Kingland Blog

Move Quicker and Spend Less on Your Data Quality

Written by Tony Brownlee | 2/24/16 9:21 PM

The approaches we’ve used for data quality in the past are outdated. We’ve all seen many efforts to improve client, account, and security master data for years. At Kingland, we’ve been wrestling with this question for some time: isn’t there a way to use what we’ve learned to help our clients move faster? With the new year upon us now, I think it’s time we really start to look at data quality projects differently through both the approach, and the data quality itself.

DATA QUALITY APPROACHES ARE OUT-DATED

We’ve all seen project plans like these. "In the next number of months we will implement a multi-phased approach to implement this vendor technology and “deliver data quality” to the organization. Here's our plan: (See list to the right)"How long does a plan like that take? I was discussing this classic data quality project approach with a Chief Data Officer last month and I said that optimistically, the fastest you could get through this is 12 weeks. She laughed. She’d happily accept 12 weeks because the reality is most of these projects take 6 to 12 months!

So why can’t we move faster? In our view, the most important step is step 10 – Identify and Prioritize Issues. If projects would simply start with the data itself and the issues we have, we see the investment in time reduced dramatically.  This saves budget, prevents rework, and delivers results faster.  

DAYS, NOT MONTHS                                

So why can't projects start at step 10 - Identify and Prioritize issues? Most cannot because they lack the tools and expertise to move quickly. However, I believe we can start at step 10 if we start with the common data quality issues that have the highest probability of existing. If we utilize the experience we have gained and the patterns we've seen from looking at hundreds of millions of entity records from clients, vendors, and regulators over the years, we can focus on the identify and prioritize steps. Then while resolution is occurring, more identifying can occur, effectively creating parallel progress throughout your program. When it comes to legal entity data, we know the data quality issues that exist and by using a scientific, pattern-based approach and starting with the data, we can predict with some certainty the types of issues that will exist in any legal entity database, and can now change the project plan. If we know the data and we can predict the types of issues, we can quickly identify those issues and prioritize them (step 10). In my view, this should take days, maybe a week at the most. 

Why should something that can take a few days, take a few months? In this new year of 2016, let’s resolve to tackle data quality differently. Data quality is critical, and we all can move faster and spend a lot less on this problem, but we have to change how we start. 

Request a consultation with a Data Quality Expert to determine what solution is right for your business.

 
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