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. 

Why “Phased” MDM Implementations are More Effective

As more and more companies embark on new master data management (MDM) initiatives, few truly realize just how complicated the deployment stage of their project will be.  Even the most solid and well-thought out plans are likely to face obstacles, require changes, and experience other issues once the implementation actually begins. 

 

That’s why many experts agree that the “phased” approach – as opposed to a “big bang” deployment – is the best way to go.  The MDM Institute, in their December, 2008 Market Report describes master data management as a critical strategic initiative, and strongly recommends that it be carried “across multiple lines of business, multiple channels, and therefore across multiple years.”

 

Companies who have attempted to execute on their entire MDM strategy all at once have run into significant problems, such as:

• Project delays
• Cost overruns
• Loss of end user productivity
• Unplanned drain on IT resources

 

Why so many issues?  Because master data management isn’t just a set of technology solutions to be installed and forgotten about.  It’s a rigid discipline that spans both IT and business.  It requires an evolution of both culture and process – and those changes simply can’t happen overnight. 

 

The implementation of MDM on an enterprise-scale will also undoubtedly impact back-end systems, disrupting core business activities. Deploying MDM across the business, in every department simultaneously, can bring critical operations to a screeching halt.  On the other hand, a well-timed series of smaller roll-outs will affect only one or two divisions at a time, making it easy for the company to create a contingency plan, and minimize losses from the temporary reduction in output.     

 

Additionally, broad-reaching MDM implementations are highly inflexible because they fail to give project leaders the opportunity to assess the viability of the strategy, and make changes along the way. But, incremental implementations make it easy to assess goals – and whether or not they can actually be met by the current plan – before the entire initiative has been executed upon.  Corrections and adjustments can take place “on the fly”, as the deployment is in progress, ensuring success in both the short-term and the long term.   

 

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

Data Validation: What It Means to MDM

While the creation of master data is important, and the seamless dissemination of it to end users is even more important, it is the accuracy and quality of that data that is crucial to the success of your master data management (MDM) strategy. 

Yet, few companies carefully consider data quality as they are developing their MDM plan, and fail to put the proper validation mechanisms in place upon execution of that plan.  This cannot only seriously hinder MDM success, it can have a severe impact on core business operations. 

Why are validation and quality control so vital? Because information is generated from many sources.  There is application data, which is maintained in various back-end business systems, as well as the metadata that describes its attributes.  There is transaction data, which is created in the course of “live” events or automated messages, and the reference data that provides detail about it.  Then finally, there is master data, which links these together to facilitate the creation and centralization of a single, consistent set of values across all sources.   

Take, for example, a client’s location.  While a customer relationship management (CRM) system may display one address, an accounting package may show another. Yet a third address may be included in an electronic document, such as a purchase order, transferred during the course of a business-to-business transaction.  These types of inconsistencies, if not detected and corrected in a timely manner, can cause major setbacks in MDM projects.  In other words, bad data will ultimately lead to bad master data. 

And, when master data is poor, businesses won’t achieve the levels of flexibility and agility they set out to reach, since they’ll be basing both tactical and strategic decisions on information of sub-par quality. 

How does validation work?  Automated validation can work in several ways.  It can scan the environment to uncover inaccuracies, such as those mentioned in the above example, across multiple data sets, and flag them for review.  An IT staff member can then manually take a look, and make any needed corrections to promote accuracy throughout the business. 

The more advanced quality control techniques allow for the use of dynamic business rules.  These rules can be proactively applied to back-end systems, to ensure that bad information doesn’t enter the environment in the first place.  For example, it can prevent end users from entering client last names that include numbers, or mailing addresses that don’t have enough characters.  These business rules can also be used to automatically “cleanse” bad data after the fact, instantly reformatting or altering it, based on pre-set guidelines, once it has been discovered. 

In order for an MDM initiative to deliver optimum returns, fully-automated controls and validation must be put into place, to ensure that master data is accurate and up-to-date at all times.  However, these controls must be broad-reaching, governing not only how data is handled once it has been created, but how it is generated and updated throughout its lifecycle.

Why Business User Involvement is Critical to Your MDM Initiative

As a growing number of businesses are embarking on comprehensive master data management (MDM) strategies, more and more of them are realizing the importance of having business users actively participate in all facets of their project.  In fact, the experts agree that the most successful MDM initiatives are those that come to fruition through a close collaboration between the IT professionals who will deploy and support the environment, and the non-technical workers who will actually use the master data generated. 

A recent study from Ventana Research indicates that it should be business people – not IT staff – that serve as the driving force behind MDM.  The analyst firm claims that a company’s success will hinder greatly on its ability to rally support all the way up the chain of command – from end users and department managers, to senior and C-level executives. 

But, why is the involvement of functional representatives so important?

Relevance 
Master data must be directly tied to day-to-day activities, as well as broad-reaching corporate goals and objectives, in order to be truly useful.  David Loshin, president of Knowledge Integrity, Inc., a consultancy that specializes in enterprise-scale MDM development states that “MDM has to be driven by business needs, otherwise it may turn out to be just another database that must be synchronized with all the other ones.”

