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Companies are competing hard to innovate and fasten their products and service delivery given an evolving digital economy. But only a few have successfully achieved this supremacy. According to a survey of 224 companies, four in ten organizations lack any data quality efforts.
Within the chaotic realm of quality data analytics, there’s an easy way out. Just make sure you fix these six most common data quality issues using a coherent assessment plan, and you pave your way for truly qualified data-driven decision making.
What’s Data Quality & how it looks like?
Data quality is a set of measures that tells to what degree data fits the requirement of a business user.
According to Talend (a world leading data health tool), best data quality practices condition on data so that it fulfills six quality standards (also known as quality dimensions). A perfect quality data is when it’s: accurate, consistent among all copies, complete in details, up to date, robust in form, and exists in a standard format.
Human error is the most common source of data quality issues. For example typing errors can lead to inaccurate and non-standard format of data.
Lack of a coherent data strategy in growing organizations creates huge and uncontrolled data quality issues. The existence of data marts can cause each department to use their own version of data, disallowing the rest of the organization to access consistent, complete and current data.
Companies save huge costs
A latest Research by Gartner shows that companies pay an average of $12.9 million every year, repeatedly because of poor data quality. An estimate by MIT Sloan shows that 15-20% of revenue goes wasted as a consequence of decisions based on bad data.
Fortunately, there are tested ways project managers and entrepreneurs can use to achieve best data quality in less time thereby saving them massive costs in the form of time, effort and revenue.
Using Data Quality assessment for Business Success
There are three essential steps in any data quality assessment process. First is the planning stage where data quality analysts work around one fundamental question “How my ideal data would look like?”. The answer lies in when you align the six quality dimensions of the data with your business processes.
For example, in a master data repository, there’s a high probability of human error and thus the accuracy element of data quality is most important here. On the other hand, the products information (intended for an ecommerce webpage) requires the completeness element of the data quality to provide satisfactory information regarding the products.
Six Quality dimensions of Data
- Accuracy
Is information available to business decision makers correct? Human error can cause data inaccuracy creating a picture that depicts an unrealistic scenario. A discrepancy in data may exist in the form of the wrong age and gender.
- Consistency
Consistency in data requires synchronization among all copies of data. When two databases are not synched, they’ll present conflicting information.
- Completeness
Does the data carry all the relevant details? First names, last names and age are important for a flower shop but a fashion business must require gender information.
- Conformity
It’s important for a business to make standards for data formats. This avoids confusion and streamlines data analysis efforts down the line. For example, using both English and American date formats can make analysts spot wrong months and days leading to poor decision making.
- Timeliness
Timeliness of data is how up to date it is. Customer behavior and preferences can change faster than ever now as the competition in the business landscape has increased. It’s important for businesses to collect the most recent data and use the latest version in decision making.
- Integrity
Data integrity defines all relevant relationships between multiple smaller tables so that they could be combined together to create a complete dataset when required. Redundancy in data elements is reduced completely. Smaller tables with primary and foreign key relationships also help preserve data integrity during updation, deletion, and insertion.
Once the planning stage is completed and you’ve identified which dimensions of data to take care of and at which stage of the business process, it’s time to move on to the assessment stage.
Gather around your team that may include: data collectors, data engineers and data analysts. Use the following quality checks to assess data quality dimensions and fix your data accordingly.
Data Quality checks
- Track data accuracy by setting value ranges. This checks scores, values, figures and calculations are within the valid range.
- Enable conformity among data by setting standard formats for each column attribute. Also make sure a given column represents similar data in another table with the same column attribute.
- Consider all important relationships among tables during the logical data model (LDM) stage. Make sure the normalization represents your business process.
- Ensure all data marts are synched together.
- Enable cron jobs for frequent updation of data.
- Perform normalization on data to preserve data integrity and improve efficiency of transactions.
Learn to craft Data Quality on your own!
Learn data quality management with our expert at Dice Analytics. Our Data Warehouse and Business Intelligence course is aimed at equipping you with the core data quality and data analytics skills so that you can enable data-driven business growth. Starting from a business problem, our expert takes you through manipulating data using SQL and building a complete data warehouse.
There are exactly no prerequisites required as we take you through data warehouse and data quality concepts from scratch.
In the end you’ll learn:
- Database and Data warehouse (Teradata) in-depth theoretical knowledge
- Data Manipulation using advance SQL
- Understanding and taking forward a set of business questions
- Understanding column attributes and cleaning the data
- Ensuring column value ranges and conformity to data standards
- Understanding and implementing Logical Data Model (LDM)
- Perform data normalization up to 3NF using SQL
- Set up cron jobs for schedule upload of data
An additional value adding module that offers complete data visualization hands-on experience is provided in the next module on Power BI and Tableau.
Don’t miss out on discounts. View Data Warehouse and Business Intelligence course details now.
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