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Data Naming In this chapter, we focus on transforming the data elements gathered in the collection process into a cohesive set of column names. Although this chapter has sections for the various steps we should accomplish for efficient data naming, there is no specific order in which to apply those steps. In fact, the whole process is broken down into steps to shed some light on each one in turn, but the actual naming process applies all those steps at the same time. Moreover, the division between the naming and grouping processes is somewhat artificial you’ll see that some decisions about naming influence the grouping phase, which is the subject of the next chapter. Data Cleaning Having gathered information elements from various sources, some cleaning work is appropriate to improve the significance of these elements. The way each interviewee named elements might be inconsistent; moreover, the significance of a term can vary from person to person. Thus, a synonym detection process is in order. Since we took note of sample values, now it is time to cross-reference our list of elements with those sample values. Here is a practical example, using the car’s id number. When the decision is made to order a car a Mitsou 2007 the office clerk opens a new file and assigns a sequential number dubbed car_id number to the file, for instance, 725. At this point, no confirmation has been received from any car supplier, so the clerk does not know the future car’s serial number a unique number stamped on the engine and other critical parts of the vehicle. This car’s id number is referred to as the car_number by the office clerk. The store assistants who register car movements use the name stock_number. But using this car number or the stock number is not meaningful for financing and insurance purposes; the car’s serial number is used instead for that purpose.
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Car gas tank filled-up: date (2007-05-30) Car delivered to buyer: date (2007-06-01) Other Notes Do we include in the model some information about the old car that the customer exchanges for their new car? Boundary: during the interviews it was decided that, for now, the model will not include the dealer’s car rental activities, nor their repair service, although much of the information about cars could be applied to those activities. The subsequent chapters will put order in the naming aspects of this data and will explain grouping techniques. Summary Building a comprehensive collection of data elements is essential to the success of a data structuring activity. However, we need to know the exact limits of the analyzed system. Then, by gathering documents and proceeding with interview activities, we can record a list of potential data elements our future column names.
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year: (2006) serial number: (D45TGH45738) price of the exchange: (12,000) Down payment: (4,000) Interest rate: (9%) Interest amount: (6345) Type of credit rate: fixed/variable Dates of first and last payments: (2007-07-01, 2011-06-01) Number of payments: (48) Financial institution’s information: name, address, postal code, phone number From the Store Assistant A store assistant assigns a car number to each vehicle that enters the floor. This helps to manage which set of keys belongs to which car, we refer to physical keys here the keys needed to unlock and start the car, not the database keys. The car number does not refer to the car’s serial number; it’s assigned sequentially and used internally only. Store assistants also prepare a delivery certificate which contains the following information: Buyer’s name: (Joe Smith) Dealer’s number: (53119) Vehicle id number: (1400) Key number: (81947) Four signatures and dates, from the buyer, general manager, salesperson, and the store assistant Finally, the store assistants keep a register about all car movements. For each car, a card-index contains: Id number of the car: (432) Car ordered: date (2007-02-03) Car arrived: date (2007-02-17) Car placed in the show room: date (2007-02-19) Car washed: date (2007-05-30)
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From the General Manager Our friend the General Manager keeps surveys filled by buyers about their buying experience as a whole. Those surveys contain remarks about the salesperson behavior. Evidently, this information is confidential, as only the General Manager and the office clerk have access to it. Survey information includes: Date: (2006-01-02) Salesperson’s name: (Harper, Paul) Buyer’s name: (Smith, Joe) The points to evaluate: courtesy, quality of information given, etc For each point, the mark given by the buyer from one to ten. From the Salesperson The main form prepared by a salesperson is the Sales Contract, and this person surely hopes to prepare plenty of these! Here are the elements present on the Sales Contract: Buyer’s information: name, address, postal code, phone number Dealer’s information: name, address, postal code, phone number Salesperson information: name, address, postal code, phone number Quantity of vehicles for this sale (usually 1) Car description: brand, model, year (Fontax Mitsou 2007) Car condition: new/used Car serial number: (D34HTT987) Car……………….. color: (aquamarine) Selling price: (32,500) Insurance company name: (MicMac Car Insurance Inc.) Insurance policy number: (J44-5764, but each company has its own code system for this) Preparation cost: (800) Tax amount: (2,400) Total price: (35,700) Vehicle giving in exchange: brand: (Licorne) model: (Wanderer)
