Why organizations must transition into data powered enterprises: Mahesh Zurale, Accenture

In an interview with Dataquest, Mahesh Zurale, Accenture, says data plays a very crucial role in the changing business landscape induced by the pandemic

Supriya Rai
New Update

The ongoing COVID-19 pandemic has ensured that business as usual is no longer possible. While organisations across the world are getting accustomed to the new normal, data will unquestionable play a crucial role in helping companies get their businesses back on track. In an interview with Dataquest Mahesh Zurale, senior managing director, Lead – Advanced Technology Centers in India, Accenture talks about why it is important for enterprises to adopt a strong data strategy in the post-COVID world.


DQ: Why is it important for organizations to transition into data powered enterprises?

Mahesh Zurale: Becoming a truly data-driven enterprise involves linking a data strategy to clear outcomes and prioritizing data as a strategic asset.Yet today, most companies use only a small proportion of their enterprise data effectively, and there is a huge potential to harness, capture and curate right data sets from new data sources to generate intelligence that can open new opportunities, enhance efficiencies and ensure maximum compliance.

With COVID-19, the push for digital transformation has accelerated drastically, with lockdowns across the worldurging organizations to rethink the future of the workplace, even as they reinvent themselves to address evolving customer needs, at speed and at scale. Data plays a crucial role in every part of this changing business order.


Adopting a strong data foundation is essential for organizations to outmaneuver these immediate challenges and build future resilience. By embedding data, analytics and artificial intelligence at their core, organizations candrive innovation, enable timely decision-making, and benefit from a complete and accurate view of what’s happening across their enterprise at any given moment.

DQ: What are the technologies and processes needed to improve data management?

Next-gen Data Management involves five areas -Intelligent Data Governance, Master Data Management, Data Cataloguing, Data Veracity and Machine led compliance.


Data Management within an organization looks at effectively governing data, including optimizing, protecting and leveraging data as a corporate asset across its lifecycle. Organizations can also look at maximizing value from data by using multi-domain master data management (MDM), which has further evolved into MDM for big data and MDM on cloud to meet the needs of data in the new. It is important to ensure data is trustworthy before using in the decision-making process. Data Veracity focuses on avoiding the pitfalls of inaccurate data by measuring the provenance, context and integrity of data; curating the data using machine learning techniquesand making data trustworthy.Machine Led Compliance focuses on automating the compliance process including the solutions for privacy laws such as GDPR.

Technologies in the data management space are ever evolving and growing, especially on cloud to meet the new age data requirements.Applying Artificial Intelligence and Machine Learning (AI-ML) to data management can provide further efficiencies and reduce burden on humans. However, just having the right tools and technologies are not enough. Organizations should focus on creating Data Capital Management framework to connect the dots between insights and technologies, and drive innovation, growth and increased efficiency.

DQ: Is the current workforce skilled enough to take advantage of the opportunities data provides?


The exponential growth in data usage in the recent times has accelerated far beyond the skills and confidence of employees who are required to use it. According to a recent research by Accenture and Qlik, even though nearly all surveyed employees (87 percent) recognize data as an asset, only 37 percent of employees trust their decisions when based on data, and almost half (48 percent) frequently defer to a “gut feeling” rather than data-driven insights when making decisions. Further, only 25 percent of them believe they are fully prepared to use data effectively, and just 21 percent report being confident in their data literacy skills — i.e., their ability to read, understand, question and work with data.

While organizations are rapidly designing processes and their systems to improve data management and efficiencies, they must invest now in skilling and building a data literate workforce tosuccessfully use data and unlock new opportunities for renewed growth and competitiveness in the future.

DQ: How is Accenture supporting its clients’ journey towards becoming an intelligent enterprise? Could you give us an example?


In the current context, reinventing theenterprise digitally is extremely critical and requires organizations to rethink their strategy to drive data-driven reinvention.

Accenture partners with clients in their journey towards becoming a truly data driven organization by helping re-imagine products and services by solving complex problems with data through AI and analytics. We assist them develop cloud-based data and analytics platform at an enterprise level that can break data silos to allow for the interoperability of cross-functional data. We help them integratedata from several sources and create data sets in real-time to offer compelling customer experiences and dynamic pricing, based on value and demand. By gaining one view of their enterprise data, organizations can quickly adapt, even customize, their products and services to meet changing customer preferences, augment internal operations, and comply with evolving regulations.

For example, Accenture modernized configuration testing for Ducati Corse by combining two disruptive technologies – Artificial Intelligence and Internet of Things (IoT) – to create a mobile application capable of simulating and monitoring a motorbike’s performance under a vast array of track and weather conditions. To date, around 4,000 sectors of racetracks and 20 different racing scenarios have been analyzed, with a wider roll-out of the solution expected. The insights generated from this data has helped Ducati to transform their MotoGP racing set-up, thus dramatically reducing the time needed to perfect each bike’s performance.