Archive for the ‘Business Value-Driven’ Category

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When it comes to Data Quality Delivery, the Soft Stuff is the Hard Stuff (Part 5 of 6)

March 11, 2013

In my last posting I discussed why an understanding of corporate financial concepts is so important to data quality success. In this blog, I will examine knowledge of commercial enterprise applications as a key enabler of effective data quality delivery.

Packaged applications for ERP, CRM, MRP, HCM, etc. were first introduced decades ago to provide tightly integrated business management functions, standardized processes and streamlined transaction processing. While one can argue whether or not these applications have lived up to all of the hyperbole, the reality is that they have been successful and are here to stay. As these backbone systems continued to evolve and mature, lessons learned from thousands of implementations were incorporated into the model solutions as best practices. These best practices spawned industry standard processes and specialized variants were born (e.g. vertical systems solutions). With the widespread adoption of these solutions, the days of custom building an application to meet the business’s needs have largely disappeared (although exceptions do persist to support specialized needs).

Since these software applications are built to address specific business function needs, their underlying data models are a direct reflection of the functions they enable. Basically, they are process-driven data models. As these applications have evolved, so have their data models – the two are inextricably bound together. When these applications are implemented, the system is designed for each client’s specific use. Along with any industry data standards, this client specific use dictates the data requirements and business rules – the data’s intended use or purpose. However, it is important to note that these data requirements are within the construct of the packaged application’s data model.

From my perspective, harvesting business rules is one of the more challenging aspects of data quality deliver. Therefore, a firm understanding of how a particular packaged application works, how it can be configured or setup and how it integrates from module to module is very critical when it comes to understanding the data’s business rules. Typically, the analysts who design the system also specify the functional data requirements needed to enable and support the intended functional design.  Therefore, who is in a better position to know which data is important, and what the likely business rules are than a functional analyst who has deep experience designing and implementing a certain packaged application? In contrast, having technical data analysts compile or determine business rules without packaged applications experience is largely a trial and error approach that is highly inefficient and can contribute to an incomplete understanding of the data’s intended purpose. This deficient set of business rules can lead to the identification of false data defects, or data defects that remain undetected. Either way, there can be a profound impact on business performance.

In my next post, I will examine the benefits of having operational experience when it comes to effectively delivering data quality.

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When it comes to Data Quality Delivery, the Soft Stuff is the Hard Stuff (Part 4 of 6)

November 28, 2012

In my previous post I emphasized the importance of demonstrated project management fundamentals as a key enabler of effective data quality delivery. In this blog, I will discuss why an understanding of corporate financial concepts is so important to data quality success.

Despite the continued evolution of data management technologies and the growing awareness of the challenges and promises of data quality, business buy-in is still a major barrier to the widespread adoption of data quality as another lever to achieve operational effectiveness. One of the key reasons for limited adoption is a clear linkage between data quality and a business’s performance, which is measured in a myriad of ways from operational metrics to managerial reports to formal KPI’s. But, eventually, the enterprise’s performance is summarized in three key financial statements; the income statement, the balance sheet and the cash flow statement. Positioning data quality impacts or improvements in the context of these financial statements begins to “connect the dots” and moves data quality from the abstract to the concrete and from the theoretical to the practical. To illustrate this point, let’s take a look at the impacts and implications of a simple data quality issue like “undeliverable” billing addresses.

If billing address date defects within a company’s billing system prevent the delivery of customer invoices, there will be an increase in a company’s return mail volume. The obvious implication of this additional return mail is an increase in a company’s shipping and handling expenses associated with the analysis and correction of billing address data defects and subsequent invoice reprocessing. While a localized problem like this is typically not material from an accounting standpoint (i.e. important/significant), when combined with inefficiencies and costs associated with other data defects found throughout the enterprise, the impacts can manifest themselves as higher operating expenses on the income statement, which reduces operating income.

Another impact associated with “undeliverable” billing addresses is the delay in invoices being received by the customer. However, the implications of billing delays are less obvious. If the undeliverable billing address issue is large enough, invoice delays will likely have an adverse effect on the company’s Days Sales Outstanding (DSO – the average number of days it takes to collect revenue after a sale has been made), and therefore on the Cash Conversion Cycle (CCC – the time between outlay of cash and cash recovery). Additionally, an increase in DSO can have a negative impact on collections since there is often a correlation between the age of receivables and write offs. To counter the increased risk associated with customer non-payment, the company will have to increase its “reserve for bad A/R” on the balance sheet. The net implication of delayed billing will be a weakened cash position as reflected on the company’s cash flow statement. This “tightening” of cash increases the company’s need for, and cost of, borrowing for expansion, inventory, product development, sales and marketing efforts, etc.

