April 29, 2021
April 29, 2021
Data is invaluable. Data defines results. It is necessary for robust business decisions. It is essential that it is accurate.
This blog takes you through the problems that bad data can cause your business in terms of inefficiencies, loss of time and impacts to revenue. You will learn the basics of how you can identify, fix, and prevent bad data, and which is the best option. You will also see some examples of how automating several processes to orchestrate your data cleansing not only provides more usable data, but also real growth benefits to your business.
Bad data is any data within a system that is incorrect, inconsistent, outdated, or missing information because of:
When your organization has bad data, it prevents you from having a complete picture and impacts your business decisions and direction.
Bad data leads to revenue loss through inefficient resource allocation, time spent fixing the problem, and missed opportunities.
"Data quality affects overall labor productivity by as much as 20%"
All companies face bad data at some point.
20-25% of an organization’s contact database becomes outdated every year (2013 State of Inbound Marketing Report).
This data decay costs US companies an estimated $3 trillion annually. According to some industry surveys around 35% of sales reps spend an hour manually fixing, updating, and validating decayed data each day.
If not treated, bad data saps precious time from your sales and marketing teams. The ‘5-minute window’ you have to build a customer relationship and surpass your competitors with speed and strong service becomes unachievable – the effects on your business can be drastic.
If your database is filled with inaccurate, outdated data, leads aren’t routed properly. Sales reps spend valuable time validating and updating data, potentially losing opportunities. According to ZoomInfo, the savings to sales from clean data are up to 27% of their time which is approximately 550 hours per year per rep.
If your processes are not working properly or how you expect, you may have a bad data problem.
Ask yourself these questions:
Your problems may be the result of an outdated database, too many duplicates, unenriched leads or non-standardized data. If left unchecked, your problems can grow exponentially. Reps spend hours to update and reroute leads, or even worse never get those great leads. Extra duplicates may eat up your storage space very quickly and with platforms such as Marketo, storage costs can skyrocket.
Once you locate where your processes are stopping or causing a clog – take direct action before you are drowning in broken data.
Use the following five steps to undertake your data quality audit:
A complete data audit provides you with more knowledge about the quality of your data and the impact it has on your processes and business. Once you have this knowledge you can build a business case for data orchestration in your business. Ensure you select a vendor that can help you solve what are potentially multiple data problems simply and easily within the one platform, while also scaling with you as your data needs change and your business grows.
Engage with your senior leaders so they understand your business specific requirements and support your need for improving the quality of data in your organization.
While having a spotless database should be the end goal for any business, a question you may have is, “Should I just fix my bad data or should I prevent it?” The 1-10-100 rule helps you answer that question.
$1 to prevent data entering your database
$10 to correct data once it is in your database
$100 to do nothing.
If your data is automatically cleansed, deduped, standardized and enriched with quality data at the point of entry in real-time, and channelled through an intelligent routing solution it will flow seamlessly.
Automated data enrichment and cleansing means less manual work for your reps
So…manually retroactively fixing bad data takes time and costs money. $3 Trillion to be exact in the US alone. If bad data is the sickness and the cost of time and money is the cure, then as Benjamin Franklin said,
Organizations that have older databases, have poor record entry processes, that do not identify and merge duplicates in real time, or use poor quality 3rd party data vendors are vulnerable to duplicate records. Inefficient duplicate matching leads to the break-down of processes and increases lead contact time.
Retroactively fixing a duplicate problem with a single major cleanse program or scheduled batch deduplication tasks gives you the opportunity to cleanse your database before embarking on duplicate prevention as data enters in real-time.
Altium Limited discovered their bad data situation when existing accounts were being routed to the new business developers who then were wasting time rerouting.
By applying RingLead’s all-in-one data solution services, they cleansed their database and standardized incoming data so leads could be routed in real-time. They included complex cleansing options, with custom and cross object deduplication to ensure the surviving field information they needed.
Altium successfully used RingLead Cleanse initially – with over 200,000 duplicate records removed their database they saved hundreds of hours in manual processes.
The result mean Altium was able to:
By using Prevent to protect their database ongoing, they have experienced growth within the market that they would not have otherwise.
"Preventing bad data with RingLead had a direct impact on pipeline and revenue growth"
- Iryna Zhuravel
Customer and Marketing Operations - Altium
To prevent bad data as it enters your database you need to consider all data entry points and establish processes to stop the bad data in its tracks. There are real-time solutions that you can orchestrate within in your operations to automate prevention processes at these entry points.
|Feature||Use when you have…|
|As-You-Type prevention|| |
Manual entry points.
Call centres, back office processing, or customer facing services where duplicates and unstandardized data is entered into your Salesforce can benefit from the prevention of duplicates or bad data as it is it typed by using data validation to prevent before saving.
|List Import|| |
Data entry via list import.
Automate list import that simultaneously ensure no duplicate, incomplete or unstandardized data enters your database.
Connected systems where data needs to flow in the right format.
Using API triggers you can standardise, deduplicate, enrich and validate records as they are created or modified.
Operational processes that require correct and specific data formats e.g. routing, segmentation and other automated processes.
Normalization improves the flow of your data and provides improved duplicate matching and merging. Preferably normalize in real-time as records are created or modified.
|Prevention for webforms|| |
Webforms as a data entry point.
Preventing bad data prior to web submission prevents duplicates and updates your before entry to your database ensuring you have the right records with the right information.
Specific data requirements for your operations.
Missing or incomplete data can play havoc with your operations, Correct enrichment at all points of entry improves the flow of your data and provides improved duplicate matching and merging.
Watermark Insights, an educational intelligence company, orchestrate their data using a variety of features. They have been able to deduplicate data entering via webforms & manual entry, create a relationship between leads and accounts and solve their incomplete data challenges. By combining deduplication with RingLead’s lead score add/combining features they have gained accurate scores across their entire database – not only improving the quality of their data but increasing their MQLs.
You think you have bad data. Is your data holding you back, and what does it mean for you?
According to the Experian – 2020 Global Data Management Report, having a healthy data culture means:
This data culture underpins the continuation of trends using automations in business.
"Investing in data analytics software, AI, and machine learning tools should drive greater automation, which in turn will reduce time spent manually analyzing data sets"
You cannot maintain contemporary operations without a foundation of clean data, let alone be able to continue to harness the future technologies of your business.
78% of companies have identified issues pertaining to data within their systems, yet only 24% of organizations have implemented strategies to manage them. With around 28% of current customer and prospect data suspected to be inaccurate in some way, it’s vital for organizations you to be data-driven to gain that competitive advantage.
Investing in a single comprehensive data platform, is the easiest way to manage your data at every stage. Automated orchestration removes unnecessary duplicates in your Salesforce and Marketo, while also preventing duplication at the source. Cleaning dirty data increases your efficiency & accuracy. Having a solution that automates multiple prevention processes and in synergy allows you to solve your data problems of today, and also provides opportunity to scale with your business as you grow.
With seamless Salesforce and Marketo integration, RingLead can help your business today.