By Simon Hall Jul 29 2021 Blog

The Pitfalls of Stitching Data Together Manually

Businesses today have a significant amount of relevant data they can utilize to improve their internal processes, sales, and marketing initiatives. However, this data often comes from a variety of different sources⁠—online, offline, CRM tools, marketing tools, third-party sources, and more. In order to get a complete understanding of the customer journey, it’s imperative to integrate these disparate data types for uniform analysis.  

What Is Data Integration?  

Data integration is the process of consolidating and harmonizing business data from different locations into a single source of truth. Successful data integration is infinitely more complicated than simply copying spreadsheets into a single file. All sources of data must be validated, and the data must be organized and checked to ensure accuracy before merging. Whoever’s managing the data must also scrutinize it to ensure consistency and eliminate redundancy.  

Businesses can take on this data integration task manually or rely on data management solutions to automate the process. Manual data integration often involves creating hand-coded scripts to automate some aspects as well. Most teams that opt for manual data integration tactics do so to save money, yet taking this route comes with some serious pitfalls that can make it a much more expensive option in the long run. 

Whether you’re doing everything by hand or creating your own SQL scripts, here are six of the main challenges manual data integration efforts pose for businesses.

1. More manual errors

The most obvious issue. Human error is a reality of any data management initiative that is executed manually. Particularly when you have more than one person working with data sheets or building SQL scripts, the chances of miscommunication and mistakes are high. This in turn can lead to inaccurate or incomplete data after integration is complete.  

What’s more, it’s possible for team members to never actually realize that the integrated data has inaccuracies in the first place. So, you end up running erroneous analyses and making important marketing and sales decisions based on incorrect data.

2. High risk for inconsistencies or redundancies

In the same vein, stitching data together manually using SQL scripts creates visualization challenges since there’s no way to illustrate potential data integration flaws. This creates a higher risk for inconsistencies and redundancies.  

By employing a data management tool, however, you can gain access to a visual design environment that empowers you to easily check and corroborate whether there are any inconsistencies in data collected from disparate sources. This is incredibly important in cases of integrating online and offline consumer behavioral data. If you’re not receiving consistent customer information from different channels, it becomes impossible to truly understand the context of their engagement and deliver a personalized experience across marketing campaigns.

3. Higher maintenance and support costs

The main reason some businesses still avoid investing in data integration tools is cost. If you have an in-house team with the ability and skills to manually integrate your data, then you can save some money in the short term by not using additional software. 

The reality is, though, that the long-term maintenance and support costs of doing things manually will undoubtedly outweigh the initial software investment. When multiple people are involved in manual data integration, there’s no guarantee their approaches will be consistent which can increase the complexity and cost of long-term maintenance.  

Even in a case where only one person is involved in data integration, what happens when that person leaves the company? Now it becomes much more difficult for remaining team members to understand the integration or how to fix problems with it. This ends up sapping more time, money, and resources in maintenance that could otherwise have been avoided.

4. Slow insights

Stitching data together manually is an inherently slow process as it requires individual data extraction, validation, and integration. This makes it very challenging to continuously provide the key insights your business needs to remain competitive in the market. This is true even when creating your own coding scripts to automate the process.  

How frequently do your business needs change or do new data sources/types emerge that are relevant for your analyses? Each time this happens, coding scripts need to be updated to adapt to these new changes. This takes much more time than relying on data integration technology to adapt to these changes for you.

5. Lower confidence in data insights

Relying on individual team members or code designers to aggregate data means that few team members understand what business data is being used, where it comes from, and how to manage it. There’s no clear customer data strategy in place that all members of the company fully understand.  

This makes it difficult for decision-makers to have confidence in data insights, meaning they are more reluctant to utilize them to inform key business strategies. This problem accentuates when manual data management leads to inconsistent reports based on incomplete or even redundant data that isn’t trusted by anyone in the business. Failing to have confidence in data insights means your business cannot make key optimizations to improve marketing, sales, and the customer experience.

6. Challenges in scalability

While manual data integration may work well enough for your business at this point, what about in the future? In the long run, you’ll likely want to scale your efforts by adding in new marketing channels and strategies that require more complex data integrations. Relying on in-house teams to manually create reports or build code to keep up with this is very time-consuming and borderline impossible. 

Your current data processing framework also might have compatibility issues with future tools and initiatives you invest in. For example, say you want to start incorporating big data insights or use another type of cloud platform in the future. In order to take full advantage of these new data points, you’ll likely need to completely redevelop your framework to fit these tools. As opposed to investing in maintenance, you’ll need to start over from scratch.  

If you depend on a data integration solution at any stage of your business development, you’ll be able to easily connect it with whatever new tools or technologies you need in the future. There will be no need to rework legacy code processes to get up to speed with the latest tools and channels you utilize. 

The Best Approach to Data Integration

Effective, scalable data integration is one of the foundations of business success today. Once you choose a strategy to implement, it becomes challenging and time-consuming to change your approach. Investing in a data management solution can help you avoid the pitfalls of trying to stitch your data together manually. 

In order to select the right tool and maximize its value for your business, you first need a clear understanding of what you want to achieve with data integration. Outline your marketing, sales, and other business goals to highlight what kind of data you need and what sources you’ll collect it from. If your goals are to understand the full customer journey, reduce attrition, drive more sales, or deliver an omnichannel brand experience, you will need to collect data from a variety of channels to get the insights you need to succeed. 

With your goals and data sources in mind, the next step is to research different data management solutions, compare their features, and select one that can help you achieve your goals.  

Here are some key features to consider:  

  • The ability to process data from all relevant sources (online, offline, cloud data, third-party data, from your other marketing management tools, etc.) 
  • Capabilities to identify and eliminate data quality issues, such as removing duplicate entries, effective data matching, and flagging other data-type errors for review 
  • The ability to continuously update reports in real-time from ongoing data streams 
  • Dashboard performance summaries that highlight key insights at a glance
  • The ability for users to access data reports from the cloud using any device
  • Collaboration tools that give key individuals access to the features they need 

Maximize The Value of Effective Data Integration  

Using an advanced data management solution is well worth the investment to avoid the perils of stitching data together manually. The value of such technology, however, extends way beyond that. Fully utilize the features your marketing intelligence software offers to maximize the benefits for your business.  

The keys to success include effectively onboarding employees to utilize your data management tool, making key strategic decisions based on data insights, and automating optimization. Once you can automate aspects of your optimization efforts, the potential to drive more conversions and meet other key marketing goals becomes much greater. 

With Centro, you can leverage over 130 API integrations across programmatic, search, social, direct, and connected TV using the most comprehensive platform in advertising: Basis. Interested in learning more? Get in touch with us today!