Data Analytics

Elevating decision support for your
business units to the next level

Achieving a Data-Driven Pole Position

In todays competitive arena, fast and reliable decision-making is essential. Knowing how you can use your data and leverage them gives you a strategic advantage. Our analytics solutions address both the Explore mindset for learning from data and the Exploit aspect for earning, guaranteeing value delivery to the business. Camelot’s holistic data analytics approach addresses all facets from integrated data strategy to modernizing your data and analytics landscape into an AI/ML-ready platform, capable of handling value-added use cases in advanced analytics or automation. We tailor our proven approach to achieve the business outcomes you’re looking for. You will benefit from our industry-specific expertise and capabilities that ensure you stay on the cutting edge. 

"Analytics is Dataland. Let's cultivate for AI to prosper."

Thorsten Warnecke, Head of Analytics, Camelot Management Consultants

Business Analytics

Empowering business users with analytics capabilities

The traditional methods of analysis such as standard reporting, dashboards, BI, and planning applications are still the norm in many companies.  

In today’s increasingly demanding work environment, these methods are no longer sufficient.  

We are currently witnessing an evolution in BI tools for business analysts, now offering smart discovery and smart insights as pre-configured standard functions. In-depth statistical expertise is no longer needed. In a way, it is an evolution from descriptive to built-in predictive functions. 

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Smart Discovery

Smart discovery provides you with a guided data exploration and analysis to discover insights quickly and efficiently. Camelot supports you in the BI tool selection process typically comparing SAP Analytics Cloud (SAC), Microsoft Power BI, Salesforce Tableau, etc., and its proper application to enhance data literacy. 

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Smart Insights

Smart insights offer automated analysis and interpretation of data to uncover trends and patterns. 

We provide expert support to user groups and promote transparency regarding the statistical methods employed in the smart features. 

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Extended Planning & Analysis xP&A

An advanced approach that goes beyond traditional methods

Extended Planning & Analysis (xP&A) encompasses a broader range of activities and capabilities, including financial planning, budgeting, forecasting, scenario analysis, and performance management. xP&A leverages advanced analytics, machine learning, and artificial intelligence using SAP Analytics Cloud (SAC) to provide deeper insights, enhance decision-making, and drive business performance. Camelot advises on integrating and aligning various business plans seamlessly in this context. 

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Faster Decision-Making

Streamline your decision-making processes by leveraging real-time data and predictive analytics, enabling faster responses to market changes. Benefit from our extensive practical experience in the integrated planning environment.

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Improved Accuracy

Improve your planning accuracy with advanced forecasting models and scenario analysis, which reduce errors and bolster confidence in decision-making. With a holistic approach, we help you steer clear of common pitfalls associated with pursuing local optima.

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Enhanced Collaboration

Foster collaboration across domains by providing a centralized platform for planning and analysis, facilitating alignment and coordination. Integrated planning always involves collaboration, which we directly embed into processes through Camelot’s proven methodologies. 

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Agility and Adaptability

Quickly adapt to evolving market conditions and business requirements with agile planning capabilities, guaranteeing resilience and competitiveness. The Time-to-Value is optimized when working with system environments that are both robust and adaptable. This stands as our benchmark for the data architectures and models we design and develop for your organization. 

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Decision Intelligence

Mastering data analytics and human expertise to improve decision-making

In the fast-paced business world, predictive analytics and decision intelligence are indispensable. When traditional statistical approaches fall short, tailored analytics catapults you into innovation and strategic decision-making, particularly when operating on scalable cloud architectures. 

Moving from predictive analytics to decision intelligence involves transitioning from solely predicting outcomes based on data to integrating predictive insights with human expertise and contextual understanding. This ultimately enables more informed and effective decision-making across various domains. 

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Utilizing Data-Driven Insights

Employ advanced analytics techniques and contextual understanding to guide your decision-making processes. Within our Data and Analytics Value Journey offering, we work side by side with you to ensure you derive maximum value at every step. After initial envisioning workshops, we tailor and develop use cases and approaches to deploy advanced analytics capabilities in your business context. 

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Leveraging Human Expertise

Harness our domain knowledge, experience, and intuition to complement data-driven insights and enrich decision-making. We involve people in all the analytics automation we build, using a “Human-in-the-Loop” approach, which allows them to review, potentially modify, or approve system predictions. 

 

Through a synergy of advanced analytics and human cognition, decision intelligence empowers organizations to navigate uncertainties with confidence, optimize strategies with precision, and drive innovation with purpose.  

 

We help our clients by seamlessly integrating their cognitive insights with the analytical capabilities of cutting-edge algorithms. 

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Considering the Broader Context

Incorporate market conditions, industry trends, and stakeholder perspectives to contextualize data analysis and guide decision-making. 

