5 Simple Steps for Higher Quality Data

Picture this: You’re making vital choices that impact a significant project, and need to use data to better inform those choices. Unfortunately, you’ve got legacy systems galore, your database is being fed by multiple sources, all of them have different rules, and you’ve got no time or budget to embark on a serious data journey without first proving its value– it’s the catch-22 of our digital age. If this sounds familiar, it’s because these are the most common problems our clients across a wide range of industries have told us they face. Without conducting a data quality exercise, how do we or trust our measurement and insights, or optimise our marketing and customer experiences programs?

Luckily, there are 5 simple things you can do today to enhance the quality and ultimately the usability of your data:

1: Review Your Input Channels

Before you begin cleaning up your data, you need to know more about where that data is coming from. For every input channel, you should find out:

  • What rules and restrictions apply
  • Whether or not the channel conflicts with any others
  • If there are is any specific order the import needs to happen in
  • If the channel is attached to any legacy systems and is at risk of being decommissioned
  • Who has access to the input channel
  • What current processes input processes are being followed
  • …and anything else unique about that channel

This step ensures you won’t be hit with any surprises down the track, and can adequately prepare for any challenges you may face.

2: Create Standard Input Rules

Different input channels added to a database over time may have different rules, which results in duplicated data, fields that can’t be properly crosschecked, and other difficulties. In this case, the data may be technically correct – it’s just not very useful. Usability is a significant part of data quality, because there’s no point having a database if no one ever references it. Take a look at every field in your database and determine what it should look like, no matter what source the data comes from. Some key rule examples would be ensuring only numbers can be submitted in a phone number field, email addresses must contain an @ symbol, and names must contain only letters.

3: Eliminate Free Text (As much as possible!)

When free text entry to a database is available, the potential for human error to invade your meticulously maintained data rises exponentially. We know everyone has the best intentions, but inputting illegal characters or phrases that cannot be searched, matched, or validated is simply too easy to do, either though accident or misunderstanding. We recommend splitting free text input into individual fields for common responses or using pre-defined lists. Pre-defined lists are a great alternative to free text because they allow you to provide longer-form input into a single field with variety and nuance, without risking illegal characters or unmatchable entries.

4: Remove Redundant Fields

It seems obvious, but when was the last time you looked at every field you collect and mapped it against the last time you used that field? If it doesn’t get used and will not be needed for historical analysis, for example any duplicate fields you discover during the previous steps or a field that refers to old systems or processes, it’s time to let it go. This will improve your database speed, as well as make it infinitely simpler to cross-reference. While this can be a time-consuming step, now that you’ve standardised input and taken out free text it should become immediately clear as to which fields no longer add value.

5: Appoint a Data Champion

A Data Champion is someone in your organisation (maybe even you!) who is responsible for maintaining data quality. Your Data Champion is also someone who, when a new project is proposed, will always ask “how does data fit into this?” Every business group benefits from a strong data strategy. Data-focused decision making drives stronger ROI across marketing activities, validates business process, improves resources management, and frees up valuable time for employees to focus on their core competencies. A passionate Data Champion is integral to ensuring everyone in the organisation understands the value of high-quality data, creating a pathway for even more improvements in the future.

Now that your data is efficient, standardised, and being given the attention it deserves, you can use your data to prove its own inherent value and embark on more ambitious projects with confidence.

If you need someone to do the heavy lifting, want some advice on project planning, or are simply short on resources, DCA’s local Melbourne team have the expertise you need to complete data projects of all sizes on time and in budget.

Contact us today  or give us a call on (03) 9320 9000.


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