It Opens the Door to the World of Advanced Analytics: More Profitable and Sustainable Models

Extracting data from corporate systems using advanced analytics services, Data Lakes, Data Warehouses, Machine Learning, and Artificial Intelligence.

The ability to harness data to its fullest potential has become a crucial differentiator for organizations. In this context, SAP systems not only serve as fundamental pillars for business management but also host a wealth of data that can drive strategic and operational decisions. Efficiently extracting SAP data is crucial for organizations to leverage the power of their data. Therefore, extracting data from SAP to analytics services is not just an advanced option but an imperative necessity to innovate and maintain competitiveness in dynamic markets. Its significance lies in the capability to transform complex data into actionable insights.

In a market where speed and accuracy are paramount, the ability to analyze data effectively makes the difference between leadership and obsolescence. Below are some reasons why data extraction from enterprise systems is crucial for business:

• Advanced Analytics – Corporate systems often contain a large amount of transactional and operational data, which when transferred to a Big Data environment, enables companies to conduct more advanced and complex analyses such as predictive analytics, trend analysis, and customer behavior analysis.
  
  
• Scalability – Big Data services are designed to handle large volumes of data and scale horizontally as needed to effectively manage vast amounts of information without compromising performance.
  
  
• Variety of Data Sources – Corporate systems often store structured data in relational databases, but there is also a wealth of unstructured or semi-structured data from sources such as social media, web server logs, IoT sensors, etc. Big Data services can easily integrate these different data types for comprehensive analysis.
  
  
• Real-Time Processing – By leveraging Big Data streaming tools, companies can process data in real-time and gain instant insights into what is happening in their business operations.
  
  
• Cost Reduction – By moving data to cloud-based Big Data platforms, companies can take advantage of pay-as-you-go models and avoid the high costs of maintaining on-premises infrastructures.

Direct Access to Information: Easy Data Extraction

Data extraction from the SAP ERP system can be performed using a variety of methods, ranging from ad-hoc queries to integrated data storage and analysis solutions. The choice of method will depend on the specific needs of the company and the data to be extracted.

Some more common options include:

1. Ad-Hoc Queries – There are ad-hoc query tools that allow users to query directly from the database, such as SAP Query, SAP QuickViewer, or SAP ABAP Query. These tools enable users to create and execute custom queries to extract data based on specific criteria.
   
   
2. Standard Reports – This includes a wide range of standard reports that provide information on various aspects of the business, such as finance, sales, inventory, human resources, etc. These reports can be executed directly from the system and can be customized as needed.
   
   
3. Data Extraction via SAP BW/4HANA – Data storage and analysis solutions that can integrate with SAP ERP to extract structured data and perform advanced analysis. It offers data modeling capabilities, integration with external data sources, and advanced analytics tools.
   
   
4. APIs and Connectors – Application Programming Interfaces that allow developers to integrate external systems with SAP ERP and programmatically extract data. Additionally, there are connectors available that facilitate integration with third-party tools, such as business intelligence or data integration tools.
   
   
5. External Data Extraction Tools – Companies can use external data extraction tools to access the SAP ERP database and extract data in an automated manner. These tools may include data integration software, ETL (Extract, Transform, Load) tools, or data replication solutions.

Opening the door to the evolution of advanced analytics: Data Lakes, Data Warehouses, and Analytics Services

These new trends in real-time advanced analytics are characterized by the adoption of modern technologies such as cloud, automation, and artificial intelligence to fully harness the potential of data and enhance business decision-making. They offer greater flexibility and efficiency, enabling companies to store and analyze large amounts of data in a scalable and secure manner. Additionally, they bring businesses closer to utilizing technologies like Machine Learning and artificial intelligence, which have proven to be the new drivers of corporate innovation in recent years. Therefore, it is important not to limit oneself and explore these options that maximize the value of data.

👉  Data Architecture

• Event-Driven Architectures: Integration of event-driven architectures to capture and process data in real-time, enabling faster and more precise decision-making.
  
