5 Data Integration Methods to Streamline Black Friday Operations

Modern data integration methods have made it possible for businesses to achieve operational efficiencies that would have been unheard of a decade ago.

For retail platforms, this is especially important when there’s a sudden spike in business. According to the National Retail Federation, 90.6 million Americans made an online purchase on Black Friday 2023 - one of the biggest spikes of the year.

Inevitably, this poses several technical challenges for ecommerce operators. Here, we investigate those challenges and explore five data integration methods that can help your business cope when the time comes.

The technical challenges of Black Friday

Efficient data management is an essential component of any online business. For ecommerce companies this can become especially challenging when Black Friday (and the following Cyber Monday) rolls around. Even for enterprise-scale companies, the huge spike in business volume poses a few tricky technical issues.

Surge in web traffic

First, there’s the sudden surge of traffic to your website. You can mitigate many potential problems in advance usingsmart web design, but the rush of customers arriving all at once can still put a strain on your resources.

The underlying issue here is that your network reliability is key. To deal with sudden changes in growth, traffic, and demand, you have to make sure your data processes are scalable and flexible.

More security threats

It’s not just your customers who look forward to getting a bargain on Black Friday – the bad guys do too. The spike in online activity creates more opportunities for hacking attempts and scope for data breaches.

This means a focus on data security is more important than ever. Implementing robust firewalls and carrying out extra security audits are good ideas. But it’s also crucial to make sure you have a sound data integration strategy in place, so your entire network is on a secure footing to begin with.

Complex inventory management challenges

Getting inventory levels spot on is a challenge at the best of times. But with Black Friday, it can be particularly difficult. If you overstock, you’ll be left with a surplus that may be difficult to shift afterwards except via serious discounting. On the other hand, understocking means lost sales.

Of course, modern ecommerce companies usually have dedicated inventory management systems in place to ensure efficient stocking. A crucial element of that is using effective data integration solutions so that customer orders and stock levels can be reconciled in real time.
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Spike in concurrent transactions

If enough customers visit your website, you may find there’s another technical issue to deal with. That is, hundreds of them clicking on the buy button for one specific item at the same time, putting an unusual load on one area of your database.

As a result, there could be a slowdown in the order processing time for each user, leading to higher cart abandonment rates. It’s clear this could turn into a serious customer experience optimization challenge. Once again, making sure your data integration process is as efficient as possible can help.

But what exactly is data integration, and how can different data integration processes impact overall site performance? Let’s take a look.

What is data integration?

Data integration is the process of bringing together data from disparate sources into a unified format and location. The objective is to make it easier to work with for a variety of operational and analytical purposes.

Optimized data pipelines consist of a number of discrete processes. Here’s a rundown of the main components of data integration, along with an explanation of what each one involves.

Source identification: First of all, you need to identify all your disparate data sources. For an ecommerce company, that could mean your CRM, inventory database, and other business tools like ERP platforms. There might also be some operational or business intelligence data held in spreadsheets or APIs you want to integrate into a centralized repository. If you run special events for customer engagement and brand promotion, then your event ticketing solution can also be a source of critical data.  

Extraction: Once you know which data you want to integrate, you need to extract it and move it where you want it. Unless all your data is already held in a single location, it’s likely this will involve a number of processes. For instance, you might set up queries on an in-house database, as well as pull files from various cloud platforms.

Validation: Data quality checks are vital. Data validation refers to the process of scanning the extracted data for inconsistencies and other errors that could cause problems with accuracy further down the line. This step is crucial to ensure the data you’re integrating is as reliable and accurate as possible.

Transformation: So, you’ve found the data you need, extracted it, and run checks to make sure it’s accurate. The next step is to convert it into a consistent format to make sure the different streams of data are compatible with each other when the time comes to use it. Data cleansing, enrichment, and normalization all form parts of the data transformation step.

Loading: Once the data has been transformed, it’s ready for loading into your chosen location for analysis or reporting. There are two main approaches to the loading phase. You can use batch processing, loading sets of data at punctuated time intervals. Or you’ll load the data continuously and in real time. Either approach can work well depending on your overall data integration objectives.

Analysis: Finally, you can now begin to run analysis on the data or use it for your preferred reporting purposes. For instance, you can give business intelligence tools access to generate customer data unification profiles for marketing purposes.

5 data integration methods to help optimize performance

When it comes to data integration, there’s no one right answer. Instead, there’s a number of options available and the one you choose will depend on several factors including the size of your business, how fast you expect to grow, and the technical resources you have available. Five of the main methods include:

1.   Uniform data access

The basic idea behind uniform data access is that users can access data from different sources in a unified view without you actually moving the data, which stays in its original location.

This method has the upside that you don’t need additional storage space, so it can be a good solution for smaller-scale operators which run relatively simple operational systems.

On the other hand, it’s only really suitable for integrating data sources that are similar. If you use a diverse range of software applications and data formats, it may not be the best choice.

2.   Manual integration

It’s possible you could have data that comes in a very specific format that’s difficult to consolidate. For instance, suppose when your sales agents make outbound calls, they use a specialized note-taking system to record in-depth observations about their conversations with clients. In that case, manual interventions of some kind could be necessary.

Traditionally, manual data integration involves a human taking responsibility for every step of the data integration process, from collecting the data, to cleaning it, and loading it into the chosen database.

It’s quite a time-intensive process, but - depending on the scale of the project - it can be quite cost-effective because you don’t need to pay for dedicated integration software. So, it can be an option for smaller operators that have unique data formatting needs.

3.   Data warehousing

Sometimes called Common Data Storage (CDS), this is one of the most popular data integration methods. It refers to the integration technique of taking data from multiple sources and loading it into a central repository known as a data warehouse.

One big plus of CDS over a method like uniform data access is that it allows you to execute more complex analytics processes without having to worry about overloading transactional databases. This gives ecommerce operators a lot more flexibility when dealing with high-volume periods like Black Friday.

​​​​​​​4.   Application integration (API)

Application-based data integration uses programs designed to find, extract, and integrate data between different software applications. It’s most useful for situations where you want to share data between apps that need to work together, such as your CRM and order processing system.

The advantage is that it’s fairly simple to use, but implementation does become more complex as you add more system interfaces and data types. So again, this type of data integration method works best for operators that use a relatively small number of data platforms.

5.   Real-time data integration

Perhaps the most important factor influencing how successful your Black Friday approach will be is making sure your systems have access to data updated in real time. With so much data being generated over the day, it’s crucial to be able to derive actionable insights on a moment-by-moment basis.

For instance, analyzing clickstream data to monitor customer behavior on your website gives you real-time feedback about how well optimized your site is for maximizing sales. If you can integrate that data and act on it immediately, you’ll put yourself in a good position to give sales a boost during this crucial period.

You can use real-time data streaming techniques like change data capture to apply any updates to the data in source systems to centralized repositories such as data warehouses.

Final takeaways

While there’s a wide variety of integration technologies available to ecommerce platforms to cope with periods like Black Friday, finding the right one for your business can be a  challenge. The key is to assess your business objectives first, then consider how best to allocate your resources.

With the right system in place, you’ll find that it’s much easier to streamline all your Black Friday operations and achieve better sales than ever - plus, you’ll have all that data on hand to analyze later, and improve your business for the next year.