There’s little doubt the effect computers, the web and technology have had on modern business. However, as the digitalization of commerce continues apace, the need for so-called ‘clean’ data is becoming more and more important.
It’s estimated the majority of businesses currently store vast screeds of outdated, duplicated or useless data. With the increasing trend towards cloud IT services and Software as a Service (SaaS) models, which are mostly charged on a per-use basis, having data integrity is now more essential than ever to save squandering money.
Moreover, as the use of Artificial Intelligence (AI) and machine learning become more common in firms, the importance of good data hygiene is beginning to play an increasingly important role. But just what is data hygiene, and what steps can you implement to ensure your firm is only storing what it needs? Read on for some top tips used by the pros.
What is data hygiene?
Not so long ago there was a widely held (but erroneous) belief that the more data a company held, the better it would be able to operate. However, significant shortfalls have been revealed in this ethos. With the gradual shift to digitalization, systems are often overwhelmed or rendered ineffective with poor data. While automation is undoubtedly transforming today’s commercial landscape, as a general rule, the quality of output from these systems is largely determined by the quality of the data they’re supplied in the first place.
Take the all-too-common scenario of outdated client data – contact names, phone numbers, email, etc. If you set up a mailing list using an automated email marketing service, you run the risk of using incorrect contact details for clients or even possibly causing offense by addressing mails to the wrong person.
However, data hygiene goes far beyond just potentially causing distress or annoyance. For example, if your company is storing inaccurate stock data or inventory figures, you could run the risk of making errors of judgment in terms of ordering new materials. Likewise, if your expenditure vs income figures are off the mark, you’ll likely end up overestimating your profit lines.
The importance of quality data
In today’s increasingly data-driven world, the quality of your data is almost more important than the amount you store. There’s little point in having page after page of inaccurate information – in fact, as outlined earlier, bad data could end up harming your firm. From incomplete to inaccurate or outdated data, the decisions you make as a company could be in jeopardy if you can’t be sure of its quality.
Our interconnected, digital world has come to rely more and more on data to operate, so you should do all you can to ensure the information you store is accurate and up to date.
Steps to improve the integrity and quality of your firm’s data
Experts suggest data is now the world’s most valuable commodity – outstripping the worth of even traditional heavy-hitters like oil and gold. Better yet, a recent study by IBM points to the fact that the amount of data collected by companies is doubling per year, which only serves to further exacerbate the potential problems caused by poor quality information.
If you suspect your firm is currently storing bad data, below are a few steps you could take:
Check the accuracy of your data: You need to know your stored information is up-to-date and reflects the current situation. Unfortunately, there is no better way to do this than by manually checking record by record. While this might seem like an impossible task, bear in mind, your firm will continue to collect more and more information so, while it might seem a challenge now, it will only get worse if it’s not done now. If you need help checking data accuracy, you could look to working with a qualified data specialist that might employ Microsoft BI tools to take an impartial view of your datasets.
Check the consistency of your data: Whenever data is collected by more than one person or multiple departments, there is always the chance that irregularities in formatting or consistency could arise. You should try to gather individuals together to agree on a common format to store your data – and also check for duplicate entries.
Verify how complete your data is: As well as the problems above, it’s quite common to find missing elements in records. Again, unfortunately, the only way to rectify these mistakes is via a manual check to ensure everything is where it should be.
Check the data is uniform: The same applies to how data is recorded. For example, some people prefer to use centimeters and meters over inches and feet – however, most automated systems will struggle to differentiate between the two. When you come to clean your data, you should gather all interested parties together to agree to a uniform format – whether that be in size and weight measurements or even preferred currency for financial figures. Establishing these rules now could save you months of work further down the line.