Rupa Mahanti

Rupa Mahanti

Doctor of Engineering
Publisher of The Data Pub Newsletter- https://thedatapub.substack.com/ Author of several books on data.

About

56
Publications
43,287
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361
Citations
Introduction
Dr. Rupa Mahanti is a business and information management consultant and author with extensive and diversified consulting experience in different solution environments, industry sectors, and geographies. She has expertise in different information management disciplines, business process improvement, regulatory reporting, and more. Her research interests include quality management, information management, software engineering, environmental management, simulation and modeling, compliance and more

Publications

Publications (56)
Book
While good data is an enterprise asset, bad data is an enterprise liability. Data governance enables you to effectively and proactively manage data assets throughout the enterprise by providing guidance in the form of policies, standards, processes and rules and defining roles and responsibilities outlining who will do what, with respect to data. W...
Book
This book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data. Considering the bu...
Book
This is not the kind of book that you ll read one time and be done with. So scan it quickly the first time through to get an idea of its breadth. Then dig in on one topic of special importance to your work. Finally, use it as a reference to guide your next steps, learn details, and broaden your perspective. from the foreword by Thomas C. Redman, Ph...
Book
This is a collection of humorous data quotes related to data, big data, statistics, and data science, from different sources and a wide array of cultural figures, thought leaders and key influencers across the world. Makes the perfect gift for data and technology professionals or anyone interested in data. A book which you can just pick up anytime...
Article
This article explores the critical success factors for implementation of data governance in organizations and presents the results of a survey
Article
Metadata helps answer fundamental questions about the data, such as who, when, what, why, where, and how. Metadata are the first input to the process of measuring data quality, and hence also need to be of good quality. thedatapub.substack.com/p/why-is-metadata-and-its-quality-important
Article
Data quality has been defined as fitness for use or purpose for a given context or specific task at hand. Despite the fact that fitness for use or purpose does capture the principle of quality, it is abstract, and hence it is a challenge to measure data quality using this definition. Data quality (DQ) dimensions are intangible characteristics of da...
Article
Organizations have a large number of data entities and data elements and large amount of data stored in repositories and also flowing in and through the organizations’ data pipelines. However, treating all the data elements equally in terms of governance and managing quality, is not feasible approach to managing data. Hence it is important to prior...
Article
Dimensions of data quality focus on measuring data’s representational effectiveness (how well people understand what it represents), characteristics of its presentation and its suitability for particular purposes. Better define metadata for clearer expectations and measurement criteria.
Article
Change is the only constant in life, and technology, civilizations, and culture have evolved over history. What has not changed are facts. With the passage of time and the evolution of technologies, civilizations, and culture, the methodologies used to capture, store, process, and use facts have evolved. Similarly, data (a representation of facts)...
Article
“Data quality is data accuracy” is one of the most common myths of data quality. The general misconceptions are that data quality is synonymous to data accuracy, or that data quality is only about data accuracy. However, data quality is about striking a balance between all data quality dimensions. Depending on context, situation, the data themselve...
Article
Takeovers, mergers, acquisitions, stock market crashes, accounting scandals, and globalization, have triggered the need for stricter governance, and creation and enforcement of regulations to improve the accuracy and credibility of corporate data with an intent to prevent scandals and financial disasters. As data continues to evolve as well as grow...
Article
Organizational culture has an impact on short term performance and long term effectiveness. Misaligned culture is one of the primary reasons Six Sigma and other quality initiatives fail. Since Six Sigma is a management program rather than a technical one, aligning culture should be an important part of it. A survey of researchers and software profe...
Article
Much has been written and heard about the U.S. government’s healthcare site, HealthCare.gov. The issues with the launch, admissions about project management failures, and excessive waste — to name a few — have been hashed over in blogs, debated in government hearings, and analyzed in the press. The project drew a focus to all areas of software deve...
Article
Risks of fraud and corruption increases considerably during a crisis situation. The COVID-19 pandemic has seen an increase in the risk of fraud and corruption. While technology and data has enabled fraud during the digital age, with the situation made much worse by the COVID-19 crisis, in ways not possible in the non-digital age, data and emerging...
Article
Data science is an expansive discipline that touches on nearly all business domains, from finance to utilities, from manufacturing to healthcare and life sciences. Data science is one of the most popular buzzwords in the current digital world and age. However, there is a lot confusion around the term and different people define the term in differen...
Chapter
Data governance framework (DGF) is one of the key factors in the successful implementation of data governance. The three main components of data governance are people (roles, responsibilities, working groups and committees), processes, and tools and technology. In this chapter we will discuss each of these components and how they interact with each...
