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Information Technology Assignment:Developments In Data Management & Digitalization


Task: Your task is to prepare an information technology assignmenton the topic “Data Management & Digitalization – A key aspect for all organizations – sometimes referred to as information management; How has it changed over time, particularly with the development of the internet and web? Digitalization – what exactly is it? What is its impact on organizations and everyday life?”


The information technology assignmentis focused on providing a comprehensive review of developments in data management and digitalization in recent years. The essay provides clear understanding about terminologies about the agenda. Data always made internet and digital world become more diverse and bigger than before. Digital technologies and data alongside plays an important role in different ways for daily lifestyle (Ma et al. 2021). Observing digital world as it is today, it is evident that data management has become a relevant part of the development. In this development journey, importance of data management lies and data is vital and of value for driving digital transformation at its peak. Good ‘data strategy’ can help to deliver appropriate insights and actionable information to the right authorities for right reasons (Rossi and Hirama 2022). Data management methods can initiate data analytics and it helps with digital business with building digital economy.

A question arises here, whether data management is related with data governance or not. Data management is identified as development, planning, execution, policies, programs, and practices that can help to deliver, control, protect and increase the value of data and information assets in data management lifecycle (Kilicet al. 2022; Diyanet al. 2022). Data governance is a key part of knowledge area of data management where it is entirely related with data quality as a data management discipline. Data governance strategy includes different processes to manage quality data in its lifecycle. Data management foundation lies in data governance where data handling techniques are defined, purpose of data management, and accuracy of data, data security and data compliance are ensured.

Background of Data Management
Data management is mainly about ‘managing’ data however, it should be made sure that data is being used properly and it delivers right value at right time for right reasons to the right people. Hence, data management only is successful when it can be used, delivered, used for a reason, and it can help certain people. Moreover, data utilization should be ethical and it should be under right people’s possession (Iaksch, Fernandesand Borsato2021). These concerns are also part of data management to ensure data privacy and safety. Data management refers to the idea that data can be anywhere and it can be collected from several sources, and data can be held useful before it becomes information for a purpose (Jayagopal and Basser 2022). Even if data were not required to be used, someone still would manage it making a decision on whether it should be used or not.

Data Management is considered as a lifecycle for data that can be used, acquired, and needed. Discipline of data management would include several data management areas such as data governance and architecture to data quality, data storage, and data security (Sezer, Thunberg and Wernicke2021). Data management is multidisciplinary area where, data management has several challenges and evolutions with new methods’ introduction. It is evident that collaboration with data management is highly likely. Holistic view of data management with extraction of value from data would need collaboration with data experts with several data-related disciplines (Annosi et al. 2021). Moreover, data consumers, data creators, and data experts would be connected in a cycle as well. Data creators and data consumers are more connected in the era of fastest data generation. Intelligent devices and machines would consume data with automation along with other devices.

Evolution of Data Management and Data Management Disciplines
Data Management is considered as an evolving domain where big data analytics, artificial intelligence, machine learning and modern applications would be considered as relevant disciplines in data management (Singh and Singh 2022). Data management has improved over longer period of time, however it has built different disciplines with importance of analytics. Evolution of data-driven digital economy has grown where data is considered as main asset (Inkinen, Helminen and Saarikoski2021). Data protection can measure how data is essential and considers need for data storage as relevant. Data management has become more complex and critical with new data age where data can be used for more diverse tasks and volumes with data becoming more complex than before.

