Data warehouses and the process of data warehousing is the latest development in the field of reporting tools, examples of which can include Oracle and Microsoft Excel. Whether this is 'merely' what they are is an issue that will be investigated in depth during this essay. The other main topic that will be discussed is if they can only benefit businesses that operate in the retail sector, or if they can offer any advantages to organisations operating outside that particular industry.
A data warehouse can be defined in a number of different ways. Kimball (2002,p.310) describes it as "a copy of transaction data specifically structured for querying and reporting." The website Systems Services Corporation - Data Warehousing gives a much more comprehensive definition, saying a data warehouse is "a structured extensible environment designed for the analysis of non-volatile data, logically and physically transformed from multiple source applications to align with business structure, updated and maintained for a long time period, expressed in simple business terms, and summarized for quick analysis."
Another definition given by Hume (1999, p.251) classifies a data warehouse as "a non-volatile time based repository that allows retailers to observe sales and inventory." Using these three definitions, a simplified and easier to understand description of data warehouses is that they are extensive and complex databases that allow users to manipulate the data contained within to provide information analysis and aid decision making.
There have of course been tools that have given users and organisations the opportunity to manipulate data to their advantage before data warehouses. They are the latest development in what is known as 'reporting tools'. Examples of these can include Oracle and Microsoft Excel, which are reasonably similar to data warehouses in the functions they offer, and also CorVu, Accuate and Impromptu, which are somewhat disparate from the first two. In a general sense, reporting tools allow users to manipulate and display data in the most suitable format for their purpose. Not all of these tools have the same capabilities as each other of course, but they all conform to this broad definition.
The tools mentioned above, in relation to data warehouses, could be described as simplified versions of them. The tools may be able to offer some of the functions available through a data warehouse, or less complex varieties of these functions, but they cannot offer everything to an organization that data warehousing can.
Before data warehouses, organisations placed the primary emphasis on their operational systems and the data that they processed, giving very little thought on how or even if they would archive this data. Systems used ranged from IBM mainframe computers such as COBOL and IMS in the 1970's, to mini-computer platforms such as AS/400 and VAX/VMS in the 1980's, to UNIX in the 1990's. Even though the last in this list, UNIX, represented a leap in technology as it introduced client/server architecture that allowed greater access and control over data, this still could not offer organisations the benefits that data warehouses can today.
In very recent years, the increasing popularity of personal computers has not only narrowed the gap between the programmer and end user but has also given this end user access to tools proficient in business analysis. Any number of desktop applications can offer spreadsheet and database facilities, as well the ability to graphically represent data.
Organisations use these types of facilities, broadly classified as Decision Support Tools or Systems, in order to incur a number of benefits. Keen & Morton (1978, p.vii) define a DSS as "computer systems designed to mesh with managers' existing activities and needs while extending their capabilities."
These benefits themselves are quite general in the context of business, but they are very real and very important nonetheless. Decision support tools allow organisations to ensure everything is 'okay'- basically the queries and reports present within the tool confirm to the user that everything is operating as expected. They also serve to validate the hunches of the end user. Very rarely does the information produced by these tools reveal anything unexpected, it usually just confirms what has already been suspected.
Another benefit is that these tools allow information to be conveyed in an appropriate and understandable manner. Some people may already know what the information means, but presenting it in a particular way will allow them to comprehend it in more depth, and will also help those who have no knowledge of the meaning of the data. Decision Support Tools also allow different types of information, customer details and financial reports for example, to be compared in any number of ways. A series of measures can occur, identifying the most or earliest for instance, or there can be simple side-by-side comparisons. It also lets users contrast data from different time periods, whether it be weekly, monthly, quarterly or yearly comparisons. Another very important function of these systems and most definitely an advantage gained from them is that they give the user the capability to obtain relatively small pieces of data from large volumes of it with ease.
The users can use the tools to check performance against their goals or constraints as well. This can be achieved by simply measuring what actually happened against such measures as budgets, quotas and forecasts.
