Top Data Preparation Challenges And Solutions

data preparation

data preparation

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?

An explosion of data is available in the modern enterprise to analyze and act on to improve business operations. However, data for analytics applications is frequently gathered from internal and external sources. It is most likely formatted differently and contains errors, typos, and other data quality issues. Some of it may be unrelated to the task at hand.

The data must be curated to meet the planned analytics uses’ cleanliness, consistency, completeness, currency, and context requirements. As a result, proper data preparation is critical. Without it, BI and analytics projects aren’t likely to produce the desired results.

Data preparation must be done within reasonable time constraints. “Perfection is the enemy of progress,” Winston Churchill once said. The goal is to make the data fit its intended purpose without getting stuck in analysis paralysis or striving for perfect data indefinitely. However, it cannot be ignored or left to chance.

To succeed, you need to know the problems with data preparation and how to solve them. Many data preparation challenges could be grouped under the data quality label, but it’s helpful to separate them into more specific issues to help identify, fix, and manage the issues. With that 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 surprised by the state of the data – or, worse, have their decisions influenced by incorrect data that they were unaware of. One of the key steps in the data preparation process, data profiling, should prevent this from happening. However, there are several reasons why it might not, including the following scenarios:

The people who collect and prepare the data assume it is correct because it was previously used in reports or spreadsheets. As a result, the data is not fully profiled. However, they are unaware that SQL queries, views, custom code, or macros are manipulating the data, masking underlying problems in the data set.

Because of the time required to profile the entire data set, someone who collects a large volume only profiles a sample. However, data anomalies may go undetected in the sample data.

Custom-coded SQL queries or spreadsheet functions used to profile data are insufficient in detecting all anomalies or other problems in the data.

How to overcome this obstacle. 

Solid data profiling should be the first step in the data preparation process. Data preparation software can assist with this: As part of data curation, they include comprehensive data profiling functionality to examine the completeness, cleanliness, and consistency of data sets in source and target systems. Data profiling, when done correctly, provides the information required to identify and address many of the data issues listed in the following challenges.

2. Incomplete or missing data

Fields or attributes with missing values, such as nulls or blanks, zeros representing a missing value rather than the number 0, or an entire field missing in a delimited file, are common data quality issues. These missing values raise data preparation questions about whether they indicate an error in the data and, if so, how that error should be handled. Is it possible to substitute a valid value? If not, should the record (or row) with the error be deleted, or should it be kept but flagged to indicate an error?

Missing values and other forms of incomplete data may have a negative impact on business decisions driven by analytics applications that use the data if they are not addressed. They can also cause data load processes not designed to deal with such events to fail. This frequently leads to a mad dash to figure out what went wrong, undermining trust in the data preparation process.

How to overcome this challenge. 

First, you must perform data profiling to identify missing or incomplete data. Then, based on the planned use case for the data, determine what should be done and implement the agreed-upon error handling processes. A task can also be done with a data preparation tool.

3. Invalid data values

Another common data quality issue is invalid values. They 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 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 may result in incorrect data analysis.

How to overcome this obstacle. 

The tasks for locating and correcting invalid data are similar to those for dealing with missing values:

  • Profile the data.
  • Decide what to do when errors occur.
  • Implement functions to deal with them.

Furthermore, data profiling should be performed on an ongoing basis to detect new errors. This is a challenge in data preparation, where perfection is unlikely to be reached. Some mistakes will always get through, but the goal should be to do whatever it takes to ensure they don’t hurt decisions based on analytics.

4. Name and address standardization

Inconsistency in the names and addresses of people, businesses, and places is another data quality issue that complicates data preparation. This difference isn’t because of a spelling mistake or a missing value but because the data isn’t all the same. But if these mistakes aren’t caught when the data is being prepared, they can stop BI and analytics users from getting a complete picture of customers, suppliers, and other entities.

The following are some examples of name and address inconsistencies:

a person’s full name vs a shortened first name or nickname, such as Fred in one data field and Frederick in another; middle initial vs middle name; differences in prefixes and suffixes, such as Ms. vs Ms, Mr. vs Mister, or Ph.D. vs PhD; spelt-out vs abbreviated place data, such as Boulevard/Blvd, suite/ste

How to overcome this challenge. 

The source data schemas must be looked at to see which name and address fields exist. The data then needs to be profiled to see how big the differences are. After that, the three best ways to standardize the data are as follows:

  • Use a data preparation tool’s string-handling functionality to create customized standardization processes; 
  • Use a data prep tool’s pre-built name and address standardization features;
  • Or use a tool from a software company that specializes in standardizing names and addresses, preferably one that works with the tool you use to prepare data.

5. Data inconsistency across enterprise systems

Inconsistent data is also standard when multiple data sources are required for analytics. In this case, the data may be correct within each source system, but inconsistencies arise when data from multiple sources are combined. It is a pervasive challenge for those who prepare data, particularly in large enterprises.

How to overcome this obstacle. 

When data inconsistency is caused by an attribute, such as an ID field, having different data types or values in different systems, data conversions or cross-reference mapping can be used to make a quick fix. When business rules or data definitions differ between source systems, an analysis must be performed to determine data transformations that can be implemented while preparing the data.

6. Data Enrichment

Enriching data is critical to creating the business context required for analytics. Data enrichment involves calculating business metrics and key performance indicators (KPIs), filtering data based on business rules that apply to the planned analytics, adding more data from internal or external sources, and getting more data from an existing data set.

However, enriching data is a difficult task. Deciding what needs to be done in a data set is frequently complicated, and the necessary data enrichment work can be time-consuming.

How to overcome this challenge. 

Before starting to enrich data, you should know exactly what the business needs and goals are for analytics applications. This will make it easier to find the business metrics, KPIs, augmented data, and other enrichments needed to meet those needs. You can then use filters, business rules, and calculations to create the enriched data.

7. Keeping and expanding data preparation processes

Data scientists and other analysts do a lot of one-time tasks. Still, the more important data preparation work they do becomes a recurring process that gets bigger as the analytics they produce to become more useful. But organizations often have trouble with this, especially if they use custom-coded methods to prepare their data.

For example, if there is no documentation of the process, the data lineage, or where the data is used, what happens and why in a data preparation process is typically known only by the person who created it. Because the organization depends on these people, they have to spend more and more time on these tasks, which makes it hard to keep the data preparation work going after they leave.

Also, adding new code to a data preparation process makes it riskier and harder to keep up to date when changes or improvements need to be made.

How to overcome this obstacle. 

Tools for data preparation can help you avoid these problems and have long-term success with data preparation. They help with productivity and maintenance by having things like pre-built connections to data sources, the ability to work together, tracking data lineage and where it was used, and automated documentation, often with graphical workflows.

Finally, here are some thoughts on data preparation and its difficulties.

To be good at data preparation, you must first know what data an analytics application needs and the business context of that application. After getting the relevant data from source systems, the key steps to preparing it are data profiling to find problems with data quality and consistency, data cleansing to fix those problems, and data transformation and enrichment to give the analytics the proper business context.

As you go through those steps, do what is appropriate and possible in a reasonable manner, especially when it comes to data cleansing. Remember that perfection isn’t always attainable or may not be worth the cost of achieving – and that it can be the enemy of data preparation progress.

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