Data warehouses are the central data repository that allows Enterprises to consolidate data, automate data operations, and use the central repository to support all reporting, business intelligence (BI), analytics, and decision-making throughout the enterprise. But designing a data warehouse architecture can be quite challenging.

Analysts and data scientists use SQL queries to pull data from the data storage underbelly of an enterprise. They mold the data, reshape it, and analyze it, so it can offer revenue-generating business insights to the company. But analytics is only as good as the material it works with. That is, if the underlying data is missing, compromised, incomplete, or wrong, so will the data analysis and inferences derived from it.

HubSpot is one of the leading CRMs for fast-growing companies. It allows you to run your marketing and sales pipelines smoothly from a single web application. But there is a problem. HubSpot offers limited tools to track and analyze your data. To answer your most pressing analytical queries you need to work with the raw data that is hidden in your CRM.

When your marketing team manages a myriad of social media platforms (from Facebook to TikTok), it is hard to keep an eye on the ROI of your marketing efforts. Each platform comes with its own set of dashboards that provide marketing analytics. But the trends and insights from those dashboards are limited to each specific platform. Knowing how you performed on Google Ads tells you very little whether you should increase your Facebook advertising expenditures.