Business Intelligence Advantages

How Your Business Culture Impacts Your Data Governance Strategy

Many companies make one huge mistake when implementing their data governance plan.  They assume that once they develop related policies and implement the needed technology solutions to support the strategy, the rest will take care of itself.

These organizations are, unfortunately, in for a rude awakening.  What they don’t realize is that existing business cultures will have a profound impact on how those initiatives are carried out.  In other words, a company’s “personality” and working environment may have as much to do with data governance success as any other factor in the plan.  For example, the willingness – or lack thereof – of both IT and business stakeholders to embrace new initiatives can make or break the strategy.  Or, pre-existing tension between departments and business units can halt the collaboration needed to get the project off the ground in the first place.  

What are some business culture “problems” that can have the greatest impact on a data governance strategy?

Lack of Communication
Many businesses suffer from poor communication across various levels and departments.  And, others are so eager to get their critical projects into play, they often dive right in without properly informing and educating their employees about the plan.  When it comes to data governance, this approach can create major problems.  For example, if stakeholders don’t understand why data governance is important, don’t know how it works, or don’t see how it applies to them, they are likely to be lax when it comes to complying with related policies and procedures.   

Too Many “Cooks Stirring the Pot”
While contribution and consensus among all departments that will be affected by data governance is critical, companies who are prone to forming “mega-committees” to spearhead important projects may see their data governance efforts fail.  Action and execution will end up taking a back seat to meetings, bureaucracy, and debate, and these businesses will likely never get past the policy-making phase.  

Failure to Synchronize and Coordinate
Employees get used to working a certain way, and asking them to significantly alter how they perform their day-to-day activities is likely to be met with some resistance.  Yet, many companies simply demand that employees follow certain data governance processes – no matter how different from current workflows they may be – without any consideration as to whether or not those staff members are capable of carrying those procedures out, and how other responsibilities will be affected.  What these organizations are forgetting is that governance processes are not separate and distinct.  They must be seamlessly integrated into any related IT and business activity they will impact.  

Out of Sight, Out of Mind
Countless companies make the mistake of introducing a major strategy with much noise and fanfare, then executing on that plan quietly, without keeping employees informed of new developments, results, etc.  When it comes to data governance, this is a surefire way for employees to lose interest, because they’ll associate the lack of “hoopla” with a lack of importance.  Conducting ongoing training on new data governance techniques, or setting milestones that track and measure the benefits a data governance strategy is delivering can help keep the initiative at the forefront of employees’ minds, and maintain focus on their goals and responsibilities in carrying out that plan.   

To learn more about governing your data, or for tips to help optimize your data governance strategy, visit our Web site at www.croyten.com.   

Passive vs. Active Data Governance

There are countless ways to oversee and manage your corporate data.  But, the approach you take will likely determine the success or failure of your data governance initiative.

 

Some companies will take a “passive” approach, giving users the ability to freely interact with information in back-end systems, then monitoring the results of those interactions – after the fact – using various reporting and data assessment tools. Others will be more proactive, monitoring the way data is created, modified, and handled in real-time as these tasks are performed, so problems can be detected and corrected before “bad” data is introduced into the operating system environment.   

 

In this post, we will discuss the many problems that may arise when companies take a passive approach to data governance, and highlight the reasons why active data governance is the more effective route to take. 

 

The key issue with passive data governance is the fact that, once invalid or incorrect information enters a database, it’s potential to create problems immediately begins.  In the time between when a user adds the bad data, and a tool identifies its existence and “cleanses” it, an incorrect bill may be sent to a customer, an inaccurate work order may be transmitted to a field service technician, or even worse, an invalid report may be sent to a regulatory body.  Therefore, catching corrupt information beforehand is critical, particularly in heavily regulated sectors such as financial services and healthcare. 

 

Bad data within a corporate environment can also lead to interruptions in core business processes.  Downtime can be particularly significant if the poor quality information in question triggers or is consumed by automated workflows.  These disruptions not only drain worker productivity, they can negatively affect revenue generation. 

 

And, most importantly, the validation and correction of bad data after the fact will require more resources than the proactive monitoring of data activities.  The amount of time and cost associated with scanning a massive database for integrity issues, then cleansing or deleting corrupt information, far surpasses the resources needed to flag suspicious data interactions as they occur, and validate them on the fly. 

 

While passive data governance can serve as a highly effective secondary measure, catching the one or two errors or inconsistencies that may mistakenly bypass more active monitoring measures, the truth is that active data governance is the only true way to promote optimum information accuracy, timeliness, and completeness across an entire business. 

 

The value of data is simply immeasurable to today’s corporations, and the speed at which information flows throughout and beyond an organization leaves little room for error.  Even the slightest problem with data integrity, even if for a short period of time, could be detrimental to operational efficiency, profitability, and regulatory compliance. 

 

To learn more about governing your data, or for tips to help optimize your data governance strategy, visit our Web site at www.croyten.com.   

Data Governance: Your MDM Plan Won’t Work Without It

At the heart of every successful master data management (MDM) strategy is master data that is complete and accurate at all times. But, the optimum quality and consistency of master data can only be secured if comprehensive data governance plays an integral role in its creation, collection, storage, handling, and administration.

 

The Data Governance Institute, a provider of in-depth, vendor-neutral information about best practices in the management and stewardship of enterprise information, has defined data governance as "a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

 

And, the experts all agree that MDM initiatives that lack formal data governance policies have a higher likelihood of failure. Why?  Because data governance not only helps to ensure the integrity of the master data that stakeholders use to formulate important business plans and make critical day-to-day business decisions, it aids in effective compliance with regulatory and information disclosure demands.

 

However, Gartner predicts that 90 percent of organizations will not succeed at their first attempts at data governance.  This failure can be caused by a variety of common factors, including:

• Too much reliance on IT.  According to Ventana Research’s Mark Smith, responsibility for data quality is not just IT’s job.  It is up to information consumers within functional business units – who have insight into the context in which master data is used – to help administer these assets.   
• No clear documentation.  Data governance policies and related procedures must be defined and documented in a way that both technical and business stakeholders can easily understand, and must be readily accessible to all those who generate or interact with master data.    
• Poor enforcement. Data governance processes that are loosely enforced – or not enforced at all – are not likely to be adhered to.  Documentation must not only account for what the rules and guidelines are, but what the possible penalties will be if they are not properly followed.

 

In some scenarios, bad or invalid master data may be worse than no master data at all.  In order to preserve the correctness and consistency of master data across an organization, companies must implement a formalized data governance program that includes strict “checks and balances” that are overseen by a council of key stakeholders from both the IT team, and various business units.  Only then can master data be optimized to ensure accuracy, comprehensiveness, and most importantly, relevance to all those who rely on it to support core business activities.     

 

To learn more about best practices in data governance and master data management, visit the Croyten Web site at www.croyten.com.