Data has gone from being a sparse resource to a commodity. Many companies try to collect as much data as possible, leading to questions about the best way to keep data.
Data warehouses allow companies to keep their data in one, unified place. Data warehouses are capable of storing large amounts of data at an affordable rate.
Data warehousing has become an increasingly common tool in digital companies. But we already have databases capable of storing large amounts of data. So why are data warehouses such a big deal?
The problem with datasets
Before data warehouses became popular, datasets were stored in separate places. Some datasets were incompatible with other datasets and couldn’t be used for the same analysis. In other cases, datasets were owned by third-party providers, who offered only limited interaction with the data.
Why did this happen? Data originates from different places. For example, you may keep a database with all the information related to your product such as account credentials, profile information, product information, etc. But you also have a database for keeping track of the interaction users have with your product. On top of that, you could be gathering information on business-related topics such as financials, emails with leads, marketing clicks, and so on. At the moment of creation, one database seems to have no relation to another database.
Eventually, companies started to notice that interacting with each dataset individually was too clunky. Companies wanted the ability to maintain this information in one, unified place. This is exactly what data warehouses were created to do. When all your data is in one place and in the same format, you may synthesize data from various levels of organization and conduct a holistic analysis. Data warehouses have fundamentally changed the way companies gather and analyze data.
How to leverage data warehouses?
Once you have a data warehouse, there are two main avenues for analysis:
- Business Intelligence tools & SQL queries
- Automated Analytics & Business Analysis tools
Business Intelligence & SQL queries
In order to answer specific business questions with a data warehouse, you need to know SQL. SQL gives you the flexibility of querying the data in virtually any way but with the drawback of requiring coding skills.
In order to let non-technical roles gain insight from data, companies traditionally rely on data analysts. However, as more and more roles need frequent access to company data, data analysts have become a bottleneck.
Automated Analysis tools
This increasing reliance on data insights by non-technical roles has created a space for a new breed of products: automated data analysis tools. Lantern is a part of this group. Data warehouses offer easy and structured access to all the data of the company, which is a perfect setup for automated tools to offer simple answers on specific topics.
Automated tools do not require SQL or a data analyst to answer your questions. This combination returns control to both technical or non-technical data consumers alike.
The category of automated analysis tools is relatively new and has been well-received by companies. Besides Lantern, which focuses on serving startup product teams, other automated analysis tools target various teams in larger enterprises such as Anodot, Sisu, and Outlier.
Warehouses have transformed the way modern companies use data. You can now afford to track all your data in the same place. This unlocks a whole new level of analysis, allowing you to put data together from various points or perform a more holistic analysis.
A key win enabled by data warehouses has been the emergence of a new breed: automated analysis tools, which pro-actively send you insights about changes in your data.
If you are interested in giving a try to such an automated analysis tool for user behavior and product usage, you can access Lantern's early access program here.