Goals of a Data Quality Assurance Program
A data quality assurance program is an explicit combination of organization, methodologies, and activities that exist for the purpose of reaching and maintaining high levels of data quality. The term assurance puts it in the same category as other functions corporations are used to funding and maintaining. Quality assurance, quality control, inspection, and audit are terms applied to other activities that exist for the purpose of maintaining some aspect of the corporation's activities or products at a high level of excellence. Data quality assurance should take place alongside these others, with the same expectations.
Just as we demand high quality in our manufactured products, in our financial reports, in our information systems infrastructure, and in other aspects of our business, we should demand it from our data.
The goal of a data quality assurance program is to reach high levels of data accuracy within the critical data stores of the corporation and then keep them there. It must encompass all existing, important databases and, more importantly, be a part of every project that creates new data stores or that migrates, replicates, or integrates existing data stores. It must address not only the accuracy of data when initially collected but accuracy decay, accurate access and transformation of that data, and accurate interpretation of the data for users. Its mission is threefold: improve, prevent, monitor.
Improvement assumes that the current state of data quality is not where you want it to be. Much of the work is to investigate current databases and information processes to find and fix existing problems. This effort alone can take several years for a corporation that has not been investing in data quality assurance.
Prevention means that the group should help development and user departments in building data checkers, better data capture processes, better screen designs, and better policies to prevent data quality problems from being introduced into information systems. The data quality assurance team should engage with projects that build new systems, merge systems, extract data from new applications, and build integration transaction systems over older systems to ensure that good data is not turned into bad data and that the best practices available are used in designing human interfaces.
Monitoring means that changes brought about through data quality assurance activities need to be monitored to determine if they are effective. Monitoring also includes periodic auditing of databases to ensure that new problems are not appearing.
Structure of a Data Quality Assurance Program
Creating a data quality assurance program and determining how resources are to be applied needs to be done with careful thought. The first decision is how to organize the group. The activities of the group need to be spelled out. Properly skilled staff members must be assigned. They then need to be equipped with adequate tools and training.
Data Quality Assurance Department
There should be a data quality assurance department. This should be organized so that the members are fully dedicated to the task of improving and maintaining higher levels of data quality. It should not have members who are part-time. Staff members assigned to this function need to become experts in the concepts and tools used to identify and correct quality problems. This will make them a unique discipline within the corporation.
The group needs to have members who are expert data analysts. Analyzing data is an important function of the group. Schooling in database architecture and analytical techniques is a must to get the maximum value from these activities. It should also have staff members who are experienced business analysts. So much of what we call quality deals with user requirements and business interpretation of data that this side of the data cannot be ignored.
The data quality assurance group needs to work with many other people in the corporation. It needs to interact with all of the data management professionals, such as database administrators, data architects, repository owners, application developers, and system designers. They also need to spend a great deal of time with key members of the user community, such as business analysts, managers of departments, and web designers. This means that they need to have excellent working relationships with their customers.
 |
There is a strong parallel between the emergence of data quality assurance to the improvements made in software development in the 1970s and 1980s. Software development teams back then consisted mostly of programmers. They wrote the code, tested the product, and wrote the user manuals. This was the common practice found in the best of software development groups.
In my first job at IBM I designed, developed the code, tested, wrote user documents, and provided customer support of a software product (Apparel Business Control System). It was a one-person project. Although the product had high quality and good customer acceptance, I believe it would have gone better and been a better product if I had access to professional writers and software quality assurance people.
In response to the continual problems of poorly tested products and very poor user manuals, companies started dedicating some of the programmers to ensuring the quality of code (testing) and began to hire professional technical writers. There was an immediate improvement in both the code and user manuals. As time went on, these two areas became established disciplines. Software development companies specialized in building tools for these disciplines; colleges offered classes and tracks for these disciplines.
The programmers that tested were no different from those that wrote the code in the beginning. They made huge improvements only because they were dedicated to testing, worked with the programmers throughout the entire project, and brought another view to the use of the code. In time, they became even better as they developed very effective methodologies and tools for testing. Testing became a unique technology in its own right.
The cost of these programs is clearly zero. Every serious development group today separates code quality assurance from code development. Projects finish earlier, with higher-quality results. The projects spend less money (much less money) and use up less time (much less time) than they would if programmers were still doing the testing.
Data quality is emerging as a major topic 20 years later. The same evolution is happening. Making data quality the responsibility of the data management staff who design, build, and maintain our systems means that they do not become experts in the methodologies and tools available, do not have the independence to prioritize their work, and do not focus on the single task of ensuring high-quality data. Data quality assurance must be the full-time task of dedicated professionals to be effective.
|
One way to achieve a high level of cooperation is to have an advisory group that meets periodically to help establish priorities, schedules, and interactions with the various groups. This group should have membership from all of the relevant organizations. It should build and maintain an inventory of quality assurance projects that are worth doing, keep this list prioritized, and assign work from it. The advisory group can be very helpful in assessing the impact of quality problems as well as the impact of corrective measures that are subsequently implemented.
Data Quality Assurance Methods
The first component is the quality dimensions that need to be addressed. The second is the methodology for executing activities, and the last is the three ways the group can get involved in activities.
