Data integration has become an increasingly important topic for governments, businesses and organizations of all sizes due to the increasing amount of data produced. By combining multiple disparate sources of data into a single unified system, organizations can gain insights from their large datasets, which can inform decisions, improve operational efficiency, and reduce costs. However, it’s more than just gathering up different bits and pieces of information; integrating these isolated data systems must be done in a secure and reliable way that ensures accuracy throughout the organization.
In this blog post, we will explore what exactly is meant by ‘data integration’, how it is tied to data matching, and what techniques can be used to ensure the flexible integration of data across systems.
Let us illustrate the concept of data integration with an example:
Fig 1.0 data integration creates a unified view
Consider an e-learning organization LearnersToday, that partners with organizations to offer courses in professional training. LearnersToday is an American company, but now wants to expand to the UK. To do so, they acquired ULeaners, a UK-based educational content production company that has recently started an online platform for professional certifications in real estate, food & beverages, and other consumer-facing centers.
The acquisition means LearnersToday would be inheriting ULearners’ databases.
In an acquisition like this, databases become the most challenging to manage. Both companies differ in terms of data formats as well as data sources. LeanersToday has a different schema and format, recording similar information to ULearners, but according to a US standard.
Similarly, the UK-based organization has different ways of recording its data. With the acquisition, the LearnersToday team will have to decide how to integrate and create master records of their data especially if they want to use the same experts for different courses.
To do so, they will need to work on both data sources, transform the data to meet one standard (either UK or US), and categorize SMEs according to subjects. For this, they would also have to match lists before “integrating” it to make a master record of SMEs and their subject expertise.
In this case and many others like it, data matching is required to integrate data from multiple sources. Once done, these records can be stored as unified master records. Combining data from multiple sources can offer opportunities for a company to obtain a customer 360 view, giving them a competitive advantage and find opportunities for improvement.
Following the example given above, the primary goal of data integration is to ensure multiple data sources can easily be plugged into a single source to easily transform, process, or match data sets easily. Creating a cohesive whole helps with transformation and with high-level objectives such as visualization, predictive analysis and much more.
It’s important to mention here that data integration can have multiple meanings. Speaking strictly in terms of data matching, data integration is the first step of the process. Without the ability to easily integrate multiple data sources into a single view, it would be impossible to carry out an effective data matching process.
In common instances, data integration would mean taking data out from different sources, then ingesting it into a central repository. In this approach, the data can be transformed at its source before it is ‘integrated’ into the system. Whichever meaning you derive though, data integration fundamentally means ‘the ability to connect disparate data sources into a unified view.’
Disparate data is a constant struggle for companies that have data streaming in from multiple sources. Where you have your own organizational data, you also have third-party data, partner data, vendor data, supplier data and many more sources that you may need to integrate to get an accurate overview of your entity. But therein lies the challenge. Disparate data is not easy to unify; mainly because some key challenges often become overwhelming.
Data integration then becomes a necessary component of data quality – but it cannot be done without addressing these concerns. You cannot simply pull data from its sources and ‘integrate’ it if you don’t clean the data, have a robust matching system in place, and a governance framework that ensures the integrated data is reliable and usable.
Data integration’s importance lies in the fact that it can help businesses achieve multiple goals – from ensuring quality, to having comprehensive views, and finally to make the data meaningful.
Data integration is a complex, daunting, and often overwhelming task. You’ll find data and IT teams spending weeks on collecting, processing, and transforming data of just one source before they move on to the next. Even today, most businesses rely on manual file sharing, uploading and downloading; coding and programming, or running queries to integrate data. Generally, there are three most common techniques of data integration:
1 . Through manual processes: Analysts and IT professionals create scripts and queries to call data from different sources into a dashboard for the purpose of visualization or creating master records. Some companies also have data entry resources manually keying in data from different sources. This involves copy/pasting the data or using spreadsheet formulas to quickly move data from one source to another. Manual processes consume excessive time and manpower and also significantly increases chances for errors.
2 . Plug-and-play: The easiest technique is plug-and-play. If you’re using a solution like WinPure, you don’t need to code, run scripts, or do any kind of manual data processing. You can simply plug in your file sources (without transforming them) and go ahead with processing or matching as needed.
3 . Use an automated solution: There are different integration-as-a-service (iPAAS) platforms that automate processes and the flow of data across different channels.
While you can opt for any technique that meets your requirement, it’s important to remember that integration involves data matching, data consolidation, and data cleaning. You would need a solution that allows you to perform all these tasks effortlessly!
Data matching can happen either during the data integration process or after. If you’re integrating data to create unified views in a business intelligence software like PowerBI or Tableau, then data matching would be required during the data sourcing, collecting, and processing stages. If you’re integrating data for the sole purpose of transformation, data matching happens after you’ve connected all your data sources into the dashboard.
Illustrating the example above, a data matching solution like WinPure can be used to perform key tasks as:
When dealing with relational databases, the problem of data matching arises when two or more tables need to be linked together. This can mean joining two tables with identical schemas (e.g., when merging databases), or connecting rows from sources with different schemas (e.g., when linking subject matter experts to subject courses being sold, such as all project management trainers for project management courses). In both cases, data matching makes sure that only relevant and correct data points are used in the integration process.
