A modern data stack, or a modern data architecture, is a suite of tools that helps businesses manage, integrate, and analyze their data to proactively uncover new areas of opportunity and improve efficiency. Data infrastructure is typically focused on internal data sources across disparate segments – data warehouses, data lakes, and databases.
An organization's data architecture needs to be equipped with the right data, at the right time and in the right format to improve its decision-making processes. With the help of new technologies, data teams can incorporate external data into their workflows, and modernize their company's data architecture. This is especially important for predictive analytics and machine learning use cases.
Overlooking external data is a missed opportunity for companies. It provides important context not always captured in internal data: economic trends, consumer preferences, weather, reviews, social media trends, competitive intelligence, and more. The insights generated create a competitive edge, keeping companies a step ahead of industry peers by improving customer acquisition, streamlining operational efficiency, and managing risk.
This blog post will outline the core components of a modern data stack and explain why it is essential to have an external data platform as part of that stack.
Data storage and low-latency processing or compute are foundational requirements for the modern data stack. Data engineers and analytics engineers leverage metadata to build data catalogs which provide an inventory of available data assets within their organizations. Cataloging uses metadata to data helps data scientists and data analysts collect, organize, access, and enrich datasets.
In the first stage, internal data is sourced from various data ecosystems such as RDBMS, Files, Web services, and Apps. It then undergoes ingestion and is stored as raw data in data warehouses and data lakes. Obtaining the services of a cloud data storage provider has become a more accessible and affordable option over on-premise storage. Raw data is increasingly being deployed into the cloud. Many organizations rely on SQL cloud databases such as Snowflake and BigQuery to store their data. Some startups may even consider data storage solutions such as Databricks, which are pioneers of storing data in open-source cloud data lakes.
Datasets stored across multiple data warehouses and data lakes are integrated into a single table or view, called a database schema. Guided by new technology, large companies and startups have more flexible ways to support demand for new data in real-time. Modern methods to achieve this include data virtualization, message-oriented movement, replication, and streaming data integration.
Data preparation and quality are the two critical steps to take before the datasets get processed by data analytics tools. The datasets will undergo data transformation, formatting, and cleansing to ensure accurate and reliable results for an organization's predictive models.
A newer component of the modern data stack is external data management. External data platforms enable BI tools to automate and streamline steps required to effectively incorporate external data into the overall data and analytics strategy. This function is essential to boost business intelligence and machine learning models since it adds important context that businesses cannot find in internal data.
The end-users of the modern data stack are data consumers, consisting of data analysts, data scientists, marketers, etc. This layer is where most people in an organization would interact with data visualizations and analysis. Users at this stage, such as SaaS providers, can access data catalogs and external data platforms to uncover insights and patterns of consumer behavior and market forces, in order to improve an organization's decision-making process.
Traditional data acquisition is a lengthy process adding challenges to the data science process. Data scientists spend 80% of their time on data wrangling.
Companies today understand the need to look beyond their four walls for external data sources that provide crucial insights, enrich their internal data, and create a deeper understanding of market conditions.
When leveraging external data, manually conceptualizing, testing, and performing analysis for each project takes too much time and leads to tunnel vision. Organizations may place undue importance on hoarding every gigabyte of internal data and thus make the incorrect assumption that internal data is the only data available to them.
Subsequently, all of the datasets stored in data warehouses or data lakes eventually become too voluminous to integrate into a single dashboard. It takes data scientists and data analysts months to process data.
This timeframe has impacted data preparation and quality. Since data pipelines are too vast, it becomes impossible to clean and prepare data for analysis in a reasonable time frame. Add on the need to manually tag data with metadata for easy retrieval; the effort required is not scalable, even for the largest corporations.
When the process of cleaning, preparing, and cataloging data isn't performed in real-time, businesses are no longer using real-time data insights. Outdated data is insufficient to drive time sensitive business decisions. This trickles down, negatively affecting other aspects of the data stack.
The solution is an automated external data platform that delivers improved external data discovery, automates access to thousands of pre-vetted data signals, and curates them for quality and reliability. The datasets form a single and collective catalog that eliminates the need to match and integrate each dataset separately. The data catalog then connects end-users like data scientists and data analysts to the data sources. It also helps end-users find their way around them, suggest the most relevant data points, and provide ways to match and integrate them with internal data sources automatically.
Explorium's External Data Platform is designed to deliver rich external data discovery. The platform improves data analytics and machine learning models by automating access to a multitude of data signals from a variety of proprietary, premium, and public data sources while adhering to privacy laws and ensuring compliance.
The advantage of implementing this platform into your modern data stack is that the data sources have been vetted and curated for quality and reliability. This external data platform forms a single and collective catalog. Since the data as a whole is treated as a single resource, it empowers data marketers to choose whether to enrich existing datasets or create new ones.
All these features aim to remove roadblocks associated with finding and acquiring the right external data. This will make it easier for data engineers and analytics engineers to build data pipelines and leverage data to be ready for analytics processes.
As for the end-users' interaction with the data catalog, the visualization of the impact of the data signals will help evaluate the uplift in machine learning models before deploying a new strategy for an organization's marketing decisions.
Learn more about modernizing your data architecture and incorporating an external data platform in your modern data stack. Download our white paper now: “External Data Platforms as Part of the Modern Data Stack.”