  

👉  Data Lakes

• DataOps: Adoption of DataOps practices to enhance collaboration and efficiency in the data lifecycle, from acquisition to delivery.
• Unstructured Data Management: Focus on managing unstructured data, such as social media data, images, and videos, for a comprehensive understanding of the data landscape.
• Process Automation: Increased use of automation tools for data ingestion, cleaning, and transformation in data lakes, speeding up delivery time and improving data quality.
• Cloud Data Lakes: Migration to cloud-based data lakes to leverage scalability, flexibility, and cost-efficiency of the cloud.
  
  

👉  Data Warehouses

• Modern Data Warehouses: Evolution towards modern, agile, and cloud-oriented data warehouses capable of handling large volumes of structured and unstructured data.
• Data Warehousing as a Service (DWaaS): Increasing adoption of data warehouse solutions as a service, offering flexibility and scalability without the need for on-premises infrastructure.
• Enhanced Metadata Management: Greater focus on metadata management to improve governance, traceability, and data quality in data warehouses.
  
  

👉  Real-Time Analytics Services

• Real-Time Predictive Analytics: Greater adoption of real-time predictive analytics to identify emerging patterns and trends and make proactive decisions.
• Artificial Intelligence and Machine Learning: Integration of artificial intelligence and machine learning techniques into real-time analytics services to enhance accuracy and relevance of insights.
• Real-Time Data Streaming: Increased capability of real-time data processing through streaming technologies like Apache Kafka and Apache Flink for continuous analysis and real-time event detection.
• Prescriptive Analytics: Advancement towards real-time prescriptive analytics, which not only identifies what is happening but also recommends specific actions to effectively address events in real-time.

Unlocking the Power of Data: Innovation and Competitiveness

In a modern and constantly growing business, the adoption of new technologies is a common practice every year. However, not all of these technologies integrate with SAP, creating information silos that can limit collaboration and efficiency. For this reason, having a data repository and the right services that enable extraction from each of the company's tools will facilitate the integration of data in a short time and its integration with datasets that are part of the core of the business. This capability of centralizing, integrating, and unifying data is a key advantage that enhances collaboration, optimizes efficiency, and enables a comprehensive and coherent view of business information, adding value to decision-making based on precise and timely information.

By accessing data from different sources and transforming them into formats compatible with advanced analytics tools, process optimization is achieved, and areas for improvement are identified through the analysis of historical data and trends, leading to increased operational efficiency and significant cost savings. Thus, companies can continue on their path of innovation and competitiveness by having a data repository and suitable data extraction services that foster innovation, explore new ideas and opportunities based on data analysis, and keep them competitive in an ever-evolving business environment.

Building the Future: SAP Data Extraction, Essential for Business

Companies manage a wide range of business processes through their SAP ERP, such as finance, sales, human resources, logistics, production, and many more. Therefore, data extraction is the only way to provide a comprehensive view of the business that allows gathering information from multiple functional areas into a single data repository to achieve:

• Regulatory Compliance – In most financial and regulatory compliance areas, data extraction from SAP facilitates financial reporting and auditing, helping companies comply with regulatory and compliance requirements.
  
  
• Integration with Other Platforms – Data extracted from SAP can be integrated with other platforms and business systems, enabling greater interoperability and efficiency throughout the organization. This facilitates collaboration between different functional areas and the creation of a unified view of the business.
  
  
• Informed Decision-Making – The availability of updated and accurate data from SAP facilitates informed, data-driven decision-making, which helps improve operational efficiency, profitability, and competitiveness.

Similarly, extracting data from SAP and integrating it into a single data repository enables companies to unify and standardize data according to the needs of different business areas, significantly reducing the time and effort that business users need to invest in obtaining complete and even standardized datasets required for their daily tasks.

Over the past years, McKinsey & Company has published reports and surveys highlighting the need to transform every company into "data-driven companies." One of the key features emphasized is that "Data assets are organized and supported as products, whether they are used by internal teams or external clients." This is because data products evolve continuously in an agile manner to meet consumer needs, leveraging DataOps (DevOps for data), integration processes, and continuous delivery tools. To begin building data products, it is essential to leverage what we already have; the ERP system is a good starting point for data extraction and thus enhancing:

• Control and Measurement – Implementing data extraction as a corporate initiative to create data products significantly reduces the number of files with different perspectives exported by each business user from the source systems. This greatly enhances data governance by enabling companies to have complete, integrated, and updated datasets that support operations, with metrics for access, querying, and even downloading. Additionally, data unification facilitates the search and retrieval of precise and up-to-date information, which is crucial for informed decision-making and process optimization.
  
