TRILLIUM SOFTWARE INSIGHTS

Lean Mean Data Governance Machine – Waste Prevention - Part 3 of 3

By Kiran Gill, Senior Strategic Consultant, Harte-Hanks Trillium Software

In my last blog, I covered Waste Elimination in Data Governance using Trillium’s Data Governance Discipline  “Data Management” as an example.  To create truly lasting Lean Data Governance, Waste Prevention activity is crucial.

Reminder: Focusing on Trillium’s 5 Data Governance Disciplines enables the exploration of key parts of the programme using Lean principles that help identify waste:
1.    Strategy
2.    People and Organisation
3.    Process
4.    Tools and Technology
5.    Data Management

The 5 principles of Lean help us prevent the occurrence of waste. 

The following 5 principles, when adopted as part of the Lean Data Governance approach will assist the business lay the fundamental foundations to streamline governance processes and feed into the prevention of unnecessary activity. Using “Tools and Technology” as an example we can see how going through this exercise can add value to your Lean Data Governance agenda:

1. Specify Value as seen by the Customer:  Delivering value at the right time to the internal data customer is a crucial requirement for Lean Data Governance. Define value accurately by speaking to your customers. Liaise with business users of the systems and tools. Are your internal data customers getting what they need from their tools or are they undertaking additional work to get to the end result? Understand outputs and ensure that these are in line with what the internal data customer needs.

2. Identify and Create Value Streams: Value streams need to be investigated and refined in order to make the overall Lean Data Governance initiative free of wasteful activity. Create new ones if the old ones are not good enough. Map the activity and the processes users need to go through to retrieve data and information. Pinpointing and refining the process flows will prevent wasteful activity and wasteful outputs.

3. Make the Value flow from source to Customer:  “Flow” enables the value to be delivered with minimal stages and activities. A seamless flow is a key requirement for Lean Data Governance. Investigating and mapping the flow of information and data within the tools helps identify unnecessary breaks and manual intervention. Eliminating these stages and introducing automation is key.

4. Create Pull: The internal customer must demand before you create the supply. Ensure your tools and technology is not producing excess information or outputs where these are not requested. Are there activities in the background that are unnecessary? This will help eliminate waste of time and resource including people, processing and storage facilities.

5. Strive for Perfection – Continuous Improvement:  Perfection is only achieved when feedback is received and tweaks are made. Consistently review the performance of the tools and technology. Implement a reviewing schedule and a reviewing process that is owned by the appropriate team. The business needs are ever-changing and the tools employed to produce correct outputs need to be adapted to facilitate these changes.

Applying this methodology to all 5 disciplines of Data Governance and adopting this approach leads to successes in overall data management, data quality and the flow of this data within the business.
Lean Data Governance will deliver value to the internal data customers and consequently the external customer base will receive a service that is fuelled by a well-oiled and perfectly tuned Lean Data Governance Machine. The hub of the organisation is data – data is an effective asset only when governed and managed meticulously. 

Remember, this approach is ACTIONABLE:
•    Not a scrap and start again approach
•    No need for workforce to be trained on LEAN
•    Adopt what you need and Adapt how you see fit
•    Does not have to be enterprise wide
•    Apply to specific processes in your remit

Lean Data Governance is a way of thinking

Look out for the publication of Trillium Software’s Lean Data Governance White Paper.

 

In Claims Data We Trust?

By Michael Chochrek, Insurance Solutions Principal Consultant, Harte-Hanks Trillium Software

Michael Chochrek advises insurance companies on enhancing the quality of claims data and more effectively managing key business processes. He has more than 25 years of experience overseeing insurance claims management and metrics, as well as managing claims teams to ensure the integrity of protocols and best practices. He worked at several national insurers including Allstate, MetLife and Liberty Mutual where he held multiple positions including Multi-Line Claims Representative, Bodily Injury Road Adjuster, Claims Supervisor and as Claims Director for Commercial GL claims.

 

To quote an old adage, “numbers never lie.” But what if you can’t trust the accuracy of those numbers?

Today’s insurance claims professionals can be overwhelmed with the data their department produces. From analyzing allocated expense budgets to identifying files with subrogation potential – or determining the proper staffing model for an office – claims people are slicing and dicing massive amounts of claims data on a daily basis.

Compounding the situation, claims departments have never been under more pressure as they must cut expenses and mitigate losses to help improve the bottom line due to the challenging and highly competitive market for insurers.

