6 Step Guide to Ensure Quality Data Capture
Leading global companies are aware that efficiently utilizing their data is the key to creating better products, providing a fulfilling customer experience and gaining a competitive advantage. Despite this fact, most of these companies still employ data capture strategies which are not cost effective, scalable and prone to errors.
What every company in real estate, retail, eCommerce, manufacturing, hospitality industry needs to understand is that quality data capture is a multi-step process. It requires organization wide involvement for successful data capture and utilization. When it comes to data capture, everyone assumes employing an effective data capture technique is all it takes to yield the desired results. But as most business have learnt the hard way, that is not the case.
Hence, here I would like to quickly take you through six quick steps on how you can ensure quality data capture within your organization:
1. Comprehensive Data Collection Strategy
Data collection strategy decides how to set out to capture and utilize your customer’s data. But, a successful data capture strategy ensures your company receives high quality customer data with the minimal of efforts.
Strategizing data capture is an organization wide exercise. It starts with understanding the company’s data requirement from CEO, CDO, marketing managers and other relevant stakeholders to implement the project with least operational glitches. Answer these questions to formulate a data collection strategy:
· What kind of data is required?
· What is the state of existing data?
· How to identify new data collection opportunities that suit your business needs?
· What are the ways to understand and guard your customer privacy?
· Do you need to incentivize customers to get data?
· How can you validate the collected information?
Need help to formulate a data collection strategy for your company? Get in touch with us now.
2. Keep Data Quality at the CENTRE
Your customer relationship management or marketing plan is going to be as good as the quality of your data. If the data capture strategy has flaws that let bad data creep in, then the whole exercise is bound to fail. Hence, a special reminder about how important it is to keep data quality at the center of all the planning and implementation of the data capture process.
3. Choose the Right Data Capture Process
To truly reap the benefits of Big Data, selecting a data capture method ideal for your organization is of utmost importance. Data capture methods range from the age old manual data entry to new automation solutions that utilize machine learning and analytics to deliver new levels of operational efficiency.
· Manual Data Entry
In this process, you need to hire manual data entry operators to key in, or double key in; the required data into forms, excel sheets or web based applications. This age old ritual of manually entering information is prone to human errors, is very expensive and time consuming.
· Automation
Rapid developments in data capture is now providing companies with automated solutions which are faster, cheaper and more accurate. Moreover, with the onset of machine learning and AI, a totally new approach of using man + machine to improve data capture is coming to the forefront. Some of the automated data collection solutions are:
a. Word Recognition Technologies
These technologies read the characters from PDF documents or hand written images, convert them into machine text and enter them automatically into the programmed field in an .XLS or .CSV files. It is categorized in three types- Optical Character Recognition, Intelligent Character Recognition (ICR) and Intelligent Word Recognition (IWR).
This technology to collect and enter data has been around for years but companies need to invest a lot of time and money in perfecting these solutions. They need to feed the system numerous documents for the machine to gradually learn and understand the form format and words- eventually leading to error reduction.
b. Magnetic Ink Character Recognition / Magnetic Strip Cards
Banking industry prefers using magnetic ink to speed up check processing. Collecting data from magnetic stripes is also a highly accurate technique to capture information, although its utility is restricted to certain processes only like debit cards and smart cards.
Although very effective when it comes to reducing errors, this technology ends up being highly expensive if applied at a place with a lot of text involved. For example — an insurance form.
c. Mobile Data Capture (MDC)
Users can now click a picture of a document with their mobile, send it to a data capture server which will extract the information from the image. Mobile Data Capture (MDC) significantly reduces the operating cost and is also flexible, scalable and fast.
Although this solution is ‘handy’, it is preferred by smaller companies with lesser data collection requirements.
All in all, the company should choose a data capture solution which captures data faster, has low error rates, eliminates redundancy and is flexible and scalable as per unique needs.
4. Cleanse and Cleanse Again
Data decays overtime.
Customer contact numbers, Email IDs, residence address, office address, job designation and other contact information change over a period of time and this time is not something you can predict. Hence, it is of high priority that once the data is captured it is cleansed and validated on a regular basis.
Data cleansing helps to:
· Identify and remove human errors
· Remove and rectify duplicates
· Complete the missing data
· Remove outdated information
Thus, maintaining the data quality of your company.
To avail data cleansing services for your data management system, get in touch with us now.
5. Remove Discrepancies in the Data Collection Process
Many problems in the data collection process employed by you will creep up at the stage of implementation. I am just mentioning a few below so that you can keep be prepared for some.
· Issues in capturing data from the front desk executives
· Privacy conscious customers reluctant to provide information
· Integration issues with CRM (Customer Relationship Management) and DMS (Document Management System)
· Solution specific errors
These discrepancies need to be dealt with as and when they appear, otherwise the quality of your data will be compromised.
6. Create a Budget Plan for Data Capture
Depending on the solution you choose, it is imperative that the budget for data management may turn out to be high. Convincing the management might not be an easy task. But, on comparing the cost of bad quality data v/s the benefits of high quality data, securing budget is going to be an easy task.
If you have any queries related to ensuring high quality data within your organization, please get in touch with us now.
