It’s true. Your insights, analyses and predictions are only as good as the data you use. If you have bad data flooding in, then you’ll have bad results flowing out.
And nobody wants negative customer experiences or bad results. Nor to miss out on all of the benefits that come with clean, accessible data.
Industry 4.0, Web 3.0 and the push for decentralisation place data at the core of modern success. With AI and digital transformation an accelerant to economic natural selection, It’s essential to craft a culture centred around quality data-driven decision-making.
That being said, data cleaning is a must in getting the best out of your data.
Awesome! But What is Data Cleaning?
Having data at the centre of any organisation’s decision-making requires a strong combination of multiple data sources.
SImply put: the more inputs you’re able to get, the better your predictions become.
And data is super important, because according to Mckinsey Global Institute:
- Data-driven organisations are 23x more likely to acquire customers;
- Up to 6x as likely to retain customers; and
- Are 19x more likely to be profitable.
But because the data comes from so many sources and in many different forms, there’s plenty of room for error. With everything from XML, to CSV files, as well as text documents and spreadsheets, there is a lot that can go wrong.
Data can be duplicated or mislabeled. It can be incorrect, or broken. And any issues with your data leaves algorithms and models, and thus predictions, inaccurate and unreliable.
So to deal with all of that messy, chaotic data, data cleaning is a massive requirement.
The process focuses on fixing or removing any incorrect, corrupt, wrong format, duplicate, or incomplete data within datasets.
A few Common Issues
Data is almost quite literally everywhere. It’s also been collected for decades from various platforms, sources and outputs. So there are bound to be plenty of issues.
From ancient, slow legacy systems to faster and more effective ones. And now, to the best tech, code and compute that our world has to offer, data has been collecting and moving around for decades. Often, with no standardisation.
This results in a load of issues data-related issues:
Duplicate Data
Data comes in from all directions. Constantly. With it, there’s a lot of duplication, redundancy and overlap.
The problem with that?
It skews analytical results and has a negative impact on machine learning models.
Customer experience comes to be significantly affected by having duplicate profiles, details and transactions. Nagging phone calls and information gaps can be annoying.
You also lose out on potential leads and prospects as marketing and sales teams run into error and brick walls.
Inaccurate Data
Data accuracy is critical to a wide range of industries. Especially highly regulated ones like healthcare, where medicines and prescriptions have to be developed and correctly distributed.
With inaccurate data, you’re not only stuck with an incorrect view of the real world, but you can’t help plan an appropriate response.
To add insult, if your customer data is inaccurate, then you can forget about offering personalised customer experiences. It can even go so far as to damage your marketing campaigns and overall credibility.
With human error, data decay and data drift, there’s no doubt you’ll come across data inaccuracies.
Data Downtime
With heavy reliance on data to power operations and back decision-making, downtimes can be dangerous. And the losses can be horrendous.
Downtimes are short durations when data is either unreliable or simply not ready. Especially during events like infrastructure upgrades and migrations. Running into data downtime has a largely negative effect on business, bringing in customer complaints and half-baked analytical results.
Data downtime can vary from software or hardware changes to migration issues. Data pipelines can be challenging, too. The complexity and magnitude of them take data engineers and data scientists a significant portion of their time.
Best way to deal with it is by monitoring data downtime continuously and minimising error through automated solutions.
Benefits of Having Squeaky Clean Data
Whether you’re using in-house data engineers or reputable data engineering solutions, the benefits that come with clean data are simply great.
A More Efficient Sales Team
Sales teams are meant to be closing. Not stuck doing mundane admin.
By having cleaner data, sales teams don’t have to run through databases to ensure consistency in contact details. With better data, they avoid unnecessary and strenuous admin work, saving on valuable time and resources.
The same goes for things like segmentation and market analysis, where data plays a pivotal role in targeting specific industries, producing accurate insights and improving overall productivity.
Improvements in Customer Experience
Customers are now driven by frictionless experiences. They want it quick and they want it seamless. With more engagement through data-driven, digital platforms (websites, apps and social media), there’s an increased dependence on comfort.
And by simply having marketing and sales aligned with your buyer’s journey, you better allow for your customers to feel seen and heard. You allow them to feel comforted by your brand.
Birthday wishes. Tailor-made messages or emails. Gifts. Special Offers. Having the correct information is critical to adding meaning and value to your interactions with customers. The result is a far more memorable and impactful experience.
Better Marketing Decisions
When your marketing department has access to accurate, clean data, their overall decision-making takes on a massive improvement.
Having a clear view of prospects, leads and opportunities is essential to the formation of engaging experiences. Data adds context. With that context, marketing efforts become more focused on the key areas that matter to customers.
It’s also important from an evaluation standpoint. Your data becomes important to the feedback that your team gets. Knowing whether a campaign, idea or strategy works or doesn’t work is as important as coming up with one in the first place.
The better the data, the better the decision.
Don’t Let Bad Data Bring You Down
Sure. Unclean data is everywhere.
And while it isn’t exactly easy to scrub away all of the grime, it can be simpler.
They specialise in cleaning and commoditising your valuable data, paving the way for better decision-making, higher productivity and more accurate predictions.