As a former claims executive, I was always concerned about the quality of the data I was using as the basis for my decisions. Experience taught me that data could be corrupted by a variety of different factors. Some of these include the migration of data to a new claims handling system, third party data, data entry errors or mergers & acquisitions that result in combined data points from separate systems. 

In order to avoid making decisions or designing processes based on inaccurate data, or simply on our perception of a situation, we would look to identify bad data through manual remediation processes. This time intensive process took valuable resources away from adjusting claims and in the end was not always successful in correcting the issue.

Just think for a moment about the value of implementing an automated, repeatable process to
systematically discover data issues and then apply logic to improve data quality. Taking this approach could have a profound impact on such areas as loss reserving, allocated expense management, litigation controls, claims segmentation and subrogation optimization. In addition, this would give claims executives the ability to make the right decisions while keeping the front line troops doing what they do best – handling claims.  

Many different industries have instituted data quality programs to help ensure they run more efficiently and are equipped with the right information to make the best decisions. It is time for insurers to step forward and make a commitment to improve their data governance protocols. With claims departments being the largest producer of data within insurers, they should lead the way.

It's All About the Data, or Is It?

By Nigel Turner, Vice President, Information Management Strategy, Harte-Hanks Trillium Software

I, along with several Trillium Software colleagues and around 250 other delegates from across the globe, attended Europe’s largest annual data governance & MDM Conference recently. 

During the three day event I had the pleasure of running a tutorial on how to make the case for investment in enterprise-wide data governance and MDM programmes.  In this full day interactive session, truly multinational teams assembled & presented three alternative business cases to the CxOs of a fictional but all too realistic hotel chain whose data was about as good as the chances of Botswana topping the 2012 London Olympics medal table. 

Great fun was had by all, and by the way all the cases received the investment they sought, albeit paid in Monopoly bank notes. Next year, if Europe’s economy continues to decline at its current rate, I may be able to hand out Euros.  

The overall event contained its expected miscellany of expert deliberations, customer case studies and vendor presentations. Many sessions focused on how data can be improved through:
•    Better data problem identification & quantification
•    Data remediation & monitoring
•    Creating and operating business processes and behaviours that enhance & preserve, and not diminish & damage, data
•    Using best of breed tools to cleanse, integrate, enrich & maintain data and support all the above activities.

However, an underlying theme frequently emerged. It’s not just all about the data after all. Time and time again several presenters and attendees bemoaned the lack of business awareness, involvement and investment in their data management improvement efforts. Many still seem to operate in darkened silos, banging their heads against intractable walls of indifference or downright sceptical hostility from the very people who should be championing their cause. 

Yet, conversely, it was clear in the event that more organisations are increasingly embracing the latest trends of Cloud Computing and Big Data.  The new data challenges these throw up will create an even greater need for effective control & management of data assets, so we are faced with, as Winston Churchill once observed about Russia, “a riddle, wrapped in a mystery, inside an enigma.” 

But Churchill also added, “Perhaps there is a key…” So what is the key to unlock this paradox for us as data management professionals?

It was clear that those who were involved with the most successful data governance & MDM projects highlighted at the event had a number of things in common:

First, they seemed to have a strong awareness of the business drivers which impel their organisations forward. As a result they could directly relate their efforts to those goals and prove their value. They also had a better handle on the business problems they are trying to help solve.

Second, they explained their successes and aspirations in plain language that a non-expert could make sense of. With some speakers, in contrast, I observed a direct correlation between the complexity of the slides and language used with apparent lack of business buy in. No coincidence I contend.  

Last, the winners presented their stories with enthusiasm and passion, celebrating their successes and learning from their mistakes. Passion is infectious, rubs off on others, and wins friends. It also helps people to get where they want to go.

Overall, this was a successful data management event. It again proved that data management is not just about the data, important though that is. It’s also very much about the people who seek to improve the data.

Defining Data Governance Dashboards

By Michael Stiffler, Strategic Consultant, Data Governance, Harte-HanksTrillium Software

In reflecting on my recent data governance dashboards webinar, we need to keep a few things in mind to help put our dashboards approach into context. If we truly understand why we are creating a data governance dashboard and the importance of the dashboards to our organization, then we can better prepare ourselves for all the necessary tasks. We need to have solid answers to the “why” question before embarking on this initiative.

Overall, the dashboard creates awareness and transparency of the data and any governance issues.

