5 Brilliant Strategies To Improve Your Data Quality
The craze over innovative “everything” is pushing enterprises in a more data- driven direction. Organizations are now performing complex analysis on their data. It helps them analyze the market trends, develop streamlined operations, and enhance the customer experience.
This is a no brainer that quality data will lead to beneficial information and appropriate insights for your organization. But, obtaining high-quality data is not an easy task if you have no clue where to start.
Let's have a look at the Top 5 Strategies to improve your Data Quality:
1. Accuracy
To ensure and sustain Data Quality you need to be accurate and precise. If you're struggling with how to secure accuracy, see if your data have these 5 traits: Accuracy, Completeness, Reliability, Relevance, Timeliness
2. Quality
Data quality is crucial — it assesses whether information can serve its purpose in a particular context (such as data analysis, for example).
Data that is appropriate for use with one application may not be fit for use with another. It is fully dependant on the scale, accuracy, and extent of the data set, as well as the quality of other data sets to be used.
The recent Spatial Data Transfer Standard (SDTS) identifies five components to data quality definitions. These are: Lineage, Positional Accuracy, Attribute Accuracy, Logical Consistency
Lineage: The lineage of data is concerned with historical and compilation aspects of the data such as the:
- source of the data
- content of the data
- data capture specifications
- geographic coverage of the data
- compilation method of the data, e.g. digitizing versus scanned
Positional Accuracy: The identification of positional accuracy is important. This includes consideration of inherent error (source error) and operational error (introduced error).
Attribute Accuracy: Consideration of the accuracy of attributes also helps to define the quality of the data. This quality component concerns the identification of the reliability, or level of purity (homogeneity), in a data set.
Logical Consistency: This component is concerned with determining the faithfulness of the data structure for a data set. This typically involves spatial data inconsistencies such as incorrect line intersections, duplicate lines or boundaries, or gaps in lines. These are referred to as spatial or topological errors.
3. Cleanse data regularly
Dirty data is perhaps the biggest culprit of low-quality data and poor data analysis. Data cleansing is a process in which you go through all of the data within a database and either remove or update information that is incomplete, improperly formatted, duplicated, or irrelevant. Now you might be wondering how to start data cleansing process:
Make Use Of Functions: It can be difficult to clean up every single error or outdated piece of data manually. When working with your spreadsheet, make use of functions and let your program work for you:
If you are using Microsoft Excel (I hope you do), there are many functions to choose from that will do some of the “cleansing” for you.
What about a tutorial? Look no further, we got you covered!:
4. Segment data for analysis
Data segmentation is how you divide and organize your data into defined groups, so you can sort through it and view it more easily.
If you’re not sure whether to spend more time on data segmentation, then this case study from the Harvard Business Review could help change your mind.
How can you apply segmentation to your data?
To implement the perfect kind of Data Segmentation and to communicate more effectively with your target group requires a blend between having the right processes and technology in place (such as data quality tools and customer data validation).
A key requirement of Data Segmentation is high-quality data, in terms of it being both accurate and does not lack basic information.
Another challenge with data segmentation is the lack of data in general, which in most cases is a false alarm. If you have paying customers you definitely figure with customer data as well. If you’re a startup without any customers and a low amount of traffic, then a lack of data will clearly be a challenge. But, you can ensure your sales outreach is as personalized as possible by segmenting your B2B Data Lists.
5. Reviewing Data Quality
Whilst you may have put in place good practice to maintain a high standard in the quality of data, it is still important to carry out periodic reviews. You can ensure that any rules/guidelines deployed are still valid and being used. For example, it may be worth verifying that the mandatory fields for record entry are still valid. As the business evolves, some of those fields may now be redundant.
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Training and Reminding
Whether your data entry standards are formalized or not, everyone should be made aware of the procedures that the company wishes to follow.
The adoption of procedures will be enhanced if personnel can appreciate the benefits to themselves and the business, so highlight the benefits. Include the education on these procedures in the training plan of new personnel and provide feedback, to all concerned, following your scheduled data quality review. If you're a Data Scientist make sure to check up on your colleagues to see if there's room for any improvement in terms of Data Quality.
These 5+1 Strategies might help you in the next meeting in pursuit of better Data Quality in your organization! Good luck and let us know if you find these tips valuable :)
Thank you for your time ❤
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