Chapter
This chapter introduces to the audience data governance and other data management functions in a concise fashion as these have been discussed in great detail in the second book of the series- Data Governance and Data Management. This chapter also presents the evolution of the data management discipline, the business drivers for data governance, dat...
Chapter
Data governance metrics is one of the key factors in the success of data governance. Data governance is often seen as a lot of overhead and hence, you need meaningful metrics to show progress periodically. A section in this chapter has been dedicated to discussing the characteristics of a good data governance metric. Organizations often do not real...
Chapter
Implementing data governance is not rocket science. However, it does involve a significant amount of effort, time, investment, and cultural change. Data governance is an ongoing endeavor and has several angles to it. Hence, implementing and sustaining the initiative can be challenging and time consuming. Data governance implementation in organizati...
Chapter
Strategy plays a critical role in the success of data governance and the role of strategy in data governance is elaborated in this chapter. Before an organization takes a leap of faith and embarks on its data governance journey, it needs to have a data strategy and a data governance strategy. This chapter discusses DG readiness (as in how ready is...
Chapter
Data governance is transpiring as a key success factor in not only data centric organizations such as finance and professional services, but also in organizations that seem to be less data centric, such as manufacturing and utilities. This is because data is important in every organization irrespective of the industry sector, and data governance en...
Chapter
It is very important to understand an organization’s current data governance maturity when planning to implement formal data governance. This chapter discusses the concept of data governance maturity and data governance maturity model. Several data governance maturity models have been proposed by different industry practitioners, which can be used...
Chapter
This chapter discusses in brief—data and its governance, the data governance stakeholders, how data governance ties together the data governance functions and initiatives, and the data governance success factors.
Chapter
Data governance tools and technology is one of the critical success factors in the successful implementation of data governance. Data governance tools and technologies can form an important part of an overall data governance strategy and implementation as they can automate repetitive activities and processes, enhance productivity, and reduce operat...
Chapter
This chapter introduces the audience to the evolution of data, importance of data, data governance, and data management.
Chapter
This chapter introduces to the audience, the basics concepts related to data and discusses the proliferation of data, key terms related to data, and the organization of data. We also discuss the concept of assets, why data can be considered as an asset, the unique properties of data, the data asset life cycle, how data differs from other fixed asse...
Chapter
The data management discipline has several functions or components with data governance being identified as one of the core components of data management tying together the other data management functions and data initiatives—for example, data architecture management, data modelling and design, master data management, reference data management, dat...
Chapter
As discussed before, interviews were conducted to elicit the experience and unique perspectives of highly experienced professionals—thought leaders, researchers, and consultants for the DG book trilogy entitled Data Governance: The Way Forward. These people have shared their thoughts on data and data governance. The questions asked and their respec...
Chapter
This chapter introduces the readers to the concept of governance, corporate governance, the impact of the digital age and data on governance, compliance and performance, and discusses in detail the difference between management and governance.
Chapter
One of the main business drivers of data governance is compliance. Compliance is about following laws and rules to prevent wrong things from happening as well as detecting and mitigating incorrect actions of the past. Regulations have made organizations much more aware of the issues regarding the data they hold. With new regulations coming into the...
Chapter
Regulations are rules or laws created by government or authorities in order to control behaviors or way activities are conducted. They are essential for proper functioning of the society and economy. Over the period of time regulations have evolved with changes in technology. In this chapter we discuss some of the standards and regulations.
Chapter
The concept of governanceGovernance is the exercise of authority and control to ensure accountability and promote transparency. It has been around in some form since ancient times and has evolved with the requirements of the respective periods. This chapter explains governance and corporate governance followed by the evolution of corporate governan...
Book
This book sets the stage of the evolution of corporate governance, laws and regulations, other forms of governance, and the interaction between data governance and other corporate governance sub-disciplines. Given the continuously evolving and complex regulatory landscape and the growing number of laws and regulations, compliance is a widely discus...
Chapter
Corporate governance is a broad discipline that has several subdisciplines which include, but are not limited to operational governance, financial governance, HR governance, risk governance, security governance, risk governance, IT governance, and data governance. This chapter discusses the corporate governance approach and the different subdiscpli...
Chapter