This transparency leads certain stakeholders to actually assume accountability for the data issues and not pass the buck. Additionally, the dashboard may help eliminate some of the fire fighting emergencies that often occur when a data issue becomes catastrophic. Lastly, the dashboard creates confidence in the data. When the business owners and executives are confident that the data is within accepted tolerances of critical business rules, they can make the best decisions about their business.

There are a number of attributes that must be present in order for the dashboard to be effective. The dashboard must be actionable – the purpose of the dashboard is to create activity around improving data quality. In order to make decisions on what kind of action should be taken, there needs to be enough detail to fix the issues.

Additionally, the dashboard should have a flexible architecture so it can adapt to changing business requirements. It should also contain technical metadata highlighting statistics about the issues as well as business metadata about the business rules, definitions, data stewards, etc. Other necessary metadata should be included by way of historical trending. This way, you can show improvements over time.

Lastly, visual indicators provide a quick way to summarize the health or quality of the data, but a data steward should provide analysis and comments about what the trends really mean.

By building your dashboard around these attributes, you have therefore made it relevant to the business. The business will also want the actual data to be relevant too. Help them understand how data governance can improve inefficiencies in their processes, and how data is tied to their KPIs, initiatives, goals, and objectives. You will also want to use the same data they are using. Figure out where their reports are housed and where they source the data.

These activities are already built into our Trillium Data Governance Life Cycle, because the dashboard is a necessary deliverable of the program.

During the initiation phase, we identify reporting as part of our data governance strategy and include policies related to reporting and dashboards. Additionally, we complete a data profiling policy (and subsequent profiling activities), and define data stewards and maintenance owners.

Moving on to the Analysis phase, we identify the key data attributes and business rules to be monitored. We also identify owners and ensure alignment to the business’ KPI, goals, and objectives. We then create documentation for the thresholds and business rules as part of the data quality service level agreement. Lastly, we will create processes for the corrective actions and escalation processes.

During the last phase of the Data Governance Life Cycle, we complete the final steps to bring the dashboard vision to reality. This includes creating templates, updating processes, monitoring the data, and adding other controls around our processes.

For the presentation layer, we need to adopt one or more reporting frameworks. There are a number you can choose from and you may need more than one to meet your business requirements. All of our frameworks share a common theme for different levels of summaries -- Executive, for the highest level of summarization; Operational, containing high level monitoring within key policy areas; and Tactical, which contains the lowest level of detail.

You may have the most success with a framework that monitors data quality aligned to specific business KPIs, goals, and objectives. And you may need to go through a number of iterations in order to fully capture the changing requirements of the business. It all depends on your unique implementation.

 

Lean Mean Data Governance Machine – Waste Elimination (Part 2 of 3)

By Kiran Gill, Senior Strategic Consultant, Harte-Hanks Trillium Software

In my last blog I discussed the synergy between Lean and Data Governance. The crucial stage in achieving Lean Data Governance is the Waste Elimination activity.

Taiichi Ono’s seven wastes, as applied to Toyota, include transportation, inventory, motion, waiting, over-processing, over-production and defects. The seven wastes provide a comprehensive list of areas of waste elimination which can be applied to most every business or business process. Each of these definitions would be unique to your own business and data governance disciplines, and must be interpreted to suit your business needs.

As a Reminder: Focusing on Trillium’s 5 Data Governance Disciplines enables the exploration of key parts of the programme using Lean principles that help identify waste:
1.    Strategy
2.    People and Organisation
3.    Process
4.    Tools and Technology
5.    Data Management

For example, when exploring People and Organisation, “Transportation” may be categorised as the physical movement of a paper file from one department to another. In Data Management however, this may be classified as the movement of data from source to central repository. These definitions need to be developed to suit your organisation.

Trillium’s 5 Data Governance Disciplines should be explored for practices that may be generating wasteful outputs. These should be subject to a TIMWOOD assessment. This mnemonic helps conduct an in-depth assessment to identify waste which enables the business to implement a Lean Data Governance approach.

Using Data Management as an example, we can begin to understand how you can categorise waste in each of the 5 Data Governance Disciplines:
1.  Transportation - The waste of transportation includes activity such as the movement of data and information from different systems and repositories to its end destination.

2.  Inventory – Responding to customer demand ensures that the business is not holding surplus data, over producing reports which are then archived, or holding product codes that are no longer used

3.  Motion – This can be unnecessary movement of data or information within a process. Using tools and technology to move, manipulate and interrogate data during processing for quality assessments or analytical purposes is a key area to look at.

