Many organisations invest considerable time and money to implement data collection systems but then fail to continuously monitor and manage the data they collect.
Having considerable volumes of data can be counterproductive and does not mean that it is of relevance or value to your business. Reliable data provides better customer insight to ensure marketing and business decisions are made on relevant facts.
Collecting excessive data is akin to holding excessive inventory stock – it takes up valuable storage and is at risk or decay and obsolescence. More is not always best and if you manage data like effective inventory control then you will reap similar benefits such as reliable information and relevant customer insights.
A regular business has on average up to 50 different data types and this number will continue to grow. Organisations need to stay on top of this proliferation of information to maximise its value and understand the necessity to implement ongoing data cleansing processes.
The data cleansing process
While there is no perfect solution for removing bad data, you should undertake a data cleansing process regularly because good data management helps ensure that customer records are current, accurate and filled with valuable, usable information.
Companies can begin their data cleansing process by analysing information to identify the types of errors and inconsistencies that are occurring. By understanding what is currently happening in a specific area of the organisation to identify issues and recognise where things are going well you are better placed to establish a way forward.
To limit the occurrence of duplication it is good practice to ensure a search for duplicates occurs before entering new data. It is more difficult and time-consuming to match and merge duplicate data once it is in the system.
Creating a consolidated record may require matching existing records from different systems then determining the best way to merge the data. At this point, the most recently accessed records and ones with more completed fields or external reference numbers are likely to produce a higher quality record.
For inventory control, companies would undertake a physical stocktake to gain a true picture of inventory stock and adjust records accordingly. Ensuring customer records are current would require contacting each customer to confirm their details are correct.
The primary cause of bad data is clerical errors made when data is manually entered into systems. By automating processes, you can significantly reduce the occurrence of bad data.
Online inventory management, barcodes and RFID scanners are just some of the many tools available to help automate inventory control and reduce the rate of bad data.
Customer Relationship Management software is another way to automate data collection and cleansing. Integrated loyalty cards scanned at POS provide information on customer purchasing habits and automated emails provide a means to seek customer feedback and check the reliability of customer data.
You can start the data cleansing process during data entry, by creating a consistent format to help identify and prevent duplicates. By establishing a process of standardisation, you are creating clean, aligned data sets that are ready for analysis and to be put it to work.
Investing in a robust system of procedures and standards with regards to how data is collected will improve data consistency and eliminate the risk of duplication.
Common standardisation includes consistent categorisation of inventory stock, only using industry-specific acronyms, standardising dates, removing punctuation and initiating routine checks on data quality. Making changes to data collection forms also ensures you are only gathering relevant information.
When it comes to bad data, the issues invariably occur due to incorrect manual entries, technological glitches, deliberately misleading information from customers, and numerous other reasons. By undertaking a periodic data audit, you can review key customer touch points and assess overall customer experience.
Audits should review how the data is being collected and used including:
- Reviews of the forms and processes used to collect data and eliminating the collection of unnecessary or redundant information
- Ensure the collection of data is consistent across all touchpoints such as websites, social media, advertising and POS
- Have an expiry date on data and creating an alert to notify when it is time to either update or delete out-of-date records
- Maintain standardised definitions and variations so that data entry is of high quality in the first place
The way to turn bad data into good data is to approach it systematically, using processes that result in a unified, consistent view across all areas of the organisation.
Clean and orderly
With your data cleansing process complete you are now better able to customise relevant marketing and promotional activities based on clear customer data. Marketing activities can be integrated with online inventory management to ensure promotional activities support inventory control which, in turn, is the key to exceptional customer service delivery.
5 ways to enhance your data quality
So, how do you ensure that the data you’re using is good enough for your business? We’ll go through five ways to enhance your data quality to make sure you get the best results possible.
1. Automatically validate your data
One of the best ways to create good quality data is to automatically validate it. This means having the system scan for missing, inconsistent and unanticipated information.
Make sure all of the formulas are working and the calculations aren’t broken. This will give you consistency and reassurance that your data is one step closer to being better quality.
2. Minimise the use of free-form text
Try and decrease the amount of free-form text in your system. This included names, addresses, short descriptions and notes.
Let’s say you’re tracking inventory stock. Rather than writing out the wetsuit vendor and style of wetsuit in free-text, give the wetsuit vendor a code like 501 and the style 443. So when you are tracking inventory stock, it would be coded to 501.443 rather than having free form text of the name and style.
Your inventory stock data will be in a better place because human spelling error will be decreased. This will enhance search function accuracy.
For instance, someone could spell wetsuit correctly, but type it out wet-suit, using a hyphen instead of one word.
3. Amalgamate your software systems
If you have a variety of systems tracking your data, it’s relatively likely that these will get mixed up and give you unsatisfactory data.
One of the best tactics to improving data quality is to consolidate all of your data. Bring all of your systems into a single programme or better yet, consider moving to cloud-based systems that integrate with each other. That way it’s easier to control and track what’s going in and what’s out of your system.
4. Put one person in charge
If anyone can make changes in your data software, chances are that they will also make mistakes on your data software. Instead of having your software completely open to everyone, put one administrator in charge of the database.
They will have the overarching power to make changes and the data becomes their responsibility. If anyone wants to make changes in the system, they need to get it approved by the nominated administrator.
5. Run regular reports
Reports are a consistent measure to ensure the reliability of the data. If you have a base measure of reports that you conduct on a regular basis, you can compare these and see if there are any outliers or unexpected pieces of information in the data.