There are some primary drivers to run data management evolution however, there is more factors that can work as well. Some drivers are mentioned as following:
1. Data is driving digital business as a primary asset
2. Digitalization and digitization in the field of data management
3. Digital transformation as an important phenomenon
4. New challenges to extract data from growing data (Big Data) where numerous data sources are considered (social media, Internet of Things, and others) (Hendriyatiet al. 2022)
5. Growth of data analytics and importance of insights gathered from Big Data
6. Increasing concerns over data privacy, security and protection
7. Requirements of Artificial Intelligence and Machine Learning
8. Organizations are migrating towards cloud computing and enterprise-level cloud data management is growing (Steinberget al. 2022)
Some data management disciplines are mentioned here as following;

Data Management Disciplines in Thomason 2022 pp

Figure 1: Data Management Disciplines
(Source: Thomason 2022, pp. 488)

Data Architecture: Data architecture in data management is highly important as handling data requires a specific format to keep data stored for usage.

Data Modelling and Design: Data modelling and Design provides usage of Big Data for meaningful patterns identification and understanding actual insights from data (Liuet al. 2022; Thomason 2022). Data Storage and Operations: Data Storage works with terminals, connection with storage devices for direct access or access through a network. Computers can be used for accessing data store data from devices; this is a foundation of data management.
Data Security: Data security is important as securing data can keep organizational data safe and massive volume of data can be useful for research and development for the organization (Smithet al. 2022; Burnette 2022). Data Integration and Interoperability: Data integration requires connecting different applications for accessing data from one system to another. Whereas, data interoperability refers to real-time data exchange between different systems without using third-party or middleware.
Document and Content Management: Data management requires handling documents; their extraction, retrieval, ranking, indexing, and storage for analytics or research purpose (Annosiet al. 2021; Iaksch, Fernandes and Borsato 2021). Reference and Master Data: Master data represents business objects that can include valuable and crucial information stored for sharing across different organizations and reference data can define set of values that can be used in different data fields.

Data Warehousing and Business Intelligence: Data warehousing and business intelligence is known as process of storing data over internal or external databases from several sources to focus over the analytics and generating actionable insights using Business Intelligence tools (Inkinen, Helminen and Saarikoski 2021; Kammet al. 2021).

Metadata: Metadata is considered as information regarding aspects of data, metadata can be used for summarizing information from data and it can be used for tracking and working with data easily. Data Quality: Data Quality is known as a measure for showing how well a dataset can be used for a specific reason. Measures of data quality can be considered as certain characteristics of data such as validity, accuracy, timeliness, uniqueness, consistency, and completeness.

Data Management Systems Today
Organizations should consider data management as part of digital transformation and this strategy is applicable for businesses so that it can use digital models of operation with digital heritage. The digitization can involve several types of data management with primary component for making digital transformation (Lichtenthaler2021). Organizations that relied on digitization can achieve quality of data available for their disposal. Exponential rise in information availability can combine with data storage cost and cloud computing (Kamm et al. 2021). Majority of the organizations consider involving pre-embedded analytics with descriptive analytics for improving their decision-making. Business restructuring can introduce digital-first setup and it is viewed as primary factor for introducing effective data management with successful data transition to replace efficient data architecture. Today, organizations should require data management solution that can provide efficient way to manage data over unified data tier. Data Management Systems can be built with different data management platforms such as data lakes, data warehouses, big data management systems, data analytics, and others (Maet al. 2021; Merkt, Thiele and Dinges 2021). Data Management Platforms are known as foundation system for massive volume of data collection and analyzing large volumes of data in an organization. Commercial data management platforms include software tools for management that is developed by database vendors and by third-party vendors (Sezer, Thunberg and Wernicke 2021). Data management solutions can help IT teams and DBAs for performing tasks such as;

a) Diagnosing, resolving, identifying, and alerting issues in database or infrastructure
b) Database memory and storage resource allocation
c) Performing changes in database design
d) Optimizing responses for quicker application performance
Currently, cloud-based platforms allow organizations to scale up or down faster in a cost-effective manner. These platforms can be used as a service enabling organizations to reduce cost and time for its deployment (Rossi and Hirama 2022; Singh and Singh 2022). Based on cloud, autonomous databaseis introduced that utilizes Artificial Intelligence (AI) and Machine Learning for automating data management activities from DBAs with management of database backups, performance improvement, and security (Steinberget al. 2022). It is also known as self-driving database with significant benefits such as lower cost, reduced complexity, less human error potential, better database reliability, improved operational efficiency and data security.