4.  Waiting - Waiting for essential outputs, be they data, reports, alterations, or something very specific to the governance process, results in a negative impact across the organisation as well as wasted time that could be used to do something more productive.

5.  Over processing - Data goes through many processes, some of which are necessary and some that are performed out of habit. These are embedded in the business but no one really knows why this is done.

6.  Over-Production – This is the production of more than what the internal data customer demands. Whether it’s producing excess data, reports, analysis or excess number of meetings, emails and documentation, overproduction leads to wastage in time and effort.

7.  Defects – Defects in data can occur regularly and repeatedly. These may go unresolved at source as the teams deal with it at a later stage, using well established workarounds. Data defects are the immediate and obvious consideration but there are other areas in data governance that are impacted by different types of defects in information management processes.

The elimination of waste will allow the business to optimise data governance and the associated processes. Evaluating each Data Governance discipline within a process will help you pinpoint areas where waste is occurring, eradicate that waste and focus on Waste Prevention.

My next blog will cover the 5 areas of Waste Prevention. The key to ensuring that Lean Data Governance survives in a business is to explore Waste Prevention and undertake necessary activity to avoid waste generation in Data Governance.

 

A Data Quality Revolution is Required to Achieve Microsoft’s Vision

 By Michele Goetz, Vice President of Product Marketing, Harte-Hanks Trillium Software

While attending the Microsoft Convergence event earlier this year, the most poignant phrase I heard was “…moving from a system of record to a system of engagement…” during the keynote. Music to my ears.

Why is this so important? It’s not just because it illustrates Microsoft’s vision of a user experience across their platform of applications and operating system. No, more critical is the fact that the phrase actually gets to the heart of what we as data quality managers really need to do in order to help our users gain the most out value from their data quality user experience – by providing the right information and insight in an actionable, dynamic and real-time way.

In our world, the system of record is the data source – the master data that is the bedrock. Yet, the notion of engagement is much more fluid, alive…and value producing. The data is ever-changing and constantly being updated, so it has to be accurate and on-target too.

This drastically alters how we provide data quality programs within the Microsoft Dynamics CRM system for example. We need to expect that users are always actively and frequently engaged with their customers, partners, and vendors. Context is king.

The data must be there in real time to educate instantly, helping users discover opportunities and support value in every interaction. Validating and improving data on the fly needs to be much more in line with the context of use and communication, and not just relegated to form and fit of data elements of an entity or record. Data quality is the foundation of an ever-evolving profile to generate a 360º view of every engagement on the fly and in real time.

Data has primarily been thought of as the by-product of a business that has to be housed and filed for some future retrieval. The retrieval of data is often a secondary thought to how the information provides value to the business. In this day and age, that perspective and approach is out-dated. 

A data quality revolution needs to occur that puts business value first, and then can be translated into the actual processes and programs that make data not just fit for purpose, but “fit for engagement.”

 

Lean, Mean, Data Governance Machine – Introduction (Part 1 of 3)

By Kiran Gill, Senior Strategic Consultant, Harte-Hanks Trillium Software

Toyota’s desire to become more disciplined, organised, and streamlined to improve overall customer value very much depended on the “Lean” manufacturing process it created in the 1950’s and 1960’s. This “Lean” methodology was designed to identify and eliminate waste in business processes and manufacturing for improving efficiency and promoting effectiveness. Through the application of “Lean Thinking,” the company has become as profitable as all the other car companies combined, became the largest car manufacturer in 2007, and grew from a small automobile manufacturer to one of the ten largest companies in the world.   

Lean methodologies have been adapted and adopted by some of the largest and most successful organizations around the world, and have also been utilized for service organizations as well as manufacturing to help improve their business processes.

The Lean Approach is commonly applied in parts of a business where the business process is visible and known to the organisation. There is, however, an area in a business that is unlikely to be subjected to Lean because in many companies it performs business as usual functions in the background – that is Data Governance.

What a shame – because there is a perfect synergy between the objectives of Lean and those of Data Governance. Both disciplines have core principles focused on driving efficiencies, minimising waste and delivering robust, reliable and timely outputs. So, by applying a Lean methodology to data management and governance, you can enable your business to maximise the value of data and process outputs by eliminating and preventing wasteful activity.

This approach focuses on the creation and refinement of processes that deliver robust data and information to the internal data customer. The method enables a business to review and refine each process stream within their Data Governance programme. Whether a business has a Lean agenda or not, it is important to remember that a Lean approach to Data Governance can be adopted independently of any enterprise-wide Lean implementation.

