by Debashis Rana -
The mission, scope, and scale of your business is unique to your brand, but there is one vital element that nearly all of today’s organizations share: operations are driven by data.
The key question is if your team knows how to capture, consolidate, and harness that information to make the data-driven decisions needed to maintain their competitive advantage and deliver the experiences your customers expect.
That’s where the field of data engineering and application of artificial intelligence (AI)—and the professionals who enable them—come into play.
So what is data engineering and how can your team use it with AI to take your operations to the next level?
What Exactly Is Data Engineering and Its Role in AI?
Data engineering as a competency is generally divided into two parts:
- The operations that need to occur from the moment data is created, transformed, and consumed until it is archived or destroyed
- The software engineering aspects of collecting these data operations and artifacts from analytic activities into pipelines that can be managed using standard practices such as continuous integration and deployment (CI/CD)
This second element is sometimes referred to as DataOps, which is a blend of the existing DevOps approach with data engineering practices taken into account. For example, AI-development activities frequently integrate these concepts, as the underlying models can only be as good as the quality of the data provided to train them. Data engineering helps to provide this strong foundation by supplying high quality, continuous data that covers the breadth of scenarios that an AI solution is expected to act upon.
What Are the Benefits of AI Empowered by Data Engineering?
With the necessary infrastructure and the best data engineering practices in place, AI has the power to give your business the ability to:
- Enable faster, more accurate data-driven decisions
- Provide more agility and responsiveness to market and customer demands
- Support the development of more efficient business processes
- Leverage existing and real-time customer data to tailor services, advertising, experiences, and predict future trends
- Better protect the availability and security of data through investments in collecting and controlling information across your organization
What Investing in Data Engineering and AI Means for Your Operations
As AI’s role in enhancing business operations has evolved, it has meant that many organizations have had to adjust how they resource, structure, and manage their teams and workflows.
Here are some of the key ways that shifting to a more AI- and data engineering-driven approach could shape your operations:
Structuring Teams to Value the Power of Data and AI
Each organization is different in its mix of data analysts, engineers, scientists, and AI engineers. While there isn’t a single right answer, the critical capabilities that teams must have1 include the ability to:
- Consolidate and ingest data
- Prepare data for analytical usage
- Develop analytical models
- Operationalize the analytics
Each organization needs to invest in and find their own balance with these skills. It is also important to keep in mind that the mix will change as the organization achieves data-driven maturity.
Inventory Business Processes and Prioritize Opportunities for Ai-Driven Improvements
In order to fully leverage the power of AI, organizations need to evolve from just collecting data to transforming, labeling, optimizing, and processing2 it.
Organizations can kick-start this process by clearly documenting their business workflows—including the data that each uses, creates, manipulates, and generates—and by identifying any inefficiencies or data chokepoints. For example, these inefficiencies could be rooted in redundant steps or manual intervention, each of which can be evaluated as a candidate for improvement using automation and AI.
Scalable and Powerful Infrastructure
At the same time, doing AI at enterprise scale requires significant computing resources. It can be prohibitively expensive for some organizations.
For those that can, the cloud provides the scalability and flexibility to capture and process high amounts of data quickly and reliably. Coupled with elastic infrastructure, and Infrastructure as Code (IaC) concepts, the cloud can provide the much needed automation to achieve this balance.
Model Evolution and Governance
Businesses change over time, and so do the types of data that they need and the analytics they produce. Therefore it is critical that the data models used to implement and maintain AI-enabled technologies are updated and retrained to keep up with these changes. Lack of evolution will cause a solution to make recommendations or decisions that are not up-to-date.
Take the Next Step with a Trusted Partner
It’s one thing to understand the ability of data to empower your teams, streamline your processes, and drive enhanced customer experiences. It’s another to put the necessary processes, roles, and technology in place.
That’s why no matter where your organization is in its embrace of data engineering and AI technology, RCG is ready to be your trusted partner. Our deep bench of experts is ready to help guide your organization through the next phase of its digital journey and identify the best tools and investments that you can use to achieve the best outcome for your business.
Ready to get started? Then click here to set up your own meeting with an RCG expert now!
Works Cited
1. QuantHub (2020) "What is Data Engineering and Why Is It So Important?" Retrieved from https://www.quanthub.com/what-is-data-engineering/
2. Google Cloud (2021, Sept 23) "Building the data engineering driven organization from the first principles" Retrieved from https://cloud.google.com/blog/products/data-analytics/building-the-data-engineering-driven-organization