Data Management Challenges
Several challenges in data management originate from faster business growth and massive volumes of Big Data. Expanding variety, velocity, and data volume to the organization making them to develop more effective management tools. Some challenges are mentioned in this section as;

Data Management Challenges

Data Management Challenges

Severe difficulty in maintaining performance of data management

Organizations capture, store and use massive data always; for faster response over expanding data management tier, organizations should monitor different type of questions and answers from database without affecting its performance.

Need for optimizing cost and IT agility

Cloud data management systems can help organizations to choose data storage or data analysis in on-premises environments. IT organizations require to evaluate cloud environment performance to analyze cost and IT agility.

Lesser data insights 

There is increasing number and variety of data sources; and data is being captured and stored from sensors, smart devices, social media, and video camera. However, if data insights are not gathered then organization cannot determine use of collected data.

Requirement for easily process and convert data

Identifying and collecting data would not be valuable if organization does not process it. Processing Big Data would take huge amount of time and massive effort to convert data into information. Therefore, easier way to process and convert data is required; otherwise, value of data would be lost.

Several challenges with changing data requirement compliance

Regulations are complex and they can change or modify constantly; therefore, organizations require reviewing the data and identifying its compliance. Therefore, organizations should track and monitor strict regulations for their data.

Increasing need to store data effectively

New world of data management would require organizations to store data in multiple systems along with data warehouses. Unstructured data lakes can store data in certain format in a single repository. Therefore, data scientists in an organization would face the challenge of constant need to quickly transform data into its original shape before storing for analysis.


Table 2: Data Management Challenges
(Source: Smithet al. 2022, pp. 877)

Data Management Best Practices
Data Management challenges are addressed and now to deal with some of them, data management best practices are mentioned in this section. The practices require better planning and comprehensive ways to recommend for organizations (Thomason 2022). Specific best practices can depend on type of data involved in industry and following are some best practices recommended to organizations today.

Data Management Best Practices
Discovery layer for identifying data Discovery layer should be created on organizational data tier that can allow analysis and hence, data scientists can search and identify datasets for making use of data. Data science environment should be developed for effectively reuse data Data science environment can automate data transformation work with streamlining data creation, evaluation of models. Set of tools can be used for minimizing data transformation so that data can hypothesize and test new models.

Autonomous Technology should be used for maintaining performance levels to expand data tier Autonomous data capabilities can incorporate AI and machine learning for monitoring database queries continuously with index optimization. This can help with databases to maintain databases rapid response times and data scientists can be relieved from time-consuming manual activities.

Data Management Best Practices

Discovery layer for identifying data

Discovery layer should be created on organizational data tier that can allow analysis and hence, data scientists can search and identify datasets for making use of data.

Data science environment should be developed for effectively reuse data

Data science environment can automate data transformation work with streamlining data creation, evaluation of models. Set of tools can be used for minimizing data transformation so that data can hypothesize and test new models.

Autonomous Technology should be used for maintaining performance levels to expand data tier

Autonomous data capabilities can incorporate AI and machine learning for monitoring database queries continuously with index optimization. This can help with databases to maintain databases rapid response times and data scientists can be relieved from time-consuming manual activities.

Converged database should be used

Converged database includes native support from all modern data types and latest development models can be built over one product. Converged database can be used for running huge workloads from blockchain, machine learning, IoT, and others.

Common query layer can be used for managing multiple and diverse forms of data storage

New technologies are using data management for working together with developments. Common query layer should be used for increasing different kinds of data storage enabling data analysts, scientists, and applications for accessing data without requirement of understand where data is stored and it would not require manual transformation into usable format.

