Why Your Business Team Needs a Self-Service Data Preparation Tool to Manage Customer Data

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Why Your Business Team Needs a Self-Service Data Preparation Tool to Manage Customer Data

Self-service data preparation tools are the future. Empower your business teams to manage customer data without relying on IT or complex ETL processes.

· Big Data Zone ·
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Before you continue to read the rest of this article, take a moment, log in to your CRM, or open, and scan the data columns. 

Does it look like this? 

 Advanced issue found


Poor Customer Data: Image Source: Data Ladder

If it's a resounding yes, you have a bad data crisis and your business is at risk of making flawed decisions if you continue using this data. 

Wait. Before you call up IT or your marketing lead and hold them accountable, I want you to reflect and think again. 

Your team is already aware of this problem. They're working with this data every day. Chances are one of your junior sales or marketing reps is actually fixing this data on a spreadsheet. Or the new data analyst you've hired to help you with insights and BI is spending 80% of his/her time cleaning up this data. 

If this is the scenario, your teams are not operationally efficient and they are not equipped with the right data preparation software. Worse, they are fixing data based on gut and gumption than on established standards. The fixes are at best superficial. They are not able to remove duplicates like the above or validate addresses or combine different data to get much-needed insights. 

 It's time you sit down with them to understand the problem and how you can empower them with resources, self-service data preparation tools & leadership support. 

Keep reading this post and I'll show you how. 

1. Assessing and Acknowledging the Bad Data Problem

The first step to redemption starts with discovery! 

Ok, a bit dramatic, but you get the point. 

You cannot solve what you don't know. In the case of bad data, your teams know it but they aren't aware of the full impact of the problem. In rare cases, even if they are aware, they don't have the right tools to solve the problem. And if they can't solve the problem, they'll learn to ignore it. 

The outcome? Embarrassing mistakes. Annoyed customers. Poor operational efficiency. 

How do you know what is bad data? Simple. 

Any data that has the following problems is bad data (refer to the image above again): 

  • Incomplete information: Missing phone numbers, ZIP codes, titles, names etc. 
  • Inaccurate information:  Misspelled names, typos, abbreviations, wrong titles etc. 
  • Invalid information: Contact data that does not exist or has not been validated. 
  • Duplicate information: Records of one entity duplicated several times.
  • Disparate information: Different versions or records of the same entity stored in different ways across different systems. For example, in the ERP,  it's John Smith. In the CRM, it's John.S

Got a headache already? Yea, bad data does that to you. Imagine being the one having to clean this data up every day! 

Bad data has a ripple effect that companies don't realize until it affects their business goals. So before you take any remedial steps, try to understand the scope and source of the problem, its impact on your day-to-day operations, and how your teams are handling the matter. 

2. Understanding the Data Preparation Process 

Before data is used, it must be prepared - meaning, the typos must be fixed, the format must be aligned (Street vs str vs ST), the duplicates removed, and any disparate data must be consolidated. 

By definition, data preparation is the process of cleaning, organizing, and transforming your data into a format that can be used for the data's intended purpose. 

This cleanup process is performed in multiple ways. Companies that use data warehouses use ETL - Extraction, Transform, Load to make this data eligible for insights and analytics. 

In recent years though, data preparation has overtaken ETL. While there are many reasons to that, one key reason is that ETL is too complex and too outdated a method to manage modern day's data needs. Moreover, ETL is highly restricted to the IT department's expertise, making it impossible for business users to work on their data. 

If you're wondering why the IT team should not work on business data, here's an excerpt from the Harvard Business Review explaining the reason well. 

“The two most important moments in a piece of data’s lifetime are the moment it is created and the moment it is used. Most of these moments don’t occur in IT. They occur in the trenches, when a salesperson signs up a new customer; in middle management, as a group struggles to understand and improve market share; in the analytics group, when a data scientist is seeking a new discovery in big data; and in an executive’s office, as she works the numbers to decide whether now is the time to add staff. The really interesting and important moments for data occur in the business, not in IT.”

