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Data Management Challenges to Avoid upon Data Ingestion

With more than 2.5 quintillion bytes of new data generated every day, it’s no wonder US martech decision makers cite customer data integration and customer data unification as the most valuable technology capabilities to achieving their marketing priorities. But managing that data and ensuring its quality can be even more of a challenge.

In this article, we’ll confront some key obstacles related to data ingestion in your data management ecosystem. And then we’ll dive into how to avoid them to ensure your marketing, sales, and service teams have accurate, clean, and organized data to deliver great experiences for your customers and prospects.

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Data Quality: Data not formatted correctly

Mixed formatting within your data can often be a nightmare to ingest, causing errors, duplication, or in some cases, completely ingesting the data incorrectly. Some programs will reject this erroneous data, and some will override existing data within the customer profile or account if refreshed. So without a proper QA, this could become a serious problem. To rebound from this situation, an IT team would have to go back and reverse the action, wasting precious time retracing steps that most busy organizations do not have.

To make sure that the jobs are scheduled or run accurately, quality assurance (QA) checks must be built into the data onboarding process to ensure files are inspected before moving forward with ingestion. Often, a developer or business systems analyst (BSA) could be the one to do the QA.

Duplicates exist in your data

If duplicates in your data exist, you should perform roll ups to combine these records. A data steward should perform this work and receive sign off on it before pushing to the operational or master table. If not, you could inaccurately overwrite valuable data with incorrect or outdated data.

On a practical level, if there are duplicates of the same customer, the organization may not know the correct or most updated profile to use within marketing efforts. An identity resolution solution could help with this. It can clean the data before being ingested, providing you with the correct records and a bonus of having additional third-party attributes to bolster your customer’s profile.

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No understanding of your data

One of the first steps in managing data quality is understanding the data that you have today. Your organization should understand what the data is used for and where it came from, especially in our privacy-centric world where it is essential to have a detailed record and the ability to erase data fast. If there is no documentation on your companies’ data or the documentation is outdated, then making updates should be the first step. Build out a data dictionary that details all of this vital information, so everyone internally is aligned and can easily understand all they need to know about the data. Yes, this will take time and energy from your team. But it will also prove to be an extremely valuable tool and is the best practice to maintain data management as your organization’s data efforts scale and become more complex.

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Manual/Automation (Real Time): Human error in manual data ingestion

Errors can be and frequently are made in data ingestion. People are human. Things happen. For example, a team member may accidentally upload active customer data into a table detailing inactive customers. To avoid situations like this, it is best practice to always ingest into a temporary table before updating into the master table. This is one way you can mitigate the risks in manual data ingestion. Another way is by looking to purchase solutions or develop custom automation workflows to do this for you. Using standard naming conversions when saving files can also help mitigate human error before it happen

Building Pipelines via ETL or API for automation

Another option to help avoid human error is to automate ingestion uploads, cutting out risk of error. Humans make mistakes, and as a former professor once told me, “The code you write is only as smart as the individual who wrote it.” Meaning, even if you automate something, it still has the possibility to make errors. However, if you have a data steward watching over your processes and protocols, everything should run smoothly, helping to increase productivity and revenue.

Expanding Storage from Siloed Data: Storage expands from duplicate data sets

Siloed databases are never a great idea, for many reasons. If groups within your organization go rogue and operate their own databases, it opens you up to errors and missed insights. Plus, if you are not careful, it can lead to legal liability issues. Having multiple environments means more storage and fees. When organizations collect and store data in siloes, marketing teams will not have access to all the data they need to gain a holistic view of your customer and prospect audiences, which often causes a lot of frustration for everyone involved. Cloud-based data lakes are a seamless solution to connect all data sources in one environment, relieving the marketing team’s headaches and saving money on storage costs. By following best practices to build and then utilize a data dictionary, your organization will have a guide to eliminate silos that may exist, gain a record of the data they have (and where to find it), and uncover gaps needed to activate future use cases.

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