The figure highlights the top line of each component to show where a concentration on data accuracy lies. Data accuracy is clearly the most important dimension of quality. The best way to address accuracy is through an insideout methodology, discussed later in the book. This methodology depends heavily on analysis of data through a process called data profiling. The last part of this book is devoted to explaining data profiling. Improving accuracy can be done through any of the activities shown. However, the one that will return the most benefit is generally the one shown: project services.
Any data quality assurance function needs to address all of the dimensions of quality. The first two, data accuracy and completeness, focus on data stored in corporate databases. The other dimensions focus on the user community and how they interpret and use data.
Both of these methodologies have a goal of identifying data quality issues. An issue is a problem that has surfaced, that is clearly defined, and that either is costing the corporation something valuable (such as money, time, or customers) or has the potential of costing the corporation something valuable. Issues are actionable items: they result in activities that change the data quality of one or more databases. Once identified, issues are managed through an issues management process to determine value, remedies, resolution, and monitoring of results. The process of issue management is discussed more fully in the next chapter.
Inside-Out Method
The inside-out method starts with analyzing the data. A rigorous examination using data profiling technology is performed over an existing database. Data inaccuracies are produced from the process that are then analyzed together to generate a set of data issues for subsequent resolution.
The analysis should be done by a highly qualified data analyst who understands the structure of the data. The methodology starts with a complete and correct set of rules that define data accuracy for the data. This is metadata. It consists of descriptions of the data elements, values permitted in them, how they relate to one another in data structures, and specific data rules that describe value correlation conditions that should always be true within the data. All of these categories are discussed at length in later chapters.
Of course, such a rigorous rule set for any operational database does not exist. The metadata that is available is generally incomplete and most likely inaccurate. The data profiling process described in later chapters is a process that completes and corrects the metadata, along with using it to find evidence of inaccurate data. This intertwined process has a very valuable by-product: accurate and complete metadata.
The process of determining the correct metadata inevitably involves conferring with business analysts and end users. The data analyst will detect a behavior in the data and require consultation to determine why it is so. This often leads to modifications to the metadata. These consultations are always productive because the question is always backed up by information from the data.
The data analyst should identify who in the user community will be the most valuable in consulting on issue identification and form a small, dynamic working group with them. In the end, they should always agree on what the final metadata is, and agree on the inaccurate data facts derived from the comparison with the actual data.
The inaccurate data evidence produced is a collection of facts. It may be explicit cases of wrong or missing values, or it may identify rules that fail without being able to say what values are wrong. For example, one fact may be that 30% of purchase order records do not have a supplier ID. Another may be that the employee birth date field has values that are invalid: too long ago or too recent. Another might be that the percent of the color BLUE in a database is too large. In this case, the analyst does not know which instances are correct and which are wrong; only that some of them must be wrong.
The facts are aggregated into issues. Some facts are issues by themselves. For example, the supplier ID problem may be the basis for a single issue. Others are aggregated into a larger issue. An example is that customer demographic fields in a marketing database contain numerous errors in all fields, possibly indicating a general problem with form design.
Outside-In Method
This method looks for issues in the business, not the data. It identifies facts that suggest that data quality problems are having an impact on the business. It looks for rework, returned merchandise, customer complaints, lost customers, delays in getting information products completed, high amounts of work required to get information products produced, and so on. Interviews are done with users to determine their level of trust in the accuracy of data coming from the information systems and their level of satisfaction with getting everything they need. It may also include looking for decisions made by the corporation that turned out to be wrong decisions.
These facts are then examined to determine the degree of culpability attributable to defects in the data. The data is then examined to determine if it has inaccuracies that contribute to problems, and to determine the scope of the contribution. This examination is generally pointed at the specific problem. It is generally not a thorough data profiling exercise, although it could be expanded to that if the evidence indicates a widespread quality problem with the data.
This approach is generally the work of the data quality assurance team member with skills as a business analyst. It involves heavy participation on the part of outside people. It also requires conference sessions with user community experts. The result is a collection of data issues that are then tracked on the same path as those from the inside-out methodology.
Comparison Of Methods
Neither approach is superior to the other: they both bring value to the process. However, they do not get to the same end point. Data quality assurance groups should use both methodologies as applicable.
Inside-out is generally easier to accomplish and uses less people time. A single analyst can analyze a great deal of data in a short time. The data quality assurance group can accomplish a great deal with this approach with the staff within their own department. The outside-in approach requires spending a lot of time interviewing people in other departments.
The inside-out approach is nondisruptive. You just get a copy of the data you want to analyze and do it offline. The outside-in approach requires scheduling time for others, thus interrupting their regular activities.
The inside-out approach will catch many problems the outside-in approach does not catch. For an outside-in approach to catch a problem, it must manifest itself in some external behavior, and that behavior must be recognizable as being not good.
An example of a hidden problem is a case in which missing supplier ID numbers on purchase orders causes a company not to get maximum discounts they were entitled to from suppliers. The purchase order volumes were summarized by supplier ID and, because the field was missing on 30% of the records, the amounts were low. The company was losing millions of dollars every year because of this and was completely unaware that it was happening. The inside-out approach catches this; the outside-in approach does not.
