In brief

In brief

  • So long as poor data veracity is looming large in the enterprise system, trustworthy insights will remain a distant goal.
  • “Right” data is the bedrock of AI and analytics.
  • Data veracity is critical to running a truly data-driven organization and anticipating new business opportunities.

Establish data veracity or “right data” implies …

You have the ability to grade the trustworthiness of data, establish lineage and auditability of data throughout its lifecycle, and determine the context in which data lives, is used and maintained.

Many organizations today are struggling to organize their business around data. They tend to use a number of point solutions to capture, curate and consume data, and hence face challenges such as:

  • Inaccurate customer and contact data, leading to wrong promotions and offers.
  • Duplicate data, leading to multiple views of the truth, thereby delaying decisions.
  • Obsolete data, leading to incorrect insights (for forecasting and planning) and non-compliance with changing industry regulations.
  • Manipulated data, leading to stiff penalties.

We believe that if organizations are to gain complete visibility into all aspect of enterprise data and base business decisions on “right” data, the data value chain itself must be conceptualized, planned and operationalized as a continuous, iterative and collaborative process.

"This has been the thinking behind Accenture’s Intelligent Data Suite—a comprehensive solution that helps establish data veracity and streamlines the entire data journey from capture to curation to consumption on demand."

– JAYANT SWAMY, Chief Data Architect, Data Business Group – Accenture Technology

The Intelligent Data Suite:

  • Uses its machine-led scanning capability to discover and capture details about an organization’s data landscape, and it detects the data lineage, patterns and outliers. At a deeper level, the solution discovers critical data attributes and generates knowledge graphs of connected data across an enterprise.
  • Assesses and analyzes data against three key attributes—quality, risk and relevance—and their sub-attributes before grading data by assigning a data veracity score. Such a comprehensive exercise is fundamental to establishing data veracity. (Figure 1)
Data Veracity score is determined by considering quality, risk and relevance of data.

Figure 1

Consequently, businesses get a very comprehensive “as is” view of their data veracity map and of high risk areas that need immediate remediation. Furthermore, to help companies know where they stand vis-à-vis others, the Intelligent Data Suite compares their data veracity scores against that of the peers in their industry.

"The most common view is that if we improve the quality of data, it becomes trustworthy. This could not be further from the truth. Quality is just one parameter, and establishing data veracity calls for a holistic approach to data."

– SANJAY KUMAR JOSHI, Global Data Veracity Lead, Data Business Group – Accenture Technology

Game changing insights

Establishing data veracity is critical to running a truly data-driven business. And, when that happens, your data-driven insights could well reveal game changing possibilities you have not yet imagined. This could take the form of making regulatory compliance at speed a business differentiator, building agile business models around new product lines and services, reconceptualizing your entire customer journey and marketing campaigns, or having the most productive workforce and profitable business ever. So why not make “right” data a top business priority?

Jayant Swamy

Chief Data Architect – Data Business Group, Accenture Technology

Sanjay Kumar Joshi

Lead – Global Data Veracity, Data Business Group, Accenture Technology


Becoming a data-driven enterprise
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