Blog October 31, 2017
3 Reasons Why Data Engineering Is More Important Than Data Science for Companies Today
Today, data science gets all the headlines. If you open any newspaper, you are likely to find an article about some facet of it. Elon Musk states we should be worried about AI; yet, Bill Gates says it will make life easier.
From cool functions, like the self-driving car, to the more mundane tasks, like identification of spam mail in your inbox, new applications of Machine Learning (ML) are changing the way we live and work. In fact, 81% of Fortune 500 CEOs considered AI/ML important to their business in 2018. If data science is changing our lives, then why is data engineering so important?
Even with all that hype and buzz, none of the concerns raised by people like Musk are a reality today. Instead, we're seeing the development of all the technology that must come first to make data science applications successful.
That is where data engineering comes in.
- With No Data Engineering, You Have No Data Science
Without data engineering, there is no data. Without data, there is no machine learning and no AI. Data science needs data upon which to apply algorithms. - Data Engineering Gives Your Data Velocity
Stale data doesn't allow you to make real-time decisions to more accurately predict things such as customer retention, churn, fraud, etc. It isn't helpful to identify fraudulent credit card activity three weeks later. We not only need to have data to have data science, we also need timely data. - More Data Means Better Predictions
In the world of big data, more managed data means more accurate predictions. A lack of data and the ability to manage what is available inhibits many of our clients. Good models, good machine learning, and good AI are impossible without well-governed data pipelines in place. Frankly, our Fortune 500 customers don't have these pipelines - yet.
It's exciting that our clients are headed this way. We are in a transformative time in the data space. Most companies are in the middle of moving from a traditional architecture to a modern data architecture. These organizations are using data engineering to build brand new data pipelines with new technologies that can scale and run in the cloud.
In the past, we were building traditional data warehouses, delivering BI reporting, and doing enhancements and maintenance on those platforms. Today, we're building with new tools for the modern world. In the old world, things were very expensive and wouldn't scale. If you ran out of space in your on-premises data center, you would have to go buy another expensive appliance before you could add data or computing capacity. That would take months of time, effort, energy, and cost. In the modern data world, you just spin up another cloud-based service in a matter of minutes, and you can immediately scale your ability to process data.
That's why we don't build data warehouses any more. Instead, we're building data lakes and real-time data streams. We need data engineering to build pipelines that fill up these lakes. Pipelines connect data from sensors, connected devices, social media, and more. Yet we aren't just abandoning old sources. Pipelines are needed to get data from legacy systems, existing warehouses, and legacy applications to one place where it can be leveraged. If you don't want your company to be left behind, make sure you're paying attention to your data engineering now so that you can move on to advanced analytics and data science before it's too late.
