Author: Ruth Cawdron

This blog post is an extension of the Salesforce Trailhead Trail: Prepare Your Data for AI, specifically the module Data Quality.

To follow along in an org, you will need to install a free trial of AddressTools Premium from the AppExchange. If you can’t do that right now, we invite you to read through the steps, check out the screenshots and watch the videos below!

If you’ve landed here from our previous article Why bad data is bad for AI – welcome! If you’ve arrived straight here, you’re welcome to jump right in and let’s learn how to standardise address data in Salesforce!

How to standardise your address data in Salesforce

Without further ado it’s time to dive into our step-by-step guide for making your address data consistent in preparation for AI. Helpfully, Salesforce has already laid out an address data inconsistency problem that we will work through below: 

Source: https://trailhead.salesforce.com/content/learn/modules/data_quality/data_quality_getting_started?trail_id=prepare-your-data-for-ai  

In this scenario, we see a chart of Accounts By State but when we scrutinise the bars, we can see that the data is very inconsistent. California is represented by a number of different values including “Surfin’, USA” “Calif” “California” and “Cali”.

Note: the following steps are completed using the 5* application AddressTools Premium from the Salesforce AppExchange.

So let’s get to work and standardise these inconsistent values!

Select a standardised state format 

There are two formats for us to choose from for this state standardisation task: 

• Full name – California.
• Sub-code value – CA.

For this scenario, we will standardise our inconsistent California values to the sub-code value i.e. CA.

• Go to the Countries tab.
• Choose All.
• Select Edit next to the United States country record.
• Check Use Sub Country Code in State field.
• Select Save.

Now we have set up our United States state values to standardise to our desired format: the sub-code value.

Enable state standardisation for an address block 

It’s time to enable standardisation. Head to the address blocks section to enable the state standardisation for listed address blocks.

• Navigate to App Launcher | AddressTools Administration. 
• From the sidebar, navigate to Address Blocks. 
• Select Edit next to the address block you want to modify.
• Scroll to Global Settings. 
• Check Standardize State. 
• Select Save.

Turn on standardisation in the package

Yes, this is different to step #2! So far we’ve set our standardisation format and enabled our address blocks for state standardisation. Now, we need to enable the master setting for standardisation functionality within the AddressTools package. 

• Go to App Launcher | AddressTools Administration. 
• Select Settings from the sidebar.
• Click the pencil next to Standardization Enabled.
• Tick the Standardization Enabled box. 
• Select Save. 

Add the alternative state names 

So far we’ve set up our solution so that state values in the US will be standardised to the sub country code e.g. CA, TX. But right now this will only work for recognised values I.e. “California” and “Texas”. 

What about these pesky state values like “Cali” and “Surfin’ USA” that we can see on our Accounts report? 

We need to add these values to our Alternative State Names object so that our solution will accept them and standardise them too. 

Video walkthrough: How to add alternative state values

• Go to the Countries Object. 
• Select United States from the Countries List.
• Select Related then select California. 
• Select Related then add a New Alternative State Name. 
• Add each value e.g. “Surfin’, USA”, “Calif”, “Cali”. 
• Select Save.

Now when we enter any of these state values, they’ll all be accepted and standardised as CA. Hello consistent address data!

Video: The results of state standardisation

Add your alternative state values to the installed State object and see how these values are accepted and standardised to the preferred value – in this instance “CA”.

Enjoy consistent address data

And there you have it! In our previous blogpost, Why bad data is bad for AI, we learned about the impact of inaccurate, biased and inconsistent data on AI and then took a closer look at the particular pitfalls of poor address data. In this article, we walked through how to standardise address data with AddressTools, solving Salesforce’s own example of inconsistent “California” state values. 

As AI tools become more accessible, and Salesforce invests more resource in its own AI offerings, the importance of high quality data only grows. 

We hope this has inspired you to continue on your data journey and we’re always on hand to help you tackle your address data in Salesforce. 

Good luck!

Resources 

When it comes to AI, it all starts with the data. If you need a place to start your AI journey, we recommend the Salesforce Trailhead Trail this example was taken from: https://trailhead.salesforce.com/content/learn/trails/prepare-your-data-for-ai  

If you’re looking to skill up, Salesforce offers a certification called AI Associate which is designed for individuals already familiar with Salesforce CRM. You can learn more about it here: https://trailhead.salesforce.com/en/credentials/associate  

If you like the look of how we easily standardised our address data in this article, you can check out AddressTools Premium on the Salesforce AppExchange and get started with a free 2-week trial: https://appexchange.salesforce.com/appxListingDetail?listingId=a0N30000002zt9uEAA 

PS. This post was not written by AI 😉

This blog post is an extension of the Salesforce Trailhead Trail: Prepare Your Data for AI, specifically the module Data Quality.

In this article we’re going to take a look at data quality in relation to AI – why bad data affects AI and where to begin when it comes to preparing your data for using AI in the future.

Salesforce Trailhead’s module lays out the landscape clearly: “Data quality plays a major role in shaping the outcomes and reliability of AI systems.” An article in Forbes from February 2023 puts this even more seriously: “Getting highly accurate AI model outputs relies on one thing—good quality data as the input.” 

So, data quality is not simply beneficial for the successful use of AI, it’s fundamental.