While automating the process of data integration using techniques such as ETL (Extract Transform Load) helps in reducing manual errors, but it also has its share of challenges, such as ensuring the accuracy of integrated information. This is where data matching comes into play.
For instance, if two customers have the same name but different addresses, then there needs to be some way of distinguishing them correctly so that no incorrect assumptions are made while combining the records.
By employing advanced data-matching algorithms during this process, we can ensure the accuracy and reliability of the integrated dataset. Furthermore, these algorithms may also help us find potential matches even when there are minor discrepancies between two datasets by applying certain thresholds, thereby reducing any manual effort required in validating each record separately.
There is always a higher chance of duplicate data occurring during a data migration process. Assuming the company is migrating data from one platform to another (such as preferring one CRM over another), there are high chances of duplication.
For example, you need to match data to remove duplicates from ULearner’s data before merging it into LearnersToday’s database. Once done, you might want to ensure the newly moved data does not have duplicates with the data already existing in the LearnersToday database. This is especially important in cases where companies have international presence and customers can cross-shop at different locations.
The technical aspects of data integration such as implementation and maintenance of software, hardware and databases can be complex enough, but it is the conforming of business definitions or metadata that can pose the greatest challenge.
Business metadata refers to the data about a company’s operations and processes—such as customer names, products offered, locations, and prices—that describe how a company does business.
A key challenge in effective data integration lies in determining which business definitions should be used across all sources. These may differ from source to source due to various factors such as local cultural norms, legacy systems or conservative approaches towards change management. If there are discrepancies between the source system’s values and those of the target system then mismatches will occur during the conversion process leading to incorrect results or inconsistencies in reporting.
Another challenge with data integration is versioning. When dealing with multiple similar but slightly different versions of the same system, there needs to be a way to distinguish them and ensure that when integrating these different versions that their records remain unique for aggregation purposes. This often requires significant effort depending on how many versions exist and how much variation between them there is.
For example, a financial services provider may acquire multiple small companies over time, with each having its own set of software applications for managing investments, customers and accounts. Integrating all this disparate information into one consistent view requires careful analysis to identify what specific elements should be included from each application before mapping them into a single repository where they can be managed as a single source of truth.
Depending on the complexity, this could involve anything from simple matching algorithms such as matching customer account numbers, to more intricate rules, such as identifying whether two customers have the same address even though their spelling may be slightly different due to regional differences in names for streets or towns etc..
Many companies may have existing legacy systems which contain outdated or inaccurate information that needs to be upgraded in order for it to be integrated successfully with other source systems. This requires an understanding of how legacy systems work, as well as their associated restrictions, limitations or conditions that need to be taken into account during the integration process.
There are also challenges surrounding governance and control over how integrated data is used once it has been transformed into one unified structure. Without proper governance procedures in place, integrated datasets could potentially contain sensitive or confidential information which could lead to security risks if not handled properly. Policies should therefore be put in place prior to any integration taking place in order ensure any shared datasets remain secure at all times while being compliant with applicable regulations such as GDPR (General Data Protection Regulation).
There are three main types of data integration: transactional and business intelligence (BI).
Transactional integration involves combining multiple databases into one system with the aim of ensuring that all changes made in one database are reflected in the other.
This type of integration is typically used to maintain accuracy between multiple systems within an organization.
An example of this could include synchronizing customer order information between an ERP system and a CRM system.
Business Intelligence integration focuses on combining data from various systems to create an ultimate view or report which exhibits facts, trends, opportunities or threats. It helps organizations understand their core business processes by analyzing patterns in their current operations, as well as predicting future results based on past trends. Some examples include creating predictive models for sales forecasting or performing detailed customer segmentation analysis from customer purchase history.
In addition to transactional and business intelligence types of data integration, there are also other forms, such as semantic web technology-based applications and cloud-integration solutions, that are becoming increasingly popular among businesses due to their scalability and cost savings capabilities.
Semantic web technology-based applications allow users to access multiple sources at once through an interface that integrates them seamlessly together to create meaningful insights about their environment.
Cloud-integration solutions enable organizations to connect public cloud platforms such as Amazon Web Services or Microsoft Azure with their existing on-premises IT infrastructure to provide more efficient service delivery at lower costs than traditional methods require.
Regardless of which type is chosen, it is important that proper measures are taken when designing any kind of data integration project to ensure that it meets security requirements as well as provides accurate results without introducing any errors.
Data Quality processes are also essential for successful data integration. They should be planned carefully for each system or department to identify any anomalies or inconsistencies in the dataset before integrating them into larger systems or databases.
Apart from using a software data quality solution, there are several best practices organizations should consider when developing a comprehensive strategy for improving their end-to-end Data Quality & Data Integration processes:
Overall, many challenges are associated with integrating large volumes of disparate data across multiple sources; however, if approached correctly, these issues can often be overcome using modern tools and frameworks such as ETL or Master Data Management systems (MDM). It must be noted though, data integration isn’t as much of a technology problem, as it is a data quality and data governance problem. You can integrate data using any solution that can perform advanced and accurate data matching. Still, you cannot ensure positive outcomes if you don’t take care of the basics – such as data quality, data governance, and ensuring you have a strategy to merge data.
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