Information networks are becoming more complex and bringing all of your information together is critical for the growth of your business. Data integration offers you a reliable way to harness the power of the data that is spread out across your systems and applications.
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What is data integration for your business?
Data integration is the process of bringing data from different systems and sources together to be able to be compared, combined and shared. Data integration enables you to pull information and knowledge from different applications and programs and present it uniformly and usefully.
To achieve data integration, a data integration tool is used to collect data and transform it into a consistent format, which can then be accessed from the master tool or application.
Contemporary information networks are spread out and messy. Large organisations often have data stored in more than 100 solutions, systems and individual apps that have been acquired to suit specific business processes. From payroll and recruitment products to social media accounts and specialist apps for work allocation, reporting, processing and storing, it can feel like you have an “app for everything”. Even small to medium businesses are finding that they have between 20 and 50 solutions used to manage business data. Data integration brings these solutions together so your staff can access it from a central location.
Many organisations are also finding an increasing need to share data and documents outside of their internal networks. Data must be accessible and useful for third-party partners, contractors and customers. There may be a need for an external party to have access to a report for review and editing. This needs to be done in a program both parties can use, and updates can be made to a master document without breaching network and security protocol. Data integration opens up the possibilities for sharing data outside your systems.
What is the importance of data integration for businesses?
The process of data integration gives you a more complete, accurate and comprehensive view of what is happening in your business. Integrating information from different sources gives you a better understanding of business operations including the real cost of activities and comprehensive profits and loss reviews. This in turn makes it easier to identify trends and opportunities and get a clear picture of the state of play rather than guessing about what all that information really means. In this way, data integration enables better data management and decision-making.
Most business owners know that having data separated across different systems doesn’t give them good insights into the business. They also recognise that confusing programs and services are less than ideal for their customers and clients.
Data integration gives you access to:
- Comprehensive data sets - by combining information that you have stored in any number of apps, tools, solutions, files or formats.
- More reliable data sets - because the integration process will identify data held in duplicate data that is incorrect, flawed or inconsistent. Problem records are flagged and even sometimes resolved form within the system. This means you can trust the accuracy of the information your systems generate, and risks such as entry error can be mitigated.
- Better analytics and reporting - integrating data from different sources can enable you to perform more comprehensive and accurate analysis, leading to better insights and reporting.
- Cost and time benefits - by using a system to integrate data, you can save time and human resources on manual data entry and analysis processes. Data sets can be combined to pre-populate records with information passed from one system to another. This leads to cost and time savings through a decrease in the need for staff to carry out data management and quality control.
Data integration also has benefits for customer relations and marketing. Through data integration, you can have easy access to customer records- be they from emails, social media, enquiries, purchase records, transaction data, profile details and even website visits. All of this knowledge of your customer enables you to see their customer journey, respond to any contacts with a full understanding of their exchanges with your business. It allows you to personalise your interactions with them. Customers today expect this level of service from their favourite brands and businesses.
What are the types of data integration?
Data integration can be achieved through a variety of processes, including ETL (extract, transform, load), ELT (extract, load, transform) and data replication. Each method has its strengths and weaknesses, and which technique you choose will depend on the specific needs of your business, as well as the volume and variety of data you require to be integrated.
There are several types of data integration techniques, including:
- Extract, Transform, Load (ETL): The most common approach to data integration, which involves extracting data from various sources, transforming it into a standardised format and then pushing it through to the final data solution or database. The ETL process is typically automated and can be scheduled to run at regular intervals.
- Extract, Load, Transform (ELT): This is a process similar to ETL, however the steps are completed differently. The data is accessed and gathered, and then pushed through to the data solution or database. From within that system, it is modified and transformed for the user. This model is useful in situations where the data resides in systems that are out of date or have low processing power. Often the transformation tool has significant processing power.
- Data replication: This technique involves replicating data from one system to another, enabling you to create a repository of compiled and combined data, which is useful for disaster recovery, backup, or reporting purposes.
Another tool is the Enterprise Service Bus (ESBs) which enables communication between different applications and systems. So while they don’t exactly integrate data, ESBs provide a standard interface for data exchange.
What are the challenges of data integration and how do I overcome them?
There are several common challenges that you may face in setting up and delivering data integration. Some of these challenges include:
- Technical complexity: Data integration can involve multiple systems, databases, and applications, making it a technically complex process. It may seem too big a job to handle, especially if you have limited internal capacity or knowledge to oversee the integration process. If you need more resources and capability, it makes sense to get external help. Specialist agencies, like Stimulus, can assist you in bringing your data systems together. There are also steps you can take to prepare for an integration project, such as establishing data rules and seeking to set up your data in the most uniform way possible.
