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Main Subject - How Non-Quality Data Can Cost Money
Introduction
When viewed from a high level, the cost of poor quality data can affect a company’s bottom-line in two ways. First, there’s the cost of scrap and rework, and second, missed opportunities.
An example of scrap and rework costs might be when an agent errs in recording a customer’s address details, and consequently a marketing premium is sent to the wrong address. Later, the customer calls to complain.
The complaint needs to be handled (extra call center time), the address details then need to be entered a second time (rework), and a second
When viewed from a high level, the cost of poor quality data can affect a company’s bottom-line in two ways. First, there’s the cost of scrap and rework, and second, missed opportunities.
An example of scrap and rework costs might be when an agent errs in recording a customer’s address details, and consequently a marketing premium is sent to the wrong address. Later, the customer calls to complain.
The complaint needs to be handled (extra call center time), the address details then need to be entered a second time (rework), and a second
According to USFDA, a combination product is one composed of any combination of a drug and device; biological product and device; drug and biological product
premium needs to be sent. The initial premium is scrapped.
An example of missed opportunity costs might be a credit card that is not granted because the calculated credit score (erroneously) falls below the cutoff score, and the customer is rejected. The opportunity to make a sale is lost, when marketing costs were already incurred.
In this whitepaper, I attempt to supply a comprehensive list of potential data quality costs.
Cost Categories of Information Quality
The costs of data quality can be broken down in 3 categories:
1. Immediate costs of non-q
An example of missed opportunity costs might be a credit card that is not granted because the calculated credit score (erroneously) falls below the cutoff score, and the customer is rejected. The opportunity to make a sale is lost, when marketing costs were already incurred.
In this whitepaper, I attempt to supply a comprehensive list of potential data quality costs.
Cost Categories of Information Quality
The costs of data quality can be broken down in 3 categories:
1. Immediate costs of non-q
; or drug, device, and biological product and fixed dose combination would include two or more combinations of drug.
Examples of combination products may in
Examples of combination products may in
uality data. This happens when the primary process breaks down as a result of erroneous data. Or, information scrap and rework, when immediately apparent errors or omissions in the data need to be circumvented in support of the primary business process. For example, data entry of a non-valid ZIP code requires back-office staff to look this up again and correct it before sending out a product.
2. Information quality assessment or inspection costs. These are costs/efforts expended for (re)assuring processes work properly. Every time a ‘suspect’ data source is handl
2. Information quality assessment or inspection costs. These are costs/efforts expended for (re)assuring processes work properly. Every time a ‘suspect’ data source is handl
lude drug-coated devices, drugs packaged with delivery devices in medical kits, and drugs and devices packaged separately but intended to be used together.
ed, the time spent to seek reassurance of data quality is an irrecoverable expense.
3. Information quality process improvement and defect prevention costs. Broken business processes need to be improved to eliminate unnecessary information costs. When a data capture or processing operation malfunctions, it requires fixing. This is the long-term investment needed to avoid further losses.
1. Immediate costs of non-quality data
Process failure
For example, capturing erroneous customer data like address, contact information, account details.
<
3. Information quality process improvement and defect prevention costs. Broken business processes need to be improved to eliminate unnecessary information costs. When a data capture or processing operation malfunctions, it requires fixing. This is the long-term investment needed to avoid further losses.
1. Immediate costs of non-quality data
Process failure
For example, capturing erroneous customer data like address, contact information, account details.
<
here is enormous increase in the number of combination products entering the market in the recent years. Combination products have proven advantages but fixe
trong>- Irrecoverable costs; e.g. premiums sent in vain to non-existing customer addresses.
- Liability and exposure costs; for instance credit risk losses when data quality problems cause erroneously offering credit to a customer who is not considered creditworthy on the basis of self-supplied information.
- Recovery costs of unhappy customers; time spent handling complaints. Information Scrap and Rework
- Redundant data handling; because many processes are ‘known’ to rely on inaccurate data, it is customary for fr
- Liability and exposure costs; for instance credit risk losses when data quality problems cause erroneously offering credit to a customer who is not considered creditworthy on the basis of self-supplied information.
