The 80-20 Quagmire in Data & Analytics

The 80-20 Quagmire in Data Analytics

The 80-20 Quagmire in Data & Analytics

By William A. Lederer, Chairman and CEO, iSOCRATES®

In the world of marketing and advertising, data is king. The ability to harness and interpret vast amounts of information is the key to making informed decisions and staying ahead of the competition. For analytics services firms like ours, the process of data wrangling takes center stage. It’s a science and an art that requires finesse, precision, and a deep understanding of the challenges that come with it.

Unifying and Wrangling Data is 80% of the Problem

Unifying and wrangling data can be likened to the foundation of a sturdy building. Without a solid base, the structure is bound to crumble. Similarly, the success of any analytics project heavily relies on the effective wrangling of diverse data sets. This process involves cleaning, organizing, and transforming raw data into a usable format, setting the stage for meaningful analysis.

Unifying data from various sources, such as social media platforms, customer relationship management (CRM) systems, and online advertising platforms, is a complex task. Each source speaks its own language, stores data differently, and often comes with inconsistencies. However, it’s at this initial stage of data preparation that the success of the entire analytics endeavor is determined.

The unification and wrangling of data account for approximately 80% of the challenges faced by businesses. The sheer volume and diversity of data sources, coupled with the need for accuracy and timeliness, make this stage a critical juncture for any analytics project.

Data wrangling consists of a series of steps that will eventually present you with data for analysis.

  • Discovery: This initial phase involves getting acquainted with the data to grasp its potential utility, akin to assessing ingredients before cooking. It entails spotting trends, patterns, and addressing evident issues like missing values, setting the stage for subsequent actions.
  • Structuring: Raw data often requires refinement to become usable. Structuring involves transforming raw data into a more accessible format suitable for the intended analytical model, shaping its form for better interpretation.
  • Cleaning: This step focuses on rectifying inherent errors within the data that could distort analyses. It encompasses actions like deleting empty cells, eliminating outliers, and standardizing inputs to minimize errors that could impact final analysis outcomes. Ensuring the removal of erroneous data significantly influences subsequent wrangling processes.
  • Enriching: Determining the need for additional data and incorporating values from other datasets if necessary, understanding available supplementary data.
  • Validating: Verifying data consistency and quality through automated processes, resolving issues, or confirming data readiness for analysis

It’s only after all these steps that you can make the validated data accessible for internal analysis within the organization.


Data wrangling is not for the faint of heart. The challenges are multifaceted and can range from simple formatting issues to complex discrepancies in naming conventions and data types. Some common challenges faced by analytics services firms include:

Data Inconsistencies: Different platforms may use varied naming conventions, units of measurement, or even date formats. Resolving these inconsistencies is crucial for accurate analysis.

Missing Data: Gaps in data can significantly impact the quality of insights. Identifying and filling in missing data requires a strategic approach to maintain data integrity.

Duplicate Entries: These can skew results and mislead analysts. Detecting and eliminating redundant entries is a tedious but necessary task in the data wrangling process.

Data Silos: Marketing data is often stored in separate silos, making it challenging to bring together a holistic view of the customer journey. Breaking down these silos is essential for comprehensive analysis.

The Costs of Data Wrangling

The challenges mentioned above come at a price. The cost of data wrangling encompasses both direct and indirect expenses, including:

Manpower: Skilled data scientists and analysts are required to navigate the complexities of data wrangling. The time spent on these tasks could be utilized for more strategic and value-added activities.

Delayed Insights: The longer it takes to wrangle data, the slower the entire analytics process becomes. In the fast-paced world of marketing, delayed insights can translate into missed opportunities and lost revenue.

Inaccuracies: Failure to address data inconsistencies and inaccuracies during the wrangling process can lead to flawed insights, potentially resulting in misguided marketing strategies and campaigns.

Given the critical nature of data wrangling, finding effective solutions is imperative for analytics services firms. Embracing advanced technologies and best practices can streamline the process and mitigate challenges.

Some potential solutions include: Using automation tools for routine data cleaning tasks, standardization of naming conventions, units of measurement, and data formats, and investing in data integration platforms.

However all these solutions require very significant investments of time, money and resources without having any reassurance on accuracy or quality. This affects the company’s bottomline in many ways and it can be a source of friction within the C-suite.

Most businesses, therefore, prefer to outsource it to specialist vendors who can take on the burden of data wrangling. Global offshoring models offer significant cost and time benefits that cannot be matched onshore at scale.

Why Data Wrangling Is Important

Data wrangling, ultimately, may be more important than the analysis itself, which is a mere 20% of the challenge. That is because the reliability of any business analysis hinges on the quality of its underlying data. Incomplete or erroneous data significantly undermines the integrity of subsequent analyses and the insights derived from them. Data wrangling stands as a critical shield against such risks, ensuring data reliability before analysis—a pivotal step in the analytical process.

However, this preparatory phase can be resource-intensive, particularly when conducted manually. To alleviate this challenge, organizations often implement protocols to streamline data refinement. Understanding the nuances of data wrangling becomes essential to preempt the detrimental impact of flawed data, emphasizing the need for meticulous data handling at every stage.

Solve to Win

Solving the data wrangling quagmire is not just about overcoming challenges; it’s about unlocking a treasure trove of benefits for marketing and advertising businesses

Accelerated Time-to-Insights: Efficient data wrangling paves the way for quicker analysis, enabling marketing teams to respond rapidly to market trends and consumer behavior.

Enhanced Accuracy: A clean and unified dataset ensures the accuracy of insights, empowering marketing professionals to make informed decisions based on reliable information.

Improved Resource Allocation: Automating routine data wrangling tasks frees up valuable human resources to focus on strategic aspects of analytics, fostering innovation and creativity.

Better Campaign Performance: Insights derived from well-wrangled data lead to more targeted and effective marketing campaigns, optimizing ad spend and maximizing return on investment.

Data wrangling is the unsung hero of marketing analytics. For organizations and agencies that have a glut of data to contend with, mastering this discipline becomes a strategic imperative.

We are data wranglers with a demonstrated history of driving success for the MADTech industry. By addressing the challenges head-on and investing in innovative solutions, backed by our deep industry experience and a roster of global data science talent, iSOCRATES transforms data wrangling from a daunting task into a catalyst for success.

Contact iSOCRATES to talk to an expert and to share your current and upcoming needs or concerns.

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