We develop tailored smart analytics solutions for our clients based on the modern tech stack of our software partners such as Microsoft, Amazon, Databricks, and Snowflake. 

 

This transformative journey helps our clients embrace a future where every choice is informed, every action is strategic, and every outcome is optimized.  

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Implementing effective methodologies

Utilize structured frameworks, such as risk assessment and scenario planning, to streamline and optimize decision-making. 

Our expertise covers a wide array of applications, from designing data lakehouses to developing robust architectures for big data and data science workloads in digital transformation scenarios.  

Key applications include real-time and predictive analytics in both the physical world and the digital twin. We’re eager to demonstrate our expertise and provide you with insights into these modern capabilities, offering support to your business departments.  

Camelot delivers tangible results that are quickly visible in our own or our clients’ cloud environments. Digital twins are particularly impressive in this regard. 

In this context, tokenization is a current focus, creating digital representations of real-world assets that can enhance data security, accessibility, and processing efficiency in digital twin technology, exemplified by Camelot’s showcases on cloud platforms, utilized for simulation and generating synthetic data for diverse industrial applications. 

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Related Topics

Develop, deploy, and manage data products to maximize business value through gradual metadata collection across components and the final product.   

The concept of treating data as a product and refining it with metadata enhances understanding, management, and utilization. Data product-centric thinking treats data as products, supporting data-driven decision-making, driving innovation, and boosting efficiency throughout the organization. 

Camelot offers vivid models and performance indicators that are equally motivating and mobilizing.  

We will be happy to demonstrate this in a joint workshop, where we leverage analytics and a simulation of how collaboration between the various business lines works in a data catalog, such as Collibra, Informatica, One Data.  

Following the Data Mesh concept, the Data as a Product principle amplifies the importance of data product management in the customer organization. Within our Data and Analytics Value Journey offering, Camelot applies the Data as a Product principle in practical project and organizational contexts.

Viewing data as products heightens awareness of their value, motivating employees to recognize data as a strategic asset that can propel the company forward. It’s essential to consider the pathways through which data products were created, ensuring compliance with legal requirements such as the EU Data & AI Act. Camelot’s solutions inherently address aspects like data lineage and code control, positioning us as an experienced partner and advisor in this regard. 

The structured approach and defined responsibilities associated with the Data Mesh approach foster collaboration among different teams and departments (data domains), enhancing the efficacy and success of data initiatives. As this form of collaboration also requires methodologies and committees to discuss aspects such as data (product) quality. Camelot provides a best-practice process and role model seamlessly integrated into data governance topics, while also addressing data contracts. 

Applying Data Mesh principles reduces traditional data silos, harnessing a decentralized, scalable architecture that puts data ownership and accessibility in the hands of business users, allowing them to exploit the full potential of their data assets.  
However, this doesn’t happen in complete laissez-faire autonomy; rather it requires operation within organized frameworks. From our international projects spanning various domains worldwide, Camelot has developed a pragmatic workshop series that involves domain managers, data (product) creators, and consumers.

Data Product Management

Elevating Data Product-Led Thinking

Data product management is essential for effectively managing data as a product, maximizing the value of data assets, ensuring regulatory compliance, and adhering to EU Data & AI Act requirements. Oversee data catalogs, lifecycle management, and governance processes to optimize data utilization, mitigate risks, and foster innovation while maintaining legal and ethical standards. We have successfully established a data product-led mindset in the domains at our clients, bridging the gap between IT and business departments and ensuring alignment. 

Modern Data Architecture

The modern data stack as the technical core of a multimodal architecture

A modern data architecture harnesses cutting-edge technologies and best-of-breed solutions to streamline data management and analysis, reducing IT overhead and fixed costs. By adopting a modern data stack, organizations gain agility and real-time insights, enhancing decision-making and driving competitive advantage while relieving IT burdens. Camelot supports you with data modernization by reimagining data standards and the data model, selecting the right data tools, creating a scalable data environment, and setting up cloud-based data warehouses and data lakes. 

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The Modern Data Stack

In our data architecture consulting, we aim to implement a future-proof modelling approach that efficiently supports analytical workloads globally, operating 24/7. Our priority is to foster innovation within organizations by enhancing their data processing capabilities. We use architecture patterns like Data Fabric and modelling techniques such as Data Vault, alongside complementary organizational and governance elements.  

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A Multimodal Data Architecture

The goal of a service-oriented IT is to provide its system users with an environment capable of seamlessly integrating diverse data formats, thus advancing towards modernization, and facilitating governed data democratization. 

These complementary data governance measures can be ideally complemented by introducing a data catalog, which enhances data visibility, accessibility, and governance. By centralizing metadata and simplifying data discovery, organizations effectively manage and monetize their data assets.  