For handling compliance requirements discovery should be used

Data discovery can be used as new data tools so that data can be reviewed and chains of connection can be identified for detection, tracking, and monitoring for compliance. Compliance should increase globally and capability should be increasing as it is relevant for risk and security management.

Database platform should be ensured with performance, availability, scaling up for supporting the business

Collection of data can ensure analysis better and timely analytics should be useful; scalable, high-performance database platform can enable enterprises to analyze data from different sources with advanced analytics and machine learning for making better decision-making in business.


Table 3: Best Practices for Data Management
(Source: Burnette 2022, pp. 980)

Influence of Digitalization over Different Organizations
Digitalization would be considered as essential for achieving business success and it would require business to start using digital technologies. Digitalization would occur when business models can change with providing more value-producing opportunities (Gallego and Kurer 2022). Digitalization is considered as stages to change business model with providing more revenue and value-based opportunities (Merkt, Thieleand Dinges 2021). Digitalization would involve activities and it would process with digital technologies. Marketing activities automation and with order processing, businesses can leverage digital technologies.

Digitalization helps business for improving operational efficiency so that automation can be made possible for the organization. There are limited human errors and the operational costs are limited; therefore, human resources are not required in the process (Brinker and Haasis 2022; Mattet al. 2022). The importance of digitalization lies in the way where digital technology plays major role for the business. Digital technologies can reconfigure entire business landscape where traditional transition of analog to digital occurred earlier. Some beneficial influences are mentioned as;

Customer behavior: Digitalization can improve customer behavior and buying patterns; it is shown that 81% of consumers searched about product that they want to buy before going to retail store (Bouncken and Kraus2022). With increasing trends in online buying, consumers need to opt for convenience for grabbing the product at their house however, they tries to compare other vendors’ services and products offering.

Human errors would be minimal: The digitalization can improve business workflows and business workflow can be streamlines. This would reduce manual errors and hence, operational cost would be reduced significantly.

Data Analysis would be easier: Google can provide secure cloud services and business data can be analyzed with Google Analytics (Roisse Rodrigues Ferreira 2022). These technologies are significant part of good-quality data reviewing and analytics can help with decision-making for business. Digitalization can increase business recognition: Business visibility on social media would matter over different social platforms such as LinkedIn, Facebook, Twitter, Instagram and others. Increased presence of product on online stores and social media can help to create communication channels without offline presence as well (Burnette 2022). Digitalization encourages innovation: Digitalization has influence over innovations. As technologies becoming more advanced and innovative, digitalization can satisfy customer needs with improving their sales and profits.

Impact of Digitalization on Daily Life
In worldwide and in daily life, digitalization became a tool for achieving success in business. It would automate marketing operations so that order processing can be automated with marketing operations and digital technologies can support the procedures to improve business efficiency (Liuet al. 2022). Implementation of digital technologies would introduce transitioning business into greater scale along with organizations that are more data-driven (Lichtenthaler 2021). This approach would provide organizations to store data over cloud storage and it would be competitive for gaining an advantage. Cloud computing would help to store data however, it would prevent data loss as a basic advantage; however, cloud support automatic data backup feature that would help incorporating team collaboration to ensure better performance.

The essay is concluded with its findings over the sections it demonstrated; the sections include basic definition of terminologies, its relevance of discussion, evolution of data management, new developments over the internet, and impact of digitalization over different organizations or in daily life. Considering collective benefits of data management and digitalization in business, there exists some limitations in data management. There is still data privacy and security issues that needs to be addressed from cloud service providers and cloud vendors. The essay provides some data management issues with their solutions that can be used as preventive measures. To summarize the challenges, it is viewed that performance of data management handling can be very difficult due to increasing volume of data. Cost management and lack of data-driven insights can make decision-making critical for any business. Moreover, data management processes should be equipped with processing capabilities and efficient data storage techniques to deal with Big Data. In context to these challenges, discovery layer can be used for identifying data and dealing with data compliance requirements. A common query layer should be used for management of multiple forms of data storage.

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