Focusing on Trillium’s Five Data Governance Disciplines will help guide the exploration of key parts of a governance programme where Lean principles can be applied to help identify waste:
1.    Strategy
2.    People and Organisation
3.    Process
4.    Tools and Technology
5.    Data Management

Waste Prevention and Waste Elimination are two key areas that need to be investigated in any Data Governance program. These activities demand that the business engage with its key stakeholders – identifying areas where waste is occurring, eliminating the inefficient or redundant processes, and preventing it from occurring in future.

In my next blog, I will discuss examples of how Waste Elimination activity in Data Governance can help a data-centric organisation work towards Lean Data Governance.

The Effective Advocate – Selling a Data Quality Business Case in Hard Times

By Nigel Turner, Vice President, Information Management Strategy, Harte-Hanks Trillium Software

In this irregular series of blogs on enterprise data governance and data quality (DQ), I have covered to date how to recognise the need to take action at the corporate level, how to identify specific DQ issues, how to investigate & quantify the issues, and how to prepare a case to the business for action.

Although taking these steps are all necessary, they are not sufficient to ensure success. I have seen many carefully researched, evidence-based and well-produced data quality cases fail when presented to the business. These are hard times for businesses – with cost reduction the primary driver for many. Any reason to turn down a case to spend money, even if it promises great rewards in the future, can today be enough to halt a case for enterprise DQ in its tracks. In any event, your case will inevitably be competing with others for scarce funding and resources. You need to get ahead of the “competition.”

When Charles Dickens wrote the novel Hard Times in 1854 he did so to reflect the tough economic landscape of the day. One of its central protagonists, the school headmaster Mr. Gradgrind, had an unshakeable belief in the power of facts. His most famous statement was “What I want is Facts… Facts alone is wanted in life.” It’s a well known cliché that facts speak for themselves, but this is one cliché that clashes with the reality of the machinations of corporate life. Facts don’t speak for themselves. They only become the basis of change when people believe and act on them.

To make people act requires committed, determined and influential advocates who can win over the corporate jury tasked with deciding the fate of the business case.  

So a business case for enterprise DQ needs to contain the necessary fact-based evidence, but to win over the jury requires a skilled advocate to present it. To do this DQ advocates can learn from Perry Mason. Mason, it is said, only ever lost two cases in court, both through the failings and duplicity of others rather than his own lack of adversarial competence. Not a bad track record as he was involved in over 270 cases in the original books and legendary TV series. I’d certainly put my fate in his hands.  

So how do you become a Perry Mason when presenting your business case to those in the business and IT who need to sanction it? There are some golden rules to follow. Most important is to do your homework on the people who will make the decision. Who will sit on the jury? What are their professional and personal drivers? Do they ‘get’ data quality and why it matters?  Are they likely to be allies or enemies when the case is presented?

In other words don’t start your advocacy in the courtroom. Start before the case is formally presented. Try to get to talk to each of them, outline the main tenets of the case, ask them if they have any questions or objections, and find out what you need to do to ensure the verdict you want. In some instances it may not be possible to gain direct access to the business case decision makers. Where this happens seek access to people who work for them. Ensure they are aware of the merits of the case and ask them to lobby the decision maker on your behalf.

In either scenario ensure that you can relate the business case to the specific interests and drivers of the person you are lobbying. How will the acceptance of the case benefit them and the area they control? Set out a clear vision of what it will do.  Ensure you have specific facts at your disposal to substantiate your claim – how has any previous DQ work helped their bottom line, or ensured they are compliant with law & regulation?  What will the proposed DQ case do to help them now and in the future?

The golfer Gary Player once said that “the harder you work the luckier you get.”  So put some hard work in to prepare for these meetings – practise your pitch, consider objections they may put up and prepare your counter-arguments. But be prepared to be flexible and adjust your case if they request changes. Also use these sessions as an opportunity to secure not just their commitment to the business case, but to gaining their (and their people’s) continued active participation once it gets accepted and the work gets underway.

So, hopefully, when your day in court arrives, you have effectively created a jury who already know the verdict. Perry Mason would have loved that. In hard times, hard work usually pays off.

 

 

Gartner BI Summit – Key Takeaways from the Week

 By Michele Goetz, Vice President of Product Marketing, Harte-Hanks Trillium Software

It was a great couple days at the Gartner BI Summit in LA. Thanks to all that stopped by our booth to talk about your initiatives. You all are dealing with quite a wide range of topics – from social media analytics to business process improvement to data management.

