5 Best Practices for Data Integration

Gone are the days when data integration in business was pretty simple, with data from a few sources being combined into a single, more presentable file. Today, data analytics and AI are being deployed in various aspects of businesses. CRM solutions, HRM programs, point of sales systems, marketing programs, mobile apps, and more are used within the organization to get the best results.

These programs have led to organizations needing better data integration tools with advanced capabilities – something that is fast, scalable, reliable, and able to handle complex tasks and manage data from countless sources. A successful data integration process aims to ensure the creation and maintenance of an effective, dependable, and thriving business intelligence environment for any organization.

In this article, we will discuss data integration and what it is and share some insights on best practices shared by our talented team of experts here at Data Pilot.

What is Data Integration in Business

Data integration in business isn't a set process. Still, a few things are daily in most data integration setups – data sources, a master server, and a platform through which clients request data from the master server.

Once data is requested from the master server, the server sources it from internal and external locations before consolidating it all into a single, presentable dataset. This process includes cleansing the data, setting up an ETL pipeline, and more, depending on an organization's specific needs.

The data obtained from the data integration process is then used in business intelligence models to produce the best results and functionalities for a company. For example, a data integration model might be used in a CRM platform to make a single customer's data and history from various sources available to customer care representatives for better service, seamless and improved CRM platform functionality, and more.

Without the right data integration tools and mechanisms, getting the same data available to companies through just a few clicks would involve hours of manual research and logging into multiple platforms. It would cost an organization too much to be viable or cost-efficient.

But armed with data from various sources, with the help of data integration tools, organizations can make better decisions backed by historical data and improve nearly all aspects of their operations. So it's no wonder that, over time, organizations have invested in building data integration mechanisms from a simple process to something much more complicated and efficient.

Best Practices for Data Integration

Data integration tools today are built like a complicated traffic system compared to the straight roads of yesterday. However, even though the process for developing data integration mechanisms has evolved, there are certainly best practices and methodologies you can use to ensure its proper functioning and peak efficiency.

1. Clearly Define Your Goals

One of the wisest things data technicians and experts can do in the initial data integration process is clearly to define your goals. Instead of seeing all your data, the possibilities of what you can accomplish with it, and the technology you can build, know precisely why data integration is needed in any particular case and build around that.

This thought process will also aid in measuring success and defining metrics you will need to measure for it.

The Solution: If you need data integration to increase the results for your marketing efforts, it helps to set tangible goals for the implementation stage in terms of measurable things like an increase in engagement, sales, etc. This will also allow you to build a road map to where you will get from point A – where you are at the start of the data integration process – to point B, where your efforts come to fruition.

2. Keep it Simple

The data integration process can be incredibly challenging, and building these mechanisms is no small undertaking. It can cost organizations thousands of dollars annually and hours of labor. These complications can be in the form of the wrong kind of data, security concerns, setting up fail-safes in case things go wrong, the ongoing maintenance involved in ensuring the mechanisms continue running smoothly, and scaling the data integration process as the needs of the business change.

The Solution: Experts must focus on user-friendly solutions and automating most of the complex parts of running data integration tools. Doing this will allow simplicity in operating business intelligence tools to the end user, leading to better results and better use of the tools developed.

3. Keep Future Needs and Scalability in Mind

Businesses these days are all about innovation and improvement. With technology advancing faster than ever before, it was only natural for corporations worldwide to find ways to implement said technology. The trend of constant innovation and development in the world of Business Intelligence means that any data integration tools an organization develops must be built for scalability and to handle most tasks required of them in the next few years.

Your business's needs and the content it needs you to handle can also change with time, so it's imperative that data integration tools can be scaled up according to an organization's needs.

The Solution: It's not feasible for an organization to create data integration processes from scratch every time a need arises for new techniques and tasks, which is why your data integration tools need to be easily customizable, scalable, and adaptable.

4. Think About Your Business's Specific Needs

Every business operates uniquely and needs different things. Data integration tools not made with the user's and organization's particular needs and operational norms in mind will fall short at critical moments, be hard to use, and pose significant threats to any company's operations.

This is why it is crucial to ensure data integration tools are made to fit each organization's needs, keeping in mind the industry norms for that organization as well.

The Solution: When performing data integration for a business, experts need to work closely with business executives or the people who will be using the data integration tool to ensure that the devices made are compatible with internal workflows, the tools used within the organization, what platforms the data being integrated is coming from, what formats it will be in, and so on.

5. Data Integration for the End User

Even the best data integration methods are useless if, at the implementation stage, you realize that your end users find the UI too complicated. This usually happens if your platform needs some coding knowledge or the interface seems too complex and needs to be fixed.

A study done by dataversity states that over 41% of business users find Data Integration technology hard to use, and making sure you're not part of this statistic can put you ahead of your competition – which is usually the end goal with improving any internal operations, especially when companies opt to leverage Business Intelligence solutions in any capacity.

The Solution: Focusing on building a beginner-friendly UI that doesn't require any coding from the end user needs to be an area of particular focus for anyone developing data integration technology in any capacity.

Challenges Faced When Integrating Data

Implementing change at any level within an organization is challenging, but the challenges only multiply when we're talking about trying to deploy hi-tech solutions on a company level. This is why it's essential not only to know the best practices you need to keep in mind but also the challenges you're likely to face and need to overcome.

Security Concerns

When trying to establish a data integration process, you'll likely be dealing with significant amounts of sensitive data and information. This can involve identification details, financial details, and much more information on a company's customers. Therefore, it is of utmost importance to ensure that all of this data is as secure as possible at all stages of the integration process to avoid any issues and losses for the business.

Data Delivery Delays

When data integration in a business needs to be done right, it often translates to difficulties in usage, data delivery delays, and inaccurate pipeline functioning. Data integration tools built haphazardly or without keeping the end user's needs in mind will ultimately cause more problems than they can solve.

Experts can prevent these issues by ensuring that the integration mechanisms and technology they built can provide a seamless end-user experience and can do quite some heavy lifting behind the scenes. It is essential to set up data passages capable of the load that would be put on them and is still scalable.

Lack of Resources

According to McKinsey, most IT projects deliver 56% less value than expected, and data integration in businesses is no exception. In addition, building data integration tools and processes isn't simple – it is a cost and labor-intensive undertaking. With these facts in mind, companies and experts must be prepared for any hurdles they may face.

The focus here needs to be on minimizing costs while maximizing ROI in the best way possible instead of aiming to achieve unrealistic things quickly.

Staff Training and Low Actionability

Your data integration tools are ready, but do your employees know how to use them? Or, consider an alternate scenario; your data integration tools need to be simplified and more transparent for your employees to operate.
Either of these situations can spell disaster for any organization, which is why organizations planning to implement data integration processes into their workflow need to be prepared to handle these issues as they come up and do their best to prevent them during the development process.

While implementing data integration, tools can be challenging for anyone, organizations that are ready to put in the work, invest in proactive measures to prevent issues, follow expert advice, and have realistic expectations about what data integration can do for their business are more likely to succeed. At the same time, it is vital for non-technical staff and company executives to be aware of how data integration works and to be willing to work with their technical team for the best possible outcome.

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