Data is the most valuable asset for every business. It provides real-time insights into user behavior and industry trends that businesses can leverage.

Today, businesses produce a massive amount of data at a drastic rate in the wake of accelerated digitization. This pushes organizations to become more data-driven and make data-based decisions rather than depending on personal opinions, observations, or instincts.

PwC research indicates that data-driven organizations can outperform their competitors in productivity by 5% and in profitability by 6%. Another research shows that data-driven businesses are 162% more likely to exceed their revenues as compared to their competitors.

However, becoming data-driven isn’t straightforward. In fact, about 75% of business executives do not trust their existing data. A host of complexities creep in with the growing data volume. But why’s that the case, and what’s the solution? Let’s find out.


Complexities of Growing Data Volume

As data keeps growing in size and complexity, it is extremely challenging to keep it under control and manage it efficiently. Here are some complexities that often surface due to ever-increasing data volume:

Data Storage Costs

With the increasing volume of data, IT spending in data centers is also increasing. And with the growing data storage requirements, companies are more likely to spend on local servers and multi-cloud environments. In addition, more data is challenging to search for relevant information across multiple storage locations, further adding to costs.

Regulatory Compliance

Data growth has increased many security and data handling concerns of customers, making it challenging to handle information responsibly. This translates to increased chances of data breaches and regulatory fines – which goes against the very premise of being data-driven and nurturing a customer-centric philosophy.

Data Breaches

Having more data can make organizations an easy target for digital extortionists and hackers. For example, Bitcoin demand drove a $1.4 billion ransomware industry in 2020, specifically targeting large organizations with large data volumes. The increasing data in businesses can lead to catastrophic breaches.

Deriving Useful Insights

study revealed that over 46% of businesses do not know where their sensitive data is stored. As such, it’s very difficult to categorize and analyze the data to derive useful insights without a viable solution.

What could this solution be? In one word – DataOps.


What Is DataOps?

DataOps has now become widely prevalent because it solves data growth issues. DataOps is a culture or a mindset to help data teams work together in better ways. As defined by Gartner, “DataOps is a collaborative data management practice focused on improving integration, communication, and automation of data flows between data managers and consumers across the organization.”.

As such, DataOps predominantly focuses on collaboration, cooperation, communication, experience, and integration to bring diverse teams together and help them work across diverse processes and tools. It leads to better management of data, saves a lot of time, and reduces wasted efforts.


Solving the Data Volume Complexities with DataOps

DataOps helps companies overcome data challenges by making the data processes more manageable. It brings the right methodology and structure to the fore while providing the right tools, technologies, and people.

DataOps puts the expertise of different people with different roles in a centralized team which leads to revenue growth in the long term. The key dimensions of DataOps include:

  • People: This corresponds to a major change of culture and skill set for accommodating the continuous data usage and automation of processes.
  • Technology: This reflects the facilitation of an end-to-end toolchain for deployment and integration pipeline automation from definition to production models.
  • Process: This outlines an end-to-end process revision to achieve streamlined and fully automated deployment of data.

In a nutshell, DataOps is an advanced practice to help organizations become more data-driven, agile, scalable, and sustainable. It speeds up the process of getting the right data to the right people rather than waiting for the requested data from technical teams. Most companies employ DataOps as an enablement tool across the value chain — processing, modeling, and insight generation.

It leverages automation for data transformation to reduce manual, error-prone, and time-consuming steps in the pipeline, improve performance, and allow for faster deployment and releases. In addition, the adjustments across technology, people, and processes enhance productivity and significantly reduce IT costs and time to market.


Get Ahead of Data Challenges Today

If you want to be data-driven, DataOps is a necessity for your business. At Trinus, we have been working on extensive DataOps solutions with full-fledged automation and data intelligence features. We help you navigate the complete lifecycle of data management, tag and categorize the data automatically, and extract valuable data insights. This allows you to identify duplicate and outdated files, useless data, and other junk.

Besides, we make it easy for you to search for unstructured data and help you comply with data security regulations to reduce costly breaches. Get in touch with us to transform your data management strategy.