Another type of problem are those inaccuracies that have the potential for a problem but for which the problem has not yet occurred. An example of this is where an HR database failed to capture government classification group information on employees accurately. Many minority employees were not classified as minorities, nor were handicapped employees all being identified as handicapped. No problem may have surfaced yet. However, the potential for being denied contracts in the future because of these inaccuracies is waiting to happen. Inside-out analysis will catch this; outside-in will not.
The opposite is also true. The inside-out approach will not catch problems where the data is inaccurate but valid. The data can pass all metadata tests and still be wrong. This can happen either because the rule set is incomplete or because the data hides underneath all of the rules. An example is getting the part number wrong on orders. The wrong merchandise is shipped. An analysis of the data will not reveal inaccurate data because all of the part numbers are valid numbers. The outside-in approach catches these problems better. (The inside-out approach may catch this if the analysis finds the percentage of orders returned to be higher than an acceptable threshold. This is possible if a data rule or value test has been formulated. These topics are covered in Chapters 11 and 12).
There is another class of problems not detectable by either approach. The data is valid but wrong and also produces insufficient external evidence to raise a flag. Although these generally are of little concern to a corporation, they have the potential to be costly in the future if not detected. A data quality assurance program built exclusively using only one approach is generally going to miss some important issues.
Data Quality Assurance Activities
The data quality assurance team must decide how it will engage the corporation to bring about improvements and return value for their efforts. The group should set an explicit set of guidelines for what activities they engage in and the criteria for deciding one over another. This is best done with the advisory group.
There are three primary roles the group can adopt. This is shown as the last column in Figure 4.2. One of them, project services, involves working directly with other departments on projects. Another, stand-alone assessments, involves performing assessments entirely within the data quality assurance group. Both of these involve performing extensive analysis of data and creating and resolving issues. The other activity, teach and preach, involves educating and encouraging employees in other groups to perform data auditing functions and to employ best practices in designing and implementing new systems.
Project Services
The vast majority of projects being pursued by the IT organization involve repurposing an existing database. It is rare these days to see a truly new application being developed that does not draw from data that has already been collected in an existing application. Examples of projects that involve working with existing data stores are
-
data migration to new applications (generally packaged applications)
-
consolidation of databases as a result of mergers and acquisitions
-
consolidation of databases to eliminate departmental versions of applications
-
replication of data into data warehouses, data marts, or operational data stores
-
building a CRM system
-
application integration that connects two or more applications
-
application integration that connects an older database to the Internet
There is a real danger in all of these applications of introducing errors through mistakes made due to a misunderstanding of the data. There is also a real danger in the data from the original systems not being of sufficient quality to meet the demands of the new use of the data. Both of these are classical concerns that if not addressed will certainly cause great difficulty in completing the projects, as well as unhappiness with the outcome.
The data quality assurance team can provide an invaluable service to these projects by profiling the data. By doing this they provide two valuable outputs: an accurate and complete metadata description of the data and an inventory of data quality problems uncovered in the process.
The metadata repository produced should be used to match target system requirements against the content and structure of the source systems. It is also the perfect input to developing processes for extraction, transformation, cleansing, and loading processes.
The data quality assurance team can use the inaccuracy facts to determine either whether the data is strong enough to satisfy the intended use or whether there is a need to establish new projects from the issues to drive improvements in the source systems. Of course, this applies to cases in which the source databases continue to live past the project, as is the case for replication and integration projects.
The data quality assurance team can also provide advice and oversight in the design of target database structures, as well as processes for collecting or updating data. They also have a good opportunity to get data checking and monitoring functions embedded in the new systems to help prevent future quality problems.
Why should the data quality assurance team perform these tasks, as opposed to the project teams? The answer is that the data quality assurance team are experts in data quality technologies.They are experienced in data profiling, investigation of issues, and fabrication of data quality problem remedies.
One of the most valuable outputs of data profiling at the beginning of a project is to learn that the project cannot achieve its goals because of the condition of the source data. When this happens, the project team can then make decisions about changing target design, changing target expectations, making improvements to data sources, or scrapping the project outright. This is the perfect place to make these decisions: before most of the project money has been spent and before most of the development work has been done.
Projects that do not perform a thorough review of the source data generally do not discover the match between the data and the project requirements until after much time and money has been spent. It is generally very expensive to repair the damage that has already been done and impossible to recoup the money spent and the valuable time lost.
Stand-Alone Assessments
A stand-alone assessment is a project organized for the purpose of determining the health of an existing database. The database is chosen because of suspicions or evidence about problems coming from the use of data, or simply because it is an important data source for the corporation.
The data quality assurance team will generally execute the entire project. Using the inside-out method, they will profile the data, collect quality facts, produce issues, and then follow the issues through to remedies.
The advantage of assessment projects is that they do not require as much interaction with other project teams and can be scheduled without concern for other plans in IT. Of course, it makes no sense to schedule an assessment of a database that is about to get a facelift as a result of another project.
An assessment can be quite disruptive to other departments, even if no change activity is under way for the data source. Time from them will be needed to develop perfect understanding of the metadata and to interpret facts that come out of profiling. If remedies are needed, negotiations with IT and users will be needed to get them designed and implemented. It may also be quite disturbing to people to find out that they have been using flawed data for a long time without knowing it. The data quality assurance team needs to involve the other departments in the planning phase and keep them involved throughout the process.
