How to improve data quality in Salesforce - part 1

Identify growth opportunities with accurate reporting dashboards

Siloed and disorganised data are increasingly problematic for businesses. At its best, data can give firms precise and real-time insight into which revenue strategies are working, and (most importantly) which are working best of all. Unfortunately, without a proper software ecosystem and oversight strategy, records can quickly become unwieldy and expensive to handle.

You don’t need to look far to uncover case studies from consulting firms detailing the eye-watering costs of managing huge stores or the lost opportunities from poor analytics.

If you’re considering migrating to Salesforce and intend to make the most of its powerful data capabilities, you’ll need to start by tidying your data. Read on to learn how to improve data quality in Salesforce and start using the world’s best CRM effectively.

Why is quality data useful?

Before we start talking about what makes for high-quality data, why is it important?

Computers are great at processing numbers, so why can’t you just skip ahead to importing your data straight into Salesforce? Taking the time to tidy your data is crucial for three key reasons.

1. The input-output rule

You can only achieve accurate and reliable analysis by providing your software with data that you’re confident with in the first place. Computers are good at processing information quickly and creating visualisations. Unfortunately, they can struggle with validation tasks, like determining whether a certain field should contain all numbers, positive ones only or characters instead.

For example, a person can easily tell that address fields containing ‘UK’, ‘England’ or ‘Great Britain’ all specify the same place (more or less). Yet, (without being told to do so) a computer won’t aggregate these inputs into a single collective area. As a result, you’ll receive sprawling regional reports missing key trends.

Of course, the example above is very general and Salesforce is largely able to handle such tasks. However, it helps to illustrate that you need to ensure your software can handle human error or variation from inputs and process information intelligently.

2. GDPR legislation

GDPR legislation places a number of requirements on the way businesses manage customer records. Governments expect businesses to delete records once they have no more use for them and customers have rights to request any data held on their behaviour. Therefore, you’ll need to make sure your data is easily searchable to fulfil these obligations.

3. Cybersecurity

Tidying and protecting your data with encryption helps to prevent cyber attacks, which unfortunately are an increasingly common occurrence. For example, you may wish to place higher levels of protection on some forms of information (especially personal data). Sorting and protecting your data can help you avoid legal fines and safeguard your reputation and at the same time.

What makes ‘quality’ data?

Just what is ‘high-quality data’? When considering how to improve your data quality in Salesforce, you should primarily aim for the following three features:

  1. Complete: Data values shouldn’t be missing, corrupted, or misleadingly abbreviated.
  2. Consistent: Data should follow standardised formats, applied uniformly across your organisation.
  3. Concise: Data stores shouldn’t contain duplicate records, so analysis and transfer processes remain efficient.

We go into greater depth on how to achieve this level of data quality in the steps outlined below and in part two of this blog series.

How to improve your data quality in Salesforce

Step 1: Assess your current data quality and stores

The first step on how to improve your data quality in Salesforce is understanding your starting point. Doing so will allow you to plan the resources and timeline you’ll need to undertake the later parts of this process.

We’ve included some common sources and signs of what makes for poor quality data so you can begin identifying them in your own organisation.

  • Inconsistent data

Inconsistency is one of the most troublesome issues affecting data quality, as it causes your records to become messy the quickest.

For example, you may find that your staff create abbreviations to help them enter data quickly. However, without a consistent framework, mistakes and deviations become commonplace. Similarly, without mandating input in certain fields, your records can become semipopulated, making direct comparisons and analysis difficult.

We’ll help you create a standardised framework in step 2 to solve these problems.

  • Stale data

Data quality degrades when it isn’t updated or validated regularly.

For example, the data you have on sales leads may be outdated from new people occupying the same positions, meaning old email addresses you have are no use. Similarly, clients’ titles may have changed from recent promotions, so you may be marketing with the wrong audience in mind.

  • Duplicate data

Data stores are further complicated by accidental copies.

At first glance, duplicate data seems harmless. A chart or two may be slightly off, but it won’t cause much damage. However, you can think of data as an inventory of your digital assets. An accurate tally is vital, ensuring your analysis is reliable later on. Similarly, you may be paying for more storage than you need if you have fragmented record stores. Therefore, you may be able to make savings by improving your data quality.

In part two of our series on ‘How to improve data quality in Salesforce’ we’ll discuss how to plan, apply and enforce your data management strategy.

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