  
• Optimal Resource Utilization – Leveraging options included in the SAP standard for conducting extractions without draining system resources, while largely preserving business logic, allows focusing on defining a robust data architecture and building data products, lakes, and warehouses in less time. Moreover, it enables directing ABAP development efforts solely towards extracting Z transactions or extractions not covered by standard extractors.

Towards Advanced Analytics: Discovering Insights with Machine Learning and Artificial Intelligence

Creating a unified data repository with an architecture that enables data extraction from SAP ERP is not just a technical decision but a fundamental business strategy. By centralizing and optimizing access to information, companies can better respond to market demands, innovate, and make decisions based on precise and timely data. Furthermore, this strategy facilitates the application of machine learning and artificial intelligence for advanced data analysis, which drives operational efficiency, enhances data governance, improves data quality, and provides a sustainable competitive advantage in an increasingly digitalized business world.

Machine learning and artificial intelligence play a crucial role in advanced analytics and data extraction, offering powerful capabilities to understand and leverage information more effectively, enabling deeper insights discovery, process automation, and informed, precise decision-making.

The application of Machine Learning (ML) and Artificial Intelligence (AI) in business environments can include:

• Prediction and Forecasting – Machine learning can be used to predict future outcomes based on historical data. For example, in data extraction, machine learning models can predict future demand for products or services, optimizing production planning and inventory management.
  
  
• Classification and Segmentation – Machine learning techniques can classify and segment data into meaningful groups. This is useful in data extraction when identifying specific patterns or trends in large and complex datasets, such as segmenting customers into behavior groups for better marketing personalization.
  
  
• Anomaly Detection – Machine learning can automatically identify anomalies or unusual patterns in data. In data extraction, this can be used to detect fraudulent transactions, anomalous behavior in web traffic, or issues in operational processes.
  
  
• Process Optimization – Artificial intelligence can optimize data extraction processes by automating repetitive tasks and identifying efficiency patterns. For instance, machine learning algorithms can learn from past data extraction processes to optimize future extractions and improve system efficiency.
  
  
• Natural Language Processing (NLP) – NLP, a branch of AI, is used to understand and analyze text data. In data extraction, NLP can extract key information from documents, emails, customer feedback, etc., facilitating text-based analysis and decision-making.
  
  
• Personalized Recommendations - By using machine learning-based recommendation algorithms, companies can offer personalized recommendations to users. In the context of data extraction, this could involve recommending relevant datasets or specific analyses based on user profiles and needs.

As we have seen, the evolution of advanced analytics and the adoption of an enterprise data repository help optimize business visibility, ensure corporate intelligence, manage decision-making, save costs, and initiate innovation processes. This collectively has a significant impact on sustainability, as it enables companies to identify opportunities for improvement and develop more sustainable business practices. It also fosters the creation of purpose-driven product development processes with awareness.

Moreover, this awareness helps companies manage water and other natural resources more efficiently through data monitoring and analysis. It begins to create models where supply chain and logistics optimization focus on sustainability. Companies start thinking about ensuring a sustainable supply chain aimed at reducing carbon emissions associated with transportation and inventory management. Ultimately, this leads to businesses that are more environmentally friendly and purpose-driven towards the well-being of the planet and humanity.

Transform with Avvale

At Avvale, we are open to starting this conversation to assist companies in adopting advanced analytics for extracting data from their corporate systems and embracing analytics services, Data Lakes, Data Warehouses, Machine Learning, and Artificial Intelligence as innovation processes aimed at achieving more profitable yet sustainable models.

Are you ready to lead your company towards an agile, circular, and sustainable future by applying advanced analytics that will make a difference for your company in terms of innovation and competitiveness?