- Data quality: In some cases, businesses and organisations are starting out with inferior data quality. It can be difficult to deliver effective integration when your data is full of errors and inconsistencies. It is also a problem when businesses don’t have a clear picture of where data is housed and which of multiple records should be considered the master record. Gaps in customer records can make it difficult for data integration tools to assess the validity of that record. The best thing you can do to overcome this data integration challenge is to establish data quality rules and carry out as much data cleansing and validation as possible.
- Cost: Data integration can be expensive, especially if it involves custom development or the use of proprietary tools. To overcome this challenge, it makes sense to consider open-source and cloud-based data integration solutions.
- Data compatibility: Data from different sources may have all sorts of different formats, structures, and rules, making it challenging to be able to compare and consolidate. Creating a resource for staff that clearly outlines how records should be created and maintained can help. The resource should include rules for how data such as names, addresses, reference numbers and prices are set up.
What are the best practices for successful data integration?
Here are some best practices for successful data integration:
- Involve stakeholders: Successful data integration requires the involvement of all relevant stakeholders, including IT, business, and data owners. Involving stakeholders can help ensure that the integrated data meets the needs of the business, is aligned with business goals, and has the necessary support and resources.
- Define clear goals and requirements: Before starting a data integration project, you should clearly define your goals and requirements. This includes identifying the data sources to be integrated, the types of data to be included, and the desired outcomes of the integration.
- Establish data governance policies: Data governance policies define the rules and standards for managing data, including data quality, security, and privacy. Establishing clear data governance policies can help ensure the integrated data is accurate, consistent, and secure.
- Ensure data quality: Data quality is critical for successful data integration, and businesses should ensure that data is cleaned, validated, and standardised before it is integrated. This includes identifying and resolving data quality issues, such as duplicates, missing data, and inconsistent data formats.
- Automate the integration process: Automating the data integration process can help reduce errors, save time, and improve efficiency. This includes using automated tools for data mapping, transformation, and loading, as well as automated testing and monitoring of the integration process.
- Monitor and maintain the integration: Data integration is an ongoing process, not something you can do once and forget about. It is important to regularly check in to ensure that the integration is meeting your needs and requirements. This includes tracking data quality, monitoring performance, reporting to stakeholders and resolving issues in a timely manner.
What are the trends in data integration for the future?
Within the next five to ten years, we will see huge changes in data management within our organisations. As technology rapidly accelerates and as we see even more clearly the value and power of data, businesses are going to be scrambling to integrate as many systems as possible. According to this McKinsey article “By 2025, smart workflows and seamless interactions among humans and machines will likely be as standard as the corporate balance sheet, and most employees will use data to optimise nearly every aspect of their work.”
Another change will be the overall data literacy of our workforces. The importance of data management is a hot topic in workplaces around Australia. Businesses of all sizes are delivering technology-based change programs that mean everyone needs to be able to embrace data and recognise the benefits of and need for integration of systems.
Trends for data integration in the future include:
- AI-powered data integration: We hear more every day about what AI can offer, and there are certainly opportunities for using AI in data integration. This process involves using AI to assess, scan and handle data. AI tools can assist data integration processes by using machine learning to identify any gaps, errors or flaws in data sets before it is transformed.
- Cloud-based data integration: Cloud-based data integration platforms enable data to be pulled from both on premises and cloud environments, or through a combination of both. This is an important factor and requirement for businesses that have already leapt into cloud-based data management. Cloud-based integrations are efficient and can be scaled to suit the needs of the business over time.
- Data virtualisation: Another huge opportunity, data virtualisation enables data to be accessed and used for reporting or decision-making without it actually having to be transferred or transformed into the master integration solution. Think of it as the ability to access the consolidated data you need on the fly, rather than having to go through the integration process in its entirety.
- iPaas: Integration Platform as a Service tools make the process of data integration quick and simple. Templates and connectors are installed to carry out the integration in an efficient way that doesn’t require lots of customised coding. Out-of-the-box iPaaS tools help you to integrate any number of cloud-based processes, services and applications.
Implementing data integration processes is essential for businesses with complex information networks and systems. Data integration gives you access to effective and reliable information that is drawn from right around your system and solutions.
What is big data integration?
Big data integration is a term used to describe the integration process when the data is contained in complex databases and across multiple cloud storage locations. Big data is considered a challenged because of the high number of sources, the variability in data quality and format and the inconsistencies in data structure. In some cases, data might need to be drawn from NoSQL databases which your organisation may use to store data in non-tabular and non-standard formats. While the theory around big data integration is the same, these types of integration projects can be expected to take longer and be more complex and costly than simpler integration projects.