5 Winning Data Engineering Strategies for 2022

By Data Science Salon

Data-driven, data-first–what every organization likes to be called, but only a few fit the description. Today, organizations cannot implement modern data-centric initiatives without formulating robust data engineering strategies. 

Data engineering is the process of transforming raw data, which includes data collection, ingestion, parsing, transformation, and analysis, to derive business value and make informed decisions. It encompasses all principles and practices for building a modern data stack.

Though it is difficult to craft a perfect data engineering strategy, more organizations are putting their resources into getting it right. It is estimated that the global data engineering services market size will reach $77.37 billion by 2023, growing at an 18% CAGR from 2021 to 2027. 

So, why do companies care about getting their data engineering strategies right?

Because enterprise data is increasing at an alarming rate with a greater inflow of unstructured data coming from disparate data sources. The sheer scale of data raises many concerns, including budget limitations and privacy concerns. An ambiguous data engineering strategy halts the digital transformation journey for organizations.

In this article, we’ll discuss some trending data engineering strategies that industry leaders should consider for building robust and scalable data-driven business workflows.

1. Data Reliability–Including Governance, Privacy, and Security 

Data reliability is a component of the modern data stack that refers to the ability of a data-driven organization to deliver complete, accurate, consistent, and healthy data across the entire data life cycle. It is a critical foundation for building trustworthy and dependable data applications that result in confident decision-making. 

The data life cycle usually consists of ingestion, transformation, analysis, and end-products like dashboards, models, and applications. Data reliability is an umbrella term that contains various data tools and techniques to ensure data integrity across this data life cycle. We can distribute these techniques across four data reliability components.

4 Components of Data Reliability Stack

Data reliability is a vital aspect of building robust data engineering strategies. The data reliability stack contains the following main components:

  • Data discovery: Collecting data from various sources.
  • Data observability: a framework to monitor datasets, and perform operational health checks, data profiling, and validation.
  • CI/CD & versioning: To build robust and continuous data pipelines.
  • Data testing: Set alerts and conduct automated tests to highlight potential discrepancies like outliers, missing values, and incorrect formats to enable corrective measures.

Additionally, the data reliability stack significantly relies on data privacy, security, lineage, and governance to build robust dataOps pipelines and deliver high-quality data applications. Data engineering leaders must focus on all aspects of data reliability if they want to operationalize data successfully and adopt digital transformation.

2. Data SLAs

Data service-level agreement or data SLA is a crucial segment of an organization’s data engineering strategies. It is a relatively newer concept in the data ecosystem. However, organizations have been using service-level agreements for their products and applications for many years. For instance, all major cloud service providers offer well-defined SLAs to guarantee a quantifiable standard of cloud services (like 99.9% uptime), along with possible ramifications if those SLAs are not fulfilled.  

Data SLA is a data engineering strategy that outlines the purpose, promise, requirements, measurement, and ramifications of an organization’s data offering, including datasets, data models, and data-driven applications, to internal teams or external consumers. It includes service-level objectives (SLOs) and service-level indicators (SLIs) that guarantee a certain quality of data and its availability. 

Data SLAs build data trust among stakeholders. It is needed because data is snowballing, and only organizations that are well-equipped to handle data downtime at a large scale can continue to provide reliable data services. Data SLAs offer a certain level of security to consumers during data downtime.

3. Hybrid Data Team Structures

The success of data engineering strategies relies on effective data engineering teams. For data leaders, team building is a challenge as enterprise data needs are increasing rapidly. Some organizations follow a centralized team structure, while others adopt a decentralized team structure. However, each team structure has its pros and cons.

For instance, centralized data teams offer increased collaboration and consistent practices, but they are bound within the department, making them inefficient at times. Decentralized data teams are not confined to a department, so they can adapt to changing business requirements. However, due to inconsistent standards, not everyone has the same business understanding or level of collaboration.

Data engineering teams require diverse experiences, so organizations are now experimenting with hybrid data team structures. The hybrid structure consists of a core data team that takes on challenging data tasks and a decentralized team of analysts that can offer flexible reporting. Companies are building dataOps teams that have specialists and generalists. They are building workflows that allow better cross-department collaborations.

4. Reverse ETL

Reverse ETL is a vital component of an organization’s data engineering strategies. Reverse ETL (Extract, Transform, Load) is the process of moving transformed data from data warehouses (mostly cloud-based) to operational business platforms like Salesforce, Tableau, Marketo, etc. 

Unlike a regular ETL pipeline, which uses data primarily for analysis and reporting, reverse ETL brings high-quality data to business applications your marketing, advertising, and support teams are already using. Simply put, the data warehouse serves as a single source of truth for all business tools.

If you are wondering why is reverse ETL needed? It is required because there is a disconnect between the data collected by enterprises and the value it generates for them. Organizations are not leveraging data completely. In fact, a 2020 report suggests that a staggering 68% of data available to enterprises goes unused.

In the modern data stack, reverse ETL enables data democratization across teams by enabling everyone to operationalize clean, consistent, and accurate data, allowing them to make more data-informed decisions and generating more business value.

5. DaaP–Data-As-A-Product

Decision-makers and executives don’t need data. They need actionable information that can drive sales and increase revenue. As part of their data engineering strategies, dataOps teams must start treating data like a product. Just as software development teams deliver great software for end-users, the data engineering team must adopt the DaaP approach to deliver actionable data.

Data-as-a-Product adopts product development principles like reusability, agility, and iterability. It is a methodology that enables teams to create more data value. It’s about embracing a product mindset while delivering data, i.e., making data available wherever and whenever needed and transforming data to be used across various business operations.

Importantly, Data-as-a-Product is different than Data-as-a-Service (DaaS). DaaP is a data engineering principle that facilitates the use of data, while DaaS delivers the actual downstream application which uses that data. In 2022, data leaders must embrace the Data-as-a-Product approach as part of their data engineering strategies to eliminate operational bottlenecks between dataOps teams and decision-makers.

Building a Modern Data Stack Using Robust Data Engineering Strategies

Advanced data challenges require modern data engineering strategies. Organizations must formulate a data plan to take this challenge head-on and generate more business value. As each organization is different, the five data engineering strategies we have shared in this article can be adapted by organizations according to their business requirements. Some key takeaways from this discussion are:

  • Align your data strategy with business requirements.
  • Adopt the modern data stack with a focus on data governance, security, and reliability.
  • Break out of data silos and promote data democratization across an organization.
  • Hire talented data leaders (Chief Data Officers, Chief Information Officers) to promote a top-down data-driven culture and build diverse data engineering teams.

At Data Science Salon, we discuss modern data concepts and data science practices to educate and illuminate like-minded data and machine learning enthusiasts. Join our ever-growing community of senior data scientists and technical specialists to continue reading such cool content.

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