- Recovery costs of unhappy customers; time spent handling complaints. Information Scrap and Rework
- Redundant data handling; because many processes are ‘known’ to rely on inaccurate data, it is customary for fr
d dose combinations are still in the process of convincing regulatory authority on their advantages over the single ingredient formulations.
Combination pro
Combination pro
ont-line and back-office staff to maintain little private “lists” of all sorts. These serve merely as a backup or improved version of what is available in the primary database. Apart from further problems like ‘maintenance’ and ‘recovery’ not being possible for these private lists, such activities are redundant, and non-value adding.
- Costs of chasing missing information; a field that has not been filled out properly, or not at all, needs to be looked up later on in the process. Excess time and costs, inefficiency, and not in the least place an aggravation fa
- Costs of chasing missing information; a field that has not been filled out properly, or not at all, needs to be looked up later on in the process. Excess time and costs, inefficiency, and not in the least place an aggravation fa
ucts have become life saving products for the pharmaceutical companies who doesn’t have many innovative molecules in their product pipeline and have been inc
ctor. Time spent looking up missing information is not being spent servicing the customer better.
- Business rework costs; e.g. reissuing a credit card that was sent out with a misspelled customer name.
- Workaround costs; when a primary key is missing or faulty, laborious fuzzy matches need to be performed to match records. This kind of work is challenging, and eats up precious time of the most highly skilled database workers.
- Data verification costs; e.g. costs of reworking data entry. But also, analyses by knowledge wor
- Business rework costs; e.g. reissuing a credit card that was sent out with a misspelled customer name.
- Workaround costs; when a primary key is missing or faulty, laborious fuzzy matches need to be performed to match records. This kind of work is challenging, and eats up precious time of the most highly skilled database workers.
- Data verification costs; e.g. costs of reworking data entry. But also, analyses by knowledge wor
easingly used in the product life cycle management. Even the companies having product patents are trying to extend their product life cycle through the combi
kers must begin by checking the correctness of data available before beginning analysis.
- Program rewrite costs; rewriting programs that fail to run because of invalid entries found in the data. E.g.: sometimes pre- or post-conversion scripts needed to be written to deal with the content of source systems prior to loading in a Data Warehouse environment.
- Data cleansing and correction costs; when feeds are processed to load into the Data Warehouse, these data need to be transformed for reasons that stem from quality issues. Any data cleansi
- Program rewrite costs; rewriting programs that fail to run because of invalid entries found in the data. E.g.: sometimes pre- or post-conversion scripts needed to be written to deal with the content of source systems prior to loading in a Data Warehouse environment.
- Data cleansing and correction costs; when feeds are processed to load into the Data Warehouse, these data need to be transformed for reasons that stem from quality issues. Any data cleansi
nation products and maximize the revenues. But the companies involved in this practice are overlooking that they are burdening the patients both economically
g and scrubbing that needs to be performed in the ETL process is essentially redundant and unnecessary insofar this is caused by faulty initial data entry. For example, when a mailing is done on the basis of a problematic customer file, dedicated scripts need to be run to deal with the (known!) errors in the address fields. This process needs to be repeated for every mailing. Since such customer files are often shared across departments and systems,source changes need to be negotiated with all end users of these data.
- Data cleansing softwar
- Data cleansing softwar
and physically. They need to rightly judge the benefits of the combination products and they have to even look at the risks involved when combining the produ
e costs; data cleansing software (like Vality, Ascential, etc.) is usually very expensive. However, there’s a tradeoff between scarce labor doing this ‘by hand’, and the fact that ETL data quality software to help with such tasks typically has very high license costs. Purchase may sometimes prove remarkably economical when related to (often unseen) labor costs for manually improving data quality.
Lost and missed opportunity costs
- Lost opportunity costs; when e.g. misspelling customer name on the card causes the customer to not use their card (instead of cal
Lost and missed opportunity costs
- Lost opportunity costs; when e.g. misspelling customer name on the card causes the customer to not use their card (instead of cal
ts. Some of the combination products were well accepted by physicians while others suffered. Companies involved in development of combination products are fi
ling up to complain about this) the business looses their future revenue.
- Missed opportunity costs; when unhappy customers directly influence their social environment, they generate negative publicity. This will make it harder to sell to people in the social network of displeased customers.