Thomas. C., Harvard Business Review

If you'd still like to know more about who's responsible for data quality, I've covered it in detail here. 

Today, data preparation is done via specialized tools that allow business users to clean, organize and transform their data *without* relying on the IT team. 

Enter self-service data preparation tools. 

3. What Are Self-Service Data Preparation Tools? 

There's data preparation. 

Then there's self-service data preparation. 

The difference between the two is only a matter of your choice of tool. 

There are data preparation tools that require users to know a special programming language (for instance, SAP) or to have technical knowledge of relational databases. These tools are again designed for IT users to work on large, complex databases. 

Self-service data preparation tools, on the other hand, are designed for everyone. You could be a business user, a data analyst, an IT expert and you can still use these tools to make the most out of your data. 

The key component of a self-service tool is its user-friendly drag and drop or click-based interface. You don't need any programming language or technical knowledge to use these tools, which is why they are perfect for business users. 

4. So What Do These Tools Do and Why Do Business Users Need Them? 

These tools perform complex data preparation functions with little to no fuss. But let me be precise. 

A self-service preparation tool will allow your user to: 

1. Profile and assess the quality of your data: How would you know the percentage of errors in your data columns? For instance, of a thousand records, how would you determine how many of them have basic issues like non-printable characters in names and numbers? Or how many of them are duplicates? You can't do this level of detailed profiling using Excel. Data profiling helps give visibility to the state of your data quality. You'll know how well or how bad your data quality is.

Data Preparation in Action. Picture Source: Data Ladder

2. Clean and Standardize data: Holy moly. It's hard to clean messy data, even harder to attempt to do that on Excel. Self-service tools let users clean and standardize data just with a few clicks. No coding needed. No special business rules needed. These tools usually have standards built-in so all a user has to do is make the right selections to initiate the cleaning process. 

3. Find deeply hidden duplicates and remove them: Duplicates are the primary reason behind flawed analytics and insights. For instance, a user signing up to your website using three different email IDs or phone numbers will result in duplicates. Self-prep tools also come with data matching functions that let you match data across, between, and within data sets to extract duplicates. Fuzzy matching algorithms like Levenshtein Distance (or Edit Distance), Damerau-Levenshtein Distance, Jaro-Winkler Distance and many more are used to return high match results between data sets that do not have exact matches. 

4. Merge data sources for data enrichment: Marketing, sales, and other customer-facing teams will always require the combination of various customer data to give them a 360 view of the customer. Say, the team wants to add social media data into the customer's main contact data set. To do this, the team will have to extract this information from the CRM, clean it, then combine it into the main data source and run a duplicate check to ensure there are no duplicates. Although this is a seemingly simple example, it takes days to get done. With a tool, your user can do all of this on one platform, merging and enriching data as needed. 

5. Create a Single Source of Truth: An average organization is connected to over 461 apps. You've got your CRM data, social media, website, mobile login etc. All of these give a disparate view of your customer. So any time you want to know your customer's journey with your organization, you will have to evaluate each section in isolation. This is why today companies are striving to achieve a single source of truth by combining data from multiple sources. A self-prep tool will allow the business user to work with these multiple data sets and create a source of truth that will help them gain key insights on the customer's journey. 

This is just a brief overview of what these tools are capable of. As companies deal with more complex data, ETL processes will be replaced. Sefl-service data preparation tools are the future. They remove dependencies on IT. They empower business users to manage data. They make it easy to integrate multiple data sources. Most importantly, they help companies achieve data quality goals with ease.  

To Conclude 

There are 101 reasons why you should empower business users with this tool, but the key takeaway is that customer data is becoming increasingly complex and requires more than basic ETL processes to make sense of. It's only right to train your business users in handling this data, prepping, and managing it for key business operations.

big data, customer data, data management, data preparation, data quality

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