Top 7 Data Integration Challenges and How to Address Them

Data integration challenges are one of the most difficult barriers to overcome. How to recognize these challenges? Learn now and how to improve your data usage with these helpful tips.

Top 7 Data Integration Challenges and How to Address Them

Data-driven decisions are the backbone of any successful company. The ability to successfully integrate data into a single platform and easily accessible to your team makes it easier for companies to recognize challenges, understand how to address those challenges, and improve the overall buyer experience.

Unfortunately, data integration has its own set of challenges that can make it impossible for your business to successfully use data at the appropriate time, place, and format.

Recognizing the challenges of data integration can do wonders in helping improve your business operations and overall success!

    Download this post by entering your email below

    Do not worry, we do not spam.

    What is Data Integration?

    To put it simply, data integration is the act of pooling data together from data sources, transforming that data into useful information while filtering out useless data, and then loading that data into a single interface that makes it easily digestible to different members of a team. This process is also known as ETL, or Extract, Transform, and Load.

    Data can come from a variety of sources, which your business might already use. This data includes:

    • E-mails
    • Customer service data
    • Customer metrics (such as name, age, marital status, number of children, occupation, etc.)
    • Human resource operations figures
    • Logistics reports
    • CRM, or customer relationship management information
    • KPIs, or key performance indicators

    Why is Data Integration Important?

    Data Integration is critical in order to get a bigger picture understanding of your business. For example, let’s say you understand that your customers subscribe to your newsletter, but only half of them ever read these newsletters. You also have data on when your newsletters are being sent out, and the types of ads or graphics they are utilizing, but on a separate tool.

    How can you know which graphics are working for which customers, or if they’re opening their newsletters because of the graphics at all, without data integration?

    It’s important to be able to integrate data effectively to improve your customer experience and buyer journey and get a better understanding of how to improve your business operations from the inside out.

    If data integration is so important, why is that many businesses don’t take the time to successfully integrate their data and use it effectively? Below are some of the most common data integration challenges.

    Top 7 Data Integration Challenges

    Understanding these challenges (and their solutions) can help you propel your business and access precious data while it is still valuable.

    1. Lack of Planning

    Data is only as useful as the operations it is being used for. What good is it to have information on, for instance, the number of sales during Christmas time, if you don’t use this information for your future sales or find ways to improve business during the off-seasons?

    Before you begin your data integration, it’s important to ask yourself questions about your specific business needs and data integration, including:

    • What am I integrating?
    • What formats do I have to join together?
    • How can this data be useful to our company?

    Many businesses don’t understand the importance of data integration, or the tools that are needed in order to help them achieve their goals using data integration.

    Asking yourself these questions first and foremost will help you find the best data integration tools to improve your business.

    For instance, if you work in healthcare, you might want a tool like Informatica that can integrate claims processing information, budgeting, and more to reduce costs and improve healthcare outcomes.

    2. Using Manual Data Integration

    When asked whether or not manual spreadsheets (such as those in Excel or Google Spreadsheets) played a significant role in data integration, a whopping 50% of the statement was “somewhat true” while another 14% said it was completely true!

    Although using traditional methods of data integration on spreadsheets, such as pivot tables and filters, can help smaller businesses, there are significant data integration challenges with using manual data integration, including:

    • You won’t be able to use the same types of data integration methods are your business grows
    • Prone to human error
    • Confusion about sharing data from different departments (if silos are used)
    • An incredible amount of money and man-hours spend on data integration

    The best option, instead of using manual data integration methods, is to use an automated data integration tool that collects data in real-time, processes it so you have it ready when you need it, and will be able to process data without lost man-hours.

    3. Lack of Scalability ability

    Even the best-automated data integration tools won’t be able to help a business that continues to grow…if it is not designed for scalability.

    This lack of scalability makes it impossible for larger businesses to handle an influx of data effectively. The solution is to use a data integration tool that can be scalable at its very onset.

    In fact, some of the best IT experts state that scalability needs to be at the forefront of designing and implementing data integration tools.

