Enterprises take data initiatives very seriously now.

On a conservative basis, a data analytics project costs an average of $500,000 and takes up to three years. Despite spending so many resources and so much time on these projects, 60% of these projects fail. 87% of them don’t even make it to production. What’s wrong?

Could this be a problem of plenty?

Data interactions, defined as the creation, capturing, copying, and consumption of data grew 500X in the ten years between 2010 and 2020. That included a significant spike in 2020 as enterprises became fully remote, driven by the pandemic. By some estimates, data usage grew from 1.2 trillion GB to close to 60 trillion GB. And, given that 90% of the data is unstructured and available in various formats, enterprises have an even harder time zeroing in on “usable data”.

Enterprises seem to be submerged in an ocean of data gathered from different sources and customer touchpoints. It has become difficult for enterprises to interpret the data correctly and extract actionable insights from it.

To avoid drowning in the overwhelming amount of data and gain valuable insights, enterprises need to focus on data management and data governance to maintain data quality.

How To Save Enterprises From Drowning In Data

Good quality data is essential for enterprises to make correct business decisions in time. This means that the data must be accurate, complete, reliable, clean, current, and relevant to the business.

That’s where enterprise data management and data governance helps drive better outcomes.

Data management

Data management deals with planning, controlling, and managing data. The data management team is responsible for ensuring that the people within the enterprise have access to standardized, accurate, and timely data. There are various benefits of effective data management.

  1. It ensures that the bad quality data is eliminated and that only good quality data is available to everyone.
  2. As the volume of data increases, duplicate data creeps into the system, errors appear, and incomplete data becomes visible. Most likely, this happens when the data is collected through disparate systems. This makes it hard for data analytics solutions to interpret the data correctly. Data management helps the teams to collect only the most relevant data in a unified system. The scope for duplication reduces, and the teams are able to spend time interpreting the data to improve business decisions.
  3. Data management also standardizes the data and makes it consistent. This accelerates the data initiatives within the enterprise as everyone works from the same page with confidence in the data.
  4. Most importantly, data management automates key processes. So, enterprises are able to save time on manual processing and reduce the scope of errors.

Data governance

While data management enables enterprises to manage the data, data governance maintains the integrity of data. Data governance is important because it ensures that the data is trustworthy and compliant with corporate standards as well as regulations such as GDPR, CCPA, and HIPAA. It harmonizes the data, breaks down the silos, and provides the teams with a holistic view of the data. A well-established data governance program comprises a governance team, a steering committee, and data stewards. They work together to formulate standards and policies that will help them govern the data and ensure that the data initiatives comply with the enterprise’s policies. Here are some reasons why enterprises implement data governance programs.

  1. Without agreed policies and common data definitions, the enterprise will be dealing with inconsistent data, which will make their data initiatives useless.
  2. As there are defined policies and standards, every team member will be compelled to standardize the data. That helps in reducing data errors and improves operational efficiency.
  3. Also, given that enterprise data is sensitive and personal in nature, a clearly defined data usage policy will prevent its misuse.
  4. A well-defined data governance policy also mentions how the data should be accessed and distributed. This helps in preventing unauthorized access to data and prevents data breaches.
  5. Enterprises measure data governance based on specific metrics such as data quality scores and adherence to data management standards and processes. This enables them to gauge its effectiveness and fix issues in the program to make it successful.


Data will continue to increase in the future, and enterprises will continue to receive it from different sources. To make sense of this data and take correct data-driven decisions, enterprises need to synchronize the data from disparate systems into a unified platform. As described, they need an end-to-end data management approach and a robust data governance plan to capture quality data, reduce costs, and improve operational efficiencies.

To help in this endeavor, enterprises need to partner with data management experts who can assess the business, understand the data needs and constraints, and build solutions.

At Trinus, we help enterprises with:

  • Integrating data from disparate sources to gather meaningful information
  • Implementing scalable data warehouses based on business needs
  • Migrating data from legacy systems for effective analytics
  • Maintaining the data quality
  • Building a robust data architecture to improve operations and reduce costs
  • And most importantly, establish a data governance program

To know more about how we can save enterprises from drowning in data, contact us.