Yet, IT staff members simply don’t have the subject matter expertise needed to truly understand what master data is needed, why it is important, and how it will be used.  For example, few of them know how master data will be applied in the context of customer, sales, or financial analysis, and therefore – alone – will be unable to develop and execute an MDM plan that most effectively satisfies the needs of master data consumers.  

Adoption 
User adoption is crucial to the success of any technology effort, and MDM is no exception.  If members of the user community are involved in the creation of master data at the outset, the more likely they will be to embrace the environment once it has been implemented, and to use master data regularly to support and enhance their daily operations.  And, the more business people who consistently leverage master data, the more of an impact it will have on company performance. 

Quality
MDM is an ongoing process, one that requires continuous refinement in order to deliver maximum value over time and address evolving needs as they emerge.  Therefore, the user community must participate actively in MDM maintenance, in order to ensure the highest level of quality and accuracy at all times. 

Through teamwork, communication, and collaboration, your IT team and end users can work together to ensure the success of your MDM initiative.

How Telecommunications Firms Benefit from Business Intelligence

Companies in the telecommunications industry are facing conditions that are unlike those that exist in any other sector.  Consolidation is happening at a record pace, allowing businesses to instantly expand their product lines and enter new markets.  And convergence strategies are emerging quickly, as carriers strive to serve as “all in one” providers, offering everything from voice and Internet, to video, data, and multi-media services. 

 

These shifting business models have made the need for business intelligence extremely critical for telecommunications companies – perhaps more so than in any other industry today. For example, mergers and acquisitions make it quite difficult for organizations to achieve a complete view of operations and activities across the entire business. Information from fragmented systems, housed in various geographically dispersed locations, must be collected, aggregated, and formatted in order for executives to monitor key performance indicators, and departmental managers and employees to generate the types of operational reports needed to support day-to-day decision-making.  Without a business intelligence solution in place, this will be nearly impossible. 

 

Additionally, as telecommunications providers seek to expand market share and capture new audiences, customer and marketing intelligence has become more important than ever before.  With a BI tool in place, these organizations have the insight they need to sell more services to more people.  For example, they can analyze existing accounts to identify opportunities to up-sell and cross-sell other products from their portfolio.  They can also gain greater insight into the demographics of their potential clients, so they can develop more effective and compelling campaigns and promotions.  And, perhaps most importantly, they can obtain a full understanding of customer needs and wants, so they can enhance existing offerings, and develop new ones, to drive more sales. 

 

As the telecommunication industry experiences intensifying competition and rising churn rates, firms can leverage business intelligence to boost customer service levels, thus increasing satisfaction and retention.  By monitoring key performance indicators such as peak usage volumes, as well as other network activity patterns, service providers can make sure that their infrastructure is optimized to ensure maximum reliability and performance.  Additionally, trends in activities such as technical support calls and call center interactions can be analyzed in detail, to assess service levels and identify – and correct – areas in which customers may not be receiving the kind of experience that is conducive to ongoing loyalty. 

 

The ways in which telecommunications firms can benefit from business intelligence are endless.  By implementing a comprehensive BI environment, and leveraging it across the organization, telecommunications firms can improve service delivery, boost sales and revenues, enhance customer loyalty, increase the efficiency of core operations, and gain a significant competitive advantage.

The New Role of the BI Channel Partner

In the past, when business intelligence projects were primarily considered to be IT initiatives, BI vendors realized the importance of forming alliances with a variety of implementation partners to help them gain broader market penetration.  They joined forces with technology consultants, system integrators, value added resellers (VARs) and others who would offer their reporting and analysis solutions as part of a broader, fully integrated IT infrastructure. 

 

But in recent years, BI has become more strategic.  No longer just another software application among many in a company’s arsenal, business intelligence has transformed itself into a vital and indispensable tool that helps drive corporate performance.  As a result, functional managers and senior executives – not technical professionals – are now the “owners” and key stakeholders when it comes to BI projects. 

 

This new audience is prompting many BI solution providers to reconsider their channel sales strategies.  Technology consultants, while still valuable, may no longer have the kind of influence over business intelligence strategies that they’ve had in the past. 

 

For example, in many companies today, the CFO is spearheading BI projects, because of the mission-critical nature of financial reporting regulations.  And while supporting technologies are important, what he is really concerned with are the business challenges associated with compliance.  So, he would be unlikely to bring on a pure technologist to solve his specific business problem, and would be more inclined to turn to a strategic consultant with a business-oriented focus, who can incorporate software and hardware tools into their plans and recommendations.

 

And so, the role of the BI channel partner is changing.  Companies who used to seek to align themselves with technology solution providers are now forming partnerships with firms who deliver business solutions, with technology as a component of those offerings.  You see many BI industry leaders developing relationships with the likes of consultants who focus on procedural workflows and business process management (BPM), corporate performance management and balanced scorecards, and other high-level strategic initiatives. 

 

What’s the difference?  While the former are experts in BI technologies, the latter are not only well-trained in reporting and analysis tools, but possess in-depth insight into the underlying business issues that drive their use.  This offers the end-user even greater value by providing them with not only superior BI solutions, but with the procedures and policies that enable their most effective use. 

