Quality versus quantity: Essential steps to improving your data
By Mark Dexter
13th January 2015

There’s no doubt the concept of data management and analysis has taken hold across all industries and sectors. Enterprises of all kinds want to harness the power of Big Data but the real benefit lies not just in gathering information but in its interpretation and application. So unless the data is of good quality, it’s not particularly useful in making effective business decisions — essentially, it’s a case of garbage in, garbage out.

How can you help ensure the data you’re managing is of good quality and beneficial to your organisation? Here are some of our suggestions for the Why, What and How of improving data quality.

Why are we gathering this data?

A data professional colleague recently said to us, “If you want to improve your data, start with good data.” That’s a neat way of saying that data should have a purpose. Especially given the expenditure involved, you’ll want to ensure that valuable resources are being spent the right way, on data that actually will be useful. What’s more, if the harvested data is given the proper framework and leads to positive business outcomes, you’ll have more stakeholders on board with the idea that good quality data should be a shared goal for the whole organisation, not just the IT department. Yes, to some extent it’s about justifying the data budget, but in the bigger picture, the entire enterprise benefits.

What data are we collecting?

We can’t emphasise enough the importance of documenting your data definitions, sources and methodologies. This is not only a good way to review the ‘Why’ of your data, but it helps to safeguard continuity and communication among the team, especially if the person who created or maintains the database were to leave. Data terms and values should be objective, rather than subjective, given that interpretation is critical for business strategy and forward planning – you don’t want to present inaccurate or biased data if a critical business decision is on the line. Especially in a larger organisation, it’s critical to have an objective data vocabulary that is understood by everyone involved. It’s also recommended that you establish who has authorised access to the data – make sure only the right members of staff are able to control the inputs. And that brings us to…

How are we collecting the data?

On the office copier, making a copy of a copy dilutes the quality, and the quality of the next copy decreases even further. The same can be true of data. Ideally, your reports should be generated from one original source or business process, rather than pulling in data from other copies. In other words, don’t sacrifice quality for convenience. It’s also important to engage with the teams responsible for data entry, such as call centre team leaders. It’s mutually beneficial for you to understand their process, and for them to know that getting the data correct at the source is critical. Additionally, you should conduct regular audits of databases to uncover missing information, resolve anomalies, clean up inconsistencies and capture static data for future benchmarking.

Data is not just about gathering as much information as possible. It’s about using good information to inform business decisions that will benefit the whole organisation. With a bit of vigilance, you can apply the above principles to your processes and better harness the power of your data. 

Have you had a bad experience with bad data? Have any tips for achieving optimal data quality? Share them with us below!

Comment

"I think you can add, 1) Who ( the audience) is meant for..with priority 2) When since it is prioritized." - Raja Khongthaw

"There are lots of tools , more than 30 renowned tools. But the ones that serve Big data, hadoop inclusively are what we should opt for, I think since, somewhere down the line the organization may need them." - Raja Khongthaw

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