LO1 Discuss business processes and the mechanisms used to support business decision-making.LO2 Compare the tools and technologies associated with business intelligence functionalityAssig
Introduction
To thrive in today's dynamic business environment, organizations prioritize optimizing their operations for enhanced effectiveness, productivity, and overall performance Central to this goal is the effective management of business processes, encompassing their complexity, supporting systems, data generation, and the software ensuring their seamless execution This article explores these elements, shedding light on the intricacies of business processes and the technologies that empower their efficient operation.
Analyzing the Concepts of 'Business Process' and 'Supporting Processes' (P1): 2 A Business Processes
Definition
A business process is a group of interconnected actions that culminate in the supply of a service or something to a customer Another definition of a business process is a series of actions and activities that, when finished, will achieve an organizational objective.Business processes are organized, repeatable series of steps that transform inputs into desired results to meet certain corporate objectives They stand for the efficient flow of labor, information, and materials inside an organization, enabling the creation,dissemination, and realization of value in products and services A framework for planning, coordinating, and completing work across multiple functional domains is provided by business processes.
Examples
Business processes exist in diverse forms across industries Some common examples include: a)Procurement Process: The procurement process involves organizations’ activities to acquire goods or services from external suppliers It typically includes identifying procurement needs, vendor selection, negotiation, purchase order creation, goods receipt, and invoice processing. b)Sales Process: The sales process encompasses the activities involved in selling products or services to customers It typically includes lead generation, qualification, customer relationship management, order processing, fulfillment, and invoicing. c)Manufacturing Process: The manufacturing process encompasses all the activities related to producing goods It includes product design, raw material procurement, production planning, quality control, assembly or manufacturing, packaging, and distribution. d)Human Resources Process: Human resources involve managing the organization's workforce These processes may include recruitment and selection, onboarding, performance management, training and development, employee engagement, and offboarding. e)Customer Service Process: The customer service process involves activities aimed at providing customer support and assistance It includes handling inquiries, resolving customer issues, managing complaints, and ensuring customer satisfaction. f) Financial Process: Financial processes encompass managing an organization's financial resources This may include budgeting, financial planning and analysis, accounting, financial reporting, cash flow management, and financial decision-making.
Supporting Processes
Types of data generated by business processes
Organizations can use the wealth of data generated by business processes for analysis, decision-making, and performance improvement These are the various data categories:
Information that lacks a specified structure or format is referred to as unstructured data It is typically produced by humans and must be easily entered into databases or spreadsheets Unstructured data, which includes details from emails, social network postings, customer reviews, papers, audio files, still photos, and video files, is frequently text-heavy Due to its lack of structure, this data format is difficult to examine using conventional data processing methods.
Unstructured data can offer insightful information about customer views, market trends, and emerging issues To extract usable information from unstructured data, sophisticated techniques like natural language processing (NLP), text mining, sentiment analysis, and picture or audio recognition are needed.
Data must be changed into a more structured format suited for analysis before being analyzed when it is semi-structured Examples of these approaches include data extraction, data transformation, and data parsing.
Customer reviews, social media postings, customer service chat logs, email communications, survey answers, research findings, and multimedia material are a few examples of unstructured data used in business operations.
Between unstructured and structured data is semi-structured data It complies with loose schemas or preset formats yet incorporates metadata or has some organizational structure Semi-structured data still allows for some organization and categorization while maintaining its flexibility.
Spreadsheets, XML ( eXtensible Markup Language), JSON (JavaScript Object Notation), CSV (Comma-Separated Values), and other formats are frequently used to represent semi-structured data It could have properties or tags with labels, which provide structure and make organizing and analysis easier.
Log files, web server logs, sensor data, XML or JSON files, spreadsheets with labeled columns, and data obtained by web scraping are examples of semi-structured data.
Well-organized and simple to examine is structured data It is appropriate for relational databases or data warehouses to store this information since it adheres to a set structure or schema Structured data can be numerical, category, or alphanumeric and is often shown as tables with rows and columns.
