Substantial Challenges Filling Data Teams with Dread While Maintaining Data Quality in 2022

4 min readJun 10, 2022


Data Needs to Treated as Strategic Asset for Better Management

Data analytics is no more of competitive advantage but is a pressing need for core businesses to make real-time decisions and make changes in the economic landscape. Like a product development team, data teams will adopt practices like storing, moving, managing, and visualizing data which comes as a challenge when companies want to centralize it. Not only companies working in data engineering, but for every company data is a new product, as they try to reinvigorate, try to improve retention, and deliver better results.

Once organizations are set to look at data as a strategic asset and reduce the time taken for manual processes of data management and architecture, they would be able to deliver firepower data projects. To come to an optimal data-driven state, organizations will need access to reliable, timely, and comprehensive data. Role of data engineers will seep out of its current silos.

What Challenges can Data Engineers Face in Data Management and Integration?

For digitally engaged customers and clients, knowing all about data and using it effectively will be the only possible solution. Enterprises will need to extract it from sources, apply insights over more channels, and perform all of these continuously which requires Hercules efforts. For such data governance and management practices following challenges needs to be addressed:

  1. Challenges Related to Data Sources: Not only the volume of data but growing data sources also impact organization’s ability to make data useful.
  2. Composing Data From Disparate Sources: Data acquires many forms and is a sojourner in different touch points like data warehouses, data lakes, devices, platforms and many more.

With 2.5 quintillion of data produced each day, unnecessary hefty data makes extracting and data integration a daunting task, so introducing enterprise-wide standards for data entry and maintenance would make data integration more feasible.

Data Preparation for Analysis: Data scientists spend most of their time in data loading and cleaning as data has to be made fit for the intended purpose without getting stuck in analysis paralysis. It presents the following challenges:

  • Inconsistent data across enterprises
  • Maintaining and expanding data preparation processes.

Integration of data back to SaaS Applications: Also known as reverse ETL, this is required for operational systems for improving customer experience (CX), reducing dependencies, and taking decisions in operational analytics.

Organizations lack a centrally accessible and intuitive UI that enables collaboration between user groups across expanding use cases, clear visibility, and access.

Challenges Related to Data Quality: Data management faces a major issue of questionable data quality and underlying structures. These two most frequently occurring data quality issues appear:

  1. Inaccurate Data: According to Corinium Intelligence and Precisely study, 40% is the average proportion of time devoted by data teams on data cleaning, integration and preparation. Organizations need to improve quality, as “Improving data quality is the purpose, but obtaining accurate data is the consequence”.
  2. Data Uniqueness: Enterprises suffer from problems of identifying data entities across systems and lack proactive steps to prevent duplicate records.

A healthcare company suffers complications in billing liability as it lacks systems that ensure only master or unique patient records are calculated.

Key Engineering Challenges: At an organizational level, capabilities such as business glossaries, data dictionaries, reverse engineering, forward engineering and cross-organizational collaboration, data modelling tools are needed to address the challenges.

  1. Data Integration and Data Orchestration: With increasing new use cases of analytics and digital application, enterprises require months of data discovery, data-pipe engineering, data cleansing, data documentation, and common standards.
  2. Data Catalog and Discovery: Data catalogs are unable to keep pace with new realities like automation. While data catalogs have the ability to document data, the fundamental challenge allowing users to discover and glean meaningful, real-time insights about the health of data has largely remained unsolved.

Businesses need to accelerate time to value by introducing technology that can integrate and manage data at scale, acting as a data operating system for the enterprise.


Automation and Workflow Orchestration will be Turnkey Solution for Addressing Challenges

What really needs to be revamped are: people and technology both, hence data engineering teams need to have a holistic approach for data product creation. Prioritizing the aspects of data strategy and pipeline that are under-developed is another way to improvise data management initiatives.

Vendors and companies nowadays are understanding that the needs of scale, speed, and varied use cases may also require a couple of databases. Data teams that invest in and take full advantage of the right resources, automation, and workflow orchestration will be better able to outperform competitors with data and will be prepared for the ever-changing future of data use.

Techment Technology believes in having end-to-end data quality and removing data errors which are always customer-facing. Our people are more aligned in helping our customers by building solid mental models around data. For more conversations on data projects, get our free consultation.




Techment is a digital catalyst that expedites solution development with high-velocity agile delivery model and in-depth tech expertise for global organizations.