A Data-Driven Approach to Digital Transformation

Introduction

The significance of the phenomenon that is ‘digital transformation’ can vary from organization to organization. In the simplest sense, it can be thought of as the transition of procedures, operations, and resources from the physical to the digital world. But, digital transformation may range from developing a mobile app, website, or system to reshaping and digitally supporting a complete organization.

When it comes to data-driven digital transformation, it means digital empowerment and optimization throughout a business, creating value via improved knowledge, integration, and actually implementing digital and offline data.

To that effect, digital transformation through data is no longer restricted to conventional offline companies. In reality, by bringing disparate data sources and systems together throughout a company’s larger ecosystem, a data-driven digital transformation both online and offline gives a definitive understanding of how to improve, scale, and get maximum value including the businesses that are already digital.

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Data Challenges solved by Digital Transformation

Digital transformation has a huge amount of potential. Nonetheless, common problems that businesses can seek to address through data integration include:

1. Overcome data fragmentation and minimize data gaps

Modern consumer interactions span a diverse range of touchpoints, platforms, and media. Consumers demand businesses to be aware of and updated with it all. The ultimate responsibility is on the business to identify customers across their selected interfaces at any moment, with precise, relevant communication, regardless of more fragmented channels to purchase.

By offering the data framework, strategy, and technology to assist organisations in consolidating, analyzing, synchronizing, and managing their data, digital transformation can enable businesses to overcome data fragmentation and improve customer satisfaction.

2.?????????????Incorporate online and offline data to digitise

Numerous companies these days, across all industries, recognise the importance of aligning their on- and offline data, as well as digitising their existing data environment, in order to take advantage of real-time capabilities, prevent fragmentation, and more. A preliminary digital transformation aim could merely be to facilitate the suitable data infrastructure and procedures to migrate data to a digital environment – for example, from on-premise to the cloud.

3.?????????????Generate value and perspective from data and MarTech

Another important problem in digital transformation is having the proper marketing technology tools and supporting that with authentic, unisolated, real-time data to allow informed decisions.

Consumers now have more options than ever before, thanks to increased digitalization, which has contributed to high data volumes, velocity, diversity, and accuracy. To deal with this massive data flood, the usual company manages and integrates a variety of MarTech platforms and apps at any one point. The problem is ensuring that the appropriate technology is in position, engaged, streamlined, and coordinated in order to generate value rather than introduce impediments.

A data-driven digital transformation can offer the MarTech resources, direction, and uniformity throughout all applications that are required to integrate data, improve data transparency, and guide business strategy.

4.?????????????Data administration, facilitation, and engagement

Many organisations intending to embark on a digital transformation journey face significant problems in data management and planning. It is difficult to understand how to access, engage, and deploy the right data in the right areas, at large, and in a timely manner. Most organisations pick a digital data transformation consultant that can assess and guide how to leverage their data ecosystem effectively and provide professional knowledge into the ideal technologies and services a specific business will require for getting the most out of their investment.

?Data Integration

Data integration is the practice of merging data from multiple sources to create a uniform, comprehensive viewpoint. Companies can get constant and consistent access to their data because of the merged data. It also provides a detailed overview of key performance metrics, market opportunities, consumer experiences, and so on. The following are the many forms of data integration:

1. Data consolidation

Data consolidation is technically combining data from numerous systems together and generating a representation of the integrated data in a single data repository. The goal of data consolidation is to reduce the amount of data storage sites. ETL technology helps in data consolidation.

ETL collects data from multiple sources, converts it into an intelligible format, and then sends it to another database. ETL cleans, classifies, and even transforms data ahead of implementing business logic.

2.?????????????Data Propagation?

It is the process of copying data from one place to another using applications. It is event driven and may be executed asynchronously or synchronously. Synchronous data propagation from the target and the source is typically used to facilitate two-way data exchange. Data propagation is aided by EDR and EAI technologies.

3.?????????????Data Federation

A type of data visualization is data federation. It makes use of a virtual database to build a data model for disparate data from multiple sources. All the data is gathered and reviewed from a single place. The EII technology supports data federation, which leverages data abstraction to provide a single viewpoint for the data. The data is then displayed and evaluated in a variety of ways using applications. Data federation and visualization are appropriate alternatives when data consolidation is costly or may generate too many security and compliance problems.

4.?????????????Data Virtualization

Data virtualization makes use of an interface to provide a uniform, near-real-time view of the data from diversified channels with a variety of data models. You can access data from one location, but it is not kept there. Virtualization gathers and analyses data but does not give a single point of access or consistent formatting.

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Developing Data Infrastructure for Digital Transformation

1. Developing skills

Effective digital transformations need specialised knowledge and skill sets, especially technical prowess. Hence, businesses must recognize and employ personnel with specialised data abilities. Furthermore, businesses must invest in employee training to help employees refine existing talents or learn new ones.

2.?????????????Cross-collaboration?

There is no one-size-fits-all solution when it comes to incentives and goal setting. To accomplish company goals, you must foster a cross-collaboration working environment in which all teams interact and collaborate to create data infrastructure. A company’s digital transformation does not happen all at once. As a result, a system to encourage and facilitate cross-functional collaboration is required.

3.?????????????Technology

Using data, resources, technology, and operating systems stimulates the critical processes required to generate growth and encourage innovation. The effectiveness of your data strategy is influenced by the technology you choose to incorporate data into essential business operations.

4.?????????????Interpreting data

The management of first-party data sources drives several critical use case processes that distinguish organizations. Businesses must also examine how to implement the proper governance measures to ensure that their data is managed ethically, well-structured, and secure.

First-party, second-party, and third-party data are the three types of data. First-party data is information collected gathered directly from consumers, and second-party data is information collected via the first-party data of another company. Third-party data refers to information obtained from third-party sources such as websites and platforms.

?Conclusion

All of it, from analytics to business operations, is reliant on data. In conclusion, starting a full-fledged digital transformation is impractical without employing proper data integration solutions. All business needs exposure to a comprehensive and consistent data set that will help them make better decisions and accelerate their growth.

The CEO drives the business to a more competitive and prosperous future situation, with the help of innovative executives who together support different pieces of the CEO’s strategy. CIOs usually embrace digitalization, concentrating on data integration, reconstituting enterprise resource planning (ERP) systems, and complementary apps to create advanced information systems.

Irrespective of who is spearheading transformation in the C-suite, leaders and their teams need to use financing as an essential enabler to execute a meaningful digital transformation. Modern CEOs are taking part in digital transformation in order to deliver value at a global level for their organisations. Successful digital transformation strategies must always incorporate the entire organisation and be created with a specific goal in mind.