A modern data stack (MDS) consists of automated data connectors, a cloud data warehouse, and a modern BI tool, all closely integrated. Together, these technologies solve the major engineering challenges underlying analytics. Imagine how much time your analysts and engineers would save if they didn’t have to worry about:
- Centralizing all of your data and resolving issues of fragmentation and silos
- Ensuring the accuracy of your data and understanding its provenance
- Updating data frequently so it never grows stale
- Making your data accessible to non-technical users to improve decision-making
An MDS frees analytics teams to focus on initiatives ranging from business intelligence reports and dashboards to AI and machine learning applications. By automating data engineering workflows, an MDS enables better decision-making across your organization and lays the groundwork for sophisticated, AI-based data products.
Implementing an MDS doesn’t have to be overly difficult — you can do it in three steps, actually! — but it is a meaningful commitment, and we recommend doing it carefully, with plenty of research and forethought. In this Boot Camp, we show you exactly what to consider, estimate and test, so you get the ROI you expect. We’ll walk you through the following steps: .
- Establish success criteria
- Estimate TCO for each MDS tool
- Chose your tools and test them
Let's get started!
Step 1: Establish success criteria
Before implementing a modern data stack, you need to understand the specific, concrete benefits it will offer. How much time and money will your organization save? What should you expect your analytics practice to look like after you’ve successfully implemented an MDS? Here are seven benefits any MDS should provide, along with examples of businesses that realized those benefits.
- Time, labor and monetary savings compared with the previous solution. A modern data stack should dramatically reduce your data engineering costs, primarily by eliminating the need to build and maintain data connectors and normalize data. Here are some examples of resource savings as a result of MDS implementation:
- Ignition Group saved $425,000 in data engineering costs when it decided to implement an MDS instead of building its own warehouse and data connectors. Read their story here.
- Oldcastle Infrastructure saved $360,000 by using an MDS to migrate NetSuite and SQL Server data into a cloud data warehouse. Read their story here.
- Carwow regained 50% of its data scientists’ time by upgrading to the more robust, streamlined data architecture of an MDS and eliminating ETL chores. Read their story here.
- Crossmedia saved 160 data engineering hours per week with zero-maintenance, ready-to-launch connectors. Read their story here.
- Expanded capabilities of the data team. By increasing data sources without consuming engineering or analyst resources, a modern data stack should expand the capabilities of your data team.
- Docusign tripled the number of data sources its team could analyze by deploying automated data connectors. Read their story here.
- Football Index chose an MDS instead of building data connectors internally and increased developer capacity by 10%-20%. Read their story here.
- Successful execution of new data projects, such as customer attribution models. More time and data mean your team will be able to focus on new analytics projects.
- Strava used an MDS to combine siloed data from ad platforms, attribution partners and customer data platforms, which allowed their data team to map the entire customer journey and develop a sophisticated attribution model. Read their story here.
- Chubbies implemented a modern data stack to combine seven sources of customer data and visualize it, gaining insight into ad spend by channel. Read their story here.
- Pleo leveraged an MDS to create a multi-touch attribution dashboard and better understand revenue sources. Read their story here.
- Reduced turnaround time for reports. A modern data stack should dramatically shorten report generation time, ensuring up-to-date reports.
- ALM Media generated reports 7x faster by centralizing data from multiple sources and applying business logic. [??]
- Bringg used an MDS to centralize and update its data, which reduced reporting time by 90%. [??] Read their story here.
- By using an MDS to automate reporting, Brandwatch saved 40 hours a week on report generation. Read their story here.
- Reduced data infrastructure downtime. A modern data stack should dramatically improve reliability and eliminate your maintenance burden.
- Keller Sports saved 20 hours per week by moving from manual data extraction, including pipeline maintenance, to an MDS. Read their story here.
- BAfter implementing a modern data stack, Ritual reduced data pipeline maintenance issues by 95%. Read their story here.
- Higher rates of business intelligence tool adoption within your organization. By combining automated data integration with a modern, intuitive BI tool, a modern data stack should be able to increase data literacy manyfold.
- Before Falcon.io transitioned to an MDS, only its small sales team routinely consulted analytics; after adopting an MDS, it saw active users of BI dashboards increase 10x. Read their story here.
- New metrics that are available and actionable. With additional data sources and an easy-to-use BI tool, a modern data stack should significantly boost the number of metrics used in decision-making.
- With an MDS, OutSystems increased actionable KPIs from 15 to 60 and improved data access company-wide; now the entire organization participates in defining key metrics. Read their story here.
- After Zoopla adopted an MDS, it created a KPI overview for its leadership team featuring 40 actionable KPIs. Read their story here.
Next up: Estimating TCO for a modern data stack
Now that you have a clear idea of the benefits a modern data stack can deliver, you’ll need to make sure the numbers work for your organization. In the next part of our Boot Camp, we’ll take a look at how to estimate total cost of ownership for each of the core technologies that make up a modern data stack.