It is important not to appear as an outside hit team trying to do damage to the reputation of the operational organizations. Involving them makes them part of the solution.
Teach And Preach
This function involves training information system staff members on the technology available for data quality assessment, the techniques and best practices available for building and maintaining systems, and how to develop quality requirements and use them to qualify data.
Few information systems professionals come out of college with training explicitly targeted to data quality. The principles are not difficult to understand, nor are the disciplines difficult to use in daily practice. Educating them will improve all of the work they do.
The data quality assurance group should function as the experts in data quality. They should not keep this knowledge exclusively to themselves. The more they educate others in the corporation, the more likely the information systems will reach and stay at a high level of quality.
Preaching means that the data quality assurance department should encourage and insist that quality checkpoints be put into all projects. They should encourage upper management to be cognizant of the need for data quality activities. They should collect and advertise the value to the corporation realized from these activities.
The data quality assurance group should not depend exclusively on teaching and preaching. If that is all they do, the company will never develop the focused expertise needed to analyze the mountains of data and drive improvements.
Closing Remarks
If you want high data quality you must have highly accurate data. To get that you need to be proactive. You need a dedicated, focused group.
You need to focus on data accuracy. This means you need an organization that is dedicated to improving data accuracy. You also need trained staff members who consider the skills required to achieve and maintain data accuracy as career-building skills.
You need to use technology heavily. Achieving high levels of data accuracy requires looking at data and acting on what you see. You need to do a lot of data profiling. You need to have experienced staff members who can sniff out data issues.
You need to treat information about your data as of equal or greater importance than the data itself. You must install and maintain a legitimate metadata repository and use it effectively.
You need to educate other corporate employees in the importance of data and in what they can do to improve the accuracy. This includes the following elements.
-
Business users of data need to be sensitized to quality issues.
-
Business analysts must become experts on data quality concepts and play an active role in data quality projects.
-
Developers need to be taught best practices for database and application design to ensure improved data accuracy.
-
Data administrators need to be taught the importance of accuracy and how they can help improve it.
-
All employees who generate data need to be educated on the importance of data accuracy and be given regular feedback on the quality of data they generate.
-
The executive team needs to understand the value of improved data accuracy and the impact it has on improved information quality.
You need to make quality assurance a part of all data projects. Data quality assurance activities need to be planned along with all of the other activities of the information systems department. Assisting a new project in achieving its data quality goals is of equal or higher value than conducting assessment projects in isolation. The more integrated data quality assurance is with the entire information system function, the more value is realized. And finally, everyone needs to work well together to accomplish the quality goals of the corporation.
Data Quality Issues Management
Overview
Data quality investigations are all designed to surface problems with the data. This is true whether the problems come from stand-alone assessments or through data profiling services to projects. It also does not matter whether assessments reveal problems from an inside-out or an outside-in method. The output of all these efforts is a collection of facts that get consolidated into issues. An issue is a problem with the database that calls for action. In the context of data quality assurance, it is derived from a collection of information that defines a problem that has a single root cause or can be grouped to describe a single course of action.
That is clearly not the end of the data quality effort. Just identifying issues does nothing to improve things. The issues need to drive changes that will improve the quality of the data for the eventual users.
It is important to have a formal process for moving issues from information to action. It is also important to track the progress of issues as they go through this process. The disposition of issues and the results obtained from implementing changes as a result of those issues are the true documentation of the work done and value of the data quality assurance department.
It does not matter who performs these phases. The data quality assurance department may own the entire process. However, much of the work lies outside this department. It may be a good idea to form a committee to meet regularly and discuss progress of issue activity. The leader of the committee should probably be from the data quality assurance department. At any rate, the department has a vested interest in getting issues turned into actions and in results being measured. They should not be passive in pursuing issue resolution. This is the fruit of their work.
An issue management system should be used to formally document and track issue activity. There are a number of good project management systems available for tracking problems through a work flow process.
The collection of issues and the management process can differ if the issues surface from a "services to project" activity. The project may have an issues management system in place to handle all issues related to the project. They certainly should. In this case, the data quality issues may be mixed with other issues, such as extraction, transformation, target database design, and packaged application modification issues. It is helpful if data quality issues are kept in a separate tracking database or are separately identified within a central project management system, so that they can be tracked as such. If "project services" data profiling surfaces the need to upgrade the source applications to generate less bad data, this should be broken out into a separate project or subproject and managed independently.
Turning Facts into Issues
Data quality investigations turn up facts. The primary job of the investigations is to identify inaccurate data. The data profiling process will produce inaccuracy facts that in some cases identify specific instances of wrong values. Other cases identify where wrong values exist but identification of which value is wrong is not known, and in yet other cases identify facts that raise suspicions about the presence of wrong values.
Facts are individually granular. This means that each rule has a list of violations. You can build a report that lists rules, the number of violations, and the percentage of tests performed (rows, objects, groups tested) that violated the rule. The violations can be itemized and aggregated.
