Business success once followed a straightforward recipe. Combine a clear vision, winning products or services, and an informative sales process with hard work, and the revenue would naturally follow.
Today, the use of technology and dependence on data are major drivers to success in competitive markets. Modern data, defined as collective information related to a company and its operations, is just as important, if not more so, than other aspects of the recipe for success.
Proper data engineering can deliver insights that can make or break companies. Poor data management or lack of use can spell disaster for companies trying to keep pace with the competition. Gartner estimates that poor data quality can cost $13.3 million per year. In addition, 39% of those companies can’t tell their good-quality data from the rest since no one is tracking that data and data organization is lacking.
Tools like artificial intelligence, machine learning, and automation can significantly assist in collecting and cleansing data. However, they are only helpful as part of a modern data engineering process. For example, AI is unparalleled in its capacity to help business leaders leverage data & insights. However, the data must be captured and accessible!
What is data engineering?
Data engineering is a complex field, but it’s all about simplifying and using information. Clive Humby, British mathematician and data science entrepreneur, sees data engineering as both a process and a resource:
“Data is the new oil. Like oil, data is valuable, but if unrefined, it cannot really be used. It has to be changed into gas, plastic, chemicals, etc., to create a valuable entity that drives profitable activity. So must data be broken down and analyzed for it to have value.”
The field of data engineering focuses on the entire information lifecycle.
- Data collection: Data engineers are responsible for ingesting data from databases, APIs, logs, sensors, and external data feeds. This process involves extracting data in its raw form and bringing it into a central repository.
- Data storage: Data engineers design and maintain data storage solutions that can handle large volumes of data. These storage systems may include data warehouses, lakes, and distributed file systems.
- Data transformation: Data often needs to be cleaned, transformed, and enriched to be useful for analysis. Data engineers use tools and pipelines to preprocess and prepare data for downstream analytics.
- Data security: Data engineers must implement security measures to protect sensitive data. This includes encryption, access controls, and compliance with data privacy regulations.
Most data engineering processes also include data distribution, defined as the secure sharing of information across any teams that might find it helpful. Modern CRMs enable a company’s marketing department to share data from the corporate website with sales teams before customer outreach. Insights such as time spent reading certain topics can be a significant sales qualification signal.
Data engineering has changed dramatically in recent years. For example, the newfound popularity of generative AI has unlocked natural language processing (NLP) capabilities and statistical modeling that can make internal data teams even more effective.
But the more data engineering execution changes (new data points, new AI tools, wider data access), the more its foundation in data collection, storage, and use remains the same. No matter what tools are in your toolbox, and in an era where 40% of business objectives fail because of poor data engineering, that foundation has never been more critical.
Data engineering is more important than ever before.
Data engineering is often a major challenge for businesses. In fact, 54% of all digital leaders say a skills gap on their team is preventing their company from keeping up with the pace of change. Over half of all CIOs plan to add at least one data engineer to their existing data team.
Implemented correctly, data engineering can accelerate the speed at which you can collect, access, and learn from your most valuable information — as long as you have the correct data engineering cadence in place.
How can I audit the state of my data?
The first step to an optimized Data Engineering strategy is seeking data clarity. This means shedding light on where your data comes from, how it is collected, when it’s used, and why certain data points are better indications of growth and change.
Here are a few questions to ask to audit the state of your data:
- How do you prepare your data for use?
- How does your organization guard against information bias?
- How well does your data accommodate industry governance policies and regulatory requirements?
- How does your data storage process reinforce security and prevent loss and decay?
- How do you justify your cloud, hybrid, or on-premises data storage strategy?
Don’t have the time or resources for an audit? Terazo can lead you through it. Sign up for a complimentary 30-minute consult with a member of Terazo’s data engineering team for better insight into your collected information. It’s the fastest way to understand better your organization’s data-related strengths, weaknesses, and opportunities — and how a comprehensive data engineering roadmap can elevate your team’s productivity and revenue pipeline.