Data Integration Challenges and How to Overcome Them

The efficient functioning of the sales team purely depends on the real-time deployment of quality data. Have you ever been to a car dealership? You must have come across cases, where a specific car model is unavailable, so the sales representative checks up in real-time to see whether it’s available at their other dealership. The sales representative can do this due to the smooth process of data integration which happens companywide.

Data Integration Explained

The evolution of tech has enabled companies to gather an immense amount of data consistently from multiple sources. If you have a global business, the data involved would be similar to a confetti bomb. Sorting out relevant data for use would be slow and require a great deal of manual work. Here is where data integration comes into play. The data integration process is employed efficiently, to assemble and catalog data from multiple sources. Fortune Business Insights projects the global data integration and integrity software market to grow to $30.21 billion.

The Impact of Data Integration on Sales

As per a combined study by Forbes and McKinsey, organizations relying on data are 23 times more likely to acquire customers, and 6 times more likely to retain customers. Data integration merges all available data and makes it easier for the sales team to access data in real time and across all verticals. You can use data integration to manage consumer data, their current stage in the sales funnel, purchase intent, previous purchases, and more. Through this unified data, you can easily chart out a sales strategy to target existing consumers and even convert leads into customers.

The Challenges

A bulk of companies have become data-driven, so integrating multiple data points is a must. While data integration is a potent process, it also comes with a lot of challenges.

  • Diverse Data Formats

As data integration gathers data from multiple sources, the data formats are liable to be different. Suppose you have a sales team in the US and another in India; the sales team in the US will enter the date in mm/dd/yyyy format, while the sales team in India will enter the date in dd/mm/yyyy format. The different data formats will make the data comparison between both teams difficult.

  • Outdated and Low-Quality Data

Without set data entry and maintenance standards, data integration combines all forms of data into a single accessible format. As a result, the data sets would include outdated and decrepit data. Data duplication is also a likelihood with multiple teams inputting the same data. The odds of this happening increase when companies don’t organize their databases regularly.

  • Using The Wrong Integration Software

Multiple data integration software exist in the market that cater to a specific set of data requirements. Using the wrong data integration software can lead to a potential data mix-up with a chance of data corruption. It might also lead to inefficient and inaccurate data collection.

  • Excess Data

If you have not set parameters for collecting data, there are chances that you might be collecting all inflowing data. It could lead to data bloating, and crucial data could get buried under scores of worthless data. The problem gets compounded when you gather data from a diverse range of sources, which could make it a colossal challenge to analyze for valuable data.

The Solution

  • Automate Data Pruning

Way before AI came into play, many processes were still automated. After AI, the automation processes became a lot more efficient and error-free. You can utilize AI to automate data pruning during the data integration process. AI can easily detect duplicate and insignificant data, and prune it during the integration process. The automation process will lead to data integration that is accurate, efficient, and easier to analyze.

  • Utilize and Scale Data Management

Setting data management guidelines can help you better manage diverse data formats gathered from varied data collection sources. Data management can efficiently scour through all integrated data. As a result, you can efficiently make accurate, data-driven decisions. Scalability is also a significant aspect of data management. Data management is a continuous process, and as your company grows, data management also needs to be scalable enough to manage an influx in data integration.

  • Choose the Correct Integration Software.

A smooth and efficient data integration process requires the correct integration software. Data integration software falls under five different types:

  1. Manual Data Integration
  2. Data Integration with Middleware
  3. Uniform Access Data Integration
  4. Application-Based Data Integration
  5. Common Storage Data Integration

You must answer three key questions to choose the correct data integration software:

Q1. Which data are you looking to integrate?

Q2. Do you require manual data pushes or automated synchronized data pushes?

Q3. Do you want data integration synced to your entire software stack?

You can also choose a mixture of two or more data integration software types based on your requirements. The right integration software will provide you with consistent data management for the data flow. It will also sync data throughout your software stack and restrict unwarranted access to confidential data.

The Endless Nature of Data Integration

Data is an integral part of the efficient functioning of any business. Your Sales team will depend on consumer data to analyze consumer behavior and prepare sales strategies accordingly. For data-dependent companies like yours, the data integration process can never cease. As technology advances, so too will the data integration process. The advanced data integration process will bring about new challenges. To mitigate these challenges, companies will require robust and flexible data integration software with the potential to scale.

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