There were three big take-always for me, that we as data quality experts need to consider in order to support the consumerization of BI and Analytics:

1) Bill Hostmann's Pace Layer Model:  He walked through analytic capabilities changes from a system of record, to systems of differentiation, and finally systems of innovation. Thus, bringing BI from reliance on heavy integration to low integration, allowing for an increased ability to change. 

2) BI Agility from Donald Farmer at QlikView: He sees the need for technical agility to support the creation, disposal, and rebuilding of analysis as discoveries are made and shared. (tweets #qlikview @donalddotfarmer)

3) Visitors to our booth are seeking the ability to utilize tools for business users to discover, define, and communicate business rules for data to IT without having to pass spreadsheets and requirement docs.

The good news, Trillium is already prepared to meet these evolutions in BI and improve support of the business and IT collaboration. All that is required is a change in view.

Supporting systems of innovation and Agile BI requires extending data quality from back office data warehouse processes and point-of-capture cleansing to data quality services, called as part of the query and presentation of insight. Trillium has been service-oriented from the start, rather than evolving from batch processes. So, you are only limited by imagination.

Many of our customers are providing data discovery capabilities to data analysts and those in risk and compliance organizations in order to support data certification needs –  operationally and for regulatory purposes. So, say good bye to spreadsheets and lengthy requirements gathering and meetings. 

We are about Business First. Improving collaboration between IT and the business is what we’ve been focused on since the release of TSS v13, and we’ll be releasing new capabilities shortly in v14

Business intelligence is rapidly evolving, and data is again at the heart of it all. Don't forget the data quality! Ensure analytic and business intelligence success with accurate data.

 

Agile Data Quality for Analytics and Business Intelligence Accuracy

 By Michele Goetz, Vice President of Product Marketing, Harte-Hanks Trillium Software

Rapid changes are occurring in the world of analytics and business intelligence. 

From the business perspective, not only are we better able to visualize our business performance, but information is more readily available within business processes. And from the technology perspective, platforms are transforming to support the exponential increase in data and improve the consolidation across sources to interpret the data within the context of the business. 

Accomplishing these changes has been no easy task, especially considering data is not under the control of IT organizations like it has been in the past. Data is increasingly managed outside the four walls of the enterprise due to the cloud, mobile communications, partner ecosystems spanning sales and distribution, and reliance on 3rd party reference data for augmenting information. The fact that technology decisions and purchases can be made by the business without IT involvement – the consumerization of IT – puts further pressure on IT to gather, align, and syndicate information from business silos to enterprise consumption.

In most organizations, data quality has been an afterthought. Yet, it really is the cornerstone for the reliability and adoption of the analysis and insight provided by your business intelligence initiatives.

Past approaches to managing data quality in this new era of analytics and business intelligence are no longer viable. The answer is a more agile services-oriented approach both in data quality processes and management. However, is your organization prepared to implement agile data quality?
Here are 4 key steps to align your data quality practice with the evolving landscape of analytics and business intelligence:

1)    Recognize the uniqueness of data sources as well as their synergies. In business intelligence projects, the focus typically revolves around the standardization of dimensions used to analyze and measure. However, the underlying architecture of a data source may not be a simple one-to-one relationship. Profiling of attributes in one data source may be required to make up a corresponding attribute that links to another data source. 

2)    Assess the data quality processes and governance across contributing data sources. The maturity to manage data quality within each data source will vary greatly. In addition, the process for prioritizing data to be corrected and improved may be in alignment with what you need for analysis or it may not. Identify the synergies and gaps in data quality programs and processes, and shape governance programs that not only build alignment to your analytic needs but assure data quality moving forward.

3)    Assess the data quality within each contributing data source. Typical data quality programs address single data sources that feed a particular business process or report. These programs may not take into account corresponding data quality processes for similar data in other sources or account for downstream dependencies. Moving forward you will want to not only assess these sources individually but need to reconcile quality across sources through relationship analysis both at the master data level that defines entities and dimensions, but also transactional and numeric data that feed your measures and calculations.  

4)    Establish data quality services at the point of consumption. Building data quality processes at the data source level is one approach to support business intelligence and analytics.  Although, this approach does not give you an agile means to align real-time analytics and operational business intelligence requirements.  Build data quality processes that are aligned to the query needs and can be called on a service basis when analytics and report calls are made to present information and analysis.