5 technical challenges for building a data warehouse and how to overcome them

The end of third-party cookies is a concern for marketers, as the volume of information at their disposal will be greatly impacted. And yet, companies collect and use data from various tools, such as CRM, web traffic, emails or social networks. This data could be a big help!

Unfortunately, data can quickly become siloed and difficult to manage. Building a data warehouse can help consolidate this data, provide valuable insights for marketing teams, and enable them to step into data-led growth. However, this process can be complex and present significant technical challenges, particularly when managing vast amounts of customer data.

In this blog post, we will explore five of the main technical issues that B2C companies may face when building a data warehouse for their marketing team and provide strategies for overcoming them.

Integrating data from various sources is the first and most significant challenge companies face when they engage in setting up a data warehouse. Marketing teams rely on a wide range of data sources, including customer data which is usually on a CRM; website analytics typically based on a tagging tool such as GTM; social media metrics taken from various social media platforms such as Facebook, TikTok, Instagram, etc., and emailing related information. Integrating data from these different sources can be time-consuming, particularly when many data sources are used. Reconciling different data structures, types, and formats could be challenging and require dedicated resources to maintain it. These data providers have no incentive to standardize and make it easy to analyze their data elsewhere; they mainly want you to return to their specific platform.

Luckily, there are now technical solutions to solve this data structure puzzle: modern ETL (Extract, Transform, Load) tools that can automate data integration from multiple sources. They enable extracting data from different sources and transforming the various flows into a standard data structure before loading it into the data warehouse. By automating the integration process, companies can save time and ensure that the data is consistent across all sources. You can use both SaaS solutions or open source ones, based on your technical skill availabilities and business needs, such as data privacy constraints (legal or not) which incite some companies to keep data on their own technical infrastructure.

Data quality is another critical challenge for companies when building a data warehouse. Ensuring the integrity of data is of utmost importance to marketing teams, as inaccurate reporting and analysis can impede their decision-making process. To guarantee data quality, B2C enterprises ought to institute a comprehensive data governance framework that incorporates data validation, cleansing, and quality assurance. Certain specialized instruments, such as Sifflet, can assist in maintaining data pipelines of the highest caliber. To avoid any discrepancies in data, it is crucial to ensure its accuracy, completeness, and consistency.

In addition, we strongly recommend using a hashing function to protect sensitive identity-related data, such as emails, during transfer. This technique involves encrypting data and transmitting it as an encoded version. Although most platforms do not require specific email addresses, they can work with an encrypted substitute.

The data volumes grow. That happens either as your business grows or as you want to add more to the warehouse. One C-level we interviewed had his data warehouse become unusable as his team decided to add a sudden flow of granular data relating to each customer (a broad set of details on each transaction).

The data warehouse must scale to accommodate growth in volume and complexity. It is important to ensure that it does so without affecting performance and query speed (i.e., its usability). Furthermore, that lack of flexibility and scalability can impact business operations if that data is put in motion in your processes and workflows.

Companies can design their data warehouse with scalability in mind, using technologies such as columnar databases and distributed computing platforms. Additionally, using cloud-based data warehousing solutions can provide unlimited scalability and flexibility. Whether you design it yourself or buy a cloud-hosted solution, you must ensure that it can grow and adapt to changing business needs.

Performance is another challenge when building a data warehouse. As the data volume grows, the query response time can slow down, impacting business operations. Slow query response times can result in delayed reporting and analysis, making it difficult for marketing teams to make informed decisions. If you need to dive deep into the data, slice customer data, analyze it, and repeat it, you want a fluid tool. A slower one will incite you to grab a coffee and wait, then stop exploring the data.

There are ways to improve query performance by using techniques such as partitioning and indexing. Partitioning involves dividing the data into smaller, more manageable segments, which can improve query performance. Indexing involves creating an index on specific columns in the data, which can also improve query performance. Additionally, using high-performance hardware and optimizing database configurations can also improve query performance.

Lastly, on this topic, some data warehouses, such as BigQuery, allow you to keep some of the data in external tables without loading them in the data warehouse. While this is quite convenient as it requires much less technical work to channel the data and might be useful for data governance, it does impact performance. We would not advise such a setting if you were to query your data warehouse in real-time and re-inject instantly in customer-facing applications such as an e-commerce website.

Security is a crucial challenge for B2C companies when building a data warehouse. A data warehouse may contain sensitive customer information that must be protected from unauthorized access. Data breaches can result in significant financial and reputational damage. To mitigate this risk, ensure you have robust access control and implement best practices for data encryption and privacy.

The various data connections from your data sources to the data warehouse must also be secured using encrypted transfer protocols. Using secure connections is now a standard in the tech and data world; you might just want to ensure that box is checked. As a tip, we have sometimes seen secure connections but with some loopholes, such as transferring or exposing API keys without encryption.

You should also implement data backup, and disaster recovery plans to ensure data security. These plans should include regular data backups and procedures for restoring data in the event of a disaster or data breach.

Building a data warehouse is a complex process that involves several technical challenges. However, by addressing these challenges head-on, B2C companies can regain control over their data and leverage it to make informed business decisions. To summarize, the five technical challenges that B2C companies may face when building a data warehouse are:

By leveraging modern ETL tools, implementing data governance frameworks, using cloud-based data warehousing solutions, optimizing database configurations, and implementing robust data security measures, companies can overcome these challenges and build a robust data warehouse to get enhanced data insights and drive data-led growth.

If you're a B2C company looking to build a data warehouse for your marketing team, this blog post has provided you with valuable insights and strategies for overcoming these technical challenges. At DinMo, we help B2C companies like Diptyque, Salto, or Galeries Lafayette, leverage the data in their data warehouses to fuel their growth. Contact us today to learn more about how we can help you leverage your customer data to enhance your business.