Why does bad data affect AI?

Let’s take a look at three factors contributing to poor data quality, and how these examples of bad data affect AI. At ProvenWorks, we’re experts in address data, so we’ll keep our case studies address related! Then we’ll spend the rest of this article diving into the third factor – inconsistent data.

Inaccurate data 

One obvious statement we’ll tackle first is that inaccurate data negatively affects the resulting accuracy of AI. AI models and machine learning models are dependent on high quality data if they are to perform well. If poor data is used to train AI, this will inevitably lead to inaccurate analysis and unreliable decisions. 

Case study: how inaccurate address data affects AI 

Let’s consider a practical example in the world of deliveries. For a package delivery company relying heavily on AI for optimising routes and delivery time estimates, the impact of inaccurate data is evident. Outdated or poor-quality address information within their database can lead to highly inefficient delivery routes, resulting in wasted time and fuel for drivers. Customers may experience delays or even non-delivery due to incorrect addresses, affecting the company’s reputation and potentially causing the loss of future business.

On top of this, inaccurate data also skews estimated delivery times, leading to customer dissatisfaction and damaging the delivery company’s own trust in AI technologies.

This case underscores the critical importance of prioritising data quality in AI projects, as it not only affects performance but also trust and adoption of these valuable technologies.

Biased data 

Consider the breadth of data from studies and research that is available for an AI system. What happens when this research used to train an AI system is not representative of the real world? If that sounds dramatic, consider recruitment firms that train AI with historical data that has an imbalanced ratio towards a particular sex, race, religion or sexual orientation. Data that is based off, or comes from, one majority group will inevitably affect machine learning.

Case study: how biased address data affects AI

Imagine an e-commerce company leveraging AI to make decisions on marketing strategies and product recommendations. If the company’s address data is collected predominantly from affluent neighborhoods, the AI system might recommend specific luxury products to all its customers, assuming that everyone has similar buying habits to those in affluent areas. This biased data leads to a biased representation of inaccurate and out-of-touch perceptions of customer preferences. 

Again, we see how poor data quality not only results in the business losing possible revenue but also how it damages trust in the company by alienating entire segments of its customer base.

Inconsistent data 

In general, consistency is crucial for data quality. With consistency comes patterns and with patterns come predictions. When an AI model is trained using inconsistent data, the model might be unable to identify patterns or generate precise predictions. O’Reilly’s The State of Data Quality in 2020 survey found that the most common reason AI and ML fail in the marketing sector is due to too many data sources and inconsistent data. The importance of creating greater consistency across data values, taxonomies, data structures and meta tags is clear if AI is to succeed. 

Case study: how inconsistent address data affects AI

Helpfully, Salesforce lays out its own example of inconsistent address data in the Trailhead Trail: Prepare Your Data for AI, specifically the module Data Quality.

In this scenario, we see a chart of Accounts By State but when we scrutinise the bars, we can see that the data is very inconsistent. California is represented by a number of different values including “Surfin’, USA”, “Calif”, “California” and “Cali”. 

How are humans, let alone AI, able to determine the true record count, and therefore business value, of California accounts when the data is so inconsistent?! 

Let’s dive deeper into this idea of data consistency. 

Consistency is key, but what actually is it?

Data consistency is a crucial attribute of data quality so it’s important we understand this dimension before we go about fixing it. 

Salesforce introduces the idea of consistent data in its Data Quality module: 

Source: https://trailhead.salesforce.com/content/learn/modules/data_quality/data_quality_assess_your_data  

As Salesforce implies above, consistency covers a range of ideas from formatting and spelling to language and taxonomy. For the rest of this article, we’re going to examine data consistency by focusing on the process of standardisation. Below we will look specifically at an example of standardisation – standardisation of address data.

What is standardisation?

Standardisation is the process of making something conform to a standard. Address standardisation therefore is the process of converting multiple known values to a single predetermined format. For example, “United States”, “USA”, “US”, and “United States of America” can each be standardised to “US”. As a result of standardisation, our data becomes consistent! 

So, standardisation is a crucial piece of functionality for organisations who want to use AI in relation to their country and state data. Afterall, we know by now that data consistency is key! 

But how do we standardise this data? 

Standardise your address data for AI 

How do we turn this report into something clean, consistent and useful for our business and for AI? 

There’s no doubt that consistent data is key for successfully leveraging AI. In our next article, we’ll take Salesforce’s own case study of inconsistent state data and walk through step-by-step how you can use AddressTools to solve the problem of these inconsistent California values.

Resources

When it comes to AI, it all starts with the data. If you need a place to start your AI journey, we recommend the Salesforce Trailhead Trail that the above example was taken from: https://trailhead.salesforce.com/content/learn/trails/prepare-your-data-for-ai  

If you’re looking to skill up, Salesforce offers a certification called AI Associate which is designed for individuals already familiar with Salesforce CRM. You can learn more about it here: https://trailhead.salesforce.com/en/credentials/associate  

Now you know that bad data affects AI, it’s time to consider your own data. If you like the look of how we easily we might standardise our address data in this article, you can check out AddressTools Premium on the Salesforce AppExchange and get started with a free 2-week trial: https://appexchange.salesforce.com/appxListingDetail?listingId=a0N30000002zt9uEAA 

PS. This post was not written by AI 😉