data integration AI business intelligence quality

Conquering the 4 Key Data Integration Challenges

The ability to integrate data successfully into a single platform can be a challenge. Well-integrated data makes it easy for the appropriate staff to access and work with it. Poorly integrated data creates problems. Data integration can be described as the process of collecting data from a variety of sources and transforming it into a format compatible with the data storage system – typically, a database or a data warehouse. The use of integrated data, when making business decisions, has become a common practice for many organizations. Unfortunately, the data integration process can be troublesome, making it difficult to use the data when it is needed.

Successful data integration allows researchers to develop meaningful insights and useful business intelligence.

Integrated data creates a layer of informational connectivity that lays a base for research and analytics. Data integration maximizes the value of a business’s data, but the integration process requires the right tools and strategies. It allows a business to increase its returns, optimize its resources, and improve customer satisfaction. Data integration promotes high-quality data and useful business intelligence. 

With the amount of data consistently growing in volume, and the variety of data formats, data integration tools (such as data pipelines) become a necessity. 

By sharing this high-quality data across departments, organizations can streamline their processes and improve customer satisfaction. Other benefits of integrated data include:

  • Improved communication and collaboration
  • Increased data value 
  • Faster, better decisions based on accurate data
  • Increased sales and profits

For data to be useful, it must be available for analysis, which means it must be in a readable format. 

A Variety of Sources

Data can be gathered from internal sources, plus a variety of external sources. The data taken from internal sources is referred to as “primary data,” and “secondary data” is often collected from outside sources, but not always. The sources of data selected can vary depending on the needs of the research, and each data storage system is unique and different.

Secondary data is not limited to that from a different organization. It can also come from within an organization itself. Additionally, there are open data sources. 

With the growing volume of data, the large number of data sources, and their varying formats, data integration has become a necessity for doing useful research. It has become an integral part of developing business intelligence. Some examples of data sources are listed below.

Primary Data

  • Sensors: Recorded data from a sensor, such as a camera or thermometer
  • Survey: Answers to business and quality of service questions
  • User Input: Often used to record customer behavior (clicks, time spent)
  • Geographical Data: The location of an entity (a person or machine) using equipment at a point in time
  • Transactions: Business transactions (typically online)
  • Event Data: Recording of the data is triggered by an event (email arriving, sensor detecting motion)

Secondary Data

  • World Bank Open Data
  • (studies by the U.S. government)
  • NYU Libraries Research Guides (Science)

Internal Secondary Data

  • Quickbooks (for expense management)
  • Salesforce (for customer information/sales data)
  • Quarterly sales figures
  • Emails 
  • Metadata
  • Website cookies

Purchased, third-party data can also be a concern. Two fairly safe sources of third-party data are the Data Supermarket and Databroker. This type of data is purchased by businesses having no direct relationship with the consumers.

Top Data Integration Challenges

Data integration is an ongoing process that will evolve as the organization grows. Integrating data effectively is essential to improve the customer experience, or to gain a better understanding of the areas in the business that need improving. There are a number of prominent data integration problems that businesses commonly encounter:

1. Data is not where it should be: This common problem occurs when the data is not stored in a central location. Instead, data is spread throughout the organization’s various departments. This situation promotes the risk of missing crucial information during research.

A simple solution is to store all data in a single location (or perhaps two, the primary database and a data warehouse). Apart from personal information that is protected by law, departments must share their information, and data silos would be forbidden. 

2. Data collection delays: Often, data must be processed in real time to provide accurate and meaningful insights. However, if data technicians must be involved to manually complete the data integration process, real-time processing is not possible. This, in turn, leads to delays in customer processing and analytics. 

The solution to this problem is automated data integration tools. They have been developed specifically to process data in real time, prompting efficiency and customer satisfaction.

3. Unstructured data formatting issues: A common challenge for data integration is the use of unstructured data (photos, video, audio, social media). A continuously growing amount of unstructured data is being generated and collected by businesses. Unstructured data often contains useful information that can impact business decisions. Unfortunately, unstructured data is difficult for computers to read and analyze. 

There are new software tools that can assist in translating unstructured data (e.g., MonkeyLearn, which uses machine learning for finding patterns and Cogito, which uses natural language processing).

4. Poor-quality data: Poor-quality data has a negative impact on research, and can promote poor decision-making. In some cases, there is an abundance of data, but huge amounts reflect “old” information that is no longer relevant, or directly conflicts current information. In other cases, duplicated data, and partially duplicated data, can provide an inaccurate representation of customer behavior. Inputting large amounts of data manually can also lead to mistakes.

The quality of data determines how valuable an organization’s business intelligence will be. If an organization has an abundance of poor-quality data, it must be assumed there is no Data Governance program in place, or the Data Governance program is poorly designed. The solution to poor data quality is the implementation of a well-designed Data Governance program. (A first step in developing a Data Governance program is cleaning up the data. This can be done in-house with the help of data quality tools or with the more expensive solution of hiring outside help.)

The Future of Data Integration

Data integration methods are shifting from ETL (extract-transform-load) to automated ELT (extract-load-transform) and cloud-based data integration. Machine learning (ML) and artificial intelligence (AI) are in the early stages of development for working with data integration. 

An ELT system loads raw data directly to a data warehouse (or a data lake), shifting the transformation process to the end of the pipeline. This allows the data to be examined before being transformed and possibly altered. This process is very efficient when processing significant amounts of data for analytics and business intelligence.

A cloud-based data integration system helps businesses merge data from various sources, typically sending it to a cloud-based data warehouse. This integration system improves operational efficiency and supports real-time data processing. As more businesses use Software-as-a-Service, experts predict more than 90% of data-driven businesses will eventually shift to cloud-based data integration. From the cloud, integrated data can be accessed with a variety of devices.

Using machine learning and artificial intelligence to integrate data is a recent development, and still evolving. AI- and ML-powered data integration requires less human intervention and handles semi-structured or unstructured data formats with relative ease. AI can automate the data transformation mapping process with machine learning algorithms.

Author: Keith D. Foote

Source: Dataversity