- Lost shareholder value; information quality puts a drain on precious resources (scarce database experts), preventing knowledge workers from performing value added work towards market share growth. Scarce human resources are often a bottleneck towards p
- Missed opportunity costs; when unhappy customers directly influence their social environment, they generate negative publicity. This will make it harder to sell to people in the social network of displeased customers.
- Lost shareholder value; information quality puts a drain on precious resources (scarce database experts), preventing knowledge workers from performing value added work towards market share growth. Scarce human resources are often a bottleneck towards p
ding difficulty in defining their combination products and facing various challenges from selecting a combination to marketing it.
Following aspects would a
Following aspects would a
rogress, like running one more marketing campaign, delivering insight in a product portfolio’s performance, etcetera.
2. Information quality assessment or inspection costs
- People spend time in assessment processes when they are aware of suspect data quality; in any database project, each and every file of questionable quality needs to be inspected for data quality problems first.
This time is irreplaceable, forever lost and never recouped in any way. Merely assessing if data is of sufficient quality is specialist work. Th
2. Information quality assessment or inspection costs
- People spend time in assessment processes when they are aware of suspect data quality; in any database project, each and every file of questionable quality needs to be inspected for data quality problems first.
This time is irreplaceable, forever lost and never recouped in any way. Merely assessing if data is of sufficient quality is specialist work. Th
dd to the challenges in developing combination products:
Which markets to tap where the combination products can do fairly well?
Which combination prod
Which markets to tap where the combination products can do fairly well?
Which combination prod
s requires access to scarce resources that are often a bottleneck towards progress.
3. Information quality process improvement and defect prevention costs
- Development costs to rework existing front-end applications; data entry applications need to enforce data quality by performing validity checks, and minimizing keystrokes and eye-hand movements. On the basis of usability findings, interface improvements invariably lead to both higher efficiency and better data quality.
- Management attention to
3. Information quality process improvement and defect prevention costs
- Development costs to rework existing front-end applications; data entry applications need to enforce data quality by performing validity checks, and minimizing keystrokes and eye-hand movements. On the basis of usability findings, interface improvements invariably lead to both higher efficiency and better data quality.
- Management attention to
cts are meaningful and rational?
Which therapeutic categories to select?
Which Combinations can address unmet needs of the patients?
Do combin
Which therapeutic categories to select?
Which Combinations can address unmet needs of the patients?
Do combin
redefine accountabilities and monitor improved information quality; steering the organization towards higher data quality requires changing accountabilities and continuously monitoring improvement. This topic will need to stay high on management’s agenda to create lasting improvement.
Conclusion
Problems in data quality often go unnoticed. It can be both a source of process inefficiencies (timeliness), as well as operational costs (direct and indirect losses). In neither of these cases is it apparent that improvement is possible from enhancing data quality.
Conclusion
Problems in data quality often go unnoticed. It can be both a source of process inefficiencies (timeliness), as well as operational costs (direct and indirect losses). In neither of these cases is it apparent that improvement is possible from enhancing data quality.
tions increase the patient compliance?
What would be the developing cost?
How to tackle the risks encountered during combination product developmen
What would be the developing cost?
How to tackle the risks encountered during combination product developmen
One of the pernicious consequences of suboptimal data quality is that the cost of poor quality data is usually hidden. Lack of data quality is not obvious to those not deliberately looking for it. Quantifying costs isn’t always easy. What makes the indirect costs of poor data quality so pernicious is that the relation between data quality problems and its consequences is non-obvious, and often only occurs with a substantial time delay. Therefore, the connection between downstream consequences and poor quality data is often not made, and the problems are not attributed
t?
As combination products don't fit into the traditional categories of drugs, medical devices, or biological products, the USFDA is in the process of devel
As combination products don't fit into the traditional categories of drugs, medical devices, or biological products, the USFDA is in the process of devel
to their true cause.
The cause of many downstream data quality costs can easily remain largely hidden (e.g. data quality), and therefore insufficiently subject to management attention and intervention. Also, progress after improvement efforts is gradual, relatively slow, in large part ‘cultural’, and therefore difficult to monitor and track.