 

Camelot offers seamless guidance throughout the selection and implementation process for these components of data architecture, which we refer to practically as data product management, data cataloging, or cartography. As independent consultants, we provide valuable market insights and guide you every step of the way. 

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Selecting the right architectural concept

When it comes to choosing the appropriate architectural concept, discussions often revolve around methodologies like Data Mesh and Data Fabric. Both concepts have modern elements that complement each other perfectly.  
Decision-making entails aligning with the objectives of the Modern Data Framework, which include dismantling data silos, resolving access constraints, managing data quality, ensuring security, and enabling scalability, both at the organizational (Data Mesh) and technical (Data Fabric) level.  
 
These discussions are vital for tailoring architectures to meet specific business requirements and ensuring their resilience. Here, Camelot provides an overview of the principles and components of contemporary concepts, drawing from our extensive experience with numerous national and international client initiatives in this field. We demystify buzzwords from truly useful approaches and tailor them to your situation and specific needs. We can easily showcase such an integration using Denodo or Starburst.  

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Explore Industrial Internet of Things (IIot)

Given the prevalence of use cases emerging from the Industrial Internet of Things (IIoT), the discourse extends to topics like Edge Computing and Edge Analytics. Robust architectural scenarios are developed to accommodate the unique requirements of IIoT applications, ensuring reliability and scalability.  
Given that one of the most common use cases we regularly implement involves a mixed scenario of SAP solutions and machine data, we are able to converge these data worlds on an elastic and scalable cloud architecture. Camelot offers companies the opportunity to explore this in a data science environment or a digital twin of a production facility. 

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Example of Our Work

Data Scientists’ Paradise: Making data architecture future-proof and flexible

A leading omnichannel retail chain embarked on a journey to enhance their Enterprise Data Warehouse (EDW) by a big data and data science platform. Their challenge was complementing the current on-premises architecture with a new lakehouse architecture. Applying a holistic approach, Camelot crafted a blueprint for the new architecture, ensuring every component aligned with the customers vision and goals. The result: a flexible and scalable data lakehouse environment to store and process the retailer’s mass data. 

Value Chain Analytics

Bringing Margin Management to a New Level

Value Chain Analytics involves analyzing each step in a company’s production and distribution process to identify opportunities for cost reduction, efficiency improvement, and value creation. It supports margin optimization decisions across complex value chains, enhancing competitive advantage, and maximizing overall profitability.  

 Additionally, Value Chain Analytics leverages a data-based model for complex end-to-end manufacturing networks, using a tailor-made, agile, and interdisciplinary approach. 

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Data Domain Profitability

Data domain profitability refers to the effectiveness of managing and leveraging data assets within an organization. It encapsulates the ability of a particular data domain, such as customer data, market data, or operational data, to generate value, drive revenue, and contribute to overall business growth. Achieving profitability in data domains requires a comprehensive approach that includes efficient data collection, storage, processing, analysis, and utilization. We help organizations build and maintain a robust data infrastructure, advanced analytics capabilities, and data governance frameworks to ensure the quality, security, and compliance of their data assets. 

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Value Pricing

Value pricing within Value Chain Analytics involves modeling for strategically setting prices for products or services based on the perceived value they provide to customers at each stage of the value chain. This approach recognizes that different activities within the value chain contribute varying levels of value to the final offering, and therefore, pricing should reflect this differential value. Value Chain Analytics enables businesses to identify the cost drivers, value-adding activities, and competitive advantages across the entire value chain. Value pricing ensures that customers are willing to pay for the unique benefits and features of a product or service, rather than simply competing on price alone.  

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Dynamic Analytical Scenarios

This involves the creation and analysis of various real-time and hypothetical situations to understand how changes in the value chain can impact business outcomes. These scenarios are designed to simulate different factors such as shifts in customer demand, changes in input costs, disruptions in supply chains, or alterations in competitive landscapes. By leveraging advanced analytics techniques and algorithms, organizations can model complex interactions within the value chain and predict the potential consequences of different strategic decisions or external events. 

Dynamic Analytical Scenarios provide valuable insights that drive strategic decision-making, improve operational efficiency, and ultimately, contribute to long-term business success. 

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Margin Impact Root Cause

Margin Impact Root Cause analysis involves identifying the underlying factors that influence changes in profit margins across various stages of the value chain. This analytical approach aims to uncover the primary drivers behind fluctuations in profitability, whether they are positive or negative, and to understand their root causes. 

By conducting Margin Impact Root Cause analysis, organizations leverage data from different sources within the value chain, including sales, production, procurement, distribution, and marketing. Advanced analytics techniques, such as correlation analysis, regression modeling, and data visualization, are employed to identify patterns, trends, and relationships that may impact margins. 

Margin Impact Root Cause data-based analysis enables organizations to make informed decisions, allocate resources effectively, and drive continuous improvement across the value chain to maximize profitability in the long term.  

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