In the current environment, data quantity isn’t really an obstacle many organisations still face. More often than not, they have more data than they know what to do with.
For a long-time data has been treated as something we know we need to have, but without quite knowing why or appreciating its true value. It’s only more recently that more organisations are starting to appreciate just how valuable data can truly be.
I don’t mean this in the sense that you can sell it to, or buy it from, a third party, but more in the sense of a value exchange – I give you my data, so I can get something back – whether this is free use of a platform, more targeted, personalised and relevant marketing communications, or an improved experience with an organisation.
Modern organisations need to regard data as a valuable commodity and treat it in much the same way you would treat an expensive new piece of tech – with care, consideration and respect, and learning how to use it to its full potential. It’s also the foundation for advanced data sciences like machine learning, deep learning or AI – all which require high quality data to function properly and for organisations to realise their true value.
We’re seeing organisations that effectively use their data become more sophisticated, streamline their processes and focus their attention on more useful and profitable projects.
Unfortunately, many organisations encounter the same obstacle to using their data to its full potential: dirty data. Luckily, there are some ways you can combat this:
Revising your data collection processes
Reviewing your technology
Involving your people
Making sure your data is squeaky clean
We’ve found that the basis of any successful data project is the availability of clean and useful data.
This requires systems and processes for collecting data that not only ensure data is being entered in the right fields, or data entry errors are minimised, but also that you’re collecting data that you can and will actually use.
‘Dirty data’ can impede efforts to consolidate data into a single database as data cannot be matched and aggregated, or duplicates are created. Dirty data can come in the form of missing data, data entered in the wrong field, incorrect data, inconsistent data as well as those aforementioned duplicates.
If your aim is to become a truly data-centric organisation, dirty data should be on the top of your list to combat.
The most common place that dirty data originates is in the collection or entry of data. You’ll want to build business rules around how data is entered that ensures consistency and accuracy. This will require an audit of current processes to get an understanding of what’s currently happening, where things are going wrong, and how you can fix these up.
Check your technology
Another area that can create roadblocks to useable data is the technology you’re using – especially if you’re using multiple systems which creates data that is siloed, has different requirements and uses different structures. When you then try to integrate these systems for reporting, or for a single view of customer, you’ll likely face the issues of duplicate data, incorrect values, or missing fields where labels don’t match.
One way to combat this it to add a layer in-between your systems – like a data-lake - which aggregates your data, cleans it up, and from which you can extract the data or insights you want in the format or structure most useful to the task.
Involve your people
It’s also important to realise that while you might really care about the quality of your data, your employees or colleagues may not. They may not see any value in entering data correctly and consistently, or making sure that data fields match.
You’ll want to instil a sense of ownership and empowerment. Involve them in the process right from the start so that they can help determine and decide what good data and data collection processes look like and come to a consensus.
Showing them how quality data can drive automation and make their jobs easier and more efficient could also be something to explore, and give them the opportunity to make decisions and act upon the data without higher approval where appropriate.
This is not to say that all employees should have access to all data, but making data a part of their job will give them a better appreciation of its importance and value.
Extracting actionable insights
Now that your data is clean, your systems are in place, and your employees are empowered it’s time to start extracting actionable insights and using the data to improve business processes and customer experiences.
The value of data for businesses after all is that they can use it to make better decisions, communicate in more relevant, personalised and useful ways with customers, and create experiences that set you apart from the competition.
Whether you’re in the early stages of becoming data driven, or already well on your way, ensuring your data is clean and useable is critical. Establishing these processes early on will help minimise issues down the track, and will build a solid foundation for more complex projects in the future.
One such application is AI which requires a solid data foundation. Check out step 4which unravels what AI is, how it works, and why we don't need to worry about robots taking over the world... just yet.
Missed the last step? Click here to check out Step 2: The secret to successfully adopting agile.