Metrics
There is a strong temptation for quality groups to generate metrics about the facts and to "grade" a data source accordingly. Sometimes this is useful; sometimes not. Examples of metrics that can be gathered are
-
number of rows containing at least one wrong value
-
graph of errors found by data element
-
number of key violations (nonredundant primary keys, primary/foreign key orphans)
-
graph of data rules executed and number of violations returned
-
breakdown of errors based on data entry locations
-
breakdown of errors based on data creation date
The data profiling process can yield an interesting database of errors derived from a large variety of rules. A creative analyst can turn this into volumes of graphs and reports. You can invent an aggregation value that grades the entire data source. This can be a computed value that weights each rule based on its importance and the number of violations. You could say, for example, that this database has a quality rating of 7 on a scale of 10.
The Good
Metrics can be useful. One use is to demonstrate to management that the process is finding facts. The facts have little to no significance by themselves but can be circumstantial evidence that something is wrong with the data. When a data quality assurance department is trying to gain traction in a corporation, metrics can be a useful way to show progress.
Metrics can also be useful to show improvements. If data is profiled before and after corrective actions, the metrics can show whether the quality has improved or not.
Another use of metrics is to qualify data. Data purchased from outside the corporation, such as demographic data, can be subjected to a quick data profiling process when received. Metrics can then be applied to generate a qualifying grade for the data source. It can help determine if you want to use the data at all. This can be used to negotiate with the vendor providing the data. It can be the basis for penalties or rewards.
Qualification can also be done for internal data sources. For example, a data warehousing group can qualify data extracts from operational groups before they are applied to the central data warehouse.
The Bad
The downside of metrics is that they are not exact and they do not solve problems. In fact, they do not identify what the problems are; they only provide an indicator that problems exist.
Earlier chapters demonstrated that it is not possible to identify all inaccurate data even if you are armed with every possible rule the data should conform to. Consequently you cannot accurately estimate the percentage of inaccuracies that exist. The only thing you know for sure is that you found a specific number of inaccuracies. The bad news is that there are probably more; the good news is that you found these. If the number you find is significant, you know you have a problem.
Corrective actions have these potential consequences: they can prevent recurrence of some errors that you can detect, they can prevent recurrence of errors you cannot detect, and they can continue to pass errors through. It is also theoretically possible that you would introduce new errors that may or may not be detectable.
The conclusion is that data profiling techniques can show the presence of errors but cannot show the absence of errors nor the number of errors. Therefore, any metrics derived from the output of profiling are inexact. This does not make them useless. On the contrary, the errors found are true errors, and if there are enough of them you have uncovered true problems.
You might conclude from the previous discussion that the number of errors reported is understated. This would be great if it were true. However, poorly defined metrics can actually overstate the error condition. This occurs when a single inaccurate value triggers multiple rule violations. This is difficult to detect and impossible to quantify. When you consider that the majority of rules will find the presence of inaccurate data but will not pinpoint the offending values, you can see why it is difficult, if not impossible, to find the true number of inaccurate values.
Comparing metrics can also be misleading if the yardstick changes between profiling exercises. As analysts gain more knowledge about a data source, they will add to the rule set used to dig out inaccuracies. Comparing two result sets that are derived from different rule sets results in an apples-to-oranges comparison. All presentations of quality metrics need to provide disclaimers so that the readers can understand these dynamics.
|
The following is an example of preventing recurrence of errors you never detected. A medical clinic's internal system records a code for the medical procedure performed, as well as the gender of the patient. It is discovered in data profiling that procedures are being recorded that are not possible for the gender code recorded. These are inaccuracy facts.
However, the root cause is that the procedure codes are handwritten on paper forms and then sent to the data entry office. Many of them are illegible or missing. The data entry staff has no way of verifying the correct procedure and are motivated to get the data into the system rather than fix it. In addition to the procedure codes being invalid in the case of gender conflicts, there are probably many other procedure codes that are wrong. However, because they are valid procedure codes, they are not detected.
The remedy called for having the data entered directly online by the administrators of the doctors instead of transferring paper documents to a central data entry function. Because so many errors were noted, the new form displays a text description of the procedure when it is entered with a confirmation button. This helps the administrators confirm that they have entered the correct code.
Checks were put in for gender/procedure code conflicts, as well as other conflicts, such as invalid patient age/procedure code combinations. In addition, administrators were educated on the importance of correct procedure codes. Because of the better data entry procedures, the number of errors prevented not only included those that were detectable but many others that were not detectable through analysis.
|
An additional problem with metrics is that data quality assurance departments often believe that this is the end of their mission. They define their work product as the metrics. However, metrics do not define the source of problems nor the solutions. To improve data quality you need to follow through on getting improvements made. To hand the responsibility for this to other departments is a guarantee that the work items will sit low on priority lists of things to do and will not get done expeditiously. The data quality assurance department needs to track and drive the issues through to solution.
Metrics are not all bad. They are often a good shock factor for driving actions. When you give management a presentation that says the HR database records revealed 700 inaccurate values, this can raise eyebrows and produce a call for action. Knowing that you have 700 and that the real number is higher can be motivation enough.
Often a single fact is more shocking than statistical metrics. For example, telling management that a profiling exercise of the birth date of employees revealed that the youngest employee in the company has not been born yet and that the oldest was born before the Civil War is far more effective than a metric at getting across the point that improvements are needed now. (I did not make this up; it was an actual output of a data profiling exercise.)