These are simply some of the advantages that can be gained from employing Decision Support Systems within an organization, and obviously data warehouses have all of them. They do, however, offer many more benefits exclusive to their implementation.
There are obviously the very broad benefits that are often mentioned such as converting data into business intelligence and gaining competitive advantage, but there are some much more specific ones. Firstly, data warehouses only require limited technical knowledge in order to set up and maintain queries and reports. The basic set-up of these systems is such that the different aspects of it can be created with those of limited experience of databases in mind, making it relatively easy to use the warehouse. Linked to this, these systems make the process of writing and maintaining queries and reports, whether they are complex or simple, quicker and easier for trained IT personnel. This can be attributed to the lack of bureaucracy associated with the creation and use of data warehouses, but the fact that the warehouses are generally reasonably user-friendly also helps this.
Another benefit involves the issue of security - mainly preventing access to certain parts of the data warehouse to certain people. These systems make it possible for a person to use queries and reports without having access to sensitive areas such as transaction processing systems or logic used to maintain the database itself. This would be particularly useful to firms who only wish to allow querying and reports over a company-wide network or the Internet itself.
Data warehousing also makes it easier for organisations to perform queries and reports on multiple processing systems, transaction systems for example, and/or external data sources. This is even more of an advantage to firms who have large amounts of data, or those who require their data to be cleansed (which basically reduces data redundancy).
Due to the points discussed thus far, we do not believe that data warehouses can be described as 'merely the latest in a long line of reporting tools.' They could be classified as a reporting tool but because of the extensive amount of functions and advantages they can offer organizations, which previous reporting tools cannot, they are much more. The benefits they can provide can have a direct effect on a business' operations and the success of them, something other reporting tools cannot claim.
It is obvious that data warehouses can offer many benefits and opportunities to organisations, but do they only offer these to firms operating within the retail sector, or can other business sectors profit from them.
Firstly, in a business world where marketing has become an integral part of success, and where identifying trends and needs within the market is so important, the process of data warehousing is a definite advantage to retail businesses. They can be employed in a number of ways that offer benefits specific to the retail industry.
The Cambridge International Dictionary of English, 2000 defined retail as: "(relating to) the activity of selling goods to the public, usually in small quantities", and the website Your Dictionary defines retail quite similarly as: "the sale of goods, or commodities, in small quantities directly to consumers."
Hume believes that data warehouses can be used to: "enhance numerous business processes including assortment planning, promotion planning, category management, sales and inventory management and so on."
As mentioned earlier, data warehouses allow users to confirm or identify trends in an organisation's data. This can be done with numerous variables, such as transaction data and customer purchasing data, and over different time intervals, quarterly and yearly for instance. Retail businesses can employ this function of data warehousing to decide which products' prices should be raised or lowered, or which segment of the market a particular product should be aimed at, for just two examples.
Real life examples of this occurring is Supermarkets using their operational data, routinely collected at every transaction in their stores, to plan upcoming activities and overall strategy. Utilising information collected through such things as loyalty cards, electronic point of sale and geographic marketing data the Supermarkets can further identify particular trends within their customer base. J Sainsbury's PLC installed a data warehouse in 1999 to record the 90% of their transactions that involve customer loyalty cards, of which they have issued 9 million since 1996. the implementation of the warehouse is central to the firm's strategy - Chris Montagnon, IT and Business Strategy Directory of Sainsbury's, said "the real issue is what we do next with the data ... we are moving from scatter-gun marketing to a more focused approach" (Davis et al, 1999).
In order to identify trends or patterns, users must employ a technique known as 'data mining'. This basically involves exploring and analysing vast amounts of data in order to better understand the patterns of behaviour contained within this data. These patterns are then interpreted by experts in order to convert the data into knowledge.
