Business Intelligence Advantages

Big Data, Big Decisions

Big data. It’s a pretty broad term, but it’s used to describe data sets that are so big or complex that in order to get the most value out of them companies need to use enhanced data applications; and more importantly know how to manage all of the information.

When it comes to big data, it’s not so much about how much you have, but more about what you can do with it. Managing this data means creating a structure that can store, process, and organize large volumes of structured and unstructured data.

For example, a typical bank offers its customers multiple products; a mortgage, car loan, checking account, saving account, credit line, etc.  In today’s economy customers also conduct transactions online and via mobile devices, and provide feedback on services via social media. Should all that data be stored in different places? No, but banks and other firms are starting to recognize that having all that relevant data stored in one place can provide a wealth of insights about their customers, which is critical to better serving their customers and offering them customized products that makes sense. Having that data in one place will enable efficient data management control and a single-client view.

Regardless of the size of your company or the data being brought in, big data can provide a whole new way of approaching big decisions.

A consumer-oriented company can use big data to listen to, learn from, and leverage consumer feedback to produce targeted B-to-C campaigns. All of the feedback collected from social media and surveys allows a company to build and update consumer profiles and then execute personalized marketing and advertising campaigns.

Insight-driven organizations (IDO) need data to drive decisions. A lot of the time, though, there is so much data these organizations don’t even know where to start. It can be a very long journey to take big data and turn it into insight-driven material. As an IDO you need to figure out what insights will be most impactful with your clients. Then you need to know if you have the right data and analytics to create these insights, and if not, how do you go about getting that information? Once those insights are available how do you create a strategy to implement them in day-to-day decisions making?

Regardless of who is using big data or how it’s being used, there are always concerns about security. Most companies don’t have the infrastructure to store big data on their own IT networks, which means they are either going to be using the Cloud or a third party storage. Transferring data out doesn’t mean to companies transfer their liability. With data coming in from all avenues; social media, emails, files, etc., there are more entry points that need to be protected as along with external access points where the data is being housed.

Finding the most accurate and secure way to use big data will lead to better decision making - which will result in more efficient operations, reducing costs, and reducing risks.

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.   

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.

MDM Isn’t Just About Technologies – It’s About Business Processes

The benefits of master data management (MDM) can only be achieved if the strategy is broad-reaching and comprehensive.  But, many organizations make the mistake of viewing MDM as a technical issue – a misbelief that can lead to project failure. 

The premise is quite simple.  In order for MDM to solve business problems, it must be tightly aligned with business activities. 

The truth is, MDM encompasses both business processes and technologies.  The technical solution will ultimately support the strategy – but the strategy itself must take into account the various people, policies, and procedures that play a role in data governance, access, sharing, and administration.  In order for any MDM plan to work, it must take into call for the needed process adjustments and re-alignments. 

The formalization and enforcement of data collection and management standards across the enterprise is what truly will enable the effective execution of MDM.  For example, if the accounting department archives information in one way, yet sales and marketing handle the storage of historical data in a completely different manner, the results of an MDM program will be seriously hindered.  In other words, consistent workflows must be implemented to promote the accuracy, timeliness, and integrity of corporate information. 

In fact, in a recent post on BeyeNetwork, a popular blog that covers the business intelligence and information management industries, the following steps to reaching a stage of MDM readiness were defined as critical:
•    Document business processes and how they map to application functionality
•    Define and use common information concepts
•    Assess the organization’s capabilities as they related to data quality and governance

The purpose of this exercise?  Determine where procedures are lacking, and re-structure in a way that will most effectively support the new MDM strategy. 

And, perhaps most importantly, which processes should be reviewed and assessed?  Those that relate to information creation, updating, deletion, or archiving are the activities that have the greatest impact on data quality.  Therefore, it is these procedures that must be effectively controlled in order for MDM to deliver desired returns.  

