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People outside of IT can now analyze and create <\/span>data visualizations<\/span><\/a> 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.<\/span><\/p>\n 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.<\/span><\/p>\n In today\u2019s 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.<\/p>\n 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.<\/p>\n Data preparation<\/a> 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.<\/p>\n To succeed, it\u2019s 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\u2019s 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:<\/p>\n When performing analytics, data analysts and business users should never be caught off guard by the state of the data\u2014nor should their decisions be influenced by incorrect data they weren\u2019t 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:<\/p>\n How to Overcome This Obstacle<\/strong><\/p>\n 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.<\/p>\n Fields or attributes with missing values\u2014such as nulls, blanks, zeros used to represent a missing value instead of the number 0, or an entire field missing in a delimited file\u2014are 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?<\/p>\n 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.<\/p>\n How to Overcome This Challenge<\/strong><\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n How to Overcome This Obstacle<\/strong><\/p>\n The tasks for locating and correcting invalid data are similar to those for addressing missing values:<\/p>\n 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.<\/p>\n 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.<\/p>\n Examples of name and address inconsistencies include:<\/p>\n How to Overcome This Challenge<\/strong><\/p>\n 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.<\/p>\n How to Overcome This Obstacle<\/strong><\/p>\n 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.<\/p>\n 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.<\/p>\n How to Overcome This Challenge<\/strong><\/p>\n 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.<\/p>\n 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.<\/p>\n Additionally, incorporating new code into a data preparation process introduces more risk and complicates maintenance when updates or improvements are needed.<\/p>\n How to Overcome This Obstacle<\/strong><\/p>\n 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.<\/p>\n 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:<\/p>\n 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.<\/p>\n Explore our data services<\/a> now.<\/p>\nWhy is Effective Data Preparation Necessary?<\/strong><\/h2>\n
1. Insufficient or Non-Existent Data Profiling<\/strong><\/h3>\n
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2. Incomplete or Missing Data<\/strong><\/h3>\n
3. Invalid Data Values<\/strong><\/h3>\n
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4. Name and Address Standardization<\/strong><\/h3>\n
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5. Data Inconsistency Across Enterprise Systems<\/strong><\/h3>\n
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6. Data Enrichment<\/strong><\/h3>\n
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7. Keeping and Expanding Data Preparation Processes<\/strong><\/h3>\n
Final Thoughts on Data Preparation and Its Challenges<\/strong><\/h2>\n
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