3 Tips for a more effective data stack

Data-driven decision-making is powered by your tech stack. But here’s the thing: there’s no perfect tech stack for every organization.

Your data stack is the combination of tools and platforms your organization uses to collect, process, store, and analyze data. To make the most of your data stack, start with something simple, like an off-the-shelf product, and then continue to evolve your data stack over time.

1.  Start with 5 simple layers and build over time

Forget perfection and embrace simplicity. Your goal at first is to get things off the ground, not build a perfect data stack from the beginning. If you want to succeed in using data, get comfortable with imperfection.

That said, there are some tips that can help you get off to a good start. Generally speaking, there are five layers to your data stack.

Data source

As the first step in the data process, the source layer includes the tools that capture unstructured data to be ingested, processed, stored, and eventually turned into actionable insights. This could be first-person data (like activity on your website), second-party data (like the kind you get from a partner), or third-party data (like the information you buy from a company).

Data ingestion

This layer includes the tools that collect data from various sources, such as sensors, databases, or log files. Data ingestion tools (like Fivetran or StreamSets) collect data and transform and analyze the data. This layer also includes ETL (extract, transform, and load) or ELT (extract, load, and transform) tools.

Data storage

The storage layer includes the infrastructure used to store and manage the data. This could be databases, data warehouses, or data lakes. While this layer used to include actual warehouses, these days, cloud data warehouses, like Snowflake, BigQuery, or Amazon Redshift, offer scalable, cost-effective solutions for data teams that need to store and query large volumes of data.

Data processing

This layer includes the data pipelines or analytics platforms. Data engineers and data scientists use tools (think SQL, Databricks, or dbt) to build and maintain data pipelines, perform data transformations, and analyze data. This could include cleaning, aggregating, and summarizing the data, as well as running machine learning algorithms.

Data activation

You probably know this layer by its most famous star: Reverse ETL, which helps data teams put data to work. This allows organizations to move data from warehouses into tools like CRMs. Data activation can look like dashboards, charts, or maps. Business intelligence (BI) tools are a part of data activation, too. With analytics products, like Looker or Tableau, anyone can create interactive dashboards and reports.

2. Stick to off-the-shelf options when available

Enterprises tend to have complex, home-built platforms. And there are obvious benefits to this, like customization. But for most organizations, a home-built option will likely create more headaches than it will solve, especially when you’re just starting to build out your data stack. But pre-built platforms come with a basic set of tools that will be sufficient to get you started and will often integrate with the rest of your tech stack. And if there are ever any issues, they’ve got a whole staff of folks ready to fix things quickly.

In the past, data stacks were often built around on-premises solutions, with data warehouses at the center. However, the rise of cloud computing and the proliferation of data sources have led to the emergence of modern data stacks that are more flexible, scalable, and cost-effective.

Cloud-based data warehouses (Snowflake or BigQuery) make it easy to store and analyze large volumes of data. Data engineers and data scientists can use tools like SQL or Databricks to build data pipelines and transform raw data into real insights. No matter what you’re looking for in your data stack, most likely, the perfect tool for your needs is already out there.

Common types of marketing data stack tools

There is a wide range of data tools available to marketers, and the best tools for your organization will depend on your specific data needs and goals.

Some of the most commonly used data tools and platforms for marketers include:

• Customer relationship management (CRM) systems — like Salesforce, HubSpot, or Zendesk — help marketers manage customer interactions and data throughout the customer lifecycle.
• Data integration and ETL tools — like Stitch or SnapLogic — take data from a variety of sources into a centralized data repository, such as a data warehouse or data lake, and process the data to make it usable
• Marketing automation platforms and AI or ML (machine learning) tools — like Salesforce or ActiveCampaign — help marketers automate and optimize marketing campaigns (like email marketing, social media marketing, and lead generation).
• Web and/or social media analytics tools — like Google Analytics or BuzzSumo — measure and analyze traffic and user behavior and provide insights into customer behavior and interests.
• Business intelligence (BI) and data visualization tools — like Tableau or FusionCharts — identify trends, patterns, and insights to better guide marketing strategy and decision-making.

3. Never stop improving your data stack

You can’t get complacent with data. There are always things you can do to improve and new ways to use data to get better insights. The worst thing you can do is consider data management an issue that can be solved once and for all. Your teams will need to constantly reevaluate their relationship to data and whether the current processes and tools in place are the right ones for their needs.

In particular, keep an eye on data quality, integration, and overall data governance.

Invest in tools for high data quality

Data quality is a critical component of any data stack, and organizations should invest in tools and processes to ensure that their data is accurate, complete, and consistent. This can include the use of data cleansing and transformation tools, as well as the implementation of robust data governance policies (more on that below).

Poor data quality can lead to flawed insights and decision-making, so it is important to prioritize data quality in your data stack. Only adopt tools that take data quality seriously and are known for high data quality standards.

Keep systems integrated

One key advantage of modern data stacks is the ability to integrate data from a wide range of sources. This can include customer data from CRM systems, web analytics, or social media data. Data integration tools (like Fivetran or Talend) enable organizations to easily and efficiently collect and integrate data from multiple sources.

Integration also means incorporating a combination of first-, second-, and third-party data, for example, demographical data (e.g., age or occupation) and behavioral data (e.g., pages you visit most often on a company’s website). The wider the view you have of your customers or users, the better you’ll be able to understand and serve them.

Create forward-thinking data governance policies

Data governance includes the processes that prioritize data security and privacy — essentially, how you handle your data in regard to regulations, policies, and best practices. You want to look for tools that are in compliance with regulations, like the GDPR (General Data Protection Regulation). But it’s also important to look ahead at the coming changes to data requirements that you’ll need to meet in the future, like the impending removal of third-party cookies from Google Chrome in 2024.

Get data on tap with the right data stack

There are many different use cases for data stacks in marketing, including customer segmentation, campaign analysis, and personalization. By understanding your data needs and goals, you can design and implement a data stack that meets your specific requirements.