Intellectual Property Rights are among the most valuable assets a company owns. It is therefore imperative, writes patent expert Donal O’Connell, that data relating to those rights is treated with the respect it deserves


The term ‘data integrity’ refers to a whole or complete structure in which data should ideally be placed. All characteristics of the data – business applications, rules for how different pieces of information relate to each other, plus dates, definitions and lineage – must be correct for the data to be complete. IP data integrity must be maintained throughout any activity on an IP-management system, so that any changes introduced by data entry or data transfer are reflected across all the categories they affect. This also applies to changes triggered by storing data in a different system to the one in which it was created, or retrieving data with a different system to the one in which it is stored. In simple business terms, IP data integrity is the assurance that the data is consistent, certified and can be reconciled.

Dirty data refers to a lack of data integrity to whatever degree. Information technology (IT) professionals coined the term to describe the effects of inaccurate information, which falls into the following categories:

a) Missing data
b) Incorrect or wrongly entered data
c) Incorrectly formatted data
d) ‘Stale’ – ie out of date – data
e) Breaks in the relationship between sets of data in two or more fields
f) Duplicated data.

Any of the above is likely to make a data sample misleading.

Dirty half-dozen

There are six main factors that could allow dirty data to creep into your IP-management system:

1. Migration errors
In data migration, material is transferred from one system to another, perhaps as a result of a system upgrade or mergers and acquisitions (M&A) activity, where data from multiple systems must be incorporated. If IP data is dirty before its migration, it is likely to remain so afterwards, unless concrete steps have been taken to address this.

2. Data-entry errors
These mistakes could be made by specialist IP personnel; by non-IP personnel who are given access to the data-management system; or by external IP personnel who have been provided with access. While some human error is inevitable, mistakes could recur if not corrected. Lead times and budgets on fixes can sometimes make auditors cringe – but users may be using systems improperly while blaming the technology.

3. System-design errors
Good system design will reduce data-entry errors by catching exceptions and formatting the user’s choices and selections in a constructive way. Bad system design will stray from these points, and could create dirty data.

4. Synchronisation problems
Maintaining an IP-data operation in step with another, related operation is crucial to data integrity. But synchronisation challenges with other company systems – such as HR or finance – can lead to problems if those systems are not fully compatible. Combine this with systems belonging to an IP-renewals or annuities provider and other members of an IP-agent network, and those synchronisation challenges can be even greater.

5. Data reporting problems
Creating reports using IP data can embed dirty data into the end product if there are script errors or other bugs in the reporting software’s functionality. Misunderstandings over data structure can also produce inaccurate reports.

6. Maintenance problems
If data within an IP-management system is not updated on a regular basis, as it should be, this can lead to dirty-data problems within the system.

Why dirty data is an IP issue

Dirty data can blight any of the key IP process areas, such as creation, portfolio management and utilisation. Problems with a front-end data field in an IP-management system, for example, can have repercussions on an invention report and on patent board decisions. Patenting processes – from drafting to first filing, foreign filing and prosecution – can inherit flaws that will mar a granted patent. These flaws will affect the value of any subsequent licence agreements.

This makes dirty data a serious issue for any corporate IP department, or any IP agency, which could be faced with liability issues or a loss of rights. Data rules may not be observed in the creation of patent families, filing dates may be missed, or incorrect data could be sent to a national IP office. Correspondence relating to an innovation may be sent to the wrong person, or incorrect IP reports could be used in key decisions.

IP data is ultimately used for management purposes and must fit with the demands of a well-informed decision-making process. Meanwhile, outside the IP department, IP data drives a company’s technologies, products and services, and forms an integral part of many legal agreements and contracts. As such, it is a critical tool for senior management.

A clean break from bad habits

For a company to understand how serious its IP-data problem is, it needs to ask some searching questions: How and why has the issue occurred? What data areas is it affecting? And what should be the priorities for cleaning up? Only when those questions have been considered should the clean-up exercise be undertaken. The process may involve using dedicated IP service providers and/or developing automatic scripts and tools.

It will almost definitely involve some manual hard work, and will consist of corrective actions to fix the problem(s); pinpointing and understanding the root cause(s); and preventative actions to stop repetition of the problems (such as training, processes implementation, checks and reviews).

Above all, companies must see that data quality issues cannot be tackled in isolation. Data quality is interlinked with a number of areas, such as ways of working that have been adopted in a company, the IP systems and tools in use, wider legal matters and the employees involved. Because of that, data quality demands management and leadership.

Several best practices exist to help address data issues within an IP-management system. Companies should:
• Control the data entry
• Properly define mandatory and optional data fields
• Assign rights and roles for both IP and non-IP personnel with access to the system
• Assign levels of personal responsibility
• Keep and maintain a change history
• Design intelligent data fields
• Measure and clean the data on a regular basis
• Make data management a living, ongoing and integral process

Four steps to data hygiene

To properly address dirty data problems within an IP-management system, it is important to adopt an iterative, four-step problem-solving process:

Plan
Thoroughly evaluate and analyse if, what, where and how dirty data is becoming a problem, and what needs to be done to rectify the situation.

Do
Make the necessary improvements, usually by starting on a small scale and building from there.

Check
Review your progress and compare actual results to planned results.

Act
If differences between actual and planned results are apparent from Step Three, analyse them and pinpoint their causes.

Once a challenge has been met, don’t just forget the problem. Metrics should be defined, agreed and implemented and regular data reports created. This will help you to precisely gauge your data integrity going forward and maintain a vigilant approach.


Donal O’Connell is former vice president of research and development at Nokia. He is currently managing director of Chawton Innovation Services