Once you’ve recognized there’s bad data present and you’re ready to make the most of the above benefits, there’s a lot you can do. First, take the time to build a data management system that’s tailored to account for all the problems we’ve listed.
Here’s some of the things you can do:
- Set benchmarks for data validity: Determine specific metrics that will be used to measure data validity. This covers completeness rate, which is the percentage of expected data that’s in a dataset. There’s accuracy rate (i.e. the percentage of data that’s correct) and there’s timeliness rate, which is the amount of time between an event happening and attributes being included in the dataset.
- Improve data collection techniques: Make it easy for customers to share their data with you in several ways. Be upfront about how you plan to use their information, offer incentives (i.e. product discounts), keep data collection forms short and demonstrate that their data will be secure.
- Update existing data: Go through your CRM, ERP and other systems with a fine-tooth comb. Assess all information that you have and see what needs to be changed and plan for how you’re going to update it. For example, data-matching software can match email addresses against databases.
Scrubbing bad data is a given. But there’s no point chasing after the first shiny new data cleansing tool you see if you don’t understand how it works or how it fits your requirements.
Here are some points you’ll want to think about:
- Purpose: Do you need a tool that will provide total data management or are you only cleaning specific types of data? Are you looking to have multiple views of your data or do you need a single source?
- Ease of use: How long are you going to spend training teams on how to use the tool? How long do you think it’ll take to train employees so everyone can achieve the best outcome?
- Advanced features: Basic data cleaning solutions will point out missing data and errors. But some offer advanced data quality checking features (i.e. finding odd symbols, examining fields for incorrect numbers and automatic attribute standardization).
- Connectivity: Determine how easy it is for the data cleaning tool to connect with your existing technology stack. Can it connect to your CRM, email platform, MS Access, CSV/XML files? Can it do so easily or does your IT/technology team have to build new processes to enable this?
- Team effort: Remember that you aren’t just choosing a ‘tool.’ You’re investing in your team and are going to make their lives easier. So, anything new that’s brought in has to be the right fit for them as well. Consult multiple opinions and compare features and benefits from different tools.
Build a specialist team to train others
In some organizations, a Chief Data Officer (CDO) would be appointed to oversee a full data management strategy. But all this does is pile pressure onto the shoulders of one person and does nothing to solve the silo problem, which is a key reason why you hired the CDO in the first place.
It’s far more effective to hire a specialist team or task force with complementary skills. Imagine training a finance CDO, a sales CDO and a marketing CDO. Each of them lives and breathes the culture and language of their departments. They can inspire and upskill their colleagues while collaborating with their fellow CDOs to further improve processes.
This article was originally published on Credit Safe.
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