This is a technique, however, that can also aid organisations not operating in the retail sector, a prime example being banks. A bank will want to know certain information about their customers, perhaps those who may want a loan from them. This information would be useful in that it would allow the bank to create a specifically targeted marketing campaign rather than a more expensive and less fruitful general campaign. In order to obtain this information, detailed customer information is firstly used to detect the characteristics of those who took out a particular type of loan. This is then used to develop certain rules that can determine customers who are likely prospects to take out this type of loan. This information is clearly extremely useful to the bank.
The Natwest bank has recently started to employ data mining in order to detect patterns in credit purchases, telling customers "if we could tell from your transactions that you were a sports fan, we'd like to send you information about sports events" (Davis et al, 1999. This is an example of how data warehousing can be used for targeted marketing, which is one of its key benefits to businesses, especially financial institutions.
Another example of a non-retail business using data warehousing to their advantage is within the insurance industry. They can use the warehouses in the area of claims analysis - to see which procedures are claimed together, for instance, or to identify potentially 'risky' customers. This can help to reduce costs and liability.
Data warehouses are also used within the Healthcare industry, especially by Primary Care Trusts (PCTs) which need to support Government initiatives such as Waiting List monitoring/management, National Service Frameworks and Health Improvement Programmes. Unfortunately, most of the data required for these schemes will come from disparate sources. The PCT data warehouse that has recently been implemented allows them to obtain all the information that is critical to their operation, and also imports, validates and integrates this information.
Indeed, some of the healthcare industry's greatest advances in the field of patient care have been based on insights gained through the use of data warehouses. An example of this is cardiac surgeons using the systems to access real-time information on which to base their diagnosis, assess patient risk with greater accuracy, and reduce or even remove errors by using data from the Society of Thoracic Surgeons' data warehouse
There are, of course, certain benefits that are exclusive to retail businesses. They are much more cost-effective to those businesses than to those outside that sector. This is due to the fact that the advantages that can be gained can have a direct impact on the profits of retail businesses, whereas this is extremely unlikely to be the case with organisations in other sectors.
Other benefits that can be achieved through data warehousing include the fact that the process creates high quality data. Simply implementing a warehouse ensures data is accurate and consistent, and if data cleansing takes place these qualities are perfected and data redundancy is completely avoided as well.
Data warehouses are also largely platform independent, providing a number of choices for organisations and also removing certain constraints. A warehouse could for example be built from a high-end Personal Computer to a mainframe, employing Unix servers and a client/server architecture.
Data warehousing can benefit an organisation's information flow as well. Certain systems, such as transactional ones, are updated automatically and according to that particular organisation's needs.
It cannot be ignored, however, that there are also disadvantages connected to data warehouses. Actually setting them up is an extremely difficult process, as many different variables must be identified and dealt with. All the sources from which an organisation's data originates must be classified, how all these different sources will be entered into the system, and where and it what manner it will be stored must be regarded thoroughly. Setting up a data cleansing process is also very complicated.
There is also an extremely large initial outlay involved in implementing a data warehouse, and this simply may not be viable for some organisations. Such a project is labour-intensive and time-consuming, but it must be ensured that the data is properly integrated and of a high quality. If this is not the case, management cannot use the information correctly and it becomes almost worthless. The process of extracting, cleaning and integrating data represents 60-80% of the total costs of a data warehouse.
The costs all depend on how large and complex the data collecting and storing operation is - some data warehouse projects take years and millions of pounds to complete.
Due to the reasons discussed above, we do not feel it is feasible to say that only retail businesses can benefit from data warehouses as other sectors can employ them to their advantage. Retail businesses do obtain the most benefits from them, as more of the advantages apply directly to them, such as analysing customer trends and the area of marketing in basket analysis, and other non-marketing related aspects of business such as sales and inventory management, which can the be used to increase profits. It is incorrect, however, to say that only organisations in that industry can employ data warehousing to their aid. As mentioned earlier, data warehousing can be extremely beneficial to organisations such as banks and most especially Primary Care Trusts, who are using data warehouses to compile patient and medical information, as well as using them to predict and therefore prevent the spread of disease on a national scale.