Over the past few years data has become the currency of business, companies are even giving away products for free – just for personal data! But not all data is created equal. So, how do you distinguish your company’s $100 data from $1 data?
There are plenty of factors that go into the value of your business’ data, depending on the sort of information your company wants.
It could be internal operations data, which can affect leadership decision-making or client data, which can affect customer satisfaction.
No matter why the data is valuable to your organization, in order to get the most value out of it, it needs to be clean and clear. But what does “clean” data look like?
According to this analysis, conducted by Siemens, any data cleaning technique should satisfy several requirements:
- Should detect and remove all major errors and inconsistencies both in individual data sources and when integrating multiple sources.
- Should be supported by tools to limit manual inspection and programming effort and be extensible to easily cover additional sources
- Should not be performed in isolation but together with schema-related data transformations based on comprehensive metadata
Let’s try to translate that. Into English. When cleaning data, you should:
- Identify and remove all major errors and inconsistencies in data and the data sources
- Limit the possibility for human error and make it easy to integrate new data sources
- Make sure you’re aware of how your data cleansing will affect the rest of the data or databases that it’s tied to.
Sounds like a lot of work, yeah? Well, if you’ve ever had to work with unorganized, inconsistent, or inaccurate data you know the legwork needed to keep data clean is worth the time and effort.
If you haven’t, here are a few more reasons why “clean” data is absolutely worth the effort:
1. Improved Decision Making
The more information you have the easier it is to make decisions, that’s not a complicated concept. But when leadership can’t trust the information it’s given – well you might be better off having no information at all, instead of having data that could steer you in the wrong direction.
2. Costs Saving
Let’s take, for example, a manufacturing facility. If the data regarding inventory numbers is inaccurate it can lead to inaccurate stock orders. Now, extrapolate that over a year and you could be looking at hundreds of dollars in money your company is throwing away. (That manufacturer should probably read our article on symptoms of an unhealthy facility)
Or not just hundreds, it might cost you millions. In 2013, Gartner surveyed a wide range of companies and found that poor data hygiene was costing them $14.2 million a year. This was in 2013. In 2019, companies are gathering more data than ever.
3. Time Savings/ Loss of Productivity
We know time is money. So, having to sift through a cluttered database or unorganized CRM will cost your organization. If you plan on using certain sets of data in the future, the time it takes to organize and cleanse data will pay for itself.
4. Customer Satisfaction
B2C organizations pile up tons of customer data, so keeping your client’s data accurate and consistent can be the difference between a satisfied customer and a customer abandoning your services.
Also, if a customer is willing to trust a company with their data, it’s up to that company to do everything possible to protect and maintain the integrity of that data. If your organization deals with customers or is based in an EU country, you may also be subject to the legal bindings of the General Data Protection Regulations (GDPR).
5. Missed Opportunities
This might be the most obvious of benefits, gathering data isn’t just for keeping a repository of records, it can be used to gain distinct advantages as a company. For example, if a marketing research team conducts a survey and discovers that a new prototype product would be adopted by a majority of users – that’s data creating opportunities. Alternatively, if the survey conducted by the marketing research team returns with inconsistent and inaccurate data or data that can’t be easily analyzed – that’s a missed opportunity.
Cleaning data is a bit like exercising, it sounds terrible in theory and isn’t all that fun in practice, but the benefits are extraordinary. It could grow your business, increase profits, increase customer satisfaction and even improve productivity. Actually, that sounds way more rewarding than working off those 4 extra slices of pizza from Saturday night.
Written by Steven Garcia