MBB7003M Data analytics
MBB7003M Data analytics
Part 1: Introduction
Company Overview
The preface to the report is signified with a technology corporation as a leading provider of Artificial Intelligence (AI) and global hybrid cloud which is parallel to consulting expertise succeeding millions of effort at a fingertip. IBM streamlines to reduce costs, generates the competitive edge of the company that is based on streamlined business processes, data over 175 countries where the software of the company helped to generate more than 12% of the constant currency (Ibm, 2023). The report determines the range of employees at IBM in 2022 where the graph has included a peak of 288.3 thousand which seems to arise from 2021 with 282.1 thousand (Statista, 2023). This successive change has been determined by the flow of technological expertise which has given a shift towards better, defined and unique IBM infrastructure, consulting and software. It may be recorded that IBM has exceptionally determined the usage of over 4,000 government and corporate entities with the flow of areas of infrastructure which relied on telecommunications, financial services, and healthcare based on the uniqueness of hybrid cloud platform contrasted with Red Hat OpenShift technology. The use of the technologies has helped to evaluate the digital transformation to serve a secure and efficient manner that has negotiated with the fore more technologies such as quantum computing. IBM has been the pioneer for AI, industry-specific cloud solutions which help to build transparency, flexibility, and openness which enables trust with inclusivity and responsibility based on services (Yang et al., 2022).
Summary of the prime activity of IBM
The zenith activity of IBM seems to be indulged in providing solutions on software which has been extensively used over the designing of hardware, cloud services, application management and IT consulting. The importance of data analytics implies to provide wide range of relevant products which is asserted with collected authentic data in IBM with a cost effective notion to optimise the operations. Again, predictive analysis IBM by data analytics aids in maintaining effective decisions for enhanced data visualisation. Again, the potential of big data and data analytics to improve company operations and decision-making will also be stated in the following report which will support the real-time data insight to provide competi9tive edge to the company. Furthermore, the pinnacle notion of IBM seems to be adjacent to developing and providing seamless persuasiveness of supply chain management which seems to be developed with logistics operations, management of supply, services on outsourcing and many forth (Businessmodelanalyst, 2023). However, the main capabilities of IBM seem to be development and research which is enhanced with a prime motto to induce networking equipment, storage systems, software including computers, servers with AI, data analytics and computing (Park and Li 2021).
The following activities of IBM seem to be managed with objectives that have been designated with information through leadership based on technologies. However, the years seem to be more struggling for IBM which has been dependable on software, consulting and infrastructure which determines the slight growth in 2022 with $25.04million, $19.11 million and $15.29 million respectively whereby the growth seems to be indolent in finances and other with $0.65 million and $0.45 million singly (Statista, 2023).
Part 2: Business problems and questions
Challenges faced by IBM
There are various business challenges in IBM that stem from lower distribution of the business landscape which has derailed the professional management inclining towards disputed customer experience which were tracked with data analytics and big data initiatives (Ibm,2023). Lower talent acquisition and sustainable skill enhancement seem to be manifested which has surged the competitiveness as various companies in recent days seem to use similar technologies. The company seems to lack the proper use of data analytics such as descriptive, diagnostic, predictive and prescriptive.
Determination of executing issues with data analytics
The aforementioned four data analytics seem to be providing prominence to build safety with more use of service of the company that is based on the receiving feedback which is further altered on providing resolution to the problems therefore, the reason seems to be adjacent towards the address of data analytics and big data. Respectively for the growth of the professional fleet, the augmented support seems to be established in providing professional solutions to achieve seamless solutions.
Describing the larger innovations or competitors and regulation of IBM
The largest innovation which has been induced by IBM seems to be the IBM Osprey quantum processor which is the largest qubit count for the processor of quantum which is highly computing with complex capabilities with a faster representation of data (Ibm, 2023). The zenith context of the business of IBM is Data mining where the competitors are Wipro Technologies, Hewlett Packard, and Accenture on IT-based (Business.uconn.edu 2023). The regulation of IBM is manifested with proper maintenance of standards, regulated with guidelines on legal requirements tracked with big data and data analytics initiatives (Ibm, 2023).