Another, and probably the most significant problem caused by poor-quality information, is that it frustrates the most valuable resource of the company: its employees. Non-quality information prevents knowledge workers from perfo
The cause of many downstream data quality costs can easily remain largely hidden (e.g. data quality), and therefore insufficiently subject to management attention and intervention. Also, progress after improvement efforts is gradual, relatively slow, in large part ‘cultural’, and therefore difficult to monitor and track.
Another, and probably the most significant problem caused by poor-quality information, is that it frustrates the most valuable resource of the company: its employees. Non-quality information prevents knowledge workers from perfo
ping new procedures for reviewing their safety, efficacy and quality.
Professional from academic institutions, pharmaceutical industries, health care indust
Professional from academic institutions, pharmaceutical industries, health care indust
ming their job effectively. On top of that, it alienates customers because of wrong information about them, and to them. Customer data is the raw material that needs to be managed for what it is: a strategic resource.
Data quality is far more than accurate data entry. It stems from monitoring downstream data usage, maintaining comprehensive and up-to-date meta data, and nurturing a corporate culture of naturally doing things right at the first attempt. Only then will knowledge workers learn to expect data quality, and enforce it because it’s the natural thing
Data quality is far more than accurate data entry. It stems from monitoring downstream data usage, maintaining comprehensive and up-to-date meta data, and nurturing a corporate culture of naturally doing things right at the first attempt. Only then will knowledge workers learn to expect data quality, and enforce it because it’s the natural thing
y and representatives from various regulatory agencies are working out to design the regulatory requirements for manufacture and sale of combination products
to do. Letting data quality slide will promote a culture of negligence, and disdain for the use of one’s most precious assets: customer information.
The case for accurate source data is further underlined when one realizes that the source in and of itself does little more than support primary processes, which is fine. However, the greater value to the organization comes from enhancing these data, from deriving new information from source data.
The investment in improving information quality is recouped several times in decreased costs, and improved value of informat
The case for accurate source data is further underlined when one realizes that the source in and of itself does little more than support primary processes, which is fine. However, the greater value to the organization comes from enhancing these data, from deriving new information from source data.
The investment in improving information quality is recouped several times in decreased costs, and improved value of informat
.
As there is an increasing trend of the combination products companies manufacturing such products should be able to tackle the problems involved in the de
As there is an increasing trend of the combination products companies manufacturing such products should be able to tackle the problems involved in the de
ion to accomplish strategic business goals.
Rapid access to high quality data is the decisive factor in an organization’s ability to assess and adapt it’s business model to changing market conditions. As corporations become ever more ‘digitized’, those that get a grip on their data quality assurance processes can reap great rewards. In a highly turbulent market this may well be the critical factor in determining the survivors in a competitive business, and therefore prove to be ultimately priceless.
Resources
Larry P. English (1999) Improving Data W
Rapid access to high quality data is the decisive factor in an organization’s ability to assess and adapt it’s business model to changing market conditions. As corporations become ever more ‘digitized’, those that get a grip on their data quality assurance processes can reap great rewards. In a highly turbulent market this may well be the critical factor in determining the survivors in a competitive business, and therefore prove to be ultimately priceless.
Resources
Larry P. English (1999) Improving Data W
elopment. They need to be wiser in analyzing the market trends and the regulatory requirements.
Companies that provide selfless information through particip
Companies that provide selfless information through particip
arehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits. Wiley, ISBN 0- 471-25383-9
Jack E. Olson (2003) Data Quality: the Accuracy Dimension. Morgan Kaufman, ISBN 1-55860-891-5
Sid Adelman, Larissa Moss & Majid Abai (2005) Data Strategy. Addison- Wesley, ISBN 0-321-24099-5
Article download "How Non-Quality Data Can Cost Money"
XLNT Consulting - Turning Data Into Dollars
Jack E. Olson (2003) Data Quality: the Accuracy Dimension. Morgan Kaufman, ISBN 1-55860-891-5
Sid Adelman, Larissa Moss & Majid Abai (2005) Data Strategy. Addison- Wesley, ISBN 0-321-24099-5
Article download "How Non-Quality Data Can Cost Money"
XLNT Consulting - Turning Data Into Dollars
tion in industry events and feedback to regulatory authorities would be able to face the challenges and will be successful in developing combination products
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