The necessity for lean, up-to-date, and operable enterprise data persists in digital commerce and the SaaS world. In fact, there’s no way to carry out any strategic planning and form a solid development strategy without business data analytics.
That said, unstructured and raw data must undergo complex multi-stage processing backed up with a modern data stack before data analysts lay their hands on it. An enterprise data stack is a suite of tools utilized to collect, store, transform, and extract data. The more precise, agile, and easy to deploy the company’s data architecture is, the less time and expenditure will be wasted in attempting to operationalize the existing data.
Given that, let’s consider what tools constitute an enterprise data stack, their functionality, and how exactly they ensure data accessibility and operability.
Enterprise Data in 2023: An Ever-Growing Demand for Modern Data Stack Solutions
For the past decade or so, enterprise data has grown tremendously in volume. According to Statista, external repositories like cloud-based data warehouses have doubled the volume of stored enterprise data just between 2020-2022.
Migrating huge data arrays from rigid internal systems to fast and flexible cloud data warehouses and data lakes resolved many problems data engineers wrestled with before. It led to:
- Increased processing speed. Legacy data systems lagged behind modern data stacks due to the lack of storage capacity and computing power. Cloud-based data warehouses increase processing speed manyfold as they utilize best-of-breed hardware solutions.
- Simpler scalability. The modularity of modern data stack products provides rich capabilities to scale up storage capacity and add data integrations to an existing suite of tools.
- Easier deployment. On average, it takes 20-30 minutes to set a cloud-based data stack ready and running. Modern data warehouses and data lakes will scan metadata details automatically and categorize them in a strict order.
- Reduced cost. Switching to a modern data stack ecosystem is cost-effective as you no longer need to invest in hardware upgrades and data architecture modernization.
On top of that, building business processes upon a cloud data stack is commonly endorsed due to its vast automation capabilities. Machine learning and AI-powered tools easily integrate with modern data stacks and autonomously correct common data issues like duplicates and outdated records.
Data cleansing is crucial as it helps to keep the warehouse budget lean and, most importantly, brings clarity into data analytics and consequent decision-making. The latest study by IBM reveals that e-commerce businesses can lose up to 30% of their revenue if they rely on poor data.
An Enterprise Data Stack Overview
In substance, a modern data stack can be viewed as a warehouse-centered suite of tools. Cloud-based data storage is at the core of Modern Data Stacks and serves as a hub where data gets collected, streamlined, and represented in an edible form.
This hub unit isn’t only a place where external data pipelines converge. It also feeds collected data to analytics and business intelligence (BI) tools and reverse ETL tools, allowing business teams to utilize the processed data in external applications like CRMs and martech tools.
5 Default Tools Modern Data Stack Builds Upon
Data Sources
On average, enterprises source data from at least 400 sources to get comprehensive insights into market changes and customer preferences. Modern data stacks are fully compatible with external sources (Salesforce, HubSpot, API integrations) and internal ones (CRMs, ERPs, website events, web or mobile application log files).
Extract, Transform, and Load (ETL) Tools
ETL tools source raw data and transform it into a standardized format, typically in tabular form, before depositing it into cloud storage. ETL tools ensure the clockwork functioning of modern data stack’s external pipelines but are also responsible for data cleansing.
During the transformation stage, raw data gets validated and checked for accuracy. Modern ETL processes are also AI-powered, which allows purging data from duplicates and irrelevant records and correcting incomplete records thanks to predictive algorithms.
Cloud Data Warehouse (CDW)
As modern data stack requirements evolve, more organizations opt for managed cloud data warehouses. It means that CDWs are managed by the vendor. They handle data architecture optimization by deploying additional computing and storage capacities.
The great thing about data warehousing is that business users pay for computing and storage separately. Conversely, you can’t scale up the repository separately from the computing capacity, and there’s only an option to upgrade them altogether. Therefore, managed CDW offers much more flexible and beneficial pricing.
Data Build Tools
Data build tools or DBT are open-source frameworks empowering data scientists to bring out insightful data infrastructure models that simplify analysis for data engineers. DBTs utilize SQL to build standardized models, which then can be used to optimize SQL code that extracts data from storage. In such a way, organizations can reach consistency and integrity of their actionable data and understand how it transforms over time.
Data Visualization & Analytics Tools
Visualization tools like Qlik or Tableau provide analytics and data experts with exploration views in the form of column or pie charts. With analytics and BI tools integrated into the data stack, users can build their own dashboards and make data exploration informative and insightful.
Exploration views group and categorize data that specifies the number of sales and contracts, list of vendors, quarterly financing allocated to departments, etc. Looking into case-specific business activity metrics allows stakeholders to stay informed of organization performance and devise feasible plans for strategic improvements.
4 Types of Tools Worth Adding to Your Enterprise Data Stack
Apart from the core functionality of modern data stack, many compatible and configurable components provide additional control over data quality and reuse.
Here are examples of data stack components worth adding to a generic suite of tools.
CDPs and Event Pipes
Customer data platforms (CDPs) channel valuable behavioral data to CDWs each time customers interact with a SaaS website or mobile app. That’s why they are commonly referred to as “event pipes.”
Such behavioral signals help Marketing and Sales develop personalized value prepositions and effectively drive customer satisfaction.
Reverse ETLs
If your business demands sending collected data from CDW to an external application, you’ll need reverse ETL products. They form data pipelines that import data to business applications in a standardized, compatible format. These are typically marketing automation platforms, marketplaces, or ad networks.