While proactively eliminating billing address data defects can sufficiently mitigate these risks to business performance, gaining business buy-in and support is still a necessary first step. However, simply telling the business that they have data quality challenges that need to be corrected, without communicating the impacts and implications in the context of business performance, misses the mark and makes the job more difficult. In my next post, I will examine knowledge of commercial enterprise applications as a key enabler of effective data quality delivery.

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When it comes to Data Quality Delivery, the Soft Stuff is the Hard Stuff (Part 2 of 6)

March 30, 2012

In my first post I introduced the concepts of hard skills and soft skills in the context of data quality delivery, and I identified 5 soft skills that I think are highly critical to data quality delivery success, and which are typically underestimated; stakeholder management and communications, financial management, project management, commercial applications and operations. In this blog, I will discuss effective stakeholder management and communications as a key enabler of successful data quality delivery.

As data quality professionals, we are continually competing with others in the enterprise for mind share and wallet share. One of the quickest and easiest way to distinguish your data quality efforts from other IT and business initiatives is by clearly communicating with your key stakeholders. Why? Because very few are doing it, or doing it well. But it shouldn’t be treated as a one-time event. You need to make a commitment to stakeholder management as a regular practice and make it part of everything you do.

As a discipline, stakeholder management can be extremely nuanced and there are very sophisticated techniques and approaches. But as a primer, here are the high points. First, who are your stakeholders? The simple answer is “any person or group who benefits from or is impacted by your efforts”. Are there more elaborate definitions? Sure, but this will get you started. Now that we’ve defined a stakeholder, how do we manage and communicate to them? Here’s a high level process to guide you; 1) Identify key stakeholders, 2) Determine their roles, 3) Assess their perception of data quality or your data quality effort (e.g. advocate, agnostic or adversary), 4) Define what you want from them and where they are (e.g. influence, engagement and support) and 5) Figure out what you need to do to get them there. Once you’ve identified and assessed your stakeholders, assign team members to manage certain stakeholders, prepare the necessary communications materials and measure and refine repeatedly.

Here are a few practical tips to get you on your way to better stakeholder management:

  1. Make it a priority and stick with it.
  2. For some, it’s not intuitive or comfortable to regularly engage key stakeholders, particularly those a few levels above you in the organization. Challenge yourself and get out of your comfort zone, it will eventually become second nature.
  3. Regularly ask your key stakeholders for feedback. It’s not necessary to have formal surveys, though they are nice. A simple “how am I doing” works just fine.
  4. Become externally focused and adopt a different mindset. Think like your customers.
  5. Always be selling. Celebrate your successes and share them with customers and prospective customers. Nothing sells data quality like data quality success.
  6. Use graphs and charts as aids to communicate visually. A picture is worth a thousand words.

Once you make a conscience decision to anticipate and manage stakeholder needs, it will start to become intuitive and your skills will develop through trial and experience. More importantly, as stakeholder expectations are met and exceeded, demand for your services will increase. That’s the best measure of success.

In the upcoming 3rd part of this series I will discuss the next “soft skill” – demonstrated project management fundamentals. Stay tuned.

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The Non-Traditional Challenges to Achieving Data Quality Success – Part 5 of 5

February 10, 2012

This is the last posting in a 5 part series on the non-traditional challenges to achieving data quality.  In Part 4, I reviewed the Data Quality Perception Gap.  In this post, I will conclude with the Delivery Gap.

The Data Quality Delivery Gap

Once we have successfully marketed, positioned and sold our data quality solution, we must shift our focus to delivery. The surest way to secure additional business is to gain customer confidence and there is no better way to do this than through demonstrated competence. While there are many variables that can impact delivery effectiveness, of those that we can control, skills are the most critical. This brings us to the seventh non-traditional challenge…….successful data quality projects can be delivered with generalists. If the business needs an experienced product manager, they don’t hire a payroll specialist. Then why staff a data quality role with an accountant, or a sales operations manager, or an SQL developer? Yet, this is often what happens, and when the effort fails it is at the expense of data quality’s reputation.

But when it comes to effective data quality delivery, having the right skills is only part of the human resource equation. Which brings us to the eighth and final non-traditional challenge……data quality professionals are not equipped with the proper mindset. In a nut shell, data quality success should not be measured by the amount of data defects cleansed, but rather the degree of business improvement achieved. Having data quality professionals that understand and embrace this perspective is integral to any meaningful data quality success. If data quality is to claim its position as a valued business discipline, we need to recognize that there is more to it than just getting a few smart people in a room. Doing otherwise devalues the proposition and diminishes our profession.

