Netflix uses data to understand what its audience wants and create blockbuster series.
Uber uses data to move their drivers to areas that have a higher demand.
These are only 2 of the many companies that are using data to stride ahead of the competition. When it comes to being a data-driven organization, collecting data is the easy part. The hard part is drawing meaningful, actionable insights from the data. Poor quality data can cause companies about $9.7 million per year.
So, along with collecting enough data, you must ensure that your data meets high-quality standards. Here are 7 ways to improve your data quality.
1. Prove The Impact Of Data Quality On Business Decisions
Improving data quality takes effort and finances. Thus, before implementing any steps to improve data quality, you need to convince everyone involved about the importance of doing so. Identify a clear link between data assets, business processes and key performance indicators (KPIs). Show the different business departments how existing data quality issues are impacting revenue and the expected results of an improved data quality program.
2. Define The Quality Standards
There is no universal ”’goodquality standard. For example, a local retailer may not need the area code with the phone number but a national retailer will need this detail. Brands with international customers will also need the country code. Similar differences may emerge between different departments in a single organization.
Hence, you need to define what is ”’good’ and the ”’best fit’ for your company. Schedule periodic discussions between business stakeholders and data and analytics leaders to understand the expectations and define these standards.
For example, when the same data is being used by different departments, the data quality expectations may be different. The marketing team may need only names and email addresses but the billing department may also want customer phone numbers.
3. Profile Data Often And Regularly
Companies collect data from many different sources. Data profiling refers to examining data from these sources and summarizing the information. Doing so helps identify potential data quality issues and corrective steps that may be taken to address these points. Regular data profiling helps assess which data quality issues can be rectified at source and which issues can be put away to be dealt with later.
For example, profiling customer contact information may show that customer addresses are being entered without pin codes. This could lead to issues with delivery and be the reason for a high volume of customer complaints. Hence, fixing the data entry form can correct the problem and ease logistics as well as improve customer satisfaction.
It is important to remember that data profiling should not be a one-time activity. It needs to be practiced at regular intervals.
4. Choose Trustworthy Data Sources
Not all the data used by an organization comes from within. Some data is also sourced from third-party databases. Here the rules and authorship of data are not always known. When choosing the data sources, you must ensure that these third-party databases are trustworthy and follow high levels of data governance.
A ”’trust model’ is a better choice than a ”’truth model’. What this means is that rather than taking data as absolute, your organization must decide on its use depending on its origin and jurisdiction.
5. Build Data Quality Dashboards
Every organization that collects and uses data must have a dashboard to monitor its data assets. This may be customized to meet business goals. This will give all stakeholders a comprehensive snapshot of the quality of data being used by them.
Assessing the data held in the past and currently helps stakeholders identify trends in data usage that may be used to improve processes in the future. By comparing data performance over time, your business can tweak necessary processes to improve the outcome and achieve the desired goals.
Data quality dashboards will also show the impact of practices put in place to improve data quality and thus prove their benefits.
6. Establish Data Quality As Part Of The Data Steward’s Responsibilities
Every organization needs a data steward. He/she is responsible for ensuring that data assets meet high-quality standards and are fit for the organization’s needs. This includes the metadata.
You should also define responsibilities to include monitoring, controlling and escalating data quality issues whenever they occur and championing good data management practices. In this way, you can take systematic measures to improve data quality.
You also need to have a team dedicated to improving data quality. This team should have representatives from all the IT departments as well as business users. A collaborative team of this sort keeps efforts from being duplicated, reduces operational costs and maintains a common standard for data quality.
7. Make Data Quality A Point On Board Meeting Agendas
Improving data quality must be a continuous, consistent effort. The results aren’t always distinctly visible so it may get overlooked after some time. To keep this from happening, link data quality initiatives to progress on business projects at board meetings. This is sure to get everyone’s attention and ensure that you have the resources required to continue with your data quality initiatives.
In Conclusion
Using poor quality data affects your profit margins as well as productivity, customer service and your reputation. It’s been said that it costs $1 to keep poor quality data from entering your database, $10 to correct it and $100 to make decisions based on poor quality data. What this proves is that you should address data quality as soon as possible.
When it comes to customer data, you need a data verification tool. Data verification helps verify that the names, street addresses, phone numbers and email addresses in your database are correct, current and formatted correctly so that there are no duplicates created. It could be something as simple as standardizing the way a name is written.
When you figure John Smith, Smith John and J Smith all referred to the same person, you can create more comprehensive customer profiles and make use of the data you hold to give your company an edge over the competition.