 

BI will continue to play an increasingly strategic role in today’s organizations.  More and more, business intelligence is becoming a means of successfully achieving corporate goals and objectives, not just a tool to satisfy departmental needs.  As a result, there will be some dramatic changes in the types of channel partners that business intelligence vendors turn to for assistance in bringing their solutions to the marketplace.  

How Government Agencies Are Using BI

The role of the government has been evolving over the last several years.  Gone are the days when agencies simply served as administrative bodies that did little more than set and enforce regulations, and collect, process, and store information.  Citizens now expect better, more convenient services and programs from their federal, state, and local entities.  That’s why many government organizations are implementing business intelligence tools, to help them better understand and address the needs of the communities they serve, while operating more productively and cost-efficiently. 

How can business intelligence solutions be applied to help government agencies improve operations?  The ways in which BI can be utilized in government are virtually unlimited, but some of the most successful ways include:

Inter- and Intra-Agency Information Sharing

In today’s age of heightened national security risks, individual agencies at the federal, state, and local level can no longer operate in a vacuum.  They must be able to instantaneously share vital, timely information from a variety of back-end data systems – not only among their own employees, but with their counterparts at other agencies.  Business intelligence makes this possible, allowing data from the disparate and fragmented sources that exist within single agencies, as well as across multiple government entities, to be easily accessed, consolidated, and analyzed in real time.  

“Customer” Intelligence

While many businesses need to know more about their customers in order to more effectively market and sell to them, government agencies are taking the same approach with the citizens they serve.   This is becoming increasingly critical, according to leading industry analyst firm DataMonitor in their recent “Business Trends: Understanding Your Government Customer” report, as constituents continue to demand a higher level of accountability and transparency from their government.  DataMonitor feels that this continued shift is the driving force behind the increased adoption of such technologies as CRM and business intelligence tools by government agencies at all levels. 

E-Government

For the past several years, there has been a major focus on initiatives for e-government, or citizen self-service.  These projects are making the services of a variety of agencies – including the DMV and Social Security – conveniently available to residents via the Web.  However, in order for these programs to work, they must give constituents fast and simple access to the data they need.  This requires the support of a comprehensive business intelligence infrastructure. 

Financial and Budget Management

No organization operates on a tighter budget than a government agency.  But, effectively managing the allocation and utilization of funds requires in-depth insight into spending patterns, as well as the ability to forecast future costs and expenses.  BI systems provide those capabilities, empowering government entities to better control and administer their budgets. 

As more and more government agencies continue to adopt innovative business intelligence solutions, the techniques they use are likely to become more sophisticated, and they ways in which they use it to support their day-to-day activities is likely to grow.  This will help foster a collaborative environment among all agencies at all levels, where they operate together in the most efficient, cost-effective, and citizen-centric fashion possible.

Putting Business Intelligence in the Hands of End Users

When business intelligence tools were first introduced decades ago, they were highly sophisticated solutions that were controlled largely by IT staff.  Report requests came in from various functional users.  Information was then accessed, consolidated, and formatted by a technical professional, who delivered it back to the user in the form of a printed document. 

But, reports would often contain errors or omissions that required the report to be re-run, or lead to additional questions that resulted in the need to generate yet another report.  This created a longer list of reports for IT teams to build.  It was a never-ending cycle. 

At the time, the available solutions were very complex, and business users simply didn’t have the technical savvy required to navigate and operate their features.  So, this heavy reliance on IT staff was the only feasible approach.  But not only did this drain a company’s already over-worked technical resources, it created data latency issues that negatively impacted corporate decision-making. 

Over the last several years, end user tools such as ad hoc and OLAP have gained prominence.  And while these, in their initial form, were far more intuitive than previous solutions, they were still way too advanced for the average departmental employee.  This limited their use to a handful of “power users” – those employees that possessed a unique blend of both technical and business skills – scattered throughout the various divisions of the company. 

So, the problem remained.  High-paid IT staff (they average approximately $33 per hour, based on surveys conducted by leading compensation management statistics aggregator Salary.com) continued to spend significant amounts of time satisfying report requests from end users, instead of working on other critical, and more strategic corporate technology initiatives. 

But, today, as operational reporting strategies – those that leverage enterprise data to support near-instantaneous day-to-day decision making – become increasingly important, more and more BI vendors are beginning to recognize the need for end users to have the ability to satisfy their own information needs in real time. 

As a result, today’s BI offerings have evolved in the form of pre-packaged functional reporting applications, more simple and user-friendly ad hoc query tools, and development environments that allow for reports to be built and deployed over the Web, where basic revisions such as filters and sorts can be performed by the end user.  Applications like these have made it easier for non-technical professionals to gather, analyze, and share information, whenever they need it, without IT intervention.  

The industry as a whole has made great strides in this area, making business intelligence more accessible to more users and more user types.  But, as the unhindered flow of information becomes more and more critical to an organization’s success, vendors will need to continue to develop BI solutions that reduce reliance on IT departments, and put the power of reporting and analysis directly into the hands of information consumers.