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If we must keep the existing system, we’ll probably build a bridge between the two systems for exchanging data. In this case, do we need a one-way bridge or a two-way bridge? Chronological Events Who orders a car for the show room and why; how is the order made phone, fax, email, website; can a car in the showroom be sold to a customer? Sources and Destinations Here we question about information, money, bills, goods, and services. For example, what is the source of a car? What’s its destination? Is the buyer of a car always an individual, or can it be another company? Urgency Thinking about the current way in which you deal with information, which problems do you consider the most urgent to solve? Avoid Focusing on Reports and Screens An approach too centered on the (perceived) needs of the users may lead to gaps in the data structure, because each user does not necessarily have an accurate vision of all their needs or all the needs of other users. It’s quite rare in an enterprise to find someone who grasps the whole data picture, with the complex inter-departmental interactions that frequently occur. This bias will show up during the interviews. Users are usually more familiar with items they can see or visualize and less familiar with concepts. However, there are distinctions between the user interface (UI) and the underlying data. UI design considers ergonomic and aesthetic issues, whereas data structuring has to follow different, non-visual rules to be effective. Data Collected for our Case Study Here is a list, jotted down during the interviews, of potential data elements and details which seem important to the current information flow. It’s very important during this collection to note, not only the data elements’ names shall we say “provisional names” at this point but also sample values. The benefit of this will become apparent in Chapter 3. In the following data collection, we include sample values in brackets where appropriate.
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Finding the Right Users The suggested approach would be to contact the best person for the questions about the new system. Sometimes, the person in charge insists that he/she is the best person, it might be true, or not. This can become delicate, especially if we finally meet someone who knows better, even if this is during an informal meeting. Thinking about the following issues can help to find the best candidates: Who wants this system built? Who will profit from it? Which users would be most cooperative? Evidently, this can lead to meeting with several people to explore the various sub-domains. Some of these domains might intersect, with a potential negative impact diverging opinions, or with a potential positive impact validating facts with more than one interviewee. Perceptions During the interviews, we will meet different kinds of users. Some of these will be very knowledgeable about the processes involved with the car dealer’s activities, for example, meeting with a potential customer, inviting them for a test drive, and ordering a car. Some other users will only know a part of the whole process, their knowledge scope is limited. Due to the varying scope, we will hear different perceptions about the same subject. For example, talking about how to identify a car, we will hear diverging opinions. Some will want to identify a car with its serial number; others will want to use their own in-house car number. They all refer to the same car with a different angle. These various opinions will have to be reconciled later when proceeding with the data naming phase. Asking the Right Questions There are various ways to consider which questions are relevant and which will enable us to gather significant data elements. Existing Information Systems Is there an existing information system: manual or computerized? What will happen with this existing system? Either we export relevant data from this existing system to feed the new one, to completely do away with the old system, or we keep the existing system temporarily or permanently.
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report because we are seeking details from the persons who are involved with the daily tasks. Forms The forms, which represent paperwork between the enterprise and external partners, or between internal departments, should be scrutinized. They can reveal a massive amount of data, even if further analysis shows unused, imprecise, or redundant data. Many organizations suffer from the form disease ……………………………a tendency to use too many paper or screen forms and to produce too complex forms. Nonetheless, if we are able to look at the forms currently used to convey information about the car inventory or car sales, for example, a purchase order from the car dealer to the manufacturer, we might find on these forms essential data about the purchase that will be useful to complete our data collection. Existing Computerized Systems The car dealer has already started sales operations a number of years ago. To support these sales, they were probably using some kind of computerized system, even if this could have been only a spreadsheet. This pre-existing system surely contains interesting data elements. We should try to have a look at this existing information system, if one exists, and if we are allowed to. Regarding the data structuring process itself, we can learn about some data elements that are not seen on the paper forms. Also, this can help when the time comes to implement a new system by easing transition and training. Interviews The goal for conducting interviews is to learn about the vocabulary pertaining to the studied system. This book is about data structures, but the information gathered during the interviews can surely help in subsequent activities of the system’s development like coding, testing, and refinements. Interviews are a critical part of the whole process. In our example, a customer asked for a system about car sales and inventory tracking. At this point, many users cannot explain further what they want. The problem is exactly this: how can I, as a developer, find out what they want? After the interview phase, things become clearer since we will have gathered data elements. Moreover, often the customer who ordered a new system does not grasp the data flow’s full picture; it might also happen that this customer won’t be the one who will work with all aspects of the system, those which are more targeted towards clerical persons.
