As a former CDO, I know that CDOs and digital transformation leaders are currently refreshing their 2022-25 strategies and looking for new ways to accelerate value while continuing to chip away at their data foundation goals. The goal is to create a sustainable and agile data value engine. This post presents three new actionable ideas that every CDO and digital leader should consider to get the most out of their strategic investments.
#1 Powerful Analytics Engines are Becoming a Commodity
Data scientists have been perfecting their craft and are continuing to publish advanced analytics engines that are reusable and designed for specific industries and their associated use cases. For instance, in retail, there are analytics libraries that can be quickly put to work. We are starting to see the same in new business models as we learn to replicate and scale some of the most sought-after algorithms in areas like computer vision and intelligent vehicles.
If you are still building your own code, consider how you can leverage some of the commoditized analytics capabilities that are available. Unless you’re one of the Big Five (i.e. Alphabet (Google), Amazon, Apple, Meta (Facebook), and Microsoft), you don’t have to write or build all of your algorithms anymore. There is plenty for your data teams to do, let them focus on the more strategic analytics.
Take Action: Shift your data strategy to leverage algorithms as a commodity. This brand new mindset will give you a better return on your data-driven investments.
#2 The Data Pipeline is Your Biggest Opportunity
Our analytic capabilities have grown faster than the data supply chain. We have powerful analytics engines that are available and affordable, but we are struggling to supply them. The bigger the analytics engine, the bigger the demand for data fuel. We will not realize value from our investments in digital analytics tools if we can’t bring the data forward.
Moving data from applications into a data lake takes time, merging lakes takes time, building structured warehouses takes time and is disruptive. In a digital world where we are trying to analyze data in seconds, does a data migration program make sense? Sure, we can analyze data quickly, but it is going to take months to move it over? There has to be a better way and there is.
Leading digital organizations are leaving data at the source and implementing Data Mesh concepts. The impact is more immediate and even better, Data Mesh gives you the flexibility to adapt more quickly to future demands. Data architects need the flexibility to leverage data warehouses and lakes when those technologies make sense (i.e. cost, time, disruption vs value). Where they don’t make sense, Data Mesh enables you to quickly integrate the data source and perform fast analytics for most of your needs. As a standard framework, Data Mesh also integrates with your existing ecosystem so that you continue to generate value from the data foundation tools you already put in place.
Take Action: Learn how the Data Mesh fits into your architecture and digital strategies.
- Check out our video series: The CDOs Guide to Data Mesh
- Contact Starburst for a Data Mesh Strategy Workshop
#3 Data Democratization Moves From Hope to Necessity
We’ve been talking about data democratization for at least two years and we have all struggled to create a real plan or generate executive buy-in. We envisioned our organizations sharing data across business domains with ease and grace, solving difficult business problems, and uncovering new business opportunities. We then faced the challenges of data sovereignty, data privacy, and we got stuck between traditional business verticals that had never shared their data before. Is 2022-25 the timeframe that data democratization becomes a business necessity?
In the next three years, we are going to see the further commoditization of powerful analytics engines and the opening of fast integration across the enterprise with a Data Mesh. Our data leaders must have a plan in place to facilitate and incentivize data sharing. In turn, industry leaders will find ways to bring data sets together, discover new insights, and they will create new digital business models.
In terms of execution, data leaders will shift to managing data products vs managing fields or tables. Data products represent an aggregated data set that includes metadata, code, ownership, and provenance. We can create policies and rules for how we want to share reusable products, we can track how they are used and we can scale new products. The data democratization strategy must shift from, “How are we going to share the data?” to “How can we start creating valuable data products together?”
Take Action: Data products are key to a robust data strategy. Start small by defining data domains and then focus on the most valuable data products that are used in each domain. Engage us to help you understand how to create federation policies and self-service capabilities to help you govern.
In my next post, I’ll write more specifically about digital transformation strategies, stay tuned!