Issues
The real output of the fact collection phase is a set of issues that define problems that need to be solved. A single statistic can result in an issue. For example, 30% of the purchase order fields have no supplier ID number. Alternatively, several facts can be grouped into one issue. For example, the customer name and address data is severely flawed: 5% of name fields have invalid names, 15% of address fields are inaccurate or blank, 12% of city fields are blank, 5% of city fields are misspelled, and 12% of Zip codes are invalid or blank. This single issue rolls up several inaccuracy facts into a single issue that needs to be addressed. Addressing each inaccuracy fact is an inefficient use of time.
Issues need to be recorded in a database within an issues tracking system. Each issue needs a narrative description of the findings and facts that are the basis for the issue. It is important to identify the facts and the data source so that comparisons can be correctly made during the monitoring phase. The information needed for the data source is the identification of the database used, whether samples or the entire database were used, the date of the extraction, and any other information that will help others understand what you extracted the facts from. In tracking the issues, all meetings, presentations, and decisions need to be recorded along with dates and persons present.
Assessing Impact
Each issue that has been created needs to be studied to determine the impact it has already had or potentially may have on the corporation. Somewhere along the line someone will ask the "so what" question about an issue. It is important to justify development and disruptive efforts to deploy corrective actions. It is important to document the value returned to the corporation for the time and cost spent pursuing issues.
This needs to be updated from time to time. It is usually impossible to compute the costs and benefits up front. One approach is to look at the facts and theorize on possible impacts. A brainstorming session with data analysts, business analysts, and others may be helpful. This will lead to activities to prove that the impacts have already occurred. Because impacts have not occurred does not mean they will not in the future. As the issues are worked through the entire process, additional information about impacts may become apparent. These need to be added to the impact section.
Impacts Already Happening
The impacts may not be obvious to anyone but may be very real. For example, an issue that states that suppliers exist in the supplier's database multiple times may lead to speculation that you are not getting large enough discounts for volumes purchased over a year. Investigation may uncover that this is true (one department orders under one supplier ID and another department uses a second supplier ID for the same supplier). You can easily compute the discount difference, the volume of purchases made, and the value lost to the corporation. The cost of this type of inaccuracy is totally hidden until the issue is identified and pursued.
Sometimes an issue is created from an outside-in investigation and the cost is already known. Tying the external cost to facts is part of issue definition. For example, the external manifestation might be that the accounts receivable department spends x amount of people time per month correcting wrong information on invoices. The facts are the number of blank or inaccurate values found during data profiling. The facts back up the assertion that invoices are not being prepared properly.
Further investigation may reveal that not only is time being wasted but that payments are being delayed by a certain amount for two reasons: one is the lag in time in getting invoices out, and the other is that invoices sent out without corrections get rejected by the purchasing company, causing yet further delays. In fact, there may be a group of invoices that are never collected due to data errors on the invoices. This is an example of a single visible cost leading to facts about inaccuracies, which lead to the discovery of more hidden costs.
One point to consider is that a significant accuracy problem on a data element may indicate a bigger quality problem. In the case of the missing supplier ID, it is clear that if 30% of the values are missing, there is a real possibility that the process is flawed and that the supplier ID is not available at the time the data is entered. It is unlikely that data entry staff are that bad at their jobs. It is also clear that this field is not involved in making the purchase or subsequent payments (it appears to cause no harm). The harm is all done in the secondary uses of the data. It is easy to speculate that if the data is not available at entry, data entry staff may also be entering wrong but valid values. The problem may be much larger than it first appears.
This is why you need to match inaccuracy facts to known manifestations. By seeing the actual data values in error and the data elements containing errors, you can often speculate about hidden costs that may be occurring.
Impacts Not Yet Happening
The most dangerous impacts are those that have not yet occurred. Seeing the presence of inaccurate data can sometimes lead to speculation about problems that could occur. These can have greater impact than those that occur on a regular basis but cost little to correct.
A simple example is the inaccurate birth dates of employees. There may have been no costs that have occurred yet for a new company that hires mostly young people. However, as this population ages, all sorts of government regulations about reporting, pension programs, and changing medical benefits when an employee reaches age 65 are at risk of occurring. These errors can also make decisions about hiring practices inaccurate and lead to wasteful efforts to adjust the company's mix of ages.
A business rule may require that a fast mode of shipment be used to ship certain materials that have the potential to spoil or decay. They may require refrigeration or avoidance of temperatures above a certain number. It may be that errors in the orders have caused a number of shipments to be made that violate the rule and no dire consequences have occurred. All values are valid individually, but the shipment mode rule for the product type is violated. By speculating on the potential for costs, the issues team may speculate about returned orders, merchandise that cannot be resold, and lost customers. However, that speculation may lead to the potential for real lawsuits, as the corporation may be liable for damage done to the purchaser trying to use spoiled merchandise.
This example may have been saving the company money (lower shipping costs) but creating a potential liability (lawsuits) that could severely damage or even destroy the company. This is why speculation on potential impacts is so important.
The process of assessing impacts will crystallize issues. It may result in issues being broken apart or issues being combined. As participants gain more experience, they will be better at sniffing out impacts both real and potential. As new participants join the process, they can benefit from the documentation of previous issues as a training device.
It should also be apparent that the documentation of the impacts of issues is highly sensitive information. The issues management process should provide for a high degree of privacy and safety of the information.