By focusing on these data quality gap areas, a better appreciation for data quality’s value proposition will start to take hold in your organization and the old, traditional challenges will seem……. less challenging.

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The Non-Traditional Challenges to Achieving Data Quality Success – Part 4 of 5

February 3, 2012

If you haven’t been following along, in my previous posting I reviewed the Data Quality Positioning Gap as a non-traditional challenge to achieving data quality success.  In this post, I will discuss the Perception Gap.

The Data Quality Perception Gap

Assuming we have properly met the challenges associated with customer expectations and solution positioning, chances are that our customer’s are still not “buying” because of the fifth non-traditional challenge…….data quality solutions are perceived as theoretical or impractical. Often times, data quality solutions appear to boil the ocean and our customers become overwhelmed with the scope and complexity or rightfully dubious of the likelihood of success. While this may not be readily apparent from the customer’s objections or from their rationale for why not to proceed, it is a leading reason why data quality solutions never see the light of day. In order to win our customers’ confidence and their business, we need to be viewed as a data quality expert. Proposing solutions that strain credulity calls this expertise into question.

Even if we are successful in proposing a practical and actionable solution, we need to be mindful of the sixth non-traditional challenge…….data quality solutions are perceived as creative ways not to address the problem. If the customer’s data quality problem can be solved by targeted data cleansing in the source system, then propose a solution that does just that. If the customer is unsure of the degree and impact of their data quality gaps, then propose a data quality solution to help them quantify and qualify their data quality issues. It is never a one size fits all and there’s no quicker way to lose credibility than to propose a solution that doesn’t address the customer’s needs.

My next posting will conclude the series on non-traditional data quality challenges.  Until then….

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The Non-Traditional Challenges to Achieving Data Quality Success – Part 3 of 5

January 27, 2012

In my last posting, I discussed the Data Quality Expectations Gap and considerations for overcoming it.  In this post, I will cover the Positioning Gap.

The Data Quality Positioning Gap

Once we have identified our customers, determined what motivates them and defined the offer, we need to market or “position” our solution. And it goes without saying that we need to do this within the context of what problem we are trying to solve. Enter the third non-traditional challenge…….data quality is incorrectly positioned as an end, rather than the means. More times than not, this is the direct result of not understanding customer motivators, as outlined in the previous section on the Expectations Gap. As a result, we erroneously conclude that the customer is looking for data quality and we further perpetuate the mismatch between expectation and message.

However, if we have done a good job understanding our customers and their expectations and even realize that data quality is an enabler and not the real business goal, we still have to overcome the fourth non-traditional challenge…….data quality is primarily positioned as a technology and not a business solution. Often times I see data quality professionals leading with technical features and functions instead of business benefits. For example, terms like entity resolution, standardization, normalization, enrichment and domain integrity all ring hollow if they are not positioned relative to the business problem our customers are attempting to solve. Let’s be honest, a VP of Sales and Marketing wouldn’t recognize domain integrity in product data if it jumped up and bit her, nor should she.

In my next posting, I will discuss the Data Quality Perception Gap.  Until next time.

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The Non-Traditional Challenges to Achieving Data Quality Success – Part 2 of 5

January 20, 2012

In my first posting, I introduced the concept of “non-traditional” challenges to achieving data quality success.  I have classified these 8 challenges into 4 distinct Data Quality Gap areas: Expectations, Positioning, Perception and Delivery.  I will explore the Expectations Gap in this posting.

The Data Quality Expectations Gap

As with marketing and selling any product, service or solution, it starts with understanding our “customers” and what motivates them. We then have to match our message to their expectations. This seems pretty straight forward, right? However, that brings us to the first non-traditional challenge……customers are not excited about data quality. In fact, I have yet to speak with a customer or business person who was even looking for data quality. Yet, very often that is what data quality professionals are proposing or selling. As a result, there is a mismatch between message and motivator.

Data quality is an abstract concept and its applied meaning and business value are difficult to understand and convey even by those of us who call it our profession. So why do we expect non-data quality professionals to appreciate data quality, and why do we insist on “selling” it? Shouldn’t we define and present solutions that address our customer’s needs and expectations? This brings us to the second non-traditional challenge…….customers purchase business success, not data quality. Our customers care about increased inventory turns, reduction in days sales outstanding, improved operating margin, reduced write-offs to bad debt, cycle time reductions, reduced systems implementation risk, etc. By and large, business people care about solutions to business challenges. We need to start communicating in terms that resonate with our customers. Until we close the Expectations Gap, data quality will continue to be sold, not bought. Trust me, there’s a big difference.

In my next posting I will discuss the Data Quality Positioning Gap.  Stay tuned.