2jefsi/queue_management_system/ management.

Let our scheme star of the lines for tickets sales. We have a fact table and six dimension tables. We have added the dimension table http://oknotizie.virgilio.it/info/5054c2390b6d779e/queue_management.html of items from the previous model of the tickets sales, allowing us to analyze sales by subfamily.

Family or company, and we have not considered analyzing the lines of the tickets sold payment method dimension: not queue http://oknotizie.virgilio.it/info/5055c231dd87ccdb/queue_management_system.html management have excessive sense as a whole ticket sales is paid by the same form of payment and this information and provides us the old model of selling tickets.

Might ask why we have not incorporated the attribute No. Description in the table Item Dimension; the reason is that if we do the http://www.dzone.com/links/queue_management_2.html analysis for items and once manufacturers need to be in different dimensions, and, if they are the same, it can not do.

This model allows us to analyze the sales of the items by queue management the size of the center, by the dimension date, slots, http://linkagogo.com/go/SiteSee?i=99801636&i=99801625&t=queue+management+s..&t=queue+management employees, manufacturers of articles and article.

In our system of ticket sales information it is possible to modify the selling price of the item. Comparing information from tickets http://www.dipity.com/sherrybradys/queue-management/ sales with articles and differ per employee, we can know who the great discounts that apply. If we analyze in detail the time dimension, we see that a hierarchy of time appear in this dimension.

Some authors using various

With the information available in our model we can analyze what is the busiest time of clients, allowing us to adjust the personnel needed for different times.

Scheme star From the scheme entity relationship previous46, we will build the scheme star 47 that allows us to analyze information so that we can answer the above raised questions about selling tickets.

For the construction of the star scheme must distinguish between tables hechos48 what we want to measure or analyze and the http://ttlink.com/dwightgeorge dimension tables how we want to measure, in our case, the fact table is that of the tickets and we analyze the following dimensions: queue management time, time zone, center, employee and payment.

The Scheme star would be: If you look carefully, we see that in the table of tickets have made in our case, the Total ticket and https://www.bookmarkee.com/brettreyes identifiers of the dimensions for which we want to analyze: date, time, id employee id center payment id. The ticket number and the ID box, no need for analysis of table dimensions.

Two dimensions we call degenerate also appear. Dimension tables allow queue management us to group events based on the values http://www.plurk.com/derekwolfe of the dimension: for example, if we know the total ticket sales from an area in the dimension table center.

We have the attribute description area which allows group the tickets according to this criterion. End week. This decomposition http://www.folkd.com/detail/queuemanagementbusiness.hatenablog.com allows us to analyze if we sell the same thing every day of the week or not, or if outsells early final month.

Compare between different months, quarters or years. We also added the attribute: Vacation that will tell us if the day is a holiday, http://www.folkd.com/detail/queuemanagementbusiness.hatenablog.com%2Fentry%2F2014%2F10%2F16%2F144642 and the attribute Week End to differentiate sales during the week and week of fin.

This information should add, since it is not available in the information system of selling tickets. Center dimension: This dimension http://sfcsf.org/tech/queue-management-system/ we can analyze what is the amount of tickets sales center, centers of population or of a province, and even sort by zip code. The size will also allow us to analyze the amount of selling tickets based on the square footage of the centers.

In the center we have added dimension zone description which indicates which zone is assigned to the center and allow us to analyze http://www.gvbookmarks.com/story.php?title=queue-management the differences between the different zones. When we combine different schemes star have different fact tables.

But they share the dimensions, facts talk about constellations even speak schema galaxy. Scheme queue management http://slashdot.org/submission/3916619/queue-management Snowflake Schema star is not fully standardized.

As in the dimension table have a redundancy center is Description area means an area as centers exist in the same will be repeated http://slashdot.org/submission/3916651/queue-management-system many times. The scheme snowflake or solves this problem. The scheme snowflake supermarket example is as follows.

As shown in the diagram snowflake shows relationships between dimension tables, while the queue management scheme star only https://www.zotero.org/sonyapadilla/items/itemKey/PS52R5SE relationship between the fact table and dimensions.

Quantified wires and should queue management measure

Business models are simplifications of reality that help us understand what is happening. To we can go to different methodologies: http://segnalo.virgilio.it/url.html.php?us=1c7204de69f2e8bac5a84d77616684e0 Cost Analysis and Cost, EFQM European Foundation for Quality Management, Six Sigma, Process Analysis.

Financial Modeling, Analysis ratios, etc. If the business model is well defined enable us to answer queue management key questions http://url.org/bookmarks/crystaljordan of the management of queue management our organization. Key business KPI39 Indicators serving organizations.

To assess whether they are meeting their goals. Once you have analyzed your mission, have identified the power groups and have http://www.pearltrees.com/lilaalvarez defined queue management objectives, organizations need a system queue management to measure progress toward achieving the goals.

Are appropriate tools to carry cabo. The KPI should be improvements in activities that are http://dir.eccion.es/usuario/brucedaniels critical for the success of the organization.

The KPI should relate to the objectives and activities with the fundamentals of our organization those that allow us to obtain the http://youmob.com/mob.aspx?cat=8&mob=http://queuemanagementfree.soup.io/ results. For example, in telephone sales is essential to take the call before hanging.

Therefore, the percentage of calls answered within 20 seconds might be a KPI. From each of the tickets sales have the following http://youmob.com/mob.aspx?cat=8&mob=http://queuemanagementfree.soup.io/post/473152583/Throughout-the-book information: the ticket number, the date, the time, the center, the box, the employee.

The payment and amount total4. We should note that where the center box, the employee and the payment method we have only the https://www.google.com/bookmarks/lookup?hl=en&btnA=&sig=AODP23YAAAAAVD967Jvh-dmsh2aiYbnD1L2Oj4s_KWel&bkmk=1 identifiers; from them and relationships with other tables get the attribute values.

For example, from employee ID and the relationship with the employee table, we can know the name of the cashier and your job https://www.google.com/bookmarks/lookup?hl=en&btnA=&sig=AODP23YAAAAAVD97QUpaWNXclESrvv31T34WNmauT7NF&bkmk=1 category. The same applies to the Go Center by relating it to the table centers can obtain other information from the center.

The table centers have a foreign key zone that allow us to group the zonal centers. For further description box is not required: With http://www.bibsonomy.org/user/abrahamspencer its identifier is sufficient, since each center numbered their own boxes.

From Lines ticket sales have a table identity corresponds to what ticket and line number, the item sold, quantity, unit price and total http://alplist.com/story.php?id=3410828 amount of the line item selling. Consider the table items.

If we analyze the relationship between articles, sub-families and families we realize that articles are a subfamily, and the subfamilies http://ziczac.it/a/notizia/queue-management-2/ form a family. This is a way to group items so that they can analyze.

In the example we have also provided another way to gather, which queue management is per manufacturer. Our products may have http://ziczac.it/a/notizia/queue-management-system/ many more features, but will add to the not understanding the example.

With the information we have to ask ourselves what we want to analyze: Do we want to analyze the sales tickets, or we want to analyze the items we sell more? This information is not in the information system of selling tickets: we will have to add to our model if we want to perform this type of analysis.