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Let’s Talk Dirty About Data

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When you hear the words dirt or pollution, your mind probably jumps to all the plastic bags, landfills, and emissions from vehicle engines and factories. Sure, these are all killing our planet slowly and we definitely need to do something about it ASAP. However, that’s a battle for another day – and we will be picking bones!!!

Today though, we’re talking about the kind of pollution that could kill your business – Dirty Data. Unfortunately, keeping your database squeaky clean is not as simple as picking up litter along the freeway. To adequately tackle your dirty data problem, you need to define exactly what constitutes the filth in your system and then root it out. Now, I do have to warn you – it’s virtually impossible to completely remove dirty data from any source. But don’t fret; you came to the right place to kick start your spring cleaning.

In this segment, Veloxy delves head first into the muck to define the 7 types of dirty data most likely polluting your database and all the hygiene practices you should use to combat each type. So grab your soap and scrubbers people, because Veloxy is about to clean house.

What is Dirty Data?

Typically, most businesses are inundated with so much data that it’s easy to ignore the fact there’s more dirty data than erroneous ones and zeros. Looking at all the Google definitions out there, they call dirty data incomplete, inaccurate, duplicate or inconsistent. However, there’s much more to it than that.

According to Experian reports, companies across the globe feel that as much as 26% of their data is dirty. This translates to massive losses which can soar to over 20% of your business revenue and over $3 trillion annually to the US economy. Anyone who’s ever dealt with dirty data knows just how frustrating it can get. So, where does dirty data come from? According to the same source above, human error influences over 60% of dirty data. The rest is covered by poor interdepartmental communication and inadequate data strategies.

The most shocking part is that dirty data can remain hidden for years making it even more difficult to tackle when actually found. Unfortunately, almost 60% of businesses find out about dirty data when it’s reported by prospects and customers – a very poor way to track and solve essential issues. Fortunately, we’ll show you how to catch all these issues, carry out data cleansing practices and apply strong data governance as maintenance. Soon enough, you’ll be generating over 70% more revenue with your crispy clean data.

Types of Dirty Data

1. Inaccurate Data

Gathering consumer data is all about gaining a better understanding of your customers to inform other strategic decisions. However, all this depends on the collected data being accurate and complete. Research shows almost 70% of fortune 500 companies report that inaccurate data undermines their efforts.

Dirty data leads to decisions with drastic results for any business. Incorrect data can be good data that is stored in the wrong location e.g. numerical values in text fields. However, it’s more common to have factually incorrect information such as fake email addresses.

How to Clean Inaccurate Data

The first step towards combating inaccurate data is keeping track of all data entry points and diagnosing the cause. If external data sources are the problem, seeking an external solution might be the best option for peak accuracy. Some services allow you to override dirty data with clean data sourced from reliable vendors.

2. Insecure Data

Privacy and data security laws are always being drafted left and right giving businesses additional financial incentive to follow them to a tee. Add in the steep fines for non-compliance and insecure data is becoming one of the most dangerous types of dirty data to have lying around.

Major data privacy laws include:

Without proper database hygiene, staying within these strict regulations becomes almost impossible.

How to Stay Within Data Privacy Regulations

If your database is disorderly, that’s the most likely candidate to house insecure data. You can implement the following data hygiene practices to combat insecure data:

– Merge duplicates to prevent fragmented profiles

– Delete unusable and outdated records

– Consolidate your stack

– Automate lead-to-account linking

3. Duplicate Data

Here’s the worst offender of data pollution that beats the rest combined. Duplicates form in a number of ways such as 3rd party connectors, manual entry, data migration via exchanges, and batch imports. The most common duplicates are usually accounts, contacts and leads.

Polluting your database with duplicate data causes inflated storage, skewed metrics and analytics, poor software adoption, decreased ROI on CRM and marketing automation systems. Duplicates have no place in any data driven organization.

How to Clean and Prevent Duplicates

Long before the era of mass data accumulation, manpower alone was enough to merge duplicates and link leads to accounts. Today, you’ve got automated solutions for detecting and merging these duplicates.

4. Outdated Dirty Data

Ever come across a report that looks incredibly promising only to find out the info is several years old and all but irrelevant? Yeah, outdated data is one of the reasons why it’s virtually impossible to achieve 100% clean data on all your systems.

Data can become outdated when organizations rebrand or software systems evolve past their previous iterations. This is the nature of the modern digital ecosystem. Change is incredibly rapid. Any business that needs to be able to trust their data has to make sure it’s fresh and upto date before using it for analytics and decision making.

How to Keep Your Data Fresh

For one, you have to purge your databases of records created before a certain date or event. Likewise, data enrichment can also solve pitfalls of outdated customer records by appending fields with newer info.

5. Too Much Data

That’s right people – turns out even in data collection, there’s such a thing as TMI (too much information). Data hoarding is a real problem. Sure, you might not find yourself in a TLC show for hoarding data, but it’s an often overlooked issue that could become a huge issue in many organizations.  

Data hoarding causes issues such as inflated record counts, slower data exchange, failure to stay within storage compliance limits among others. Maintaining a sleek yet extensive database is a major part of data hygiene. It will help drive alignment between departments and improving accessibility throughout your entire business.

How to Thin Your Database

Although too much data might seem like an advantage, a large portion of the data simply isn’t usable. This translates to your team wasting a lot of time digging through all that data to find what they want. Outdated data and hoarded data go hand in hand so it’s easy to solve both in one fell swoop.

Dirty Data - The Final Word

There you have it folks; all the different types of dirty data in one place as well as what you can do to clean it up. Take the time to focus on your data and how it looks before piling more on. You’ll find that it’s easier to clean your data early on before you have years and years worth of it to sift through.

And lastly, make sure you grab the right tools to help with data updating and management. When it comes to Salesforce CRM, nothing beats Veloxy sales assistant. Veloxy Mobile and Veloxy Engage automatically log actions such as calls, emails, replies, and other relevant data making it painless to collect and store customer data as you go. Likewise, Veloxy feeds your sales rep with the right data at the right time based on their context, location, and upcoming events so they can focus on more productive tasks. Get Veloxy today and start your journey to 100% crispy clean databases.

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Samir Majumdar

Samir Majumdar

Samir is the CEO and Co-founder of Veloxy. After spending 20+ years creating corporate systems, boosting revenue, and eliminating inefficiencies, Samir started Veloxy to help sales professionals shorten sales cycles, accelerate pipelines, and close more deals.

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