In a previous CTA, we discussed building a 360-degree view by stitching together data from a la carte tools such as Google Analytics, Facebook Pixel, and Salesforce. However, for many companies, customer data is spread across a variety of cloud-based or traditional databases. How do you get that data into a single place so that your data can be analyzed for 360-degree insights?
Organizations handling multiple types of data from different sources need both a data storage and a data analysis solution. The most common data storage frameworks are Data Lakes and Data Warehouses. Let’s look at what makes up a Data Warehouse.
Consolidation into a Data Warehouse
A Data Warehouse is a repository – requiring engineers to build ‘entry criteria’ for data entry – organized according to the data models that the organization wants to analyze. The end result is a well-structured set of data forms and schema.
Because only very well-organized and specific data is collected with this approach, analyzing data in a Data Warehouse can be very accurate and powerful. This setup is harder and you may potentially miss data that will be valuable in the future because you’re only collecting what’s in the schema. But, what do you do if your data is unstructured and outside of a schema?
Consolidation into a Data Lake
Data Lakes were born out of the desire to avoid the ‘rigidness’ of Data Warehousing and better handle the needs of companies need to analyze a lot of unstructured (i.e. ‘Big’) data. Data from external sources (ie: social, video, voice, text) ‘flows’ into this lake through a number of different streams (ie: channels) all related to the customer, but not in a structured format. What you end up with is one, massive table of data unfit for the analytical tools you can use within a Data Warehouse.
Whether your organization’s requirements are a better fit for a Data Warehouse or Data Lake, you need a way to analyze all that data quickly so that your data is actionable.
How a Marketing Team Consolidated their Data in 15 Minutes
A national media publication with over 100 websites and hundreds of millions of metrics and customer segments recently needed to integrate their marketing datasets, including Amazon Redshift and Adobe Analytics. They wanted to be able to track marketing success with application downloads, email delivery, and website traffic.
Across this disparate data, they wanted to adjust advertising prices based on changing customer behavior on their site. This company invested in a data lake to house all of their critical data sources into a single location. This enabled easier reporting and in-depth investigation. However, they still needed an automated solution to discover new trends and did not want to wait until all of data was in their Data Lake to start exploring solutions.
By incorporating an Automated Business Analysis platform, like Outlier, the firm was able to integrate their three datasets into one view within 15 minutes to track unexpected changes and patterns. They can now make fast adjustments to advertising cost based on the latest trends in their data. When their Data Lake project is complete their Automated Business Analysis platform will simply point to the new location with no downtime to their process for adjusting advertising spend.