Has your data project failed to deliver the promised value? Ever wondered what could be the reason? Well, most of the time the culprit lies in your data itself. Your project success hinges on your organization’s ability to effectively capture, manage, and govern information. In short, it depends on how well you’ve planned your data integration processes – the pipelines that bring your data together in a usable form. The better you facilitate trusted and secure data, the higher the chances of achieving maximized transformation. To navigate this crucial aspect, we will discuss four common data integration mistakes to avoid in 2024 and practical data integration tips to ensure that your data project sets the stage for business growth.
Before we delve into the pitfalls, let’s quickly look at the meaning of data integration and why it is important for your organization.
What is Data Integration?
Did you know more than 8 out of 10 business operations leaders view data integration as essential for ongoing operations?
Data integration refers to the technical and business processes used to combine data from various sources into a unified and consistent format.
- Data might reside in different locations such as databases, cloud applications, and more. Data integration tends to bring all this information together.
- There are chances that the raw data has inconsistencies in format or structure. Data integration will cleanse and transform all the data to enable a standardized format across the board.
- The objective is to create a unified dataset that can be easily accessed, analyzed, and leverage for business reporting, machine learning, and business intelligence. Nearly 70% of businesses leverage data integration to power their analytics and BI initiatives.
Here’s an example to help you understand better: Let’s say you are a retail company and have customer purchases stored in two different systems such as POS and e-commerce platform. The POS system collects customer information like names, address, and phone number. Names are stored in a format like ‘Sarah Jolie’ and phone numbers are captured with or without country codes.
On the other hand, the e-commerce platform stores email addresses and uses abbreviations for states in addresses. These inconsistencies in format and structure can cause duplicate records and data quality issues. Data integration converts the names into a consistent format. For instance, “Last Name, First Name.” Phone numbers are standardized to include country codes. You can use modern data integration tools to identify matching customer records across both systems and merge them into a single entry.
“Data Integration is a discipline that comprises the practices, architectural techniques and tools for achieving the consistent access and delivery of data across the spectrum of data subject areas and data structure types in the enterprise to meet the data consumption requirements of all applications and business processes.”
Data Integration Mistakes and Tips to Avoid Them
While each data integration project is different, there are some common execution pitfalls that come in the way of your initiatives. Let’s go through them one by one.
Lack of Clear Vision and Project Strategy
Starting your data integration project without a clear business objective is like launching an arrow into the unknown. When there’s lack of direction, your team may invest time and resources into integrating data without understanding its importance to your company’s goals. This may result in a patchwork of disconnected systems and processes. Even if the technical implementation is flawless, the outcome will likely be irrelevant.
To avoid this pitfall, there are a few questions businesses should ask themselves:
- What are the specific goals of the data integration initiative?
- How does the initiative align with the overall business objective?
- Who are the key stakeholders involved? What is their role?
- What resources are needed to execute the integration plan?
Here are some steps you should take:
- Make a list of your data integration objectives and make sure they align with your business vision. It’s necessary to identify measurable outcomes and track progress. Hold collaborative sessions with key stakeholders to define and prioritize goals. Next, you need to document these objectives in a roadmap for reference throughout the integration process.
- Clearly define the roles and responsibilities of all stakeholders involved in the data integration project. You should establish clear lines of communication and ensure that everyone is working towards common goals. We recommend holding regular meetings or check-ins to keep stakeholders informed of progress, address concerns, and feedback.
- Determine the resources required to execute the integration plan effectively, including budget, technology, and expertise. You can conduct a resource assessment to identify any gaps or constraints in budget, technology infrastructure, or skill sets.
- Develop a resource plan that outlines how resources will be allocated throughout the project lifecycle. Some factors that you can consider are timelines, dependencies, and risk mitigation strategies. For example, define the steps for data profiling, cleansing, and more.
Lack of Clear Vision and Project Strategy
Studies suggest that only 16% of companies report having ‘very good’ data quality. Overlooking data quality is a significant data integration mistake because it undermines the integrity and reliability of the integrated data. When you neglect data quality, you risk making decisions based on inaccurate, incomplete, or inconsistent information.
Here’s what you can do to prevent this:
- Frame your data quality standards and use them to evaluate incoming data sources and identify areas for improvement.
- Focus on data cleansing processes. You can use data quality management tools that automate the identification, validation, and cleansing of data errors. Remember that data quality is not a one-time fix – you need to monitor it regularly throughout the integration process.
- Empower employees to take ownership of data quality within their respective roles.
Underestimating the Power of Data Governance
Did you know businesses with bad data risk losing 12% of their revenue? Data governance plays a crucial role in mitigating such risks by enabling data accuracy, integrity, and compliance. Underestimating the power of data governance is a common data integration mistake with far-reaching consequences. Without clear ownership or rules on how data is used, the data integration project can get complicated.
Different departments might use the same information differently, leading to inconsistencies and unreliable results. Think marketing using one format for customer addresses, while finance uses another. Analyzing this data becomes impossible and leads to bad decisions based on inaccurate insights.
Uncontrolled access to data creates security vulnerabilities. Without proper governance, sensitive information like customer details or financial data might be exposed to unauthorized users. This can lead to costly breaches and compliance issues with regulations like GDPR or HIPAA.
Tips to solve the data governance challenge:
- Figure out who's accountable for what data. Assign data ownership and establish data stewards responsible for maintaining quality within their areas.
- Define who sees what – You can implement access controls so users can only access the data they need for their job. This minimizes the risk of unauthorized changes or misuse.
- Develop data usage policies that define everything from data retention periods to sharing protocols. This ensures everyone understands how to handle business information.
- Leverage data lineage tools to map the origin and transformation of your data. This helps identify errors, improve data quality, and clarify the context of the data for everyone.
Not Investing in a Modern Data Platform
Legacy systems might technically connect your data sources. But they often lack the capabilities for smooth, scalable, and efficient data integration. Traditional infrastructure may lack the robust security features necessary to protect sensitive data from growing cyber threats. This leaves businesses vulnerable to breaches, compliance violations, and reputational damage.
Hence, leveraging a modern data platform for data integration projects becomes important.
Here are some tips to note:
- Perform a comprehensive evaluation of your organization's data requirements. Consider both present demands and anticipated growth. Identify gaps and inefficiencies in existing systems to inform the selection of a modern data platform that can scale with your business.
- Stay on top of data governance and security. Look for capabilities such as data lineage tracking, access controls, encryption, and compliance certifications to ensure the integrity, confidentiality, and availability of your data.
on cloud platforms or hybrid cloud environments.
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Authors
Shalu Santvana
Content Crafter
Mohd Imran
Senior Consultant
