Top Data Preparation Challenges And Solutions
People outside of IT can now analyze and create data visualizations and dashboards on their thanks to the rise of self-service BI tools. That was great when the data was ready for analysis, but it turned out that most of the time spent developing BI applications was spent on data preparation. It still does, and numerous challenges make data preparation more difficult.
Business analysts, data scientists, engineers, and non-IT users are increasingly facing these challenges. This is because software vendors have also created self-service data preparation tools. These tools allow BI users and data science teams to complete the data preparation tasks required for analytics and data visualization projects. However, they do not eliminate the inherent complexities of data preparation.
Why is Effective Data Preparation Necessary?
In today’s enterprise, a vast amount of data is available for analysis and decision-making to enhance business operations. However, data used for analytics often comes from various internal and external sources, likely in different formats and with issues such as errors, typos, and other quality problems. Some of the data may even be irrelevant to the task at hand.
To ensure that the data is suitable for its intended analytics purposes, it must be curated to meet standards of cleanliness, consistency, completeness, currency, and context. Therefore, proper data preparation is crucial. Without it, business intelligence (BI) and analytics projects are unlikely to yield the desired outcomes.
Data preparation must also be completed within reasonable time constraints. As Winston Churchill once said, “Perfection is the enemy of progress.” The goal is to make the data fit for its intended purpose without falling into analysis paralysis or endlessly striving for perfect data. However, data preparation cannot be ignored or left to chance.
To succeed, it’s important to understand the challenges of data preparation and how to address them. While many of these challenges fall under the umbrella of data quality, it’s useful to break them down into more specific issues for easier identification, resolution, and management. With this in mind, here are seven obstacles to be aware of:
1. Insufficient or Non-Existent Data Profiling
When performing analytics, data analysts and business users should never be caught off guard by the state of the data—nor should their decisions be influenced by incorrect data they weren’t aware of. Data profiling, a key step in the data preparation process, is intended to prevent this. However, there are several reasons why it might fail, including:
- Those who collect and prepare the data assume it is correct because it has been used previously in reports or spreadsheets. As a result, the data isn’t fully profiled. However, they may be unaware that SQL queries, views, custom code, or macros have manipulated the data, masking underlying issues in the data set.
- Due to the time required to profile the entire data set, someone collecting a large volume may only profile a sample. However, data anomalies may go undetected in the sampled data.
- Custom-coded SQL queries or spreadsheet functions used to profile data may be inadequate for detecting all anomalies or other issues.
How to Overcome This Obstacle
Robust data profiling should be the first step in the data preparation process. Data preparation software can assist by providing comprehensive data profiling functionalities to examine the completeness, cleanliness, and consistency of data sets in both source and target systems. When done correctly, data profiling offers the information needed to identify and address many of the data issues mentioned in the following challenges.
2. Incomplete or Missing Data
Fields or attributes with missing values—such as nulls, blanks, zeros used to represent a missing value instead of the number 0, or an entire field missing in a delimited file—are common data quality issues. These missing values raise important questions during data preparation: Do they indicate an error in the data? If so, how should that error be handled? Can a valid value be substituted? If not, should the record (or row) with the error be deleted, or should it be retained but flagged to indicate an issue?
If not addressed, missing values and other forms of incomplete data can negatively impact business decisions driven by analytics applications. They can also cause data load processes, which are not designed to handle such events, to fail. This often leads to a frantic effort to identify the problem, undermining trust in the data preparation process.
How to Overcome This Challenge
First, perform data profiling to identify missing or incomplete data. Then, based on the planned use case for the data, determine the appropriate course of action and implement the agreed-upon error-handling processes. This task can also be facilitated with a data preparation tool.
3. Invalid Data Values
Invalid data values are another common data quality issue. These can include misspellings, typos, duplicate entries, and outliers, such as incorrect dates or numbers that aren’t reasonable in the data context. Even in modern enterprise applications with data validation features, these errors can still occur and end up in curated data sets.
If the number of invalid values in a data set is small, the impact on analytics applications may be minimal. However, more frequent errors can result in incorrect data analysis.
How to Overcome This Obstacle
The tasks for locating and correcting invalid data are similar to those for addressing missing values:
- Profile the data.