    In addition, you must prepare for your business expansion and anticipate data integration needs ahead of time. For instance, if you know your company will soon acquire another business, choose the strongest data points from those businesses and integrate them with your own ahead of time.

    Taking the time to understand how data from your acquisitions will fit into your own, or data from new customers, helps prevent lag time and poor buyer experience.

    How To Use Data Analysis To Generate New Content Ideas

    4. Low-Quality Data

    If your data is erroneous or of poor quality, automated data integration tools will not be able to successfully analyze it and integrate it for use.

    This is one of the easiest data integration challenges to fix with the help of data quality management. Just like you’d use quality management to make sure the food you’re serving or the products you’re using is good enough for the consumer, data quality management does checks to make sure your data is free of errors.

    One of the best data quality management tools is Ataccama, which can help users:

    • Understand the state of your data
    • Validate your data before it is loaded or transformed
    • Improve your data

    This ensures that you’re getting only useable data and not suffering from errors during the transformation and loading process of ETL.

    If you run a smaller business and rely on manual integration, you can still do quality assurance checks with the help of a trained data quality management specialist. However, as stated before, this can only work for so long before it becomes too time-consuming and too repetitive of a task that cannot catch all errors 100% of the time.

    It’s best to begin looking at useful data integration tools and data quality management tools as well.

    5. Duplicated Data

    Duplicated data is a common error that around 94% of businesses suspect they might be suffering from. These businesses believe that their customer information is erroneous, including being duplicated across multiple platforms.

    Data duplication is the very opposite of what the goal of CRM and data integration is, which is to have a single customer view to help improve your buyer experience.

    Duplicated data can cost your company lost time and money due to reasons such as:

    • Duplicated marketing efforts. If you’ve already tried to increase engagement with customers through one marketing campaign that didn’t work, you might find yourself using that exact same campaign again to no avail.
    • The increased cost of labor and man-hours to contact customers repeatedly. For instance, your sales representative might continue to call a customer without knowing they’ve already been contacted.
    • Cluttered data that leads to an increase in data storage unnecessarily, leading to lag times and a disorganized business.

    To help address these issues, make data de-duplication a priority. On platforms such as Hubshout, for instance, there are lots of de-duplication features such as the ability to merge data, quality checks, and recognizing missing information.

    6. Data in the Wrong Format

    Similar to data duplication, data stored in different formats can be challenging to integrate into your ETL process. for instance, your human resources department might save phone numbers in the format (000) 000-0000, while your sales department might save it under 000-000-0000.

    These types of small formatting issues become even more pronounced for companies that rely on specific sets of data such as metrics, volume, and other data related to numbers.

    To fix this, make sure to stress to your different departments the importance of data formatting across multiple platforms.

    You can also use data wrangling tools that are designed to format data across different platforms into a single, usable base language. Data wrangling tools such as Talend are invaluable in their ability to transform raw data into valuable information.

    Top 10 Big Data Challenges for New Data Strategies

    7. Data Not Available When Needed

    There are two types of data integration processing methods, known as batch processing and real-time processing. Batch processing is designed to take large amounts of data and process them during a single session, producing information for a later stage.

    Although batch processing can be a good tool to use for larger sets of data, they have many downfalls, including:

    • Must be used during downtime
    • You won’t have access to the data until after the downtime
    • Can be prone to error, which will lead to errors in the entire data batch

    On the contrary, real-time processing takes smaller amounts of data, processes it quickly, and allows you to have access to this information when you need it in “real-time.”

    Although real-time processing can be difficult to design, it is well worth the investment in real-time processing tools so you can get the data as soon as it is available.

    Such real-time data integration tools include SnapLogic, which can reduce data integration times by 90%.

    Wrap Up

    Data integration is a key component of data-driven decision-making and the success of a business.

    To ensure you know how to solve these data integration challenges, consider the tips in this list to help you recognize challenges, know how to overcome them, and improve your business operations and your customer experience!

    Share
    facebook
    linkedin
    twitter
    mail

    Subscribe to our blog

    Sign up to receive Rock Content blog posts

    Related Posts

    Want to receive more brilliant content like this for free?

    Sign up to receive our content by email and be a member of the Rock Content Community!