1. If I were to design Ben & Jerry’s data warehouse I would use several dimensions of information. The first dimension would consist of the company’s products; ice cream, frozen yogurt or merchandise. The marketing department has to know which products are selling, if Ben & Jerry’s didn’t know that their T-shirts are selling out as soon as they hit the stores, then they wouldn’t be able to take advantage of the opportunity to sell the shirts. The second dimension would consist of the different areas of sales; US, Canada, Mexico, or Europe. I am not sure if they sell their ice cream in Mexico, but with data collection they can find out if their ice cream would be a better seller in the hot climate, …show more content…
The primary key for the Store file would be the store number, or code. Some of the foreign keys would be; delivery dates, order dates, sales, and customer complaints. The Employee files primary key would be an employee number. The foreign keys for the Employee files would be; start date, wage, shift, employee address, paid time off, and absences. The Product file would have a primary key of product name, for example, Cherry Garcia. The foreign keys would be product type (ice cream/frozen yogurt), amount in stock, amount sold, ingredients, and comments. Comments sounds like a useless bit of information, but I think it could be one of the most useful. When it comes to a decline in ice cream sales a comment query could tell managers exactly what flavor people really want. In the case Ben & Jerry’s received a large number of complaints that it’s Cherry Garcia ice cream didn’t have enough cherries in it. Using the business intelligence with Business Objects helped them discover a problem with their packaging, and solve the problem.
3. What is the question that any business should be asking itself? How do we cut costs? Using business intelligence, Ben & Jerry’s can look at how they can cut costs. From a financial perspective we could run a query to see what it costs to make the top 5 selling flavors, once
According to Berson and Dubov (2011), there are four typical categories of drivers that explain the need for data management: Business Development, Sales and Marketing; Customer Service; Risk, Privacy, Compliance and Control; and Operational
One of the main functions of any business is to be able to use data to leverage a strategic competitive advantage. The use of relational databases is a necessity for contemporary organizations; however, data warehousing has become a strategic priority due to the enormous amounts of data that must be analyzed along with the varying sources from which data comes. Company gathers data by using Web analytics and operational systems, we must design a solution overview that incorporates data warehousing. The executive team needs to be clear about what data warehousing can provide the company.
What information is accessible? The data warehouse offers possibilities to define what’s offered through metadata, published information, and parameterized analytic applications. Is the data of high value? Data warehouse patrons assume reliability and value. The presentation area’s data must be correctly organized and harmless to consume. In terms of design, the presentation area would be planned for the luxury of its consumers. It must be planned based on the preferences articulated by the data warehouse diners, not the staging supervisors. Service is also serious in the data warehouse. Data must be transported, as ordered, promptly in a technique that is pleasing to the business handler or reporting/delivery application designer. Lastly, cost is a feature for the data
A data warehousing is defined as a collection of data designed to support management decision making. Data warehouses contains a wide variety of data that present a coherent picture of the business conditions at a single point in time. Development of a data warehouse includes development of the systems that extract data from operating systems plus the installation of the warehouse database system that provides managers flexible access to the data. The term data warehousing generally refer to the combination of many different databases across an entire enterprise. (webopidia)
The whole process throughput time of making a dozen of cookies is 26 minutes. It takes washing, mixing and spooning 8 minutes to make a dozen of cookies. And preparation and bake time totally are 10 minutes. The final step of cooling, packing and accepting payment of cookies takes roommate 8 minutes to finish the cycle. Assume the night capacity is 4 hours, so Kristen and roommate have 240 minutes operating time. Since the oven only holds one tray, the second dozen takes an additional 10 minutes to bake. For example, first dozen of cookies take 26 minutes to make, second dozen of cookies take 36 minutes to make and third dozen of cookies take 46 minutes to make. So we can
For employees this data could be anything from human resource forms to vacation day requests. For customers the data could be products purchased, recorded marketing calls, website interaction as well as a host of other areas.
Enterprise Data Warehouses (EDW) have become the foundation of many enterprises' systems of record, serving as the catalyst of strategic initiatives encompassing Customer Relationship Management (CRM), Supply Chain Management SCM) and the pervasive adoption of analytics and Business Intelligence (BI) throughout enterprises. The role of databases continues to be an ancillary one, supporting the overall structural and data integrity of the EDW and increasing its value to the overall enterprise (Phillips, 1997). The advances made over the last decade in the areas of Extra, Transact & Load (ETL) have made it possible to create EDW frameworks and platforms more efficiently, creating greater accuracy in overall database and data warehouse performance as a result (Ballou, Tayi, 1999). The creation and use of an EDW to further drive an organization to its objectives requires that the differences between databases and data warehouses be defined, in addition to a clear, concise definition of just what data warehouse technologies are. Finally, the relationship between data warehouses and business intelligence (BI) including analytics needs analysis and validation. Each of these three areas are discussed in this analysis.
A data warehouse and business intelligence application was created as part of the Orion Sword Group project providing business intelligence to order and supply chain management to users. I worked as part of a group of four students to implement a solution. This report reflects on the process undertaken to design and implement the solution as well as my experience and positive learning outcome.
Today businesses are investing many resources in building data warehouses and data marts to obtain timely and actionable information that will give them better business insight. This will enable them to achieve, among other things, sustainable competitive advantage, increased revenues and a better bottom line.
“Welcome to Cook Out, may I take your order please?” This seemed to be some of the most used words in my vocabulary for a while now. When I was 16 years old, I got my first job at the most wonderful fast food restaurant ever, Cookout. This was not an ideal job for a 16-year-old. Most teens dream of their first job being in their favorite clothing store, or maybe even their favorite grocery store. I was that teen, but where I am from there are very limited options for 16 year olds so I had to just settle for a fast food restaurant. We have all heard these typical assumptions about fast food employees, they are all uneducated, they work too hard for little pay, and they are all rude with nasty attitudes. How could you assume such things about a person just because they work in the fast food industry? Throughout my paper, I will be talking about the main stereotypes I experienced while working at cookout and how other employees and I dealt with it.
"A data warehouse is a subject oriented, integrated, time variant, non-volatile collection of data in support of management 's decision making process". Source
Krispy Kreme’s rapid expansion may have been the reason for its rapid fall. Recently becoming a publicly traded company in April 2000, Krispy Kreme shares had seen amazing growth as they were selling for 62 times earnings. Naturally, this created a buzz around Wall Street, and an “obsession” with Krispy Kreme began as it became one of the hottest stocks on the market. Yet, analysis of the fundamentals of Krispy Kreme needed to by analyzed to see the true threats the company had brought upon itself.
Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. It is a solution package, to integrate all the existing data of organizations efficiently, provide accurate report to support high level managers to make business strategic decision.
The data collection company has a huge collection of data generally goes into terabytes. Data warehouse is the only option Though not much structured like relational databases but when used in line with them, data warehouse turns out to be a huge help. Every moment, a single page visit generates thousands of records to be saved. Data may get outdated but it cannot be deleted as few months down the line, it will be used for analysis. This analysis generated future prospects of business. And this analysis depends on - how the data is collected and how efficiently it be arranged to give clear picture for analysis.
In the year 2001, the international labor rights funds as well the steelworkers union took coca cola to federal court. The two unions also sued two others bottlers Pan-American Beverages, Inc. as well as Bebidas Alimentos. The charges were against the torture and murder of Sinaltrainal leaders who were the representatives of workers in the bottler 's company. Accompanied in the lawsuit were also survivors of the alleged murders and who had been hired. The survivors claimed that contracted security forces top carry out the tactical killings of the bottler’s leaders.