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Chapter 2 [ 19 ] be addressed, like a new report about the car sales that the Chief Executive Officer (CEO) needs by June 20? Another point to take into account is how the modules are related. Chances are good that some data will be shared between modules, so the data model prepared for module 1 will probably be reused and refined during module 2 developments. Model Flexibility Another point not directly related to our user but to us as developers is: can the data model be built to be flexible and more general? This way, it could be applied to other car dealers, always keeping in mind contract issues between the developer and the user. (Who will own the work?) Should the data structure be developed with other sales domains in mind? For instance, this could lead to a table named goods instead of cars. Maybe this kind of generalization can help, maybe not, because data elements description must always remain clear. Document Gathering This step can be done before the interviews. The goal is to gather documents about this organization and start designing our questions for the interviews. Of course, a data model for car sales has some things in common with other sales systems, but there is a special culture about cars. Another set of documents will be collected during the interviews while we learn about the forms used by the interviewees. General Reading Here are some reading suggestions: Enterprise annual report Corporate goals statement President’s speech Publicity material Bulletin board I once learned a lot about information flow from a grocery store’s bulletin board for the employees. There were small notes from management to employees explaining how to handle clients who pay by cheque (which personal information must be obtained from the client before the store can accept their cheque), and detailing the schedule for sick employees’ replacement. Also explained on the board, was the procedure to use on the cash register to give reward points to clients who pay with the store’s credit card. This information is sometimes more useful than an annual
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Data Collecting [ 18 ] An organization always has a main purpose; it can be selling cars, teaching, or providing web solutions. In addition to this, every organization has sub-activities like human resource management, payroll, and marketing. The approach to data collecting will vary, depending upon the exact area we are dealing with. Let’s say we learn that our car dealer also operates a repair shop, which has its own inventory, along with a car rental service. Do we include these inventories in our analyzing tasks? We have to correctly understand the place of this new information system in its context. When preparing a data model, the biggest challenge is probably to draw a line, to clearly state where to stop. This is challenging for various reasons: Our user might have only a vague idea of what they want, of the benefits they expect from the new system Conflicting interests might exist between our future users; some of them might want to prioritize issues in a different way from others, maybe because they are involved with the tedious tasks that the new system promises to eliminate We might be tempted to improve enterprise-wide information flow beyond the scope of this particular project It’s not an easy task to balance user-perceived goals with the needs of the organization as a whole. Modular Development It is generally admitted that breaking a problem or task into smaller parts helps us to focus on more manageable units and, in the long run, permits us to achieve a better solution, and a complete solution. Having smaller segments means that defining each part’s purpose is simpler and that the testing process is easier as a smaller segment contains less details. This is why, when establishing the system boundaries, we should think in terms of developing by modules. In our case study, a simple way of dividing into modules would be the following: Module 1: car sales Module 2: car inventory Delivering an information system in incremental steps can help reassure the customer about the final product. Defining the modules and a schedule about them can motivate users and also the developers. With a publicized schedule, everyone knows what to expect. With the idea of modules comes the idea of budget and the notion of priorities for development. Do we have to deliver the car sales module before or after the inventory module? Can those modules be done separately? Are there some constraints that must
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Data Collecting In order to structure data, one must first gather data elements and establish the domain to which this data applies. This chapter deals with raw data information that comes from the users or other sources, and the techniques that can help us to build a comprehensive data collection. This collection will become our input for all further activities like data naming and grouping. To be able to build a data collection, we will first identify the limits of the system. This will be followed by gathering documents in order to find significant data elements. The next step will be to conduct interviews with key users in order to refine the list of data elements. All these steps are described in this chapter. System Boundaries Identification Let’s establish the scenario. We have been called by a local car dealer to submit a proposal about a new information system. The stated goal is to produce reports about car sales and to help track the car inventory. Reports are, of course, an output of the future system. The idea hidden behind reports could be to improve sales, to understand delivery delays, or to find out why some cars disappear. The data structure itself is probably not really important in the users’ opinion, but we know that this structure matters to the developers who produce the required output. It’s important to first look at the project scope, before starting to work on the details of the system. Does the project cover: The complete enterprise Just one administrative area Multiple administrative areas One function of the enterprise
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