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5 Common Data Integration Challenges Businesses Face (With Solutions)
A staggering amount of data is created every day. In fact, the average person creates 1.7 MB of data every single second. That may not sound much but when you consider there are almost 8 billion people on the planet, you begin to get an idea of just how much data is actually being created. 

The issue for businesses is that they are now collecting data from multiple sources including emails, KPIs, customer data, and metrics. How do they integrate that data into something meaningful that can be easily analyzed and then acted on? What are the main data integration challenges that organizations face and how can you overcome them?

 

5 Common Data Integration Challenges and how to deal with them

1.    Poor planning

Inadequate planning can be an issue in any part of your business, from how you work with recorded cell phone calls to utilizing demand forecasting. You are collecting a huge amount of data but what are you using it for? For example, do you use sales data to plan for the same periods in the next financial year and look at ways to improve your sales?

If you don't have a plan on how you will use specific data, then your integration may be largely ineffectual.

Making plans for data integration can help you use your data better. Some questions you should consider at planning stage include:

  • What data are you collecting and integrating?
  • Does it come in different formats and how will you join them together?
  • How will you use that data? How do you make it useful?

2.    Not thinking about scalability

 

Every business wants to grow yet many do not think about that growth when it comes to data integration. You already know that the amount of data you collect is increasing so scalability needs to be a factor when it comes to considering what tools or automated processes you include in any data integration model.

When it comes to data integration challenges, this is one of the most important to overcome. There is little point in purchasing and implementing a data integration tool unless scalability is one of its main features. You shouldn't just be looking at how you use data integration now, but also how you will be using it in the future. Don't just think about the data from your current customers, think about data from new acquisitions too.

3.    Sticking to manual method

Given we are in the era of digital transformation, it is surprising how many businesses still look at manual methods, such as Excel, of integrating data. While this may work for smaller businesses or startups who are looking at small amounts of data, for larger organizations, or as those smaller businesses grow, these manual methods become increasingly ineffectual and prone to human error.

If you are still using manual methods, then you could face a number of data integration challenges as you move forward. These can include:

  • Confusion across silos when data comes from different teams and departments.
  • Human error when inputting data
  • Increasing issues when scaling your business upwards.
  • Time consuming and not cost-effective.

It's time to switch to an automated tool for integrating your data. This can help you collect and analyze data in real time so you can access it when you need it.

4.    Data duplication

Duplicated data is a common issue and one of the main data integration challenges. When you are recording or storing the same data in more than one place, then it can have a knock-on effect on decision making and other factors and can thus be a little damaging to your overall efforts. In fact, duplicated data can end up costing you both time and money in several ways:

  • Contacting customers repeatedly when they have already either made a purchase or stated they are not interested. This could also damage your brand reputation.
  • Replicating campaigns that don't work.
  • Cluttering your data storage and causing confusion.

It can help to look at data integration tools that have a feature that can help you avoid duplication in the future as well as erasing any duplicated data (deduplication) you currently have in your systems.

5.    Poor quality data

Just like poor planning, poor data will mean yet more data integration challenges. If the data is low quality, then any tools you use will struggle to analyze it and integrate it with other data. However, this is one of the easier issues to solve and involves using a data quality management tool to ensure your data is of sufficient quality.

By using a good data quality management tool, you can:

  • Check and validate data before it is loaded into your systems and databases.
  • Improve data quality and reliability,
  • Recognize and understand the state and format of any data.

The takeaway

 

As the old saying goes, every problem has a solution. From a template for a website proposal to automated email marketing tools. It's the same with data integration challenges. As the amount of data you use continues to increase, it makes perfect sense to implement an automated data integration and management tool. It can help you integrate existing and future data and can help you make more informed business decisions.

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ABOUT THE AUTHOR

Grace Lau - Director of Growth Content, Dialpad

Grace Lau 

Grace Lau is the Director of Growth Content at Dialpad, an AI-powered cloud communication platform for better and easier team collaboration with helpful services like Dialpad IVR solutions. She has over 10 years of experience in content writing and strategy. Currently, she is responsible for leading branded and editorial content strategies, partnering with SEO and Ops teams to build and nurture content. Grace has also written for other domains such as Ironhack and Jostle. Here is her LinkedIn.

Published Friday, November 18, 2022 7:33 AM by David Marshall
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