When you work with data, there are a number of misconceptions that come up frequently from peers, colleagues and clients. One is that data is related to IT, therefore the quality of data is a problem for the IT department.
But think about it. Data isn’t some ethereal entity; a mysterious being locked away in a server room. Data lives and breathes. Every day, we all consume data, generate data, alter it, manipulate it, delete it and replace it. Data holds the organisation together.
But whose problem is data quality really? And what are the consequences of leaving someone else to deal with it?
- A poor experience for the customer when data decays
- A more difficult working day for the employee when data is unreliable
- Wasted resources (time, money) when data creates confusion
- Lower profitability and missed opportunities for sales and growth
A combination of some, or all, of these factors will eventually result in a disaster for the business. Customers will find an alternative supplier that cares more about their personal data, and a bad reputation can be difficult to shake off, particularly in the age of social media. In time, businesses fail because ROI drops – perhaps due to waste and inefficiency.
To link data quality to achievable goals, the business must consider what defines clean data for them. What state would that data have to be in to be considered fit for purpose? How much of your data is in that state now? What routes can you take to good data quality? And how will you advance towards the single customer view that your colleagues crave?
Acting on data quality means educating and involving staff at every level of the organisation and making the consequences relevant to them. This practically ensures proactive involvement. From the intern to the CEO, everyone has a role to play in ensuring clean data is captured.
Over time, ongoing maintenance is required at every level. Data cleansing software is needed to prune and perfect the contents of the master record, while staff need to be continually trained and updated on best practice. Failure to invest in the integrity of the database is the number one reason data quality starts to erode, so this is an essential stage.
Aligning Data With Outcomes
When it comes to storing data, backing it up and making it available, the IT department is essential. But as we can see, the quality of the data is not actually their problem exclusively. They have a role to play, as does everyone else.
Focusing on one aspect of data quality will only benefit one group of people. For a dataset to be useful, it must be maintained by all. If everyone has a vested interest, they are more likely to invest time and energy into achieving the organisation’s data quality objectives.