Marketers want 360-degree views of customers in order to provide them with the most enticing, personalized messaging. But to have such a comprehensive view of customers, marketers need comprehensive data from a variety of sources.
However, collecting, merging and analyzing this data is a challenge for many, said Sandy Fliderman, founder and president of Industry FinTech Inc. and a former chief technology officer.
“Data integration can be a complex and challenging task because data sources may have different structures and formats and may be stored in different locations,” he said. Additionally, there may be concerns about the quality and integrity of the data, as well as the ability to handle large amounts of data in terms of scalability and performance, he noted.
Below are some of the biggest challenges and recommendations for overcoming them.
The Point: Why This Article Matters
- Policies for the win. Establish clear data governance policies and procedures to ensure data quality, accuracy and security.
- Automation's your friend. Automate data extraction and delivery to reduce errors and inefficiency.
- Determine the right toolset. Choose the right tools for data integration that align with the intended use and purpose of the data. Address inconsistent, incomplete, and low-quality data through testing and validation and invest in data cleaning and normalization tools.
Establish Clear Data Governance Policies, Procedures
Data governance establishes the processes and responsibilities that ensure the quality and security of the data used across a business or organization, while also outlining who can take what action, upon what data, in what situations, using what methods.
“One of the most effective ways to ensure data quality is to establish clear policies and procedures for how data is collected, stored and used,” said Frank Ricotta, BurstIQ CEO. This includes guidelines for data entry, validation checks to ensure data is accurate and complete, and clear roles and responsibilities for managing data.
Establishing clear data governance policies and procedures can help with data quality and fidelity, Fliderman added. “It also helps to ensure that data is properly managed throughout the integration process. Good data governance compliance will act as a clear roadmap for executing the integration plan and helping to reduce challenges due to ambiguity or a lack of clear understanding."
Related Article: How to Prepare Data for Ingestion and Integration
Automate Data Extraction and Delivery
Data extraction, the process of gathering data from various sources, is often reliant on manual labor, which can lead to errors and inefficiency, according to Odeta Pine, G7 Tech Services CEO.
“This is a time-consuming task that, through its monotony, lends itself to human error," Pine said. "I am a firm believer that data extraction can, and should, be automated.”
Automating the process can ensure that data is pulled correctly, at the ideal time and frequency, to capture the most current and accurate data snapshots for your organization to make decisions with, Pine added.
To be analyzed and used most efficiently, the data needs to be organized into visual stories — dashboards that support how the brain processes information. Otherwise, reviewing rows and rows of spreadsheet data often leads us to misread and misunderstand information, Pine added.
“As long as data extraction is automated and dashboards are set up properly, this is the last wheel in the cog to make data integration valuable,” Pine said. Reporting should be easily accessible and provided at regular intervals to support decision-making, she noted.
Choose the Right Tools for Data Integration
When integrating data, you need to think about which databases or datastores (relational versus nonrelational databases, a flat file store, or even all three combined) you’ll be using and how you intend to extract, load and transform the data, which often requires some sort of workflow tool, said Chris MacNeel, Freya Systems senior data scientist.
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“Some tools are better working with structured data, other tools focus on unstructured data, some are good for specific volumes of data which if large, could cause other tools to fail,” MacNeel explained. People often choose the latest, coolest or most popular technology tools without fully understanding the intended use and purpose of the data. This understanding is crucial before selecting the technology, he said.
Related Article: Data Best Practices: Integration, Enrichment and Integrity
Address Inconsistent, Incomplete and Low-Quality Data
All data integration efforts are for naught if the data itself is poor, which happens when data collection issues are suspect, a prime issue when there is manual data entry, which is subject to human error.
One of the biggest challenges with customer data integration is dealing with data that is inconsistent, incomplete or of poor quality, according to Ricotta. “This can be caused by a variety of factors, including differences in naming conventions, data entry errors and a lack of standardization.”
Test and validate, Fliderman recommended. “Thorough testing and validation of data can help ensure accuracy. And, when done properly, this will help identify potential issues earlier in the integration process, which can reduce costs and delays.”
Blayne Hyatt, Porter Capital data analyst, recommends investing in data cleaning and normalization tools, which can help to automate the process of identifying and correcting data errors, as well as standardizing data to ensure consistency.
“Tools like OpenRefine and Talend are popular options for this,” Hyatt said.
Final Thoughts on Better Data Integration
Data integration will continue to be a challenge as long as companies work with different data collection technologies across different channels. Those technologies tend to have variable naming conventions, different ways of collecting and measuring unstructured data and other hurdles to overcome to present a unified customer view.
Establishing data governance policies and procedures won’t solve the technical issues, but will help the organization focus its efforts to ensure everyone is working toward the same data integration goal, rather than having multiple people moving in different directions.
