5 Essential Characteristics of Data-Driven Enterprises by Subhabrata Dasgupta on May 5, 2022 528 views

Data is omnipresent – in every industry, firm, department, at every scale imaginable. The amount of data generated by modern businesses is already sizable, and this will grow to gigantic proportions in the foreseeable future. The numbers help get some perspective on this burgeoning scale of data. The world produces about 5 exabytes of data (2.5 Bn GB) every day. By 2025, this number is projected to reach 463 exabytes!

From a business perspective, data is becoming increasingly valuable, offering organizations rich perspectives on whether they are moving in the right direction, areas for improvement, implementation guidance, etc. These movements have spurred the rise of data-driven enterprises that have integrated data analysis into the core of their processes. What may these data-driven enterprises look like, you ask?

We take a deep dive in this blog. 

1. Data at the heart of every decision, process and interaction

Employees at such organizations look to leverage data to resolve challenges to save time. As a result, they avoid defaulting to problem-solving by developing lengthy processes. Instead, they are curious to know how data could solve the same problem in less than half of the time. As a result, such organizations are better at decision-making. They also automate basic daily, recurring activities, freeing up employees to focus on innovation and collaboration. 

In such a data-driven environment, there is an emphasis on continuous performance improvement to craft differentiated customer and employee experiences. 

2. Data processing and delivery, real-time

Data from connected devices is processed, queried, and analyzed in real-time in data-driven enterprises. While there are challenges due to the limits of legacy technology structures, and high computational demands in the current environments, innovations are pushing the boundaries.

New technologies like kappa or lambda architectures will enable faster, more powerful, real-time analysis — enabling the easy delivery of insights to customers, partners and employees. 

3. Flexible data stories for integrated, ready-to-use data

Data-driven enterprises leverage various database types to enable more flexible ways of organizing data. Consequently, teams can query and understand relationships between semi-structured and unstructured data quicker and more simply. This accelerates the development of new AI-driven capabilities. Further, the discovery of new relationships in the data enables innovation. 

These flexible data stories allow organizations to develop data products with sophisticated simulations using machine learning techniques.

4. Data as a product

In a data-oriented organization, data assets are organized and recognized as products. These products have dedicated teams for data security, data engineering, and the implementation of self-service access and analytics tools. These data products evolve continuously in an agile way to meet consumer needs. The products provide data solutions that can easily meet various business challenges and reduce the time and cost of delivering new AI-driven capabilities. 

5. Data management accorded full priority

With the evolution of regulatory expectations and organizational mindsets, data-oriented enterprises treat data privacy, ethics and security as required competency areas. This shift is in line with the increasing consumer awareness of data rights. Further, security incidents have become increasingly high stake affairs. All these factors contribute to the full prioritization of data management.

Consequently, organizations can foster greater trust in the data and its management. This leads to the acceleration of new data-driven services. 

Your Digital Transformation Enabler

At Argusoft, we have built and continue to build data visualization and business intelligence tools for various clients in government and the private sector. These data-driven initiatives have helped clients derive greater value from data by putting it to work. Explore the endless possibilities of your data initiatives with Argusoft. 

Four Success Factors for Digital Health Implementations In Developing Countries The Importance of WHO's SMART Guidelines In Public Health Implementations

About Author

Subhabrata Dasgupta

Head - Communications