We are witnessing an explosion of the applications each business uses to operate.
Although integration patterns and platforms may help you organize your company's data to create a Modern Data Stack by reducing the amount of data, best practices are rarely shared between companies. Data integration can be a siloed practice, even within the same company. It is often confined to specific business units or functions.
We wanted to create a guide summarizing the best data integration practices after thousands of discussions with teams from business intelligence, automation, and product.
What Are The Nine Best Practices For Data Integration?
- Concentrate on business outcomes
- Document your data sources, destinations, and pipelines
- Investment in Enterprise Data Model
- Reduce the divergence between analytics, automation, and product pipelines
- It would help if you Always were Prepared to Add New System
- Decouple Process Logic From Business Systems
- Monitoring your pipelines and data sets
- Never Compromise on Data Security
- Choose the correct data integration software for your job
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Concentrate On Business Outcomes
Prioritizing technical initiatives over business requirements is the worst thing you can do. Ensure you're tackling the most critical business issues before starting any integration project. Establish a date and time for interviews with executives and business users to understand the impact of data on decision-making.
Create a list of the top 10 business issues your company can solve with data. Then, think about how to integrate data with these goals.
If, for example, "reducing customer churn through analytics" is your company's top priority, then you should probably focus on integrating customer data into dashboards rather than integrating employee information to automate organizational tasks. You will only save time if you begin with the correct initiative.
Document Your Data Sources, Destinations, And Pipelines
You can duplicate efforts or miss critical data flows if you need to know which data exist and how they are being moved. Ask your IT department to identify which apps and systems are used in your organization. Your IT team will have an overview of your databases, applications, and legacy tools.
Take inventory of any data integration tools that may already exist. Data pipelines can be used by business users to move data from one system to another for critical workflows. Be sure to understand existing systems and data flow before you start a new project.
Investment In Enterprise Data Model
You should build an enterprise data model once you have identified the company's top priorities, its systems, and data pipelines.
What Are The Advantages Of Enterprise Data Modeling?
- Where master data is stored will be known to you
- Standard definitions can be created for concepts
- To reduce future data integration issues, entity relationships should be defined.
If you have a POS system, a Ticketing System, and CRM, then it may be challenging to determine where the organization-customer relationship is defined.
You can answer all these questions by creating enterprise data modeling. This will help you to create a framework for your data integration strategies and let your team focus on delivering value.
Reduce The Divergence Between Analytics, Automation, And Product Pipelines
Data analytics, process automation, and product development are three ways that data can be used to generate value. It is a bad idea to have different pipelines to handle the three use cases when it's optional. It is not uncommon for teams to have different use cases for data and to build a separate technology stack.
You will see a number of things happen if you choose to use one pipeline for business decision-making (like an ETL combined with a CDP) but another pipeline to automate processes (likely through an iPaaS or CDP) and a home-grown real-time system to drive your external data products.
- Different sources will give different results
- The same task will be repeated multiple times
- Security, privacy, and policy enforcement will all be three times more difficult.
Document all use cases and find the easiest way to implement them.
It Would Be Best If You Always Were Prepared To Add New System
In the world of technology, change is inevitable. Your architecture needs to be updated if you look at the world from the perspective of your current systems - data sources, destinations, and data pipelines. Make sure to limit your tool selection to the current applications, cloud environments, and data warehouses that you use. If you want to be able to purchase systems in the future, it is essential that your tools are flexible.
Our clientele shows an interesting trend on this subject. The data teams no longer sit on the sidelines, and the integration of data is not evaluated post-factum. The data team can then be sure the system will integrate into the architecture and processes of the business before it is selected.
Decouple Process Logic From Business Systems
This one best practice will help you save months of work for your team. Companies often create workflows that are based on existing tools. Dashboards are created using Salesforce data automated workflows around Amazon Ads. Oracle's products are integrated directly with marketing pipelines.
It will be difficult to remove an application if you have dashboards, products, pipelines, or other data-related applications that are tightly coupled with your own specific data applications. It is a time-consuming process. By creating an integration web connecting disparate systems, you are effectively locking your business into specific tools.
We suggest a few alternatives to building over application-specific APIs:
- Enterprise Data Model.
- Load the data in a central environment (such as a data warehouse or data lake).
- Data from source schemas can be mapped into an enterprise data model.
