6 min read
Data integration is necessary to make better decisions, gain transparency and innovate faster. A platform to centralize data for a 360° view is key for long-term business success.
Data integration as well as the consolidation and transformation of data poses distinct challenges to many companies. Data can be messy, incomplete, and difficult to access, but companies need this data to make better decisions and innovate faster.
- Data Integration Challenges
- Implications of Data Integration Challenges
- How a modern data platform fixes these issues
Data integration is not a new problem, but it has become more pervasive in an age where data comes from many different digital and offline channels. Data is collected in so many different ways that most companies face challenges in their proper use.
According to a survey cited in Forbes, Data Scientists spend up to 60% of their daily work with the organization and transformation of data. Additionally, they spend 19% of their time with the collection of data sets. This means, that only 20% of their time is spent on the actual analysis of data.
The goal of successful data integration is to make company data available and standardize it for efficient use.
Data Integration Challenges
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Below are some of the most common challenges of data integration that companies are facing.
Data may reside on different databases
One of the biggest challenges is integrating data from different sources that use different formats and structures. The data may be located on different databases, making it difficult to access and combine. In order to successfully integrate the data, it is often necessary to perform data cleansing and transformation. This process can be time-consuming and difficult, and it may not be possible to convert all of the data into a single format. This can be difficult to do accurately and can lead to inconsistency in reporting.
Different systems may not be compatible
Another challenge is integrating data from different systems that are not compatible with each other. The systems may use different data models or have different field names. This can make it difficult to combine the data into a single database. In order to overcome this challenge, it is often necessary to build custom connectors or adapters that can translate the data from one format to another.
Data may be inaccurate or incomplete
Incomplete or wrong (e.g., outdated) data can harm the analysis and even disrupt processes (e.g., personalization or qualification). The data may need to be cleansed and transformed before it can be integrated, which can be time-consuming and difficult.
There is a lot of data to process
Another challenge is dealing with the large volume of data that needs to be processed, which - in big volumes - is also called "Big Data". Big Data is more energy-consuming when it comes to storage and processing. Additionally, the consolidation and organization are much more complex and often necessitates sophisticated tools and technologies that can help streamline the data-collection process.
Integration Processes can be complex
The process of integrating data can also be complex and time-consuming. It may require changes to business processes or the development of new integration tools. It can also be difficult to ensure that the data is consistently updated as new information becomes available.
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Despite these challenges, data integration is a vital process for organizations that want to make better use of their data. Integrating data from different sources can help organizations gain a more complete picture of the business environment and make better decisions based on that information. It also helps with automation (e.g., reports). Additionally, data integration provides transparency which supports your compliance and data security regulations.
Implications of Data Integration Challenges
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The process of data integration can be complex, and it can often be challenging to ensure the quality of the integrated data. These challenges can result in a number of implications that may negatively impact your business.
Some of the most common implications that occur as a result of data integration challenges include:
Inconsistent data can lead to inaccurate decisions
Data that has not been cleansed and transformed correctly may be difficult to understand in the business environment. This can result in inaccurate decisions that can negatively impact your organization.
Data integration can be expensive and time-consuming
Integrating data can be a costly and time-consuming process. It can require changes to business processes or the development of new integration tools. It can also be difficult to ensure that the data is consistently updated (in real-time) as new information becomes available. The more data silos a company has, the more likely is the need to update manually which costs resources and time.
Data integration can be difficult to manage
The more teams and programs are involved with the processing of data without a standardized plan, the higher the probability that this data is less compatible with other systems and data sets.
Inconsistent data can delay decision processes
With insufficient data quality and transparency, decision making becomes harder, more complex and can even be halted. Since automation and reporting depend on centralized, standardized information, anything less has a direct impact on the business intelligence that impacts forecasts and decisions.
Data can be difficult to scale
A growing company also grows their processes, data sources, and channels. Many silos and missing interfaces make scaling more difficult, for example, if data still needs to be optimized manually or if individual interfaces or processes need to be developed for every new channel, data set, etc. This results in performance and quality issues that directly harm the company growth.
How a modern data platform fixes these issues
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Modern data platforms offer the ability to connect to major data sources and make data integration easy, automated and fast. Connect to your CRM, marketing automation platform, social media listening tool, website analytics platform, and more. This allows you to get a complete view of your performance and make better decisions based on that information.
Additionally, a good data platform offers a number of additional features and functions to optimize your overall data management and use.
Integration of different types of data
Integrate data from different sources, including structured and unstructured data. This helps you use data faster and develop process to structure data as well as use the data for automation and your reports/analyses.
Data cleansing and transformation
Data cleansing and transformation tools make it easy to maintain your data which saves (manual) resources and can even be automated. This way, you can optimize your data quality and structure data for easier processing.
Data governance features help you manage and oversee your data. This will help you to keep in line with data regulation and compliance and can even trigger reminders for specific and necessary activities.
Performance analytics features help you track the performance of your data integration process and identify any issues that may arise. This allows you to troubleshoot any problems quickly and efficiently.
Process large amounts of data
The process power for big data turns a modern data platform into the perfect solution for companies that deal with complex and big data volumes. That way, your own systems will not be overloaded. Since the platform is specifically optimized for big data, your data will be synchronized and processed a lot faster.
Flexibility to change the raw data structure
The flexibility of a modern data platform allows you to change the raw data structure to fit your needs. This gives you more control over your data and makes it easier to integrate with other systems.
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With a modern, flexible data platform, you can connect to all of your data sources and get a complete view of your performance. Additionally, your data management and data cleaning will be supported to improve your business performance and data quality.
In our digital age of information, companies are successful if they have a 360° view on their data and are able to integrate and process data efficiently. But the perfect data management is still a utopia for many.
One of the reasons are the many channels, tools, and systems that companies are using nowadays which need to be connected to consolidate all data, ideally in real-time.
Whereas insufficient data integration causes plenty disadvantages that can hinder processes, risk data security, and even halt a company's growth, successful data integration can not only help to optimize processes but also actively shape the success of a company.
Datorama is the perfect solution for your data integration. Our DIGITALL AI experts support you with the evaluation and implementation to transform your business data into a sure way to success. With Datorama, you can easily connect to your CRM, marketing automation platform, social media listening tool, website analytics platform, and more.