Identification of business relevance and significance of problems of IBM
The relevance of IBM’s business seems to be manifested with an optimised understanding which is mandated with browsing effectiveness, search optimisation, query capabilities and many more (Ibm, 2023). The tag to commoditization is the significance where the customers are accused of failing to make differentiation which has increased the identical competitiveness synthesising loss over quarter on its financial proclaims (Cbsnews, 2023).
Analysing and expressing the outcomes and significance of challenges and issues faced by IBM
It seems to be more crucial for IBM to utilise the flowchart for data analysis which is manifested with a higher intent of complexities and added-value contribution which helps to denominate the descriptive analysis as a lower complexity with lower value contribution. The challenge has taught IBM to supportively innovate the Automatic Requisition Tracking Management Information System (ARTMIS) which helps to track and deliver shipments in the supply chain (Ibm, 2023). Furthermore, the IBM open-source cloud platform is the deliberate outcome which is recognised from challenges which seem to be addressed as a stem for moving critical workloads to hybrid architecture. IBM has garnered the challenge with effective results which resemble the use of Business Intelligence (BI) that directs towards the innovative utility of the Internet of Things (IoT) where the alignment to descriptive analytics seems to explain the exact issues with diagnostic analysis to provide solutions. Similarly, the significance of the issues faced by IBM is the progressive change to be determined with the establishment of setting a worldwide network for valuing a business for customers oriented by Data- Driven Decision Making (DDDM). The use of data analytics seem to develop a range of profound multi-criteria optimization in learning which supports the vector machines in order to upscale the learning pattern (Sas, 2024).
3. Data Collection
Internal Data of IBM
IBM gathers or accesses information from the connected system through IBM Security Directory Integrator where it converts the data by using Java objects from system-specific types to the internal representation. In terms of gathering internal data, IBM is significantly facilitated by its Security Directory Integrator which may harmonise data representation and schema by simplifying the data through certain issues that are common when complex data structure comes along (Ibm.com, 2024). Despite that, IBM Security Directory Integrator helps the company work with the data structure in a hierarchical order by leveraging its facility of various attribute value types for any Java object and entry object with its own attribution and value.
External Data of IBM
IBM gathers external data from various databases, a SOAP API or a REST and other many data sources by applying the pull technique. Other than that, IB collects client information from client applications or by utilising the MobileFirst Server from where client data may be accessible. IBM stores those external data from external sources in a JSONStore collection internally by pulling data from external sources (Ibm.com, 2024).
Data from smart devices
The use of IoT in IBM seems to make proper change with the aid of physical objects embedded in sensor collection to enhance the network connectivity which further helps in collecting and sharing data for further assistance. IBM uses the IoT as smart device in order to monitor climate parameters with the help of real time patterns which improves the bottom line of the company. The importance of IoT which assists IBM to use is its improved efficiency, Data-driven decision-making, cost-saving technique to enhance the financial range of the company (Ibm.com, 2024).
The type of data which IBM relied on to identify the challenge
Data collection
IBM relies on the collection of data from Data warehouse, Data Lake, BI, Data Mining, OLAP, and DDDM that helps to manifest the scope of the challenge with proper identification. However, the collection of data and its use may be seen from Data Warehouse by IBM to forecast the problem with a standardised format that helps to negotiate with the change helping to recollect integration on subject-oriented databases (Al-Okaily et al., 2022). DDDM has been formatting the state of collected data in IBM with warranty and a positive and better product configuration that diagnoses readout data to gather a positive and manageable form to save the cost of data resembling and collecting data as an external for sourcing data for smart devices. The use of Data Lake has equipped the scope of a centralised repository which helps to give a proper structure to the organisational change with an optimising effectiveness over the unstructured data (Sawadogo and Darmont, 2021). The gathering of the process seems to be managed with hyper supervision that has registered with a worthwhile application that is manifested with more experimentation and observations to provide massive and effective management. The adjustment to the components seems to manage the capability notion of the data gathering which has articulated the scope to determine the change with a descriptive data analysis.
