Data is critical in order for an organization to make informed decisions. Both data consolidation and integration allow organizations to analyze all aspects of their business in order to make improvements. Data integration involves combining data, systems, and platforms to create a unified perspective of all data. The goal of data integration is to harmonize elements of important data sets and to create a process of data merging and data unification. This creates a seamless process that allows organizations to efficiently access data and share it across the business to improve their insights. In contrast, data consolidation is bringing data from multiple sources into one cohesive framework. The process involves combining data from various sources, locations and systems to reduce redundancy and increase efficiency. The purpose of data consolidation is to reduce data duplication and help to facilitate easier data access as well as management. Both data integration and consolidation involve combining data from multiple sources. However, the main difference is that data consolidation is combining data from different locations, while data integration focuses on centralizing data and reducing redundancy. There are many challenges and solutions that are related to both of these approaches that are outlined below.
Data Integration Challenges and Solutions
Data integration is a concept that benefits businesses because it allows them to get a bigger picture of what is happening within the organization. Many key issues that companies face can be analyzed using data integration. For example, if your company is sending out a survey to its customers, but only half are completing the survey, you have data that will be able to pinpoint why this may be occurring through data integration. Data integration challenges are important to resolve because it allows organizations to improve the overall customer experience and journey while using the product or service. By combining data from multiple sources, businesses capture a real-time view of the overall performance of specific strategies.
Using Manual Data Integration
Traditional manual data methods, such as spreadsheets and pivot tables can be useful for smaller businesses, but introduce various issues. This includes not being able to scale these methods as the organization grows, confusion about sharing data from different departments, and a large amount of time spent on data integration. The solution to this would be to use an automated data integration tool that collects data in real time and processes it when you need it.
Lack of Scalability
As a business grows, it can be difficult for data integration tools to keep up in terms of scalability. This causes an issue for large businesses that want to keep up with large influxes of data efficiently. This can be solved by using a data integration tool that can be adaptable to changes within a business. Preparing for business expansion ahead of time can also be beneficial. For example, if you know your company will acquire another business, choose the strongest data points from those businesses and integrate them with your own ahead of time.
Low quality data
If the quality of data is low, integration tools will not be able to analyze it or put it to use when evaluating business strategies. A solution to this issue is implementing data quality management. Using data management tools helps to check that the data is free of errors. These tools can help businesses to understand the state of data, validate the data before it is loaded and also ensure that an organization is only using data that is usable and error free.
Lack of Planning
Before beginning data integration or using data integration tools, it is important for businesses to assess the current state of their goals. Organizations need to understand what exactly they are integrating and how this data can be useful. Many businesses do not understand the importance of data integration or the tools that are needed to help them to reach their specific goals. This challenge can be addressed by conducting research prior to integrating any tools and determining which ones are most beneficial to your industry.
Duplicated data is an issue that 94% of businesses are suffering from and can result in an organization losing money and time. For instance, repetitive marketing campaigns or constantly contacting a customer when they already have been contacted are issues that many businesses face. Data duplication challenges the core purpose of data integration, which is to have a single customer view to help improve the overall buyer experience. A solution would be to use de-duplication tools and features that focus on the ability to merge data, perform quality checks, and recognize when information is either missing or has been duplicated.
Data Consolidation Challenges and Solutions
Data consolidation is important because it helps to ensure that an organization uses high quality data to make informed decisions. This practice is rooted in transforming the data into a consistent format, allowing a business to have the opportunity to analyze the data and improve the quality of it prior to it being used. It also ensures that companies have an accurate data set from every source. Both data consolidation and integration are useful for any organization because they help to provide insight into multiple aspects of the business. Specifically, data consolidation allows companies to manipulate different points of data into insights that can help an organization improve its business.
Data consolidation can be lengthy and require an entire team to integrate the process. It can be difficult for organizations to dedicate a large amount of time or figure out how to best use resources. The best solution to this would be to create an extensive plan prior to data consolidation. This can include figuring out what individuals within a department or team are assigned to the task as well as determining constraints and strengths with regard to resources.
Data security can be a concern for businesses because of data centralization, meaning all the data is in one place. Though this provides many benefits in terms of organization and accessibility for an organization, it can present a security risk. Following up on the latest security measures and implementing firewalls can help to minimize these risks that are associated with security. Consulting with data security experts to address any potential threats prior to data consolidation can also minimize problems in the future.
When using a central source, data latency can be involved. An organization may not be viewing the most accurate representation of the data because it can take time to retrieve data from the original source and then transfer it to the central target. To solve any issues related to data latency, it is best to do frequent data transfers. This can be done by communicating a clear schedule to a designated team.
Data that is generated from multiple sources is not often formatted correctly and can have issues when being used with ETL tools. Since all of this data is consolidated in a single location, it must be transformed all together to be used within analytics. A solution to this problem would be hand-coding the data in order to ensure that all data sets have consistent formatting.