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Industry 4.0 is about the digital reinvention of industry. Organizations and Business leaders are shifting their focus to digital transformation but are also concerned about the associated risks. Most companies are not able to fully realize the value of transformation initiatives, while some completely fail to achieve key objectives, leading to wasted resources and money losses making them outdated or less competitive. We found that high levels of data trust and understanding, particularly on the company’s Master Data, are crucial to the success of any digital transformation initiative. Deriving value from data does not happen in a vacuum or by accident. It requires intention, planning, coordination, and commitment. It requires management and leadership.

To achieve a successful digital transformation initiative, organizations are encouraged to institute a technology and process enabled Master Data Management office which will bring business and IT together in applying industry best and leading practices to unlock value from data assets.

Much ink has been spilled over the relationship between data and information. Data has been called the “raw material of information” and information has been called “data in context.” Imagine a layered pyramid to describe the relationship between data (as the base), information, knowledge and wisdom (at the very top)

Organizations should take a small step back to start building a sustainable MDM practice to redefine experiences and position the organization for success in the age of digital disruption.


The underlying root cause for a failed digital transformation

There is a rat race and rush for digital transformation initiatives. Disruptive Innovation is fundamentally changing the way organizations do business. Today’s market landscape is hyper-competitive; technology giants and startup ecosystem are threatening the existence of many large organizations. Winning companies are leveraging digital not only to discover and develop new products, services, and business and operating models, but also transform their core business. Companies are in pursuit of end-to-end solutions to achieve new levels of efficiencies and redefine customer experiences by integrating product innovation, engineering, manufacturing and service functions.

It is a top concern and risk to businesses since most of the digital transformation initiatives fail due to lack of visibility and importance of cleansed, harmonized, standardized contextual data. Business functions and processes are getting so tightly integrated and demanding that without having a master data management practice in place, all will lead only to only CHAOS.

In a typical transformation project, there is a strong desire to move from siloed processes and systems to connected, interactive, and collaborative functions, processes and systems through seamless data flow. However, connecting the siloes alone will not unlock all these incredible benefits. While it helps the company to gather more data, it does not automatically ensure that it is trustworthy enough to be used for business insights. This is where data management comes in. Without it, companies might not reap the rewards of investing in digital transformation

Why there is a need for Master Data Management?

When starting with data management exercise, companies often find it difficult to decide which data to focus on especially after breakout technologies such as Internet of Things (IoT), products and machines produce greater amounts of data than ever before.

The various data types are grouped according to the Enterprise Data Lifecycle that depicts post digital transformation scenarios to highlight how a wide array of data is typically utilized to gain better insights and make timely informed business decisions.

Master Data is the uniform set of identifiers and attributes that describe the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, asset hierarchies and chart of accounts. It is the core element for transformation programs and hence, this is where the journey to become an insights-driven organization should begin.

The quality of the business data and business insights depend on the quality of master data. As seen in the below infographic that it all starts with Master Data. It feeds into the ERP or OT systems to conduct business operations and day to day work. Business operations generates actionable business insights which is CLARITY through analytics aided by visualizations.

How to achieve this clarity?

Institute a sustainable, evergreen Master Data Management framework, practice and process in place. This framework lays out some key defined steps or process for successful digital journey.

  1. Data Diagnostics

  2. Data Strategy

  3. Data Management

Conduct Data Diagnostics

Organizations often make significant investments in digital transformation programs without clearly identifying target areas. Leading companies prefer to diagnose the issues before engineering the solution, which gives the project leadership and data owners faster access to key problem areas. It also enables them to start the journey of building trustworthy data as early as possible. The improved data analytics and visibility will then drive decision making in real-time. For this connect to a data source and run diagnostics to understand key issues based on the sample data. Check for data quality dimensions and parameters such as accuracy, completeness and consistency.

Develop Data Strategy

Most organizations are reactive – key actions are only taken after the business encounters severe data-related issues. To solve these challenges, there is a need to set up an integrated data governance council and structure that define, control and monitor complete data information management.

Data strategy entails the development of the vision, scope, business case and roadmap for transforming the management of data to meet the business and technical needs of an organization. Define standards and setup data governance model along with stewardship to have a complete line of sight.

Manage and Sustain

Data management is the function of controlling, protecting and facilitating access to accurate data to provide consumers with timely access to the information they need. Implementing data management practices and transforming existing practices to higher maturity levels ensure that the organization is well-positioned to improve data quality and proactively respond to changing data requirements.

Data sustainment refers to the continuing drive to develop and enhance value from data capabilities enabled by cost-effective management and growth of the people, processes and technology of the organization.

Conclusion: Delivering value through end-to-end Master Data Management

This is a proven and industry best practice to approach and execute end to end master data  transformation. Some key values include:

  1. Shape future-state capabilities, data processes and policies

  2. Support process design completion through deployment to operations

  3. Provide change assessment, stakeholder interlock facilitation and training documentation

  4. Define the baseline quality, impact to the business and identified key issues

  5. Executed interlock needs, defined stakeholder impact and managed risks and issues

If you have any queries or recommendations about the subject, please don’t hesitate to leave a comment and inform me. Your input is highly valued and will aid me in deciding which topics to include in my upcoming blog series. Thank you for your support!

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