3 most common data preparation challenges—and how to solve them

As a data practitioner, you are the driving force behind your organization’s quest to become data-driven. But if you’re facing an overwhelming number of data sources, siloed tools, tedious processes, and the increasing demand for regulatory compliance, capturing and maintaining trusted data for insights is no cakewalk.

In fact, our research shows 93 percent of businesses face challenges in preparing data, and more than one-third are unable to see ROI from data management initiatives. Simply put: Bad data is bad for business.

“Our study shows the negative impact this has on organizations by wasting resources and incurring additional costs, damaging the reliability of analytics and negatively affecting the customer experience,” Steve Philpotts, General Manager, Data Quality & Targeting, Experian AUS. “Reaching and maintaining 100 percent accuracy is somewhat unlikely, but it’s entirely possible to come close, and it’s something we should all be striving for.”

We’re here to help you conquer the most common data preparation roadblocks so you can spend less time preparing data and more time solving critical business challenges.

Challenge 1: We don’t have confidence in our data—or a complete view of our customers.
Becoming data-driven starts with going back to basics—and you first need to understand the true state of your data quality today. What’s the quality of the data you are using? If you analyze it, can you trust the results you are getting, or do you often have to spend days preparing and cleaning up the data?

Data preparation—the process of collecting, cleansing, and consolidating data into one, trustworthy file or table—is your first step. While enterprise-wide data hygiene can be your long-term goal, gaining organizational trust with quality data so you can get the support and resources you need starts with a quick win. Prioritize practical, outcome-based initiatives that improve the overall hygiene of data such as improving the quality of information at the point of capture.

First, have your business analysts and data stewards profile your data landscape. This will help them to identify and correct human or machine input errors, remove duplicates, fill in incomplete data, and merge data from several sources or data formats. This is a fundamental first step to prepare your data for larger initiatives, like a single customer view across your systems.

Once you have documented improvements and early success, this momentum helps your CDO more easily partner with business stakeholders to encourage an investment into employing the right systems, controls, training, and feedback mechanisms to ensure data accuracy does not erode over time–increasing the overall trust in data. This goes a long way to centralizing data governance initiatives and strengthening the case for data quality.

Investing in data preparation puts you on the fast track to improved operational efficiency, reduced risk and costs, accelerated usage and collaboration, reliable customer insights, and most importantly, a foundation of trusted data to run your business.

Challenge 2: We’re lacking the skills in-house to prepare data for analytics and business initiatives.
As companies look to do more with data and put data insights into the hands of business users, the obstacle is often few people are truly data literate. A skills gap has emerged in the data space, and our research shows 84 percent of companies report data literacy—the ability to read, work with, analyze, and argue with data—will be a core competency of employees over the next five years.

“It’s widely accepted that data literacy is as critical to commercial success as data hygiene,” says Paul Malyon, Head of Data Literacy, Experian UK&I.

While your business users do not necessarily need to write code or complex data workflows, they’ll need a common language understanding around data…how to access it, how to leverage it, etc. To facilitate training and adoption, consider investing in specialized data roles such as data analysts, data quality analysts, data engineers, and data scientists, to spearhead data initiatives and widespread adoption. They can also aid in developing an enterprise-wide common data language and processes for data usage and governance to empower business users with an understanding of how to best maximize and maintain trusted data.

Challenge 3: We spend too much time on manual processes and are seeing little ROI.
While the abovementioned data profiling is a critical first step, there are data quality management solutions that can accelerate your data quality efforts, saving you time and money. Consider investing in a best-in-class data quality solution that can profile your data to tell you how good it is—and where there are errors or gaps. It can then help you transform your data in real time so it’s standardized, enhanced, and error-free.

And it gets better. Then you can transform data by standardizing formats and enriching it with additional attributes for a more complete view of your customer to help with predictive analytics. This produces harmonized, standardized, and enriched data that can then be analyzed to inform business decisions such as personalized marketing to increase customer engagement or loyalty.

Data quality management tools empower you to quickly and confidently automate lengthy and expensive manual processes and re-usable data transformation rules so you can reallocate time, resources, and money toward strategy and innovation. They also give business users a user-friendly, self-service option for accessing powerful analytical insights without IT support.

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