Storage of data
OLAP (Online Analytical Processing) has been relied upon by IBM to convert the data for better supportiveness of the decision to drill down a data cube soliciting an ad hoc report from external sources that seems to be managed with smart devices as a medium of data collection. The use of BI seems to be giving the strategic decision which has been effectively denominated by IBM for further actionable insights that rely on current data from external sources for smart devices.
Data processing
However, the three tiers of the data warehouse architecture have been evolving the scope of processing the data with more persuasive data mining structure which is tiered with the front-end client in the top analytics engine in the middle and the database server in the bottom that evaluates better scope for secure data which has been observed and obtained through external sources fascinated by smart devices to collect data. The management of diagnostic data analysis has helped to follow and trace the areas of development which resembled challenges over predictive data analytics. The arrangement to resolve the data management is hyped with the effectiveness of Prescriptive analytics that has manifested to the entire scope of the process.
4. Data Analysis
Analytical Approach Chosen by IBM to Mitigate the Challenges
Currently, IBM is facing challenges related to the implementation of data analytics and business landscape disruption and many more which are disrupting the overall business productivity. Based on the information received from the brand’s website (Ibm, 2023), the brand is currently utilising Big Data Analytics which is focused on accessing large data and enhancing cost operational efficiency which is mostly utilised by descriptive data analytics. Al et al., (2022), mentioned that IBM’s data analytics strategy is majorly based on gathering a large amount of majorly descriptive data. Optimisation strategy of IBM may be analysed through the brand’s implementation of real quantum computers which has helped the brand to mitigate challenges (Carrascal et al., 2023).The brand’s data analytics strategy also includes cost-effectiveness and an improved data-driven market strategy where the brand has also utilised diagnostic data analytics which majorly mitigates the brand’s challenge related to workforce engagement.
By using predictive data analytics the brand is predicting the upcoming business risks and enhancing the business productivity. IBM has acquired an increasing number of start-ups which is propelling the increasing number of Artificial intelligence (AI) fostering the brand’s business (Bharadiya, 2023).Furthermore, through the application of the four types of data analytics, the brand is fostering business growth and critical thinking which plays a crucial role in fostering the accounting system of the brand which is mitigating the current challenge related to the brand’s competitiveness. Moreover, the brand’s approach to data analytics has provided the brand with a significant advantage related to the brand’s information related to the current trends of the market. The implementation of descriptive analytics has improved the brand’s understanding related to the brand’s software usage which fosters the brand’s economic growth. IBM has introduced analytics engine which is useful for Hyperledger fabric blockchains which has encouraged the usage of user-friendly dashboards (Altarawneh et al., 2022).
Justification for the Usage
The current challenges of the brand are mostly reflective and hinder the business growth for which descriptive analysis is suitable for the brand since the brand’s data analytics is focused on large data gatherings. Dow et al. (2021) mentioned that IBM majorly utilises descriptive, diagnostic, predictive and prescriptive analytics which predicts customer demands and purchasing intentions that foster the brand’s growth in the competitive market. Furthermore, it also helps IBM with its auditing capabilities which are crucial for the brand in mitigating the current business challenges which hinder the brand’s overall productivity. IBM has encouraged the incorporation of computing science through the application of AI which enhanced the company’s problem-solving abilities (Ivakhnenkov, 2023). Furthermore, the use of the four types of data analytics is justified since it helps the brand with predicting its future outcomes and the risks related to the business of the brand. Moreover, the usage of data analytics helps the brand with communication which is crucial for the brand’s business productivity and focuses on business productivity. The major business challenge of IBM is immense market competition and the usage of data analytics fosters the brand’s innovation which is crucial for mitigating the challenge related to business competition. Based on the brand’s nature of data analytics the brand majorly focuses on effective decision-making which is possible through the application of the types of data analytics. Business analytics software has been encouraged by IBM which enhances profitability and encouraged the brand of foster data-driven decision-making (Ibm, 2024).
