Like the Da Vinci Code's Robert Langdon, many organizations today are aspiring to crack their own code on how to achieve data quality.
While Langdon's particular quest was as unique to him as are the specific requirements of any particular organization, they will all need to embrace a set of common technology themes to locate their own keystone; and by doing so satisfy their own quest for the Holy Grail of data quality.
These themes, if applied correctly, can provide the solution to an organization's own data quality code.
Investigation is the first step of any quest and the key activity to define the scope of whether it is the support of a key business initiative or the removal of one or more business pain points. It is through this process that the key data issues, acting as barriers, are firstly identified, then quantified and finally prioritized.
Investigation utilizing a tool-based approach will allow an organization to create a process which is maintainable, scalable and just as importantly repeatable.
Representing the key set of activities which will address the issues brought to the surface by our investigation is remediation. Our chosen technology must support the full gambit of functionality, from analysis through to consolidation and monitoring, and just as critically present a business-focused persona to ensure the effective inclusion of all quality stakeholders.
Data quality activities cannot exist effectively in a vacuum or silo and focus should only be given to tools which present a high level of Integration with other data centric technologies. Adoptees of the right tools can reap enormous benefits for their data quality programs by effectively leveraging these technologies, be it near universal data access, choice of latency, or massive orders of processing scalability.
Collaboration brings all effective data quality initiatives together. Key to the success of any data quality initiative is the ability to clearly identify and maintain the linkage between the IT centric activities - which represents a significant percentage of a DQ program - and the business expectations or aspirations which are driving them. At each turn of the process we should be able to draw a clear line from a specific data fix to the business benefit it delivers and should utilize technology that supports such collaborative and interactive processes.
In the ever more regulated business environment which exists today, compliance-related activities represent an ever increasing burden that organizations must bear. Technology which can effectively support certification processes through such functionality as data lineage, impact analysis, dashboard and score-carding will do much to alleviate the cost of this burden.
Virtually every data quality program will encounter its own bugs working at distinct odds to the aims it represents. Effective and timely communication can be one of the key weapons to define and deliver the right information, in the right format, at the right time to the right person is critical to successfully evangelize the good work that is being performed.
Many data quality quests are begun with fewer people than are needed and therefore the degree of automation which can be applied to the process to maximize the bandwidth of the limited resources available. Exception-based reporting, self monitoring process, online or real-time validation are only some of capabilities which can be leveraged to make less seem like more.
While all such quests invariably represent a journey into the unknown and are reflective for each individual's own definition of the Holy Grail and are hugely dependent on the talents of the individual and the path they follow; early adoption of the right technology supportive of these themes will go a long way in reducing the risk and uncertainty of the undertaking.
Neil Gow is director for pre-sales at Informatica Asia Pacific and the company's resident data quality guru.