Our next post will highlight data validation techniques.  We’ll discuss the critical role they play in MDM, helping to ensure data consistency, accuracy, and integrity.

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.

Four Ways to Ensure Efficient, Accurate Regulatory Reporting

In recent years, the focus on – and importance of – regulatory reporting has intensified.  Accounting scandals, secretive financial dealings, and other shady business practices have lead to increasingly stringent guidelines such as Sarbanes Oxley, BASEL II, and other rigid financial reporting standards aimed at holding organizations more responsible for the way they manage, track, and account for their monetary assets.

Regulatory reporting requires companies to consolidate, calculate, and present their financial data in the most timely and accurate manner possible.  But, with revenue and expense data residing in various disparate enterprise sources such as accounting applications, order entry systems, third-party payroll databases, and budgeting and forecasting solutions – how can an organization efficiently and effectively comply with these complex regulations?

Know the Guidelines

In many organizations, few stakeholders have full knowledge of all the guidelines and rules that govern the business.  Additionally, legislation changes frequently, and the various federal, state, and local agencies are issuing new and revised laws all the time.  Therefore, it is critical that you identify those who are most accountable for overseeing compliance with regulatory reporting guidelines, and put the appropriate measures in place to ensure that they are well-informed about existing rules, and keep on top of any new or revised ones.

It is also important to note that some regulatory bodies require companies to designate a senior representative to certify report contents, holding them personally responsible for the information’s accuracy and integrity.  Choose this person wisely.

Assess Your Risk

Are your global operations, and such factors as accounting systems in multiple languages or currency conversions, hindering your ability to generate fast and consistent financial reports?  Are consolidations and calculations manual, increasing the number of errors and negatively impacting data integrity?  Or, do you lack an effective audit trail, so you can track how financial data has been entered, altered, or accessed?

Questions like these are critical to understanding where exposure for non-compliance exists, so you can take swift corrective action.  Have a third-party “expert”, such as a consultant, come in and conduct an unbiased assessment of your current reporting processes and systems, and make recommendations for new procedures and tools to help ensure effective compliance.

Leverage Your Master Data and Metadata

Master data and metadata have both proven to be effective ways to enable improved regulatory reporting.  For example, a master data management strategy allows companies to develop enterprise definitions, such as what “open” account status means in the case of a mortgage, a checking account, a line of credit, etc.  This gives financial managers access to a common view of accurate and timely financial information from systems across the enterprise.

Additionally, metadata, particularly that generated by business intelligence systems, can help ensure the appropriate storage, retention, and disposal of mission-critical financial information – including that contained in unstructured formats such as emails and images.  It also provides a virtual “audit trail” of financial information, such as what systems key accounting data resides in, how is it aggregated from various databases, or how expense, revenue, and profit numbers are calculated.

Corporate Governance is Key

Simply creating a report, and refreshing the data on a regular basis is not enough to guarantee ongoing compliance.  If the data you are plugging into your report templates in inconsistent or inaccurate, then your reports will not meet the needed standards, and severe sanctions could result.  Since financial data exists in many disparate systems across an organization, strict policies and procedures must be developed, documented, and enforced – and a governing body must be assigned to oversee them – in order to maintain the integrity of all relevant information across the enterprise.

With these four surefire tips, you can help your organization facilitate effective compliance and avoid heavy fines and penalties, as well as bad press, damage to corporate image, and decreased trust among prospects, customers, investors, and business partners.

The Top Five Reasons Master Data Management Fails – And How to Avoid Them

Not all master data management (MDM) initiatives are created equal, and some are doomed to fail for various reasons.  Before your company embarks on an MDM project, you need be aware of the most common potential pitfalls and how to avoid them.

1. Lack of functional sponsorship. 

IT professionals have the technical savvy to implement MDM, but lack the subject matter expertise needed to ensure that master data is usable and relevant.  Without input from functional master data consumers, quality will be hindered and results will be less than satisfactory.  Involve non-technical representatives from various departments and business units in your project from the very beginning, to ensure that planning and execution are addressed from all aspects.