- Create logic for dashboards, products, and procedures on the basis of a source-independent data model.
You can add or migrate to new tools, upgrade your system, or change sources without having to recreate any queries or automations.
Monitoring Your Pipelines And Data Sets
After the consolidation of data or integration, you can't stop. Data quality is essential once data flows effectively and material value for the business has been created.
To ensure that data is synced correctly, I suggest adding two different types of monitoring:
- Be sure to set up alerting and monitoring for your data pipeline.
- Then, you should monitor actual data for anomalies.
Data quality and observability are not enough to create value, but they can help your business get the best value from the pipelines that you have built.
Never Compromise On Data Security
Big data is a great tool, but it's essential not to compromise data privacy or security. When integrating data, it is essential to consider many data governance aspects.
- How are credentials protected?
- What is required to grant a system or person a certain level of privilege?
- Are the fields sensitive?
- What laws govern the location of storage?
- Access controls (Who is allowed to view the data?)
- How critical is the uptime of your system to clients?
When handling unstructured and structured data, there are many other things to take into consideration. Start with the values, company culture, and policies. Define data management procedures and dive into the controls needed to protect your information.
Choose The Correct Data Integration Software For Your Job
On the market, there are a number of data integration software tools. The majority of integration platforms cater to specific types of data, user cases, and buyer personas. Understanding your needs is critical to evaluating the features of different platforms.
When Selecting A Tool For Data Integration, What Are The Primary Considerations?
You should consider the following factors when selecting an integration tool:
- Data connectors
- The Extensibility
- Price model
- Support
- Customizability vs. ease of use
- On-premise vs. cloud-based
- Open-source vs. proprietary software
- Batch vs. real-time processing
Data integration is a complex field. In the ETL category, for example, you can find tools designed to work with Snowflake BigQuery Redshift or PostgreSQL.
Which Type Of Customer Data Integration Should You Use?
The company must determine the best type of data integration for its specific needs and size.
Three types of CDI exist:
- Consolidation: CDI is the most popular type. The CDI unifies data from different sources and stores them on a single platform.
- Propagation: CDI of this type ensures that data is copied from every possible source and passed into a single solution. Data will already exist within the sources and will also be a part of the integration solution.
- Federation: The data in this situation will come from multiple sources but will only be accessible from a single point.
Which type of integration process for customer data is best for your company? In most cases, you will consolidate data since it's the easiest and fastest way to create an advanced solution. It can cost a lot if there is a large amount of data. Consider a data federation in this situation. This is the standard solution for large enterprises that have a great deal of data about their customers.
You can choose a CDI that propagates data if you are a small company with limited data. This is the best way to enhance customer service.
Customers Data Integration Methodologies
It may be challenging to decide which strategy is best for you. You should determine if the method is simple to use if it improves data security, and how many other organizations have already used this technique.
The three methods of integrating customer data that are most commonly used:
- Integration by hand: Coded integration can be created by the IT department. Although it may take some time, the software will be tailored to specific requirements. This is only useful for small businesses. It has also become less popular due to the wide variety of integration software available on the market today.
- Integration automated: CDIs of this type use a pre-built solution to run the process. This can be a simple tool that performs the integration with one click, or it could be a sophisticated solution.
- Custom integration: Custom integration. This involves building a platform that is tailored to the needs of the business. This method is used when more than an existing solution is needed for a complex integration. A reliable partner is also needed to create a custom platform.
The Customer Data Integration Process: Steps to Follow
Next, we will discuss step-by-step directions to assist with the integration process. We'll look at the steps.
Define Your Data Sources
Identify the data that you want to incorporate into your platform or application. You can use data on website traffic, customer transactions, and other cloud-based sources. This data can come from hundreds of different sources and help to achieve future business goals.
Consider the following sources:
- Loyalty programs
- search data
- You can find out more about this by clicking here.
- Reviews
- Emails and Phones
At this point, it is essential to determine what tools are needed for customer data integration. You may need to use a particular tool in order to extract information from different sources and perform an accurate extraction of time data.
Clean Data
You will then need to organize and clean the data. Included in this is: Different sources store data in various formats. Some of these sources collect the exact same information. Databases can store transactional information, for example. Once the information has been extracted, it is necessary to delete any duplicates and clear out all of this data.