- Decide how to handle errors when they occur.
- Implement functions to manage them.
Moreover, data profiling should be conducted on an ongoing basis to detect new errors. This is a challenge in data preparation, where perfection is unlikely to be achieved. Some mistakes will always slip through, but the goal should be to minimize their impact on decisions based on analytics.
4. Name and Address Standardization
Inconsistencies in the names and addresses of people, businesses, and places pose another data quality issue that complicates data preparation. This issue is not due to spelling mistakes or missing values but rather because the data is not uniformly formatted. If these inconsistencies are not addressed during data preparation, they can prevent BI and analytics users from obtaining a complete picture of customers, suppliers, and other entities.
Examples of name and address inconsistencies include:
- A person’s full name vs. a shortened first name or nickname, such as Fred in one data field and Frederick in another.
- Differences in middle initial vs. middle name.
- Variations in prefixes and suffixes, such as Ms. vs. Miss, Mr. vs. Mister, or Ph.D. vs. PhD.
- Spelled-out vs. abbreviated place names, such as Boulevard vs. Blvd, suite vs. ste.
How to Overcome This Challenge
- Review the source data schemas to identify which name and address fields exist.
- Profile the data to assess the extent of the inconsistencies.
- Standardize the data using one of the following methods:
- Utilize a data preparation tool’s string-handling functionality to create customized standardization processes.
- Employ pre-built name and address standardization features available in a data preparation tool.
- Use a specialized tool from a software company that focuses on standardizing names and addresses, ideally one that integrates with your data preparation tool.
5. Data Inconsistency Across Enterprise Systems
Inconsistent data is a common issue when multiple data sources are required for analytics. The data may be accurate within each source system, but inconsistencies can arise when data from multiple sources are combined. This is a pervasive challenge, especially in large enterprises.
How to Overcome This Obstacle
- For inconsistencies caused by attributes, such as an ID field, having different data types or values across systems, use data conversions or cross-reference mapping for a quick fix.
- When business rules or data definitions differ between source systems, conduct an analysis to determine the necessary data transformations that should be applied during data preparation.
6. Data Enrichment
Data enrichment is critical for creating the business context needed for analytics. It involves calculating business metrics and key performance indicators (KPIs), filtering data based on business rules relevant to the planned analytics, adding additional data from internal or external sources, and expanding an existing data set.
However, enriching data is a complex task. Determining what needs to be done to a data set is often challenging, and the necessary enrichment work can be time-consuming.
How to Overcome This Challenge
- Clearly define the business needs and goals for analytics applications before starting data enrichment.
- Identify the required business metrics, KPIs, augmented data, and other enrichments needed to meet those needs.
- Use filters, business rules, and calculations to create the enriched data.
7. Keeping and Expanding Data Preparation Processes
Data scientists and analysts often perform one-time tasks, but significant data preparation work evolves into a recurring process that grows as the analytics they produce become more useful. Organizations frequently struggle with this, particularly if they rely on custom-coded methods for data preparation.
For example, if there is no documentation of the process, data lineage, or where the data is used, the details of what happens and why in a data preparation process are typically known only to the person who created it. As organizations depend on these individuals, they face increased difficulty maintaining the data preparation processes after the person leaves.
Additionally, incorporating new code into a data preparation process introduces more risk and complicates maintenance when updates or improvements are needed.
How to Overcome This Obstacle
Data preparation tools can help avoid these issues and ensure long-term success. They enhance productivity and maintenance by offering features such as pre-built connections to data sources, collaborative capabilities, tracking of data lineage, and automated documentation, often with graphical workflows.
Final Thoughts on Data Preparation and Its Challenges
To excel in data preparation, you must first understand the data required by an analytics application and its business context. After obtaining the relevant data from source systems, the key steps in preparing it include:
- Data Profiling: Identify data quality and consistency issues.
- Data Cleansing: Address and correct these issues.
- Data Transformation and Enrichment: Provide the necessary business context for the analytics.
Throughout these steps, aim for a reasonable level of accuracy, particularly in data cleansing. Remember that perfection is not always attainable or cost-effective and that striving for it can hinder progress in data preparation.
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