Effective digital marketing needs to be data-driven, but data flows in from a number of disparate sources, making it hard to see the whole picture at once. To truly understand the extent to which any of your marketing activities are performing well, you’ll need to run dynamic reports that incorporate factors like ad campaign parameters, customer lifetime sales, support ticket volume, and many other metrics from channels that lie outside the purview of the marketing department.
Generally, each information source is presented in a siloed environment with its own data structure. You have much to gain if you manage to integrate all the data derived from these varied silos into a single, 360-degree view that connects all the dots.
Table of Contents
- (Guide) Why You Need to Consolidate & Integrate Your Marketing Data?
- Data Integration as it Used to be –
- The Next Generation of Data Integration –
- Cloud-Based Data Warehouses:
- Integration Apps:
- Data Integration Middleware:
- Data Lakes:
- This is what Good Marketing Data Integration Looks Like –
- Integrated Data Produces Better Marketing –
(Guide) Why You Need to Consolidate & Integrate Your Marketing Data?
Data Integration as it Used to be –
There’s nothing particularly new about the concept of data integration. Traditionally, this was a complicated and tedious process. In smaller businesses, it was done manually, with a lot of copying and pasting into spreadsheets. At the same time, larger marketing agencies and departments mostly relied on Extract, Transform, and Load (ETL) apps to consolidate information in data warehouses.
ETL apps from vendors like Oracle, Microsoft, and IBM would extract data from each data silo and then convert (transform) it to match data formatting rules in the marketing data warehouse. Once the data was converted, it would be loaded into the target database, where marketers could analyze it against other data and mine it for insights, often with the help of a dedicated business intelligence team.
This was fine while the flow of data was relatively slow, but “big data” has opened the floodgates. One survey found that 32% of companies switched from ETL apps because they can’t keep up with the volume of data. Another 24% cite the need to provide self-service data across the organization, while 23% say they need higher data quality for machine learning projects.
Marketers at businesses of all sizes have been forced to move on.
The Next Generation of Data Integration –
Today, there’s a whole range of data integration options, some of the more sophisticated than others. Marketers need to consider the best data integration methods, depending on the specifics of your team’s data needs and sources.
Cloud-Based Data Warehouses:
Data warehouses store information from multiple sources that have already been converted into a single common format, so it’s a go-to solution for larger corporations with several silos. It’s a good way to get fast responses if your marketing department runs a lot of queries, and marketers who don’t have a strong background in data manipulation find it to be relatively easy to handle because all the data has already been processed.
You can use data warehouses to answer most data-driven marketing queries, like checking open email rates against customers who convert to premium subscriptions. However, it’s not the best choice if you want more flexibility to play around with the interaction of multiple data streams and sources, and you still need a solution for standardizing formats before your data can be pushed to the warehouse.
Application-based data integration is the simplest data integration method. It uses software to directly connect and integrate data from different sources whenever you run a query, rather than combining the data in a single repository. It’s most commonly used for on-premise databases.
Integration apps are relatively inexpensive to set up, and the better ones work well for small marketing departments. In service-based businesses that don’t have too many data silos or too much data volume, this is often a viable solution. When the amount of data or data sources grows too great, though, application-based integration can’t keep up.
Data Integration Middleware:
When it comes to data integration, middleware is similar to application-based integration, but it uses another layer of software to connect a web server with your database system. With middleware, it’s the webserver that goes directly into each data source to retrieve the data you need for your query, so while you won’t necessarily have all of your data stored automatically in a central repository, you will be able to see what you need, when you need it, with minimal lag time.
Middleware is well suited to marketing departments or marketing agencies that handle numerous client accounts because it can operate better in the cloud than application-based integrations.
This is the most sophisticated option that allows for the most connections and analysis between different data sources. Unlike data warehouses, data lakes store unstructured data that hasn’t yet been converted, keeping it in raw form in large repositories. This way, you can access every data point you need, and you can combine them any which way you like.
Data lakes are ideal for large enterprises that absorb vast amounts of data every minute from IoT devices, security sensors, or social media interactions, for example. They can support far more complex queries, like monitoring seasonal trends against economic fluctuations and email subject lines. Still, you’ll need some level of data science expertise to be able to manipulate the raw data.
This is what Good Marketing Data Integration Looks Like –
Data needs to be trusted and understood throughout the marketing department and delivered in a controlled but flexible way. That requires following specific data integration best practices:
Data decay almost as soon as you acquire it, so it’s important to set up automated data exchanges to keep your data up to date and avoid human error creeping in.
For marketing teams at companies that have the means to do so, it’s also a good idea to automate your code and process management, so that you can reuse processes and be more efficient.
Compliance requirements are rising all the time, so it’s important to make sure that you don’t lose any data points while integrating your data, even if they’re not relevant to your marketing queries.
Schema mapping involves mapping all the attributes of your data points and then searching for common attributes. It helps to ensure that no data points get lost.
Raw data is heterogeneous and needs to be converted into unified, consistent data. Record linkage involves looking for ways to connect records that concern the same person, event, or other common attributes.
For example, if you want to track the effectiveness of your email marketing campaign on conversion, you need to link the person who opened your discount email offer with the person who redeemed a discount code on the website.
Maintaining data quality is vital for data-driven marketing. You need to weed out any flaws, inconsistencies, or corrupted datasets because corrupted data can spread and affect other records.
Data fusion is the process of resolving conflicts between different data sources to identify real-world truth and remove invalid data. For example, you need the ability to avoid accidentally comparing data about Amy Winehouse’s social media activity with Amelia Winehouse’s customer support tickets and Amanda Winehouse’s purchases, so that you can track her entire purchase journey.
Integrated Data Produces Better Marketing –
When you integrate your marketing data, you can use it to drive marketing campaign decisions up a level.
Using the best new data integration method that’s best for your marketing needs – and following data integration best practices to ensure that your integrated data is consistent, compliant, high quality, and uses automated procedures – allows your team to integrate and consolidate all available data signals for optimized marketing results over time.