How Nissan Middle East FZE standardized interactive data visualization by implementing the “PowerBI Report Server” platform on AWS that drove faster and smarter decisions leading to 21% increment in net profit

We need to do more than what corporate performance management has traditionally enabled to move from strategy to strategic actions while forecasting, monitoring, and managing performance.

What is Corporate Performance Management?

Undoubtedly not a new concept, corporate performance management is often referred to as business or enterprise performance management. Gartner defines corporate performance management or CPM as “an umbrella term that describes the methodologies, metrics, processes and systems used to monitor and manage the business performance of an enterprise”. And that is indeed a broad scope as business performance does touch several areas.

Corporate performance management isn't a matter of technologies or solutions or an isolated activity or strategy—the holistic perspective is crucial. Getting strategic, financial, analytical, and business knowledge is just one aspect of the equation that will help you make better business decisions.

The four crucial techniques linked to effective performance management are listed below:

• Analysis of the chance and choose the high-impact use cases to benefit from artificial intelligence
• Automation of tasks and establishing a long-term strategy to find quick wins for an all-encompassing change with automation
• Capturing of the value by streamlining jobs to manage organisational transformation and adopt an end-to-end process view.
• Integrating Artificial Intelligence (AI) into an operational model to make a transformational impact, analyse data to make wise decisions, and develop the capacity for ongoing improvement

How has AI impacted CPM?

The face of corporate performance management is changing due to artificial intelligence, among other things, in monitoring, reporting, analysis, forecasting, and analytical intelligence (as provided by analytical applications).

• The past corporate performance management only partially made it possible to foresee the future. But the present AI advancements help in real-time analysis, predictions, and future-oriented decision-making.
• With the help of artificial intelligence, pattern recognition, statistical forecasting, the analysis of vast amounts of market intelligence, decision-making can be made outside the traditional realm of historical information. When AI tracks KPIs and makes more data-based decisions, performance management can improve significantly.

Historically speaking, leveraging data has improved over time, opening more opportunities to learn from the past and present and, most importantly, to comprehend and plan using the proper KPIs. This is true within the broader context of strategy and planning in a corporate performance monitoring, reporting, and analysis environment.

To enable analysis, produce measurements, and changes, AI is integrated into the performance management process. Better performance management procedures can be sped up with artificial intelligence, increasing transparency and sustaining employee interest. Furthermore, corporate performance management enables better leverage of all data in the organisation’s ecosystem and helps to achieve its strategic objectives.

Advantages of a CPM Software

Corporations must adopt process automation in the age of business management intelligence. Here are a few advantages of using a CPM system.

1. Immediate feedback
Intelligent dashboards built into performance management software offer all the quantifiable metrics a management team could need to make decisions. The benefit, though, is not in the variety. It lies in the capacity to interpret and use data as changes occur in real-time throughout the entire organisation.
2. Data consolidation for easy management
The tools have the intelligence to gather, group, and combine data from multiple sources, be it departments, spreadsheets, or even companies.
3. Make risk management simple
The incorporation of tools like what-if models is a crucial benefit of CPM. By simulating the best- and worst-case situations, for example, the model equips managers to reduce risks and make wise decisions.
4. Offer primary data access and feedback
Managers may easily access information through performance management systems, promoting accuracy and quality.
5. Ease of collaboration
CPM tools are locally integrated and cloud-connected for all users to keep in sync across all departments.
6. Boost the standard of talent management
Recruiters and business leaders are now concentrating on employing AI-driven performance management to enhance the caliber of their hiring. It assists them in finding the appropriate talent pool and selecting the best candidates.
7. Effective data analytics for problem-solving
AI is a real game-changer in performance management systems and helps leaders and stakeholders make data-driven decisions and solve current challenges.
8. Developing a collaborative environment
Finally, businesses succeed when they concentrate on developing a collaborative environment where there is no place for hierarchy, bias, or inconsistent behaviour. AI in performance management systems will enable executives to spend more time on their employees and provide brilliant ideas.

