Sales Intelligence Blog

The Importance of Clean Data for Sales Intelligence

Today, sales teams are faced with the challenge of extracting maximum value from unprecedented volumes of complex data. Without the help of sales intelligence tools, this can be a huge time-sink and a waste of precious manpower that could be utilized to solve strategic challenges.

sales intelligence data

How big of a problem is “dirty” data? 83% of businesses see data as integral to forming a business strategy. However, 98% of companies believe they are using inaccurate data, while 69% say it continues to undermine customer experience efforts. This is despite 99% of businesses claiming to have a data quality strategy in place. Clearly, there is a lot of room for improvement.

A sales intelligence platform that helps businesses “clean” their data can be immensely helpful in saving time & effort, improving ROI, and even increasing conversions. But what exactly is “clean” data? How can sales intelligence tools clean up your data? And, what are the tangible benefits to your sales processes?

Let’s find out.

What Is Clean Data?

Simply put, data cleaning is the process of preparing your data for use. Whether it’s for analysis or direct use, raw data rarely comes in a form that is immediately fit for efficient and accurate use. It can be messy, inaccurate, incomplete, or misaligned with your data-gathering strategies.

For example, let’s say you have a lead generation system that collects account information on top-of-funnel prospects. Typically, this would consist of data points, like the company or individual name, industry or profession, and contact details, like phone number, email, IM/social accounts, etc.

However, intentionally or unintentionally make mistakes, like leaving some fields empty, inserting the wrong data in an incorrect field, making typos or spelling mistakes, or submitting duplicate entries. This leads to data entries that do not match, are incomplete, and are difficult to use. This is what we call “dirty” data.

Data cleaning involves fixing these types of abnormalities to produce data that are standardized according to your data gathering and storage practices. Clean data is accurate, complete, easy to read, and can be used immediately to achieve business objectives.

What is Involved in Cleaning Sales Intelligence Data?

We can look at the process of cleaning sales intelligence data from two sides. The first is what you should look for to clean data effectively for maximum value. The second is the actual steps or the A->B process of cleaning the data.

Let’s first look at the elements or characteristics that you should look at to consider whether the data is clean. You can do that by asking the following questions:

●          Is the data valid? First, does the data conform to your data-handling practices or rules? Here you can look at various types of format validation, such as data type, range, mandatory/optional, pattern, or cross-field validation.

●          Is the data accurate? Some data errors, like typos, cannot be caught by data validation mechanisms. For example, a phone number with a wrong digit or area code or missing numbers. This might require extra fact-checking or validation against existing data.

●          Is the data standardized? Every organization should have standardized policies that govern how data is represented in their databases. Clean data follow standard formatting for data like dates (e.g., DD/MM/YYYY or MM/DD/YYYY), titles, phone numbers, etc.

●          Is the data complete? Incomplete data entries may be worthless if they omit key data points that allow you to contact leads, personalize sales pitches, or optimize the sales pipeline. Ensuring complete data starts at the collection phase by making specific fields mandatory and maintaining this throughout your pipelines.

Now that we know what to look for and how to fix it, let’s look at the actual process of cleaning up sales intelligence data:

  1. Audit and inspect: First, you need to evaluate your data to ensure that it meets the minimum requirements for collection and handling. A data quality audit will inspect data for inconsistencies and poor formatting, including duplicate, invalid, or missing data. This capability can largely be baked into your sales intelligence system or can be a separate software between your lead generation and sales intelligence systems.
  2. Data cleaning: Now comes the actual process of cleaning your data according to your standards, norms, and practices. Follow the above questions and systematically eliminate any issues. Beyond simply fixing individual entries, you should also aim to solve problems related to incorrect associations between different data sets.
  3. Verify, verify, verify: If you don’t verify that your data cleaning fixed the potential issues, it can cause you to run into bigger problems when you act with 100% certainty using the wrong information. So, you should always incorporate additional rounds of auditing to ensure correctness.
  4. Data profiling and reporting: You also need to analyze and profile your data regularly to have an overview of your sales intelligence data. This gathers statistics on your data, such as the number of entries, size, rate of new entries, the number of clean vs. dirty entries, etc., allowing you to monitor and continually improve your sales intelligence practices. Reports on how you use your data and what value you get from it will also help justify future sales decisions and budgeting.

The Benefits of Maintaining Data Hygiene

Although some benefits of proper data hygiene should already be apparent, let’s review them for good measure:

●          Enhanced decision making: Accurate, timely, and relevant data should inform every decision you make as a sales manager or person.

●          Optimizing marketing campaigns: Accurate and complete data will help you optimize your outreach and sales campaigns to individual clients.

●          Improve efficiency and streamline workflows: Clean data will lead to less clutter, the ability to act faster, and less frustration trying to jumble messy data into place.

●          Maximize resources: Messy data will lead to duplicate entries, and more processing is needed to fix and organize databases. Clean data will use less storage space and lead to smoother intelligent analytics.

Is Your Data Working For You?

Regarding sales intelligence, data can be your biggest ally or your worst enemy. Sticking to good data hygiene practices and making it part of your everyday sales intelligence operations will ensure that your data is working for you and not the other way around.

challenge of extracting maximum value from unprecedented volumes of complex data. Without the help of sales intelligence tools, this can be a huge time-sink and a waste of precious manpower that could be utilized to solve strategic challenges.

How big of a problem is “dirty” data? 83% of businesses see data as integral to forming a business strategy. However, 98% of companies believe they are using inaccurate data, while 69% say it continues to undermine customer experience efforts. This is despite 99% of businesses claiming to have a data quality strategy in place. Clearly, there is a lot of room for improvement.

 

Topic: Sales Intelligence Data