5. Evaluation of Practical Outcomes
IBM’s usage of Data Analytics outcomes
Through the usage of the data analytic’s practical outcomes which include risk prediction, huge information related to the brand’s growth of sales and meeting customer demands the brand has partnered up with Cloudera to provide AI-generated services to its consumers (Ibm, 2023). Furthermore, the brand has utilised the outcomes of the data analytics by the development of hybrid multi-cloud data analytics which influences common security and structural governance of the brand. Furthermore, the brand has utilised the gathered knowledge to earn flexibility and value-based services which fosters the brand’s strategy of effective decision-making. Furthermore, the brand has utilised the gathered knowledge to foster workflow information which enhances the productivity of the brand and mitigates the challenges related to software complexities. Through the application of the data analytic practical outcomes, the brand has accelerated the growth of hybrid clouding technologies which fosters brand efficiency and provides value-based services to the consumers.
Wu et al. (2021), mentioned that analytic outcomes have helped the brand with the gate model of quantum computing systems which have fostered the organisational performance of the brand. Furthermore, it has also boosted the brand’s process of decision-making which enhances the brand’s analysis of business challenges and coming up with effective solutions regarding future business challenges. It may be argued that the brand has invented a simulated detector which has fostered the brand’s strategy of predicting the risks related to the company’s business challenges and transforms the infrastructure of the organisation into a digitalised forum.
Steps by the company from the obtained information
Based on the information received from the company website through the information gathered from the analytics the first step of the brand was to pair up with Cloudera and build a data lake which enhances security and governance. Furthermore, the technical usage of data analytics has also optimised the visualisations of machine learning which has fostered the growth of the brand. IBM and CLS has partnered with each other to develop a blockchain application which ensures final testing stages of financial growth of the organisation (Sazuand Jahan, 2022).The next step of the brand was to predict failures and take preventive actions on time which has fostered the brand’s decision-making strategy in mitigating business challenges. The next concrete step of the brand was to access data and frame a complete picture based on unstructured data, images and logs. Furthermore, the brand also incorporated cost-effectiveness and reduced workloads through data warehouses and data lakes which has fostered the innovative growth of the brand. Furthermore, through the partnered program the brand fostered a collaborative data platform which provides the brand insights related to risks and opportunities associated with risks. Another major step of the brand was to foster the flow of information to accelerate the development of streaming applications and explore options for easing the workloads of IT professionals fostering brand growth. IBM has utilised data analytics for training and enhance employee performance which has impacted the employee outcome of the organisation (Arqawiet al., 2022)
6. Conclusion and Recommendations
Recommendations
Based on the current business challenges of the company and the brand’s current business process the brand should incorporate a CMMI model which fosters the quality system of the business and focuses on process optimisation. Ilin et al. (2022), mentioned that levels of digital maturity may be defined through 5 levels such as basic infrastructure, computerisation connectivity and other stages. Furthermore, the brand should currently focus on the improvement of the process and improve the quality of the IBM’s current business model. Based on the current situation the brand should focus on improving its business quality and for that the brand should implement a CMMI model to track the business performance. CMMI also improves the IBM’s operational performance and improves the quality of the services which prepares the brand for meeting upcoming business challenges.
Mölder et al. (2021), mentioned that the data analysis process also helps the brand with raw data processing and quality control of the brand which helps the brand to achieve productivity. In the future, IBM should do research based on the process of data analytics which will help the brand with its development of business analytics. Furthermore, the company of IBM should develop an understanding related to business demands which helps the company with its management. Moreover, the brand’s sustainability depends on the business process and the brand should improve its quality control which enhances its business productivity.
Conclusion
It may be concluded that the report has addressed the summary of the company and the company’s major activities which provides the report with the details of IBM business analytics. Furthermore, the report has addressed the business challenges faced by IBM and has also identified the competitors and business innovations which has a significant influence on the identified problems. The report has also addressed the outcomes of the challenges and the impact of the challenges on organisational growth. The report includes insights into the data collection and analysis approach taken by the company related to the challenge and has also provided details related to the possible outcomes of the decision.
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