What does Beinex offer?

STRACT by Beinex is an indisputably efficient tool that helps it deliver optimised business performance. It allows businesses to forecast, track, assess, and identify areas for improvement across all operations. Companies can thoroughly and comprehensively analyse all relevant financial and operational metrics across various levels of the business by consolidating data and performance metrics into one centralised database and then measuring this against their strategic goals.

STRACT is a comprehensive solution with features that allow you to create much more accurate and flexible budgets, enhancing your current business and resource planning and forecasting.

Types of LOD Expressions in Tableau

1. FIXED
2. INCLUDE
3. EXCLUDE

There are three types of LOD expressions in Tableau:

FIXED: The FIXED LOD expression calculates values based on specific dimensions, regardless of the dimensions present in the visualisation. It creates a virtual level of detail for the calculation. For example, {FIXED [Category]: SUM([Sales])} would calculate the total sales for each category, irrespective of other dimensions used in the visualisation.

INCLUDE: The INCLUDE LOD expression calculates values at the specified level while incorporating the dimension(s) used in the visualisation. It allows you to aggregate data for a specific dimension while considering other dimensions in the visualisation. For example, {INCLUDE [Region]: AVG([Profit])} would calculate the average profit for each region, considering all other dimensions in the visualisation.

EXCLUDE: The EXCLUDE LOD expression calculates values at the specified level while excluding the dimension(s) used in the visualisation. It enables you to aggregate data for a specific dimension while ignoring other dimensions in the visualisation. For example, {EXCLUDE [Product]: SUM([Revenue])} would calculate the total revenue for each product, excluding any other dimensions present.

FIXED LOD Tableau Function

In Tableau, the FIXED Level of Detail (LOD) is the widely used function that allows you to perform calculations using specific dimensions, irrespective of the dimensions present in the visualisation. It creates a virtual level of detail for the calculation, providing precise control over your analysis's granularity.

Steps to Create a FIXED Level of Detail Tableau Function

In Tableau, the FIXED Level of Detail (LOD) function allows you to perform calculations using specific dimensions, irrespective of the dimensions present in the visualisation. It creates a virtual level of detail for the calculation, providing precise control over your analysis's granularity.

The steps to create the FIXED LOD function are as follows:

• • Create a calculated field. (This can be done by clicking on the drop-down next to the “Search” bar in the dimension pane or by selecting “Analysis” and selecting “Create Calculated Field”)
• • Type in the FIXED LOD Formula in the calculated field and drop the field into the view.
• • We can obtain the syntax of the FIXED expressions when we search for “Aggregate” functions in the calculated field.

Example
The following FIXED level of detail expression computes the sum of sales per region:

Beinex+ Tableau Offerings

Beinex has 100 years of combined experience in Tableau and is led by professionals who have successfully delivered Tableau projects in the region for large private and public sector organisations. Our team of Tableau-certified consultants are real-life Tableau business users passionate about Tableau and providing a world-class experience. In addition to providing sustainable analytics solutions, Beinex will help organisations to build superior data visual analytics capabilities internally through our bespoke training programs.

What is AWS Managed Services (AMS)?

AWS Managed Services (AMS) is a fully managed service that helps businesses run AWS environments at scale. It adheres to industry best practices for operational excellence, security, and compliance.

• Key Benefit of AWS: AMS enables your company to spend less time managing the complexities of cloud operations and more time focusing on business outcomes.

AMS handles:

• Infrastructure Management: Includes continuous monitoring, patching, and lifecycle management of your AWS resources.
• Security & Compliance: Generates automated compliance checks against NIST, PCI-DSS, GDPR, and HIPAA frameworks.
• Operational Support: 24×7 incident response, backup, disaster recovery, and governance.
• Automation & Optimization: Uses custom runbooks and built-in AWS features to reduce manual labor.

Why AMS Matters in 2025

Cloud adoption is growing, but so is the risk associated with it. Even though many businesses:

• Lack 24/7 monitoring.
• Struggle with regulatory audits.
• Depend on manual vulnerability patching.

AMS offers solutions to mitigate the risks associated with cloud adoption. It resolves the issue by utilizing automation, continuous monitoring, and best practices from AWS. You can implement AMS at scale, without overloading internal teams.

Top 6 AMS Cloud Security Best Practices

1. Prevention: Stop Threats Before They Happen

• Identity & Access Management (IAM): AMS provides real-time validation of modifications, enforces least-privilege policies, and automatically corrects deviations.
• Network Security: AMS configures AWS WAF, Firewall Manager, and Network Firewall to block malicious traffic.
• Patch & Vulnerability Management: AMS automates scanning and patching, including zero-day threats, with Systems Manager + Inspector.
• Backup & Disaster Recovery: With recovery testing and ransomware prevention, AMS fortifies AWS Backup & DRS.

2. Detection: Spot Threats in Real Time

• GuardDuty & Macie: Sensitive data is protected, and anomalous activity is detected.
• Security Hub: Assists with noise reduction, alert aggregation, and issue prioritization.

3. Monitoring & Incident Response

• 24×7 global monitoring with NIST-aligned runbooks.
• Automated incident response using Systems Manager + CloudWatch.
• Integrations with ServiceNow, Jira, Slack, and Teams for faster collaboration.

4. Compliance Made Simple

Adhering to compliance frameworks such as HIPAA, PCI-DSS, SOC, GDPR, and NIST is complex for all companies. AMS embeds compliance controls from day one.

• Continuous Compliance Monitoring: Automated checks via AWS Config rules.
• Audit Trails: CloudTrail & Athena queries for reporting.
• Certified Services: AMS itself is compliant with major frameworks.

5. Governance & Reporting

• Resource Tagging: For governance, cost allocation, and reporting.
• Self-Service Dashboards: Patch compliance, backup coverage, and incident stats.
• Monthly Business Reviews: Joint sessions with Beinex + AWS experts.

6. Security On-Demand: Flexibility When You Need It

AMS Operations on Demand (OOD) lets you scale support instantly for:
• Emergency vulnerability remediation.
• Legacy OS upgrades.
• Firewall operations.

Why You Should Opt for AMS Security?

AWS provides the platform. AMS provides the tools. It ensures:

• Faster ROI → measurable security improvements within 6–12 months.
• Local + Global Expertise → deep knowledge of GCC regulations + AWS global best practices.
• End-to-End Ownership → from planning to monitoring, audits, and response.

Secure Your AWS Cloud with Beinex

Beinex’s Cloud Engineering Services help organizations maximize their AWS investments through robust security frameworks and modernization strategies.

Our Core Offerings Include:

• Cloud Security
• Security Assessment
• Strategy & Implementation
• Well-Architected Review
• Migration & Modernization
• Cloud Migration
• Cloud Automation & DevOps
• Infrastructure Modernization

Whether you are in the early stages of cloud adoption or looking to optimize your existing AWS, we help you achieve your cloud goals.
Connect with us: https://beinex.com/cloud-engineering

By proactively utilising retail analytics, companies can rapidly act upon essential insights drawn from their data, resulting in notable improvements in their business outcomes. The most successful retailers also leverage external datasets to support their business strategy.

The initial and critical step towards achieving this is establishing a data foundation that combines internal and external data sources, providing a complete view of the customer experience.

To deliver the experience customers want and to remain competitive, companies must harness the power of retail analytics. In this blog, we will cover the top trends in retail analytics retailers use to get ahead.

The Primary Developments in Retail

There are six main trends in retail analytics that companies are using to gain an advantage. These include:

1. Develop Individualized Experiences for a Single Customer
2. Utilise Predictive