Data Integration Challenges that Organizations Must Know

Data Integration
In today’s world of technology and smart business decisions, data integration plays a significant role.

Integrating data generated from multiple applications and working on it has become the flagship of some of the IT projects run by various organizations around the world. Not only that — the need for improving data accessibility, enhancing teamwork and collaboration, as well as need for reports and dashboards have given rise to the idea of Data integration.

What is Data Integration?

Data integration is the amalgamation of a systematic series of operations used to combine data from disparate applications into valuable information. However, has it ever occurred to you what would be the consequences in case if a data integration set up suddenly goes wrong? How would it affect the organization as well as the Data integrity?

With technological advancements, responsibilities do come hand-in-hand. We need to integrate data and turn data into real actions to increase its utility. Simultaneously, we need to have a keen eye on the security and integrity of the same. To do that, it is important that you understand the common problems and its consequences that users often encounter during integration.

8 Most Important Challenges of Data Integration

There are several challenges that a user faces during data integration process. These challenges obstruct the path preventing the user to have a perfect integration.  Some of the common problems faced are the following:

  1. Understanding the behavior of source and target systems.
  2. Logically mapping heterogeneous data structure between source and target systems.
  3. Processing the source data to fit into the target system based on business needs or rulles.
  4. Integrating disparate tools from different domains to bring in collaboration among different team members.
  5. Large volume of associated data.
  6. Performance of the solution.
  7. Unforeseen cost involved in changes after integration.
  8. Infrastructure problems.

Consequences Faced Because of Wrong Data Integration

Some of the consequences that follow because of wrong data integration are as follows:

A. Problem with Data Integrity

Data is structured differently in various systems – such as CRMs, ERP, DBs etc. Data integration involves synchronizing huge quantities of heterogeneous data generated from different sources.

The basic parameters that are involved in Data Integrity are: —

  • Pushing the Data from Source to Target without altering it in between.
  • Storing the Data in a secured environment.
  • Continuous auditing and verifying the data being integrated.

When Data integrity is not in place, organizations do get affected in several ways such as:

  • Authorization of Data is hampered when a data gets altered between creation and reception end.
  • Transactions of data in improper places.
  • Improper management controls related to the integrity of data being processed and the databases involved that influences customer transactions.
  • Unauthorized disclosure of Data can bring great damage to the organizations.

During integration, we need to check the format of the source as well as the target. Various data formats available from external sources continue to be added to the legacy databases. These improve the value of the information. Transformation and translation of the data is a significant task performed by the integration engine.

As often is the case, the source system contains different data in one table (e.g. — title, status, description) that must be copied to different tables in the target. On the contrary, the source may have different tables (e.g. — customer name and phone number) for the data that has to be aggregated into a single table in the target. Such mismatches are also common for the field names and data structures.

If the data integration is not done accurately, then taking into consideration the data structures of the source and the target tool, the problem of data integrity will arise. This indicates mismatch will occur between data residing in both source and target tools.

In case of such technical errors, organizations do get badly affected and the data integration tools need to have provisions for correcting such errors.

B. Poor Performance of Data Integration

Performance is always a major concern for any data integration solution. We need to keep in mind factors such as – richness of data as well as the total time consumption to process. The processing time and the response of the source and the target system heavily contributes to the performance. Several other factors such as the database structure, the quantity of data, and the distinctness of a data also add to the overall performance quality.

When the performance is not up to the mark, following are the problems that organizations can face.

  • Greater time lags
  • Slow data processing
  • Affecting the quality of data
  • Delays in real time synchronization
  • Actual expenditure increases compared to estimates.

C. Lack of Data Security

Data security should be one of the top priorities of a data integration solution. Organizations want to ensure that all data stored are secured and confidential; especially when it comes to data integration. There are certain areas that get highly affected when data integration is performed in a not so secured environment. These are as follows:

  • Loss of Revenue
  • Data Breach
  • Data Leakage
  • Loss of Trade Licenses of Organizations
  • Government Penalties and Lawsuits
  • Loss of Organizational Reputation

D. Problem with Data Monitoring

One of the major parameters of data monitoring during integration is the need to analyze the same through various metrics and reports. These reports and documents help the management to take vital business decisions.

However, when reporting and monitoring is not in place, an organization will have to face the following consequences:

  • Lack of proper view that will help management know the status of work items in a project.
  • Estimation of budget can be affected.
  • Estimation of time can be affected.
  • Errors in Documentation.
  • Incorrect information transfer across teams.

Data Integration
E. Cost Effectiveness

Cost plays an important factor in any software organization. Before going for data integration, it is very necessary to accurately calculate the cost involved in data integration solution and maintenance. Unrealistic estimation can be driven by an overly optimistic budget, particularly in case of budget shortfall and doing more with less.

When proper budget is not in place, organizations can face the following consequences:

  • Delays in projects.
  • Poor contract management.
  • Delay in Payments.
  • Dissatisfaction within workers, vendors, clients.
  • Loss in organizational revenue.
  • Affected owner-client relationships.

Put an End to Wrong Data Integration

There are some vital points to consider before you opt for an integration solution. These are as follows:

  • Planning should be done congruously.
  • Thorough ecosystem verification before the start of an integration.
  • Performance should be monitored with high volume of production data.
  • Regular validation of source and target system data.
  • Solution should be robust enough to handle any change in integration constellation and tool version changes.
  • Solution should have a firewall against unauthorized access to data and data corruption.
  • ROI for an integration solution should be calculated accurately considering the future expenses.

With these points in consideration, you will enjoy a seamless and robust integration solution.

Conclusion — Be Smart Before You Integrate Data

And yes, we all know — it is burdensome to recover from a wrong integration solution, cope up with losses, and implement the entire solution correctly. Thus, why not prevent beforehand rather than encounter the dreadful losses and think for its cure?
Be smart to choose your solution. Think before you integrate!

Vikram Gupta is a Software Engineer at Kovair Software, specializing in Designing configurations to satisfy customers' ALM use cases. At leisure time you will find him Cooking.

2 comments

  1. Great write up and sums up pretty much all the problems we face even while setting up the simplest integration pairs. We have been using vendor certified connectors for now but limited by their functionality and flexibility. What sort integration approach might help to address the problem of legacy data and the transition from the connector system to any other optimized solution? How long might that take.

    1. The problem of legacy data is a common one for most of the organizations. You can look for some migration tool, which shifts your legacy data to the required target tool. Kovair has a migration tool named “QuickSync” which is great option.

      From connector system, I suppose that you have tools from different vendor connected using some certified connectors, to move from this solution to a more optimized one, you can go for better connectors, which are more flexible. Kovair Omnibus is a good option for connecting tools from different vendor and it is flexible or else you can go for one-vendor approach, where a single vendor will provide you all the tools required and those tools will be well connected.

      The amount of time, depends on the strength of your organization and the amount of data you have, so it is not possible to comment on the time needed.

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