Ever stare at a closet of stuff and just been overwhelmed? Who has time to go through all of this? How old is all this stuff? I have no idea what any of these papers/pictures are! And what happens? Nothing. Because it is just easier to live with the mess buried somewhere in that closet, than put the effort into cleaning it up.

The same is not surprisingly different for bank databases, datasets and legacy systems.

Chief Data Officer (CDO): What is all this data used for?

Business response: Who has time to go through all of this? I have no idea what any of these columns are used for… Can’t we just decommission this application?

What happens? Nothing. Since the effort isn’t worth the cost of the initiative.

But here is the hook; a disciplined overhaul and cleanup, done once and only once, can be forever life-changing.

At Accenture, we call this transformative.

Banks and their financial services peers that have embarked on a disciplined data architecture “house cleaning” journey have seen results in the form of a decluttered environment, a dramatic drop in data quality issues, a data-driven culture and a clean data supply chain to support a clear roadmap to a more modern and productive ecosystem. A CDO office transformed.

So what lessons can we apply from Ms. Kondo’s best seller?

Here’s a few keepers:

1. Start by category, not location: At banks, start by reviewing data domains, not the System/Applications in which the data resides. Break it down by category and subcategory, then highlight the applications that house that domain data for analysis. It’s an easy way to spot data duplication, when looking at it with a domain lens.

2. For each item ask yourself, does this satisfy a use case?: Ask yourself for each data element: What use is this data fulfilling? Who is using this data? Where does this data land on a report? If the answer is “no one knows” or “we used to use that field but now we use something else,” you know immediately you can start to do some tidying.

3. Move from easy to hard items: Always start with the easy to assess data. Harder and more complex data, is data which cannot be easily aligned to a use case/report or has questionable, complex and derived origins and not easily discernable. Such data should be addressed only after the easy data is identified as necessary or to be decommissioned.

4. YODO – you only declutter once (if you do it right): Many banks don’t even get started on their decluttering journey because they think it is a never-ending task. But, once data is assessed and decluttered correctly, a strange thing emerges: a new data governance framework emerges. Within this framework, a clear and unique relationship to a use case can be established for the newly onboarded data, or a spot on a dictionary. This can dramatically simplify the entire client data onboarding process.

5. Don’t overcomplicate tidying up, let a few simple questions guide you: The following simple questions can guide your efforts to clean up any type of data across your organization:

  • What was/is the business purpose of this data?
  • How and why did this data land in the database?
  • Has the data fulfilled its business purpose?
  • Does the business consider this dataset unreliable?
  • Does this data already exist elsewhere in the organization’s data domain?

As you consider your data house cleaning journey, Accenture’s Data Advisory Team has a host of off the shelf Data Assessment Tools to fast track a bank’s CDO office transformation and create a new platform of unique and decluttered data to drive sustainable growth.

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