Top data preparation challenges and how to overcome them
Data preparation is a crucial but complex part of analytics and AI applications. Don't let these seven common challenges send your data prep processes off track.
Without properly prepared data, analytics and AI applications are unlikely to deliver the desired business outcomes. But data preparation is an inherently complex process that poses various challenges for data management and analytics teams.
Preparing data for planned uses requires substantial amounts of time and resources. Indeed, it typically accounts for most of the work involved in developing analytics applications. Large amounts of data in diverse formats collected from numerous sources must be combined and consolidated. The raw data routinely contains errors, anomalies, inconsistencies and other data quality issues. Data sets might not include all the information an application requires. Conversely, some data might not be relevant to it.
Data preparation tools -- available as separate products or built into BI and data science platforms -- enable data scientists, data engineers, business analysts and other end users to prepare data themselves. However, these tools don't eliminate the challenges of data preparation. Data leaders must ensure users are sufficiently trained on the data prep process, including common challenges.
Effective data preparation also requires a multipronged approach. To aid self-service users, data quality analysts profile and cleanse data upfront. Data integration developers run initial data transformation jobs. BI teams further transform, enrich and curate data sets for planned applications. They, too, must be prepared for the challenges of preparing data.
7 top data preparation challenges
Because of its complexity, data preparation can't be left to chance. The following are seven notable challenges that disrupt efforts to create clean, consistent and complete data sets, along with advice on how to overcome each one.
1. Inadequate or erroneous data profiling
Data profiling should prevent end users from belatedly discovering data issues when running analytics applications -- or, worse, from having the analytics results be affected by faulty data they aren't aware of. But it might not do so due to the following scenarios:
- Data team members or business users preparing data for a new application assume it's valid because it's already used in reports and dashboards. As a result, they don't fully profile the data. However, the existing uses masked underlying problems in the data set.
- Someone only profiles a sample data set from a large volume of data because of the time it would take to profile the full one. But the sampling approach doesn't detect anomalies and other issues in the full data set.
- Similarly, custom-coded SQL queries or spreadsheet functions used to profile data aren't comprehensive enough to find all the problems in the data.
How to overcome this challenge
Solid data profiling must be the starting point of the data preparation process. Data preparation tools can help: They include comprehensive functionality for profiling data sets in both source systems and the data platforms that analytics and AI applications run on.
2. Missing or incomplete data
Missing values and incomplete entries are common data quality issues. Examples include:
- Null or blank fields.
- Zeros that represent a missing value rather than the number 0.
- Other types of placeholder values.
- Partial transaction records with missing details.
- Incomplete demographic data on customers.
- An entire field or row that's missing from a data set.
Missing or incomplete data can adversely affect business decisions driven by analytics applications and create data governance and regulatory compliance risks. It might also disrupt data loading processes or cause them to fail completely, forcing data teams to scramble to figure out what went wrong.
As a result, instances of missing or incomplete data raise complicated data preparation questions. Do they represent substantive data errors? If so, can valid data be inserted? If it can't be, should affected fields be deleted or kept but flagged to show users there are issues with the data?
How to overcome this challenge
Effective data profiling identifies missing or incomplete data. Decide what to do about it based on planned use cases and the significance of the data errors. Optimally, data teams or end users should then use a data preparation tool to implement the error-handling measures.
3. Invalid data values
Invalid values are another common data quality issue. They include misspellings, transposed digits, unnecessary characters, duplicate entries and outliers, such as ages, dates and numbers that aren't within a reasonable range. These errors can occur even in enterprise applications with built-in data validation features and end up in analytics and AI data sets.
A small number of invalid values in a data set might not have a meaningful impact on applications, but more numerous errors can lead to faulty data analysis results. Cleaning them up should be a priority during data preparation.
How to overcome this challenge
Finding and fixing invalid data is similar to handling missing values: Profile the data, decide what to do about errors and implement automated functions to address them. Data profiling should also be done on an ongoing basis to identify new issues as data is updated. Perfection is unlikely -- some data errors inevitably slip through. But minimizing them will prevent bad analytics-driven business decisions.
4. Name and address standardization
Inconsistencies in the names, addresses and contact information of consumers and businesses also complicate data preparation. These are legitimate data variations in different systems, not misspellings or missing values. But if not standardized, they can prevent analytics users and AI tools from getting a complete view of customers, suppliers and other business partners.
The following are common examples of such inconsistencies:
- A shortened first name or nickname versus a person's full name, such as Fred in one data field and Frederick in another.
- Middle initial, full middle name or neither.
- Acronyms vs. full business names, such as BMW and Bayerische Motoren Werke.
- Companies listed both with and without Inc., Co., Corp., LLC and other business suffixes.
- Spelled-out vs. abbreviated address data, such as Boulevard and Blvd. or New York and NY.
- Different phone numbers and email addresses for the same entity.
How to overcome this challenge
Identify inconsistencies through data profiling, then use the standardization features built into a data preparation tool. Alternatively, data teams can create customized standardization processes with a data prep tool's string-handling functionality or use software from a vendor that specializes in name and address standardization.
5. Inconsistent data across enterprise systems
Organizations also encounter inconsistencies when combining data from systems in multiple departments or business units. The data might be correct in each source system, but differences in data formats and entries create problems for analytics and AI applications. It's a pervasive data preparation challenge, especially in large enterprises.
How to overcome this challenge
When a data attribute, such as an ID field, has different values across source systems, data conversion or cross-reference mapping procedures provide a relatively easy fix. However, if different business rules or data definitions lead to inconsistencies, more complex data transformations are required.
6. Data enrichment issues
Data enrichment helps create the required business context for effective analytics and AI uses. The following are examples of enrichment measures implemented when preparing data:
- Augmenting data with entries from other internal or external sources.
- Deriving additional data attributes from the existing ones in a data set.
- Calculating business metrics and KPIs based on the data.
- Organizing data into different structures for planned applications.
- Adding tags, labels and metadata to help users understand the data.
But enriching data isn't easy. Deciding what needs to be done is complicated, and enrichment work can be time-consuming.
How to overcome this challenge
Data enrichment requires a strong understanding of business needs and goals for the planned applications. Work closely with business executives and users to develop enrichment plans, and allot sufficient resources to the process to meet application delivery schedules.
7. Sustaining and scaling data preparation processes
While data teams and end users sometimes prepare data on an ad hoc basis, data preparation work often becomes a recurring process. Its scope also expands as analytics and AI applications grow and become more widespread -- and valuable -- in enterprises. But organizations often struggle to sustain and scale their data preparation initiatives.
Insufficient resources and skills are a problem in some cases. Using custom-coded data preparation methods is, too. If there's no documentation of a custom-coded process, its creator might be the only person who understands how it works, which makes it hard to continue the process if they leave. Also, when modifications to a process are needed, bolting on new code makes maintaining it even more difficult.
How to overcome this challenge
Ensure that data preparation programs have the required resources and that data teams and end users are properly trained. Using data preparation tools also helps avoid the traps of custom coding. They automatically document processes and track data lineage and use, while also providing AI capabilities, collaboration features and connectors to various data sources.
Editor's note: This article was originally published in 2022. TechTarget editors updated it in March 2026 for timeliness and to add new information.
Rick Sherman, who died in January 2023, was founder and managing partner of Athena Solutions, a BI, data warehousing and data management consulting firm. He had more than 40 years of professional experience in those fields.
