Enterprise modernization tends to be a popular discussion point whether planning roadmaps, defining strategy, or even optimizing costs. In recent years, the enterprise data platform has been front and center in this very discussion.
The paradigm shift is evident. In the traditional enterprise, data was seen as a utility to support the business. Now, it is a vital asset that plays an integral part in making key decisions for the data-driven organization. The modern data platform is the architectural shift needed to adhere to new demands and very exciting opportunities.
Forging ahead into this new architecture will surely yield rewards, but like many major structural changes, there can be risk factors that jeopardize adoption. Proper planning is key. So where do you start?
Once you decide to adopt the modern data platform architecture, you now have the opportunity to eliminate the pain points that currently exist in your organization. Do you have data ingestion limitations? Are there data quality issues that cause the business not to trust the data? Perhaps there are disagreements between finance and sales on what certain data points truly mean. Transforming your business into a modern data architecture is an opportunity to create a new data culture by taking the lessons learned from previous history and implementing new stable approaches that build trust in a scalable data platform.
The Data Lake
The data lake is a great place to start when visualizing what a utopian state looks like for your organization since it can be a central repository. In this stage, data governance comes into play as a key aspect of the data-driven organization.
To avoid having this brand-new data lake turn into something of a data swamp, proper controls should be in place. This will help in designing the architecture and promoting a healthy data culture. What data can we ingest? What will our data zones look like? Who has access to data as it travels and transforms into data lake zones? What is the data retention policy for this department data?
Perhaps the most important question: what does our semantic model look like for our business as we create data models into specific working zones in the data lake? Is that defined yet? As you can see, asking the right questions and getting the right people in the organization to provide an answer are key aspects of having a successful initial phase of modernizing your data estate.
The initial phases of the modern data platform implementation should build excitement for the organization, and this can begin in a parallel state without any disruption to existing processes. Compared to a traditional data platform, various types of data (not just relational) can now be ingested with considerable velocity.
Previous data inconsistencies have been identified and are on the way to no longer becoming pain points via proper data quality and master data management solutions. There is now an agreed upon definition of the semantic data model for the targeted business domains. Once matured, these data models will eventually be consumed by power users, business analysts, and even critical reporting. A single version of the truth is attainable.
Feature planning in a modern data platform is another benefit of modernizing the data architecture. Traditionally, an enterprise-class solution right out of the box may have had some features that were included in the price, but the organization may not have been ready to take advantage.
Modularization is a very intriguing aspect of this architectural approach. An enterprise can implement only needed functionality. This is evident in doing a search for modern data architecture diagrams and seeing various solutions for various needs. Beneficially, one size does not fit all. With modular functionality and consumption-based pricing models, you are only paying for what you need.
Taking our example from above and using the Microsoft Azure platform as a reference, the initial modern data architecture could look as simple as Data Factory ingesting various source data into the designated data lake zones. Multiple options are available as we get closer to the data presentation layer. Does a Data Warehouse make sense? Does the data go straight to a Power BI dataset? You have options.
Once the organization has adopted a healthy data culture and is well on the way to true data-driven maturity, new opportunities become available. The most obvious is targeted data science solutions. An immensely vital prerequisite to taking advantage of machine learning is having a robust collection of quality data to traverse over. Detecting patterns over poor data does not yield the greatest results.
Luckily, your methodical approach to building out a well-governed, well-defined semantic business data model allows you the opportunity to adopt these new technologies with a mature data foundation in place. Your newly formed team of data scientists can find valuable inferences about the business using data from sources that were not available previously.
As you can see, the end goal of modern architecture like this is to enable the business and serve customers at scale, all with trusted data. It is not easy and organizations that don’t put in a diligent effort upfront during planning and governance formation will meet certain frustrations. However, a methodical approach while asking the right questions of the business will allow you to turn your data into an enterprise asset. The data engineering team at Improving is ready to help you navigate this modern data journey.