Investigating Causes
The next logical step in the process is to discover the causes of the inaccuracy facts. Remedies cannot be fabricated until more information is uncovered. You need to perform a thorough study, in that the causes may not be what you think they are.
This chapter is not going to cover this topic comprehensively. This is a very large topic and beyond the scope of this book. However, a snapshot of some of the approaches is given to show the types of activities required.
Investigating causes requires talking to a lot of people in a lot of organizations. Assignments to investigators must to be done based on the substance of the issues. Participants from many organizations may be needed. The data quality assurance department should not try to undergo this step entirely with their own staff. Neither should they relegate this entirely to others. It is yet another place where the need for a larger team exists that gets guidance and leadership from the data quality assurance staff.
Investigation of the cause is not always possible. For example, databases purchased from vendors may be found to be defective. It is your responsibility to notify them of the problem and give them facts. It is their job to investigate the causes and correct them.
There are two basic approaches to investigating errors: error cluster analysis and data events analysis. The first is used to narrow down the sources of errors. The second is used to study the events that cause data to be created and maintained in order to help identify the root causes of problems. They can often be used together to efficiently complete the task.
Error Clustering Analysis
This type of analysis attempts to use information in the database to provide clues as to where the inaccuracies may be coming from. It starts with information about the specific database objects containing inaccuracies. For example, in an order database, it would start by identifying those orders that contain inaccurate data or that are suspected of having inaccurate data. Although many rules about data cannot identify specific data elements that are wrong, they can identify entire orders that contain the wrong data. The collection of all orders that have wrong values or rule violations constitutes the analysis set.
The analysis set may be defined narrowly (all orders violating a single rule) or broadly (all orders violating any rule). It depends on the amount of data in the analysis set and the importance of the individual rule. There is also the concept of rules having affinity. That is, for example, all rules that deal with the initial capture of the order information (a process clustering) or all orders dealing with customer name and address information (data semantic clustering).
Once the set of data is isolated that contains offending data, all of the data elements of the isolated set are used to determine if they vary in significant ways with the general population of data.
Common data elements that may reveal significant variances are data source location (branch office, geographic region, specific sales reps), customer information (first-time customers, Internet customers), dates (specific dates, days of week, range of dates), product type or characteristics (engine pats, volatile, expensive), or process steps completed (initial entry, order shipped, invoice created). You are looking for any factor that may indicate a starting point in examining the causes of the errors. Performing error clustering analysis can shorten the search for causes significantly through performing a relatively quick and simple test of data.
Data Events Analysis
This involves a review of all processes that capture data or change data. Data takes a journey from inception to one or more databases. It may have a single process event (data entry) or a number of events. The points of examination can be any or all of the following:
-
data capture processes
-
durations in which data decay can occur
-
points at which data is extracted and added to a different data store
-
points at which data is converted to business information
Data Capture Processes
The process point at which data is captured represents the single most important place data can be made accurate or inaccurate. All data capture points need to be identified and examined. Some data is only captured once. Some is captured and then updated on an exception basis. Some data is captured and the business object updated or enhanced through a work flow process that may occur over a long period of time. Some of these points may take on multiple forms. For example, an order may be entered by the actual customer over the Internet, entered by a recording clerk from a form received in the mail, or entered by a company sales representative through a company client server application. This example shows three very different and distinct ways of entering the same business object.
Building a diagram of the data paths of a business object, identifying the distinct points of data capture, and specifying the characteristics of each is a time-consuming but extremely important task.
-
Time between event and recording: In general, the longer the time differences, the greater the chance for errors. If the time lag is long enough, it also lends itself to missing or late information. Examples of long durations are cases in which forms are completed and mailed to a data entry location. The accuracy and timeliness would be enhanced if the time difference were eliminated through a more direct entry, such as through the Internet.
-
Distance between event and recording: Physical distance can also be a factor. This reduces the opportunity for the person who is entering the data to verify or challenge information. For example, if the originator of data is in Chicago but the information is transmitted via telephone or paper to Kansas City for entry, you have a distance between the person who knows the right information and the one entering it. If there is confusion, the entry person has to either enter nulls or enter a best guess.
-
Number of handoffs of information before recording: The first person to experience the event is most likely to be the one with the most accurate description of the facts. Each handoff to another person introduces the possibility of misreading written information, misinterpreting some else's comments, or not knowing information that was not passed on.
-
Availability of all facts at recording: If the person entering the information has no access to the event, to the person who created or observed the event, or to databases containing important auxiliary information, they cannot fill in missing information or challenge information they see. For example, it is better for HR data to be entered with the employee sitting next to the entry person, as opposed to copying information from a form. Another example is to have a search function for customer identifiers available for order entry personnel.
-
Ability to verify information at recording: This is similar to the previous issue, but slightly different. Can the data entry person get to correct information if they think the information provided is wrong? An HR data entry person could call or e-mail the employee if there is confusion. Sometimes the process makes it impossible to make this connection. Sometimes the process penalizes the data entry person for taking the time to verify questionable information. All entry points should allow for information to be either verified immediately or posted to a deferred process queue for later verification and correction if needed.
-
Motivation of person doing recording: This is a complex topic with many sides. Are they motivated to enter correct information? Are they motivated and empowered to challenge questionable information? Are they motivated to enter the information at all? Someone entering their own order is motivated to do it and get it right. Someone entering piles of form information they do not understand could not care less if the information is entered correctly or completely. Is feedback provided? Is their performance measured relative to completeness and accuracy?
