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December 03, 2021

Data Engineering in Startups: How to Manage Data Effectively


Edwin Lisowski

CSO & Co-Founder

Reading time:

8 minutes

Starting a company is easier said than done. You want to build a strong online reputation, increase the value of your sales, and make your brand stand out from the competition. At its infancy, several things demand your attention, and data collection might not make it to the top of your priority list yet. You’ve probably heard somewhere that you need a data strategy, but you didn’t think that you’d need it this early in your business. In this article, we are going to talk about data management best practices, even if you’ve just started.

It’s a common misconception that big data is only right for IT giants that have the financial capacity to build the infrastructure, develop tools, and train machine learning models. However, this is not always true since multiple big data mining and visualization tools make big data more affordable and convenient for startups.

data management

Tracking big data metrics does not only help businesses forecast their revenues, but it also offers valuable insights into customer behavior. The best place for startup CEOs to begin their big data implementation process is by getting data engineers to build the right data infrastructure and adopting the right data management techniques. Read on as we take you through the role of data engineering in startups and how to manage data effectively.

What is data engineering?

Data engineering[1] is a branch of data science that focuses on the practical applications of data collection and analysis. So basically, a data engineer’s job involves collecting and preparing data for operational or analytical use. They are responsible for building data pipelines and developing algorithms that help make raw data more useful to organizations. Besides making data more accessible and useful, data engineers also create predictive models that show trends in different business sectors.

Without data engineering, it would be almost impossible to make sense of the massive amounts of data available at a business’s disposal.

Data engineering also involves addressing data quality issues. Inaccurate or inconsistent data can lead to flawed insights and decisions. Data engineers employ techniques to clean, validate, and transform data to improve its quality and reliability. They ensure that data is properly structured, standardized, and integrated to enable seamless analysis and reporting.

Moreover, data engineering is a constantly evolving field. New technologies and tools emerge regularly, and data engineers need to stay updated with the latest advancements. They must be proficient in programming languages, databases, data integration tools, cloud computing, and distributed systems. By leveraging these technologies, data engineers can enhance data processing capabilities, optimize performance, and enable real-time data analysis.

Data engineering in startups

Why do startups need data engineers? Well, these professionals are needed to stitch up, maintain, and manage your data stack. It’s evident that every business faces numerous data-related roadblocks regardless of whether they’re just getting started or have been in the game for a while. This is why they need a certain degree of creativity from data engineers to help them resolve these issues.

data engineering in startups, team work

Unfortunately, startups usually have limited or unlabeled data in the beginning. This hinders founders from making significant progress on making data-driven decisions. But they can always put data to good use by adopting good data management practices.

Read more about The best data engineering tools and technologies in 2021

Data management in startups

There is a glut of data available for startups through third-party tools like Google Analytics. But not all startups that collect data end up using it effectively. Most waste time and effort collecting, storing, organizing, and storing the data, but do not manage it well. In the end, they end up being information-rich and insights-poor.

Instead of using uncertain industry KPIs, startups can always set their own benchmark. This is only possible through effective data management techniques. Data management is the process of collecting, storing, organizing, and maintaining data collected by an organization.

It includes a mash-up of different functions that work collectively to make sure that data collected in organizations is accurate, available, and accessible. Most of the work in data management is done by data teams composed of data engineers, but business operators also play a role to ensure that the data meets the needs of their organization.

Startups can also leverage automation and streamlining techniques to enhance their data management processes. Startups often have limited resources and tight timelines, making it crucial to optimize efficiency in data engineering in startups workflows. By implementing automated data collection, integration, and transformation processes, startups can minimize manual effort and reduce the risk of human error.

Data engineering in startups frameworks and tools, such as Apache Airflow, can be utilized to create scalable and reliable data pipelines. These pipelines enable startups to streamline the movement of data across various systems and ensure data consistency and quality throughout the process. Additionally, data engineers can implement data versioning and monitoring techniques to track changes, identify anomalies, and maintain data lineage for better traceability.

Startups can also explore cloud-based data engineering in startups solutions, such as Amazon Web Services (AWS) or Google Cloud Platform (GCP), to take advantage of their managed services and infrastructure. Cloud providers offer a range of tools and services, including data lakes, data warehouses, and serverless computing, which can significantly reduce the upfront cost and complexity of building and maintaining data infrastructure.

Furthermore, startups can benefit from adopting agile methodologies in their data engineering practices. Agile methodologies emphasize iterative development, frequent feedback, and collaboration, allowing startups to adapt their data engineering processes as their business requirements evolve. This agile approach enables faster time-to-insight, facilitates continuous improvement, and empowers startups to respond quickly to changing market dynamics.

Data management best practices

Assuming that you’ve zeroed in on your expectations from the data that you’ve collected, here are some of the data management best practices that you can follow to manage your data effectively:

Store your data well

Having a proper data storage system[2] is essential for any startup that intends to have easy access to the data that they’re creating. This involves finding a plan that works for your organization’s preservation methods and data backups.


Data storage is not a one-size-fits-all solution. What works for another business might not work for yours, so it’s important to keep your data needs in mind when selecting a data storage solution. There is a wide range of storage locations that you can consider. These include networked drives, optical storage, cloud storage, external hard drives, and more.

Invest in quality data management software

If you want to easily find the information you need, you have to put all the data you’re creating into a manageable business tool. A data management software[3] takes in data from various sources and aggregates it into a consistent resource. It extracts, cleans, and integrates the data without compromising its integrity. This allows you to access it in an easy-to-use format.

Using reliable data management software can help you build and govern your data. Moreover, this means that you can build trust in the quality of data available for your business, and therefore you can easily adopt a self-service analytics system.

Document your data

This is a data management practice that every startup can’t afford to overlook. In addition, it describes the collected data, making it easier to retrieve, use, and manage it. Data documentation takes multiple forms and describes data in different levels, including:

  • Project level: Explains the aim of the study, methodologies, etc.
  • File level: Documents how all the files in the dataset relate to each other, and the format they’re in.
  • Context level: Gives the project’s context. I.e., why it was created.
  • Software level: Documents the version of software used

The bottom line

Every startup business has a clean slate and the ability to plan it out. In this context, it means that they can build and govern their data. This enables them to build their own benchmarks and measure KPIs that matter to their business.

With data management, the goal is to treat data as a valuable resource and focus on bringing it together to make better business decisions. A good place to start is by adopting data management practices such as proper data storage and documentation and investing in quality data management software. So, your next step is to check our data engineering services. With our help, you will make the most of your data. Find out more today!


Data Engineering