What is the state of your data?

Zennify Team

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 and 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. Components include:

Data collection: Ingesting data from databases, APIs, logs, sensors, and external feeds.
Data storage: Designing systems that can handle large volumes of structured and unstructured data.
Data transformation: Cleaning, enriching, and preparing data for analysis.
Data security: Protecting sensitive data through encryption, access controls, and compliance with regulations.

Most data engineering processes also include data distribution—sharing information securely with teams that need it. For example, a marketing team might pass customer behavior data to sales before outreach. Metrics like time spent reading a product page can be a powerful qualification signal.

Data engineering has changed dramatically in recent years. Tools like generative AI, natural language processing, and statistical modeling have made data teams faster and more effective. But the fundamentals remain the same: collect, store, transform, and use data well.

In an era where 40% of business objectives fail due to poor data engineering, that foundation has never mattered more.

Data engineering is more important than ever before.

It’s also a challenge. 54% of digital leaders say a skills gap is holding their teams back. Over half of all CIOs plan to add at least one data engineer to their teams in the coming year.

When done right, data engineering accelerates the way organizations collect, access, and use their most valuable information. The key is building the right cadence.

How can I audit the state of my data?

The first step is data clarity. You need to know where your data comes from, how it’s collected, how it’s used, and why it matters.

Here are a few questions to ask:

How do you prepare your data for use?
How does your organization guard against bias?
How well does your data support compliance and regulatory needs?
Is your storage process secure and sustainable?
How do you justify your cloud, hybrid, or on-prem data strategy?

If you don’t have the time or resources to do this alone, Zennify can help. Sign up for a complimentary 30-minute consult with a member of our data engineering team. It’s the fastest way to understand your organization’s data strengths and gaps—and how a strong engineering roadmap can improve productivity and pipeline.