Standardize data in 4 steps: The secret to data that flows

Updated by John Kosturos on September 23, 2020

The 4 Steps to Data Standardization

Technological changes are disrupting the market equilibrium. Big data, data analytics, artificial intelligence (AI) and the internet of things (IoT) are transforming how organizations relate to and engage with their customers.

Organizations are being forced to adapt.

Throughout this disruption, we need to find ways for data to be structured in a single format so that it can be analyzed and utilized in multiple programs such as our analytics and AI tools, CRMs, and marketing automation platforms (MAPs).

Standardization is a key component to being able to pull data in from these multiple systems into a single usable format. Also known as normalization, it groups similar values into one common value for a meaningful starting point for processing, distributing, actioning, and analyzing data so it can flow through your business solutions and arrive at the right destination for the right action at the right time.

Leonardo da Vinci said that, ‘Water is the driving force of all nature.’

I say, ‘Data is the driving force for ABM.’

Data needs to flow through your technical ecosystem – Salesforce, Marketo and their connected apps and platforms – not only to enable your sales and marketing operations, but to also support customer retention and success.

Data needs to reach its consumer, either a program or a human; clean, hygienic, and fit for purpose. Data must be cleaned, standardized, enriched, and segmented for effective campaign execution and analysis leading to successful outcomes and revenue generation.

While data standardization should be undertaken as soon as possible on, or even before entry, normalization and standardization needs to be an ongoing activity within your sales and marketing operations.

Why is data standardization important?

You’re thinking, is standardization really that important? It seems to me to just be formatting….

Like with water, without continuous and effective processing for our data, we would receive a sludgy mess when we turn on the tap.

Clean data that flows through our technology stack starts with standardization.

If your sales and marketing ecosystem does not have a foundation of standardized data, formulas will fail, errors will become the norm and the progress of important business processes will slow if data doesn’t fit the platform or application taxonomy.


Data without standardization means the following for your business:

  • Multi-platform/application inefficiency or even failure
  • Poor lead scoring, segmentation, and routing
  • Increased manual processes through reduced ability for data to flow through automations costing time and money
  • Duplication of records
  • Poor marketing attribution.

All these factors combine to result in lost opportunities, ROI, and revenue.

After standardization data can be filtered and segmented to create meaningful action and outcomes for your business like:

  • Seamless and efficient flow of valuable data through your sales and marketing applications and platforms
  • Improved segmentation, lead scoring and routing
  • Effective personalization with tailored content to target segments
  • Improved analytics to track success, spend, ROI or attribution
  • Streamlined data flow to BI and AI tools.

The 4 steps to standardization

1. Understand your data and what you need it for

You need to analyse your processes and work back to understand what role the data plays at what point. Ask a lot of questions….

‘Is the data in this field useful, or is it redundant?’

‘Is this data consumed and who consumes it, what do they do with it?’

‘Does this data help make decisions, does it create action, can we make it more meaningful?’

‘Is it used by another platform/application; are there specific requirements it needs to meet to flow through?’

Establish if data can be grouped so that you can normalize large sets of data consistently e.g. State. Determine if you will use the data to infer information e.g. grouping zip codes to form a region and if the structure of the data will impact your ability to do that.

Once you have identified valuable fields and the data usage, consider if there are significant gaps in field completion, or is there specific data you need that you don’t have? If you don’t have enough completed fields or don’t have specific data, consider appending or enriching before normalization to gain the data you need.

The 4 Steps to Data Standardization

Below are some common fields that are often normalized for specific objects. While account information is not natively available on the lead in Salesforce (shown in purple), this becomes available if you use lead-to-account linking from a data orchestration provider like RingLead.

The 4 Steps to Data Standardization

The fields that you normalize are determined by the data needs identified thorough analysis of the processes, platforms, and applications within your technology ecosystem as well as end-user requirements.

2. Understand your data entry points

The complexity of today’s marketing and sales operations means that data enters from many points. These include marketing automation platforms like Marketo, website web forms, emails, 3rd party vendor enrichment data, purchased lead lists, in-house lists from conventions and tradeshows and manual entry. Each of these methods can treat the data differently so you may end up with the following variations on a single company name:

  • Ring Lead
  • RingLead
  • Ringlead
  • RingLead Ltd.
  • RingLead Inc.

This can lead to multiple account records that you cannot exact match for deduplication and rep updates made across multiple records, eroding your single source of truth and trust in your data.

3. Define the data standards that will give you the greatest flow

Data such as job title, industry, state, country are often used in several different scenarios across applications, so accuracy and consistency are essential. Here are some examples of how the data may be used.

The 4 Steps to Data Standardization

Here you can also see there are often several ways you can standardize common data.

Finally consider if are you using field data to communicate externally, and if the data needs to present in a certain way. You don’t want to send content that shouts by being in ALL CAPS, so think about the end-point.

When selecting what standard you want to use for your data, ensure you understand what you are using it for and what applications and end-users have requirements for that data. Then group and standardize your data to meet those needs

The 4 Steps to Data Standardization

4. Normalize your data with a data orchestration platform

While Salesforce does have native normalization, it relies on validation to prevent entry of unstandardized data. This can become frustrating when entering data and can slow down, or even halt processes when the data doesn’t ‘fit’. Fortunately, there are many ways you can easily normalize your data and undertake other data hygiene processes in synergy by using a data orchestration platform like RingLead.

Using batches and list imports

Batch or Mass Update normalization is a great way to standardize the data you already have in your platform.

Pre-built batch normalization templates with an easy to use interfaces allow you to unify your data to a standard format. You can customize them and use filters, field mapping and normalization rules to standardize case and format, fix typos and in-field data errors.

The 4 Steps to Data Standardization

Common use cases for batch normalization include:

  • Normalizing phone numbers to ensure auto-diallers work
  • Standardizing states to a two-digit ISO code or to be fully spelt out e.g. California = CA
  • Normalize company names to be fully spelled out GE = General Electric
  • Fix case issues in names and company names – no more shouting in emails
  • Adding string values to fields.

The List Import feature is great for importing and normalizing lead lists, trade show lists or 3rd party data lists.

The upload of data from external sources should be simple and easy whether uploading a lead list from a data provider or a trade show list. Batch normalization using List Import

features such as RingLead’s allows you to map, standardize, and merge list files to align with your existing dataset, so field specific updates can occur simultaneously and be saved for future use.

Normalizing in real-time

Where data orchestration creates enormous value is its ability to perform functions in real-time in synchronisation with other features. For example, with RingLead you can enrich using 3rd Party data automation from a Data Exchange vendor directly into your Salesforce or MAP with it standardized and segmented in real-time. Likewise, your incoming web submissions can be normalized and enriched in real-time even before entry into your platforms.

Incoming records from any other entry point such as manual entry or email can be standardized and normalized into fit your unique business requirements using rules as records are created or modified without the need for restrictive in-field validation.

The 4 Steps to Data Standardization

A streamlined sales and marketing ecosystem begins with data normalization…

Once you have set standards and normalized data within your database, it is now in a state ready to flow through your applications, platforms, and processes. Normalized data flowing through a data orchestration platform enables:

  • Automatic segmentation and routing
  • Automatic identification and merging of duplicates
  • Targeted, personalised and professional content
  • Accurate analytics.
The 4 Steps to Data Standardization

Contact us see how you can normalize your data with RingLead and harness the synergy of an end-to-end data orchestration platform in your business.

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