Governance has always been and will always be a “must have”. Corporate governance and several sub-disciplines of corporate governance came into the picture with the evolution of corporations a few centuries back. This chapter summarizes the key milestones in the evolution of corporate governance, data and data governance, and discusses the way forw...
Presentation
In this webcast, the author summarizes the case for data quality and discusses the different data quality dimensions and their role in ensuring quality data. She also explains the central role data quality plays in ensuring product quality, process quality, and compliance in the digital age. Link: https://asq.org/quality-resources/webcasts/webcast...
Presentation
Full-text available
Good data is a source of myriad opportunities, while bad data is a tremendous burden. Companies that manage their data effectively are able to achieve a competitive advantage in the marketplace, while bad data, like cancer, can weaken and kill an organization. In this presentation we discuss the evolution of data, importance of data quality, cost o...
Article
This article summarizes the case for data quality, discusses the different data quality dimensions and their role in ensuring quality data in a succinct fashion, and the central role data quality plays in ensuring product quality, process quality, and compliance in the digital age. https://asq.org/quality-resources/articles/data-quality-and-data-qu...
Article
Software development life cycle models form the heart of software engineering since software development process spans the life cycle of any given project. Even though the waterfall model forms the basis of all software development life cycle models, the demand for new models with design and implementation occurring in parallel is on the increase....
Article
Data profiling is the first step in the journey to improve data quality and is a proactive approach to understanding an organization's data. Data profiling is a process that gives the picture of the existing data quality. This article focuses on understanding the critical success factors (CSFs) driving the implementation of data profiling in the in...
Article
Purpose – Statistical process control (SPC) is a powerful technique for managing, monitoring, analyzing and improving the performance of a process through the use of statistical methods. The purpose of this paper is to present results of a survey on SPC in the software industry. The focus is on understanding the critical success factors (CSFs) for...
Article
Full-text available
Problem statement: The aim of this study was to present the results of the survey conducted with software professionals in a few Indian software companies. Approach: The study initially presents an overview of the common software life cycle models used in the software development. Results and Conclusion: The survey results revealed that the level o...
Article
Pair programming is a methodology in which two people work together and periodically switch between the roles of driver and navigator. Instead of partitioning a task into a number of activities, here each member performs a different activity alone; in pair work both partners perform each activity together. Pair programming concepts have been introd...
Article
Full-text available
Purpose The aim of this paper is to present the results from an empirical investigation of Six Sigma in the Indian software industry Design/methodology/approach The paper begins with a review of literature of Six Sigma and its role in the software industry. The importance of Six Sigma in the software domain is presented, followed by presentation o...
Article
Agility is the keyword if one needs to survive in this rapidly changing world, keeping pace and at the same time not loosing balance or control which could result in loss of quality of performance. No wonder agile methods have emerged in the field of software development as well, and they are gaining popularity in academics also. The bunches of agi...
Conference Paper
The pervasive impact of software in systems design as well as its changing character presents immense challenges for the education of software engineers. In the twenty first century, software engineers face the challenges of rapid change and uncertainty along with dependability and diversity. This paper presents the results of a study conducted to...
Article
This article focuses on the use of quality function deployment (QFD) to define the unspoken customer requirements in user interface design. The relationships between customer requirements such as learnability, speed of use, and so on, and the realization mechanisms or performance measures such as consistency, error management, and others, are shown...
Article
Simulation and modeling techniques are currently being used to address a wide variety of problems in science and engineering. Foundry operations face many environmental challenges in today's regulated society. An integrated computer model to predict the ground level concentrations of sulphur dioxide emanating from the foundry stack at different rec...
Article
Full-text available
While the Six Sigma body of knowledge has benefited a large number of organisations in improving product and process quality (Define, Measure, Analyse, Improve, Control) and even for developing new products (Design For Six Sigma), some work is still needed for managing software projects. The aim of this paper is to present the results of semi-struc...
Article
Full-text available
Purpose The aim of this paper is to highlight the application of six sigma, software engineering techniques and simulation to software development with a view to improving the software process and product. Design/methodology/approach This paper attempts to integrate six sigma and simulation to define, analyse, measure and predict various elements...
Article
This paper describes a novel neural network model to estimate the emission rate of sulphur dioxide (SO2) emanating from the stacks in foundries employing cupola for melting purposes. The model used in this research is 3-layer network with 9 neurons in the hidden layer. The model has 10 inputs and 1 output. The robustness of the proposed model is de...

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