Structured data empowers businesses to leverage data management systems and tools to query and manipulate information This data includes customer transactions, sales records, inventory data, financial statements, personnel records, website analytics, and supply chain data By harnessing structured data, businesses can generate reports, identify trends, and make data-driven decisions with ease and accuracy.
Differences between Structured, Semi-structured and Unstructured data:9 5 Some of the software used in Business Processes
Properties Structured data Semi-structured data
Technology It is based on
It is based on XML/RDF(Resourc e Description Framework).
It is based on character and binary data
Matured transaction and various concurrency techniques
Transaction is adapted from DBMS not matured
No transaction management and no concurrency
Versioning over tuples, row, tables
Versioning over tuples or graph is possible
Flexibility It is schema dependent and less flexible
It is more flexible than structured data but less flexible than unstructured data
It is more flexible and there is absence of schema
Scalability It is very difficult to scale
It’s scaling is simpler than structured data
Robustness Very robust New technology, not very spread
Structured query allow complex joining
Queries over anonymous nodes are possible
Only textual queries are possible
5.Some of the software used in Business Processes:
Organizations use various software solutions to improve and streamline business operations The following are examples of popular business software:
5.1.Data Analytics and Business Intelligence (BI) Software: Organizations can use these software solutions to analyze and interpret massive amounts of data to discover insights that can be used They include advanced analytics, reporting systems, and data visualization tools Tableau, Microsoft Power BI, and QlikView are three well-known data analytics and business intelligence (BI) programs.5.2.Project Management Software: Project management software makes planning,scheduling, and executing projects easier It helps with task management, collaboration, resource allocation, and tracking progress Standard project management software programs include Microsoft Project, Asana, and Trello. 5.3.Communication and Collaboration Tools: These tools, which include email clients, instant messaging services, and video conferencing software, facilitate effective information sharing and decision-making by improving team member interaction and communication Some examples include Slack, Zoom, and Microsoft Outlook.
5.4.Enterprise Resource Planning (ERP) Software: By integrating various business operations such as finance, human resources, supply chain, and customer relationship management onto a single platform, ERP systems enable smooth information flow and process automation Microsoft Dynamics, SAP, and Oracle are examples of ERP software.
5.5.Customer Relationship Management (CRM) Software: CRM software helps companies manage customer interactions and relationships Managing effective lead management, sales tracking, customer service, and marketing campaigns is now possible CRM applications include Salesforce, Microsoft Dynamics 365 CRM, and HubSpot CRM.
5.6.Business Process Management (BPM) Software: Businesses can use BPM software to plan, execute, track, and improve their business processes It provides tools for process modeling, performance monitoring, and continuous development BPM software includes Appian, Pega BPM, and IBM Business Process Manager.
6 Compare the tools and technologies
8 Compare the tools and technologies associated with business intelligence
10 Compare the tools and technologies associated with
12 Compare the tools and technologies associated with
Compare the tools and technologies associated with business intelligence functionality
II C OMPARE THE TOOLS AND TECHNOLOGIES ASSOCIATED WITH BUSINESS INTELLIGENCE FUNCTIONALITY
The equipment and technology used for business intelligence functions
Why utilize tools for business intelligence?
To start, anybody may now use these tools to undertake data discovery, which was previously only possible with the training of advanced analytics specialists
Furthermore, these technologies provide you the knowledge you want to accomplish a variety of goals, including expansion, handling urgent issues, gathering all of your data in one location, forecasting future outcomes, and far more.
The resources and innovations
Oracle BI (Business Intelligence)
The Oracle Corporation offers a collection of business analytics tools and software known as Oracle Business Intelligence (Oracle BI) A wide range of functions are available for data analysis, reporting, and decision-making Oracle BI offers a centralized platform for data modeling, ad hoc reporting, interactive dashboards, and predictive analytics It lets businesses to collect data from diverse sources, analyze it to derive
P a g e | 15 insightful conclusions, and communicate those conclusions to stakeholders Large businesses with extensive data requirements may benefit from Oracle BI's scalability, resilience, and integration skills.