-
Skill, training, and experience of person doing recording: People who enter the same information for a living get to learn the application, the typical content, and the data entry processes. They can be trained to do it right and to look for red flags. People who enter data on a form only one time in their life are much more likely to get it wrong. Sometimes there exists a data entry position that has not been trained in the application. This is an invitation for mistakes. Note that entry people who are making mistakes tend to make them repetitively, thus increasing the database inaccuracy level and thereby increasing the likelihood that it will be exposed through data profiling analysis.
-
Feedback provided to recorder: Feedback is always a good thing. And yet, our information systems rarely provide feedback to the most important people in the data path: those entering the data. Relevant information, such as errors found in computer checks, should be collected and provided to help them improve the accuracy of data they enter.
-
Auto-assist in recording process: Do the data entry programs and screens help in getting it right? A complex process can include pull-downs, file checking, suggestions on names, addresses, questioning of unusual options or entry information, and so on. Remembering information from the last transaction for that source can be very helpful in getting information right. Letting each data entry station set its own pull-down defaults can reduce errors. Providing the current date instead of asking that it be entered can improve accuracy. There are a lot of technology best practices that can improve the accuracy of information.
-
Error checking in recording process: Evaluate the checking provided by the entry screen programs, the transaction path, and the database acceptance routines. Data checkers, filters, and database structural enforcement options can all be used to catch mistakes at the entry point. These are not always easy to identify because they require someone to dig around in code and database definitions. Many times these are not documented. Many times they are thought to be true but have been turned off by a database administrator to improve performance. Many times they exist but are not applied to all points of entry.
It is important to study all factors at each entry point, even though the investigation started by focusing on a single set of inaccuracy facts. This process may reveal other inaccuracies that were hidden from the profiling process or uncover the potential for problems that have not yet occurred. It may also uncover some locally devised practices that are good ideas and may warrant propagation as a formal methodology throughout the data entry community.
Data Decay
The analyst needs to identify data elements that are subject to decay and check for process steps that exist that will mitigate decay. Identifying data decay candidates is a business analyst topic best handled as work sessions with participants from multiple departments.
If the investigation reveals that no procedures are present to prevent decay, the analyst needs to determine the extent to which decay has contributed to currently visible problems or whether it presents the potential for future problems.
Decay problems are often not observable though data profiling because the values in the database are valid even though wrong. However, process analysis may suggest that the data is susceptible to decay problems. Sampling the data and testing it through object reverification may reveal hidden problems. These can become the subject of new issues split off from those that got you there.
Data Movement And Restructuring Processes
Many errors can be introduced when data is extracted, reformatted, aggregated, and combined with other data. If the data source that was used for identifying the inaccurate data is not a primary data source, it requires examination of the processes that build that database from the primary sources.
The first question to ask is whether the problems also exist in the original data source, are part of the data movement processes, or are the result of an incompatibility with the target database structure or definition. Errors at this level often cause primary data sources to be blamed for problems not of their making.
One of the problems with this type of analysis is that the extraction, transformation, cleansing, and loading processes are often not well documented or are documented only in the proprietary repositories of individual products used for the separate steps. This requires expertise on each of these repositories and on the functions of the individual products used. This can lengthen the time required to perform the analysis.
Often data movement processes are locally developed without the aid of packaged tool software. The project team merely writes code for each step. In these cases, finding out what the team does may be difficult because much of it is probably not documented at all. This stresses the importance of being disciplined enough to create and maintain metadata repositories on all data structures: primary, intermediate, and summary. Information should also be kept on all processes that move data between them.
Review of upstream processes may be indicated by discovering information about quality problems in primary databases. This means that a situation discovered in a primary database that produces inaccurate data may lead to the discovery that upstream uses of this data are also flawed. You are basically asking the question "What is the data warehouse doing with this wrong stuff?" This process of examining known data flaws through to their final use can raise issues that were otherwise hidden.
Conversion To Information Products
Other places to look are the conversion of data from databases to reports, movement to OLAP cubes, staging data in corporate portals, and other business information products.
This type of review would normally only be done if the issue were created from concerns raised about these objects. Looking at wrong output does not always indicate that the data is wrong. The routines to extract the data and to compute from it, and the timeliness of this activity, can lead to inaccurate business information products from perfectly accurate data. Problems in the information products should be traced back through the system because they can often uncover previously hidden problems with other uses of the same data.
It should be clear that the process of identifying where errors creep into databases has many beneficial side effects. It can surface bad practices that are creating errors that were not detected in the initial analysis. It can detect bad practices that are not generating errors but have the potential for doing so. It can identify hidden problems in upstream copies of the data or uses of the data that were not known. This may lead to expanding the impacts section to include impacts already occurring and those that have not yet occurred. This process may lead to the consolidation of issues (discovery that the data entry process caused many of the issues) or creating new issues (the corporate portal is displaying flawed renditions of the data).
It may be helpful to document the bad practices independently for the benefit of future projects. Bad practices used in one application frequently find their way into other applications. The same team that implemented them in one case may have implemented them in other applications they also worked on. Having a list of bad practices can serve as a checklist of things to look for in subsequent investigations.