There are a couple of phases to the data cleansing process:
- Validation
- Unification
- Normalization
- Categorization
Unification Of Data
Data unification, or identity resolution, is the next step of CDI. This step ensures that data from different sources is linked to customer profiles. You will be able to access the data and previous customer activity. You can also use automated tools to unify data.
CDI has three concepts that are common to all of them:
- Identity graph to show the relationship between the different data sets.
- The same user can be identified by using deterministic matching across different databases.
- The probability of two or more records representing the same customer is measured using a statistical method.
Data unification concepts can be tailored to meet different business objectives. It would be best if you, therefore, focused first on the communication you have with your customers since this will help determine which approach to take.
Data Enrichment
Last but not least, you need to turn the information that is available into a format or platform your team can use. The data enrichment process can be used to fill in missing information or to standardize certain data types for a more efficient data grouping. Also, it is helpful to know more about clients in order to improve your communication.
Customer Data Integration Challenges
Data integration may seem simple at first glance, but it is a complex process that has many challenges.
- Create a solid integration plan: You will be extracting data from different sources. The first thing you'll have to do is standardize all of the data. You need a solid plan to integrate the data.
- Managing and accumulating such data will prove to be an enormous challenge: This can be a cause for modifying storage capacities since the data may have unanticipated needs. You will need custom software to integrate big data.
- Integrating data can increase the security of data: It also poses the risk of losing or compromising customer data. It is for this reason that businesses must pay special attention to the protection of data when integrating.
- You are integrating historical customer data with new software solutions: The goal of this type of integration is to reduce data loss. You still have to keep all historical customer data, even if your software is advanced. This data may also need to be updated.
Customer Data Integration Best Practices
Here are some tips to help you integrate customer data more efficiently.
- A data tracking plan is a must. You will be protected from the mess that can occur when data from different sources is mixed.
- Automate any data movement.
- Determine a person who will be responsible. The person responsible for your tracking plan will also be familiar with all data integrations within the organization.
- Transparency is key. Remove all duplicates.
- After the process has been completed, audit and monitor the platform. You must ensure everything is working correctly.
- You should be aware of the location and use of your data.
- Set a timeline for integration. This will allow you to manage your integration process better.
- Begin with your integration goals. This should guide you through the entire process.
- Delineate data categories before integration. This will make it easier to cleanse the data.
Why Customer Data Integration Is Essential To Business
What can you do to improve your business so that it meets the needs of your customers? This is the typical response: learn from customer experience, analyze, and then provide a data-driven solution. How do we gather all customer data in one location for analysis?
Where can you get information about a specific client for your campaign? These are the usual places to look:
- Social media
- Market research
- Website visits
- App downloads
You can see that the data you require is located in various locations.
You need a customer data integration tool to solve the problem. It will consolidate client data from different business units so that you can have 24/7 access to customer information.
More than adequately built processes can be needed for the integration of customer data. Just 3% of the integrated data from customers meets quality standards. More time should be spent on deployment.
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Why Enterprises Should Use Customer Data Integration
It is easy to see why integration software should be implemented. We will highlight some benefits which can assist small and large businesses to remain competitive and also learn from customers.
- Reduce the time and cost by automating the process: CDI tools can help enterprises become more productive as they offer a number of analytic techniques and a smooth rollout that is cost-effective and quick.
- Enhance customer experience: This will allow you to be updated in real-time on any customer issues and respond promptly.
- Reduce risks: Customer data integration systems provide data governance and stewardship to enable efficient data management, encryption, and access control.
- Data security breaches can be dealt with quickly and effectively: CDI tools allow for a higher level of protection and immediate response to threats. You can better protect your customer's data when you are able to see all the processes.
- One point of access for all your data: You don't need to gather data and analyze it from different sources. You can now have all your data in one location with integration software.
- Everyone is on the same page: The data will be accessible to managers and all departments. Data duplication is reduced, and the company knows which data it collects.
- New business opportunities are available: Once you've compiled all your data into a single view, it becomes possible to find new opportunities for growth.
- Predict trends: Marketing forecasts are based on valuable data analysis. This allows you to track industry trends.
Conclusion
We're always happy to offer advice or a second opinion when it comes to evaluating data integration methods. We Cisin a web development company is ready to help you out.