2. Failure to adjust business processes accordingly. 

Master data management is not just a technology discipline – it’s a business one as well.  Without making corresponding changes to processes and workflows, your organization won’t achieve the expected results.  Be sure affected activities are re-aligned and re-structured as needed to fully support your MDM initiative.

 

3. Lack of validation.

While most companies are quite thorough when planning the creation and dissemination of master data, many fail to put the proper validation mechanisms in place, which can result in inconsistencies and poor quality.  Proper controls and a fully automated validation process must be developed to ensure that master data is accurate and up-to-date at all times.

4. Taking an “all at once” approach to deployment.

An MDM implementation is long and complex, and problems and changes are likely to arise.  Additionally, it will impact back end systems and disrupt some business activities.  Many organizations make the mistake of taking on a “big bang” deployment, and find themselves surrounded by project delays, cost overruns, and lost productivity.  A phased, incremental implementation that allows project leaders to assess goals and progress, and make needed corrections and adjustments along the way, will allow your organization to realize significant short-term and long-term benefits.

5. Failure to create and enforce data governance procedures. 

Data governance is critical to the successful implementation and maintenance of MDM, yet leading analyst firm Gartner predicts that 90% of organizations will fail at their first attempts.  This is due primarily to lack of clarity in documented processes, or lax enforcement of guidelines.  The best way to ensure effective data governance is to create a rigid “checks and balances” program that is overseen by a council of key project stakeholders who have a vested interest in protecting the MDM environment.

 

By avoiding these “traps”, you can ensure a smooth successful implementation, and maximize the value of your MDM investment. 

Using Data Centralization to Enhance Master Data Management

Master data management (MDM) initiatives are being developed at more and more organizations.  Yet, many of these companies are using MDM as a means of replacing their existing data warehouse environment.  While this may make sense in theory, the reality is that companies should not take a “one or the other” approach in this matter.  In fact, the most successful enterprise-wide data management strategies use MDM and data warehousing together to facilitate true information consistency and accuracy across an entire organization.

Every MDM consultant and vendor out there will propose a different type of architecture.  Some will claim that master data is best managed within the operational environment, while others will suggest that setting up a separate MDM engine is best.  While these beliefs do have some merit, they are somewhat flawed because they put a higher priority on data governance and management than they do on information consolidation and standardization.

But, how can you successfully administrate data if it is still incorrect, out-of-date, or inconsistent?

This is why I, and many other industry experts firmly believe that MDM is most effective when data objects and values are organized and managed in a single, central location such as a data warehouse.  Supporting this approach is the fact that MDM relates to all the operational systems it serves.  And, in order to ensure optimum standardization of key data elements, MDM must often suggest changes to those source systems.  If the master data is not centralized in a warehouse-type environment, inherent roadblocks and challenges – such as an inability to properly “close the loop” between master data and back-end operational systems – may occur.

On the other hand, if data values are pulled from source systems using extract, transform, and load (ETL) tools, and collected and organized within a data warehouse, the process of making changes to the source environment becomes simplified, accelerated, and ultimately, less costly and time-consuming.   Companies can get a better handle on what data they have, and how it needs to be standardized.  And, the data warehouse is transformed into an efficient and effective mechanism for fully-centralized master data management, as well as distribution of feedback for exceptions and enhancements back to business applications, and the delivery of confirmed data to end users from a single, centralized location.

What are the key benefits of this approach?  Deployment time is shortened, costs are minimized, data is better synchronized, and the ability to access and govern master data is significantly improved.  Additionally, a “single version of the truth” can be more readily achieved and maintained, enabling faster and better reporting and analysis.

So, in short, don’t discard your data warehouse when you make the move to MDM.  Using your data warehouse to extend and enhance your MDM strategy is the smartest possible strategy, and will help put your project on the path to success.