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Today’s vertical take-up of data and digital transformation is profound Chinabased. The rise of digital transformation and big data in the digital economy has triggered a new era of digital transformation. In this article, we will explore the current trends and challenges of digital transformation, including its architecture, detailages, challenges and opportunities for small and large enterprises. We will also discuss the role of data as a foundation for digital transformation efforts at different scale and scope. Read on to know more about this transformational moment in time. What is a Digital Transformation? A digital transformation is an essential component of a digital economy. It is much broader than just creating new data centres or adopting new business models that are based on data. A modern enterprise needs to: • Explore new technologies—including Artificial Intelligence (AI) and Machine Learning—to drive ultimate benefit from their technology investments; • Utilize Data to Improve Business Value—throughput by connecting customers with suppliers, inventory management, customer service etc.; • Utilize Data to Drive Meaningful Business Results—throughput by connecting users with data sources via social media or other platforms; • Optimize Efficiency through Data Planning, Analytics,and Governance; and last but not least, • Plan for Digital Transformation in the Long Term through Adaptive implementations of data management strategies. Hence, aDigital Transformation is very much an umbrella term to cover all these elements under one roof. Let’s explore more contextually the five pillars of Digital Transformation

The Digital Transformation Framework

The first thing that comes to mind when one thinks of digital transformation is customer pain. What will it take to get my business digital transformation? How can I align my efforts with the needs of my customers? These types of questions tend to get answered by looking at the different Digital Transformation (DT) pillars and what they have in common. – Data Warehousing and Data Analytics: Data is the foundation for digital transformation. Traditionally, data has been controlled and stored on tape. Tape is slow, expensive, and requires frequent copying and restructuring. It’s also not easy to maintain or update. Data Warehousing and Data Analytics are a solution to this problem. Data Warehousing allows enterprises to store and analyzing data, and data analytics allows enterprises to understand their customers’ needs and serve them better. – Cloud Computing: Cloud computing has sweeping benefits for enterprises. It eliminates the need to physically and Mentally connect to remote data centres. It eliminates the need to train staff to operate remote technologies. It eliminates the need to provide oversight or control over data movement. It enables organizations to focus on what matters – serving their customers. Cloud computing also enables organizations to eliminate the maintenance and alteration of file systems and protocols (like database connectivity), and it provides visibility into the state of technology across all teams. – Algorithms and Data Analytics: Algorithms are a way of making sense of huge amounts of data. Data analytics enables the use of rules to make sense of data. algorithms are ways of making sense of data. These can be technical analysis, like making sense of data from ETL; cultural analysis, like identifying and contextualizing problems with data subjects; social analysis, like understanding other’s preferences; financial analysis, like understanding revenue from different channels; and human analysis, like understanding human behaviour.

Data is the data, and the data is the data.

For each of the Data Transformation pillars, we will examine the benefits of data and how these benefits can be implemented in an enterprise. Then, we will discuss the challenges of data transformation and the ways data transformation can overcome these challenges.

New Types of Data Warehousing

Advanced data warehouse technologies, such as artificial intelligence (AI), machine learning, and big data, are making data analytics more efficient and effective. They allow organizations to handle huge numbers of records in a very efficient fashion. Thus, organizations can collect data from a variety of sources. They can also store data in data stores, like tables, or in data warehouses, like this one: Data Warehousing allows organizations to collect data from a variety of sources, like customers, employees, vendors, and other businesses. It stores data in tables or data warehouses and allows organizations to act as if every record is owned by a single entity. To simply list a few examples: an employer can collect data on employees and assign them low-level roles; a customer can ask for information and be given a record with that same data; and a vendor can act as if each record is the same.

Digital Transformation in Practice

To truly begin to understand the power of data, it’s necessary to familiarize ourselves with the digital transformation canary. We will use this digital transformation canary to examine the effects of data transformation on a small business. Data transformation asks for a high level of commitment from all stakeholders. It requires understanding the full impact of data transformation on an organization as a whole, from top to bottom.

Achieving Digital Impact with Data

From the above-mentioned metrics, we can see that the data transformation canary has a big advantage in the short term. It can be used to show businesses that data transformation is a very serious issue. After all, this issue is the foundation of any digital transformation effort. It is the root of all digital transformation challenges. To truly tackle the challenge of data transformation in the long term, it’s critical that businesses understand their immediate challenges and funnel their best data and information into a solution. This is the process of data proof. This is the process of data adoption. This is the process of data proofing. In other words, businesses must first and foremost understand themselves as data consumers. They must understand how their data is being used, and they must ask themselves these questions: – What is the purpose of my data? – What is the impact of that data on other stakeholders? – Where can I best get help? – What can I do now? – Conclusion Data transformation can be challenging for small businesses due to various reasons. Here are 5 of the most common challenges: – Data quality is poor. Data is often not stored up to date. Data is often out of date. Data is often unpredictable. – Data is not current or doesn’t reach its intended purpose. This issue is often due to a variety of issues, but the most common is a lack of data responsibility. Data stakeholders need to take responsibility for their data. This includes the data itself, rather than relying on third-party data engineers to make sense of it. – Data management is poor. Data management is poor because data is being stored in outdated or incorrect format. Data requirements need to be defined and data resources need to be allocated accordingly. This causes data problems. Data transformation is one solution but it is not the solution to all data transformation challenges. – Data governance issues are a concern. Data governance is poor and leads to data transformation failure. Data transformation is a two-way street and data transformation issues are often connected to data governance issues. – Data quality is poor. Data quality issues are often related to data governance issues. Data transformation must meet data quality requirements. However, the requirements can be difficult to define and achieve due to data transformation challenges. Data transformation must be repeatable. Data transformation must be repeatable and scalable. – Conclusion Data transformation requires a complex web of relationships. It requires data suppliers, data consumers, and data managers. It requires data quality, data governance, and data chain functions. It is a challenging task and requires a person or team with a variety of roles: data manager, data supplier, data manager/data engineer, data engineer, data/mary/steward, etc. There are challenges such as data transformation that have been addressed in the previous section, but there is much room for improvement. This is why data transformation remains an essential component of digital transformation.