Tableau
Tableau is a popular tool for business intelligence and data visualization Users may connect to different data sources, build interactive visualizations, and share findings with others thanks to this tool Both technical and non-technical users will find Tableau to be user-friendly thanks to its simple drag-and-drop interface Users of Tableau may generate dynamic dashboards, reports, and charts that aid in the analysis of large data sets and the discovery of patterns and trends Advanced tools like data blending, geographic analytics, and natural language processing are also available in Tableau It is renowned for its powerful visual skills and adaptability when working with various data sources.
Domo
Domo is a platform for data analytics and business intelligence that runs in the cloud.
It provides a variety of capabilities for reporting, collaboration, data visualization, and integration Organizations may use Domo to connect to different data sources, convert data, and produce reports and dashboards in real time Users may explore data, produce visualizations, and share insights with others in a collaborative setting because to the interface's emphasis on simplicity of use Domo also provides tools that enable enterprises to effectively make data-driven choices, including data alerts, automated processes, and predictive analytics.
Oracle BI, Tableau, and Domo are sought-after options for businesses seeking data visualization and analytics Each tool's choice is influenced by specific business needs, user preferences, data complexity, and scalability Businesses should evaluate their requirements in terms of data integration, user interface, analytics capabilities, and cost to determine the optimal solution.
III E XAMINING THE V ARIED L EVELS OF
Informed business decisions, the cornerstone of organizational success, guide actions towards achieving desired goals The process demands a methodical evaluation of available data, encompassing both risks and potential outcomes By engaging in this analysis, organizations can navigate challenges, exploit opportunities, and ultimately thrive in a competitive landscape.
The process of business decision-making typically involves the following steps: a)Problem Identification: The first step in decision-making is identifying the opportunity or problem that requires a decision This entails identifying the gap between the organization's current and desired state, comprehending the underlying causes, and determining the need for action. b)Data Gathering: After identifying the issue, decision-makers gather relevant data and information to gain understanding and aid in the decision-making process Internal databases, industry reports, market analysis, customer feedback, and expert opinions are all possible data sources. c)Data Analysis: Decision-makers examine the gathered data in this step to identify significant trends and insights Data analysis techniques such as statistical analysis, data mining, and predictive modeling can be used to discover decision-supporting relationships, trends, and correlations. d)Evaluation of Alternatives: The decision-maker generates and evaluates potential problem or opportunity solutions This entails weighing the benefits and drawbacks of different options and their viability, risks, and possible outcomes Decision criteria such as price, lead time, quality, and strategic alignment are used to compare and order the alternatives. e)Decision Selection: Decision-makers select the best course of action based on assessing the alternatives and how well it fits within the organization's constraints, values, and goals Factors such as potential benefits, risks, expenses, resource availability, and stakeholder considerations are frequently considered when making this decision. f) Implementation: Following a decision, it is effectively implemented to put it into action This includes developing action plans, allocating resources, setting deadlines, and notifying relevant stakeholders of the decision Coordination, observation, and any necessary changes are required during the implementation process. g)Evaluation and Feedback: Decision-makers evaluate the outcomes of their decisions after implementation They examine whether the desired results were achieved, how well the decision-making process worked, and how key players contributed This feedback loop helps to improve future decision-making processes by allowing for learning from past decisions.
1.Types of Decision-Making Levels:
Operational decisions are made at the bottom of the organizational hierarchy and involve routine actions that directly influence how effectively and efficiently operations are carried out Most of the time, these options are standard, repetitive, and tactical, focusing on completing procedures and tasks as quickly as possible Operational decisions influence smooth operations, resource allocation, and adherence to established policies and guidelines.
Examples of operational decisions include: a) Production Scheduling: Deciding on the order and timing of production tasks, allocating resources, and organizing workflows to meet customer demand effectively. b)Inventory Management: To balance holding costs, select the best stock availability levels, reorder points, and replenishment strategies. c) Workforce Allocation: Choosing which tasks, shifts, or projects to assign to employees based on their qualifications, availability, and workload requirements. d)Quality Control: Deciding on inspection, testing, and quality assurance procedures to ensure that goods or services meet predetermined standards.
BI tools can aid operational decision-making by providing real-time data insights, performance monitoring, and operational dashboards Managers can use these features to
P a g e | 19 track key performance indicators (KPIs), identify trends, identify bottlenecks, and make sound decisions BI, for example, can provide users with data on production metrics, inventory levels, worker productivity, and quality control.
Middle-level managers make tactical decisions that balance long-term strategy and day-to-day operations These options revolve around allocating resources to achieve short- to medium-term goals and translating strategic objectives into actionable plans. Aligning departmental activities with overarching organizational strategies heavily depends on tactical decisions involving more precise, quantifiable goals.
Examples of tactical decisions include: a) Marketing Campaign Planning: Choosing a target market, messaging, distribution channels, and financial budget for marketing campaigns to promote goods or services. b) Budget Allocation: Deciding how to distribute resources among various departments or projects to ensure the best possible use of financial resources. c) Supplier Selection: Choosing suppliers based on quality, cost, dependability, and sustainability to meet procurement needs efficiently. d) Performance Evaluation: It is critical to evaluate team or individual performance, provide feedback, and highlight improvement areas to increase productivity and meet performance goals.
Business Intelligence (BI) tools empower decision-makers with analytics to uncover insights from historical data, market trends, and KPIs They help identify opportunities, measure marketing campaign effectiveness, monitor budget utilization, and track progress towards goals By producing reports, visualizations, and conducting ad hoc analyses, managers leverage BI to assess options, optimize resource allocation, and evaluate the impact of tactical decisions for informed decision-making.
Executives at the highest levels make strategic decisions that determine the organization's long-term direction and competitive positioning These decisions have a broader impact on the organization's overall success and sustainability All aspects of strategic decision-making are allocating resources, identifying expansion opportunities, entering new markets, and establishing organizational goals.
Examples of strategic decisions include: a) Market Expansion: Deciding to diversify and expand your business by pursuing new customer segments or geographic markets. b) Product Development: Deciding to create and introduce new goods or services to gain a competitive edge and meet changing customer needs. c) Merger or Acquisition: Evaluating potential merger, acquisition, or partnership opportunities to improve market presence, increase capabilities, or create synergies. d) Strategic Alliances: Deciding to collaborate or form alliances with other businesses to exploit complementary strengths and increase market reach.
BI is essential in strategic decision-making by providing insights into market trends, competitor analysis, customer behavior, and performance benchmarks. Executives can use it to assess market potential, identify growth opportunities, and determine how strategic decisions affect the company's bottom line BI tools enable scenario planning, performance tracking, and data-driven decisions that align with organizational goals.
Businesses require analytical help to make decisions that are supported by facts in a world where data is king At this level, significant insights from massive datasets are extracted using advanced technologies, business intelligence tools, and data analytics. Organizations can assess various scenarios and anticipate results with the use of analytical support Improved company performance results from giving decision-makers at all levels the ability to make more informed and precise judgments.
Analytical support empowers organizations with data analytics tools, business intelligence (BI) solutions, and forecasting models Data analytics tools, including machine learning algorithms, help uncover insights from large datasets, guiding decisions on customer preferences, market trends, operations, and pricing BI solutions facilitate data collection, storage, analysis, and visualization, providing interactive reports and dashboards that assist decision-making across the organization Forecasting models leverage historical data and statistical techniques to anticipate future trends and outcomes, aiding in planning and resource allocation.