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Assessing Your Data Quality Needs

So you have data quality issues. Who doesn’t? Should you embark on a data quality project? Maybe but what are your objectives? Are there service issues related to poor data quality? Marketing issues? Other major integrations or warehousing projects going on? And once you clean up your data – then what? What will you do with the data? What benefit will a clean database pose for your organization? And without clear objectives, how can you even justify another major technology initiative?

Before any data quality project, it is critical to go beyond the immediate issues of duplicate records or bad addresses and understand the fundamental business needs of the organization and how cleaner day will enable you to make better business decisions. This will help you to establish accurate project parameters, keep your project on track and justify the investment to C level executives. So where do you begin? At the beginning.

Look beyond the pain.
In most cases, a specific concern will be driving the urgency of the initiative but it will be well worth the effort to explore beyond the immediate pain points to other areas where data is essential. Plan to involve a cross-section of the departments including IT, marketing, finance, customer service and operations to understand the global impact that poor data quality could be having on your organization.

Look back, down and forward.
Consider the data quality challenges you’ve had in the past, the ones you face today and the ones that have yet to come. Is a merger on the horizon? Is the company migrating to a new platform? Do you anticipate signficant staffing changes? Looking ahead in this way will ensure that the investment you make will have a reasonable shelf-life.

Look at the data you don’t have.
As you review the quality of the data you have, also consider what’s missing and what information would be valuable to customer service reps or the marketing department. It may exist in another data silo somewhere that just needs to be made accessible or it could require new data be collected or appended.

Be the customer.
Call the Customer Service Department and put them through the paces. Sign up for marketing materials online. Place an order on the website. Use different addresses, emails and nicknames. Replicate perfectly reasonable scenarios that happen every day in your industry and see how your infrastructure responds. Take good notes on the places where poor data impacts your experience and then look at the data workflow through fresh eyes.

Draw out the workflow.
Even in small organizations, there is tremendous value in mapping out the path your data takes through your business. Where it is entered, used, changed, stored and lost. Doing this will uncover business rules (or lack of) that are likely impacting the data, departments with complementary needs and or places in the workflow where improvements can be made (and problems avoided).

Think big and small.
Management and C-Level executives tend to think big. Data analysts and technical staff tend to think granularly and departmental users usually fall somewhere in the middle. Ultimately, the best solution can only be identified if you consider the global, technical and strategic business needs.

The challenges with identifying, evaluating and implementing an effective data quality solution are fairly predictable but problems almost always begin with incorrect assumptions and understanding of the overall needs of the organization. In some cases, the right data quality vendor can help you move through this process but ultimately, failure to broaden the scope in this way can result in the purchase of a solution that does not meet all the requirements of the business.

Click here to download a comprehensive Business Checklist that will help you to identify the data quality business needs within your organization. Then stay tuned for our next post in our Data Quality Project series.

Why is Data Quality So Hard?

If you take a good look around the master data management (MDM) industry, data quality is the buzz word of the day. Blog posts, surveys, analyst briefings, white papers and testimonials are filled with commentary on the importance of good data quality. What is the importance? Well, if you’re paying attention, then you already know. Having accurate data (with no duplicates, correct addresses and the like) can save a company money, increase sales, facilitate positive customer experiences, streamline business processes and is the shining star of a strong brand.

So it’s really important to have good data right? Yes. Definitely. At least, that’s what everyone is saying.

But do they really mean it?

Yes. Of course they do. But each day helpIT systems still talks with clients, from marketers to SQL developers, who are having trouble making the case for a data quality solution. They know how important it is to keep a clean database. The marketing department is screaming for clean mailing lists. The customer relations team struggles to address client issues without a single customer view. Sales opportunities are flying out the window left and right. They get it. At any given time, at least one person in an organization gets how important this issue is. But the bottom line is, it’s still hard to sell.

Why? Here’s what we see…

  1. Data Quality is Elusive.
    Even with the best charts and examples, there is no brass ring to hand to your CEO after the installation is complete. It will take a while for true data quality to become a tangible element ? like cost savings or an additional sale. Selling the intangible requires stamina, planning and ammunition.
  2. Wanted: DQ Champions.
    Any new implementation requires one of two things, a direct order or a champion. If there is a direct order to improve data quality, it?s usually more about finding the right solution. But without that, data quality needs a hero – someone to bring it to the C-level team or the IT Director and show them what the value is. This is harder said than done.
  3. When Good Data Falls in a Forest?
    While a great database is the foundation of a strong MDM strategy, the true value will depend heavily on someone actually using the tools. Whether it?s batch cleansing or developing business rules, that champion we mentioned is likely to also be the poor soul who will need to see it through.
  4. First We Need Data (fill in the blank)
    You?re migrating data. You?re profiling data. Maybe you?re building a new data warehouse. There are lots of ?things? to do with, for and around your data. Isn?t cleaning it the last thing you do? Nope. It?s not. But some companies think so and just like cleaning the file cabinet, they put it off as long as possible.
  5. Connecting the Data Quality Dots.
    Do you have a ton of duplicates in your system already? Working with 7 databases on 2 platforms in multiple countries? Working with various data formats that you have no idea how to normalize? Sometimes a company’s current technology configuration can strangle the effort to integrate a data quality tool. It doesn’t have to, but every day we work with customers who have struggled to find a solution to work with their existing structure.

Are you a DQ champion? Facing some of these challenges yourself? Know of any other barriers worth addressing? Let us know if we can help you build the case for good data quality but most importantly, keep fighting the good fight until data quality becomes a priority in your organization.