Analytics
Enterprise analytic systems play a crucial role in numerous organizations and businesses, ensuring connectivity and coordination across processes, activities, and financials. The key advantage lies in providing easy access to analytics and data through browsers, facilitating the use of reports, visualizations, and ad hoc capabilities.
Larger companies are increasingly incorporating Industrial Internet of Things (IIoT) data into their Data Lakes and Enterprise Data Warehouses (EDWs). In scenarios where a CEO or senior leadership needs to compare and contrast business and digital data across the entire enterprise, centralizing the data in one database or data warehouse is often the most effective approach. This centralized repository allows for easier aggregation, analysis, and visualization of data from different systems, enabling comprehensive insights and informed decision-making at the executive level.
By consolidating "Business and Digital" data, organizations can create unified dashboards and reports that provide a holistic view of key metrics and performance indicators, facilitating strategic planning and alignment across departments and business units.
Analytics helps manufacturers understand their current operations by analyzing data from various sources such as production lines, supply chain, and equipment sensors. This analysis provides insights into inefficiencies, production bottlenecks, quality issues, and resource utilization. By identifying areas for improvement, manufacturers can optimize their processes before implementing digital solutions.
- As-Is environment analysis: Analytics helps manufacturers understand their current operations by analyzing data from various sources such as production lines, supply chain, and equipment sensors. This analysis provides insights into inefficiencies, production bottlenecks, quality issues, and resource utilization. By identifying areas for improvement, manufacturers can optimize their processes before implementing digital solutions.
- During system implementation: Analytics guides manufacturers in implementing digital solutions by providing data-driven insights into the impact of changes on operations. For example, predictive analytics can forecast the effects of process modifications or equipment upgrades, helping manufacturers make informed decisions and mitigate risks during implementation.
- Post-implementation monitoring: After the digital system is implemented, analytics continue to play a crucial role in monitoring and optimizing operations. Manufacturers can use real-time data analytics to track key performance indicators (KPIs), detect anomalies, and identify opportunities for further improvement. This ongoing analysis ensures that the digital system operates efficiently and delivers the expected benefits over time.
Analytics guides manufacturers in implementing digital solutions by providing data-driven insights into the impact of changes on operations. For example, predictive analytics can forecast the effects of process modifications or equipment upgrades, helping manufacturers make informed decisions and mitigate risks during implementation.
Overall, analytics are essential in the manufacturing process for both understanding the current state (As-Is environment) and optimizing operations before and after implementing digital solutions, ensuring continuous improvement and competitiveness in the industry.
Major parts of a Analytics or BI system
It's important to note that the pace of technological advancements and the specific trajectory of analytics for large corporations can be influenced by various factors, including regulatory changes, technological breakthroughs, and global events.
- Increased Integration of AI and Analytics: The synergy between artificial intelligence and analytics is expected to deepen, with more advanced AI algorithms being integrated into analytics solutions for improved insights and automation.
- Real-time Analytics: There will be a greater emphasis on real-time analytics to enable businesses to make quick and informed decisions based on the most up-to-date information.
- Enhanced Data Security: As cyber threats evolve, organizations will continue to prioritize data security and implement measures to safeguard sensitive information. This includes advancements in encryption, secure data sharing, and compliance with evolving data protection regulations.
- Augmented Analytics: The use of augmented analytics, which combines machine learning and natural language processing to enhance data analysis and interpretation, is expected to become more prevalent.
- Democratization of Analytics: There will be a continued effort to make analytics tools and insights accessible to a broader audience within organizations, empowering non-technical users to leverage data for decision-making.
- Edge Analytics: With the proliferation of Internet of Things (IoT) devices, analytics processing is likely to move closer to the data source (edge computing), enabling quicker and more efficient analysis of streaming data.
- Sustainability Analytics: Corporations may increasingly focus on incorporating sustainability metrics into their analytics efforts, tracking and analyzing environmental, social, and governance (ESG) data to inform sustainable business practices.
Organizations that stay agile and continuously adapt to emerging trends will be better positioned to harness the full potential of analytics in the coming years.
Visualizations, Dashboards and Reports
Historically, business intelligence (BI) and analytics have prioritized users' capacity to generate standard reports with swift interactivity, including "ad hoc" capabilities or a "self-service facility" for report construction. Dashboards emerged from these systems, gaining prominence in the last decade, accompanied by the rise of Mobile BI, BI Search, and Interactive Visualization tools in recent years. Furthermore, statistical client tools have consistently played a significant role throughout these periods, serving as a potent ad hoc tool that complements enterprise analytic efforts, especially in aiding in-database models and computations.
Analytics Server
Front-end visualizations, dashboards, and reports primarily rely on the Analytics Server, positioned in the middle of the overall BI landscape. The Analytics Server serves to aggregate meaningful data sets and present them to Frontend Visualizations for OLAP or static use. Functioning as a broker between users and the database, it offers a platform for user administration and content organization, encompassing thousands of users and numerous objects such as reports and visualizations.
The key strategy involves utilizing the Analytics Server as a pass-through for the intensive tasks carried out at the Database layer, ensuring quick data throughput to users. The database is responsible for filtering, aggregations, and computations whenever feasible. Furthermore, Analytic Server Meta Data should adhere to the same principle, serving as a complement or pass-through for Metadata at the Data Warehouse layer.
Experiance Curve
With 25 years of experience in enterprise analytics projects, Alexicon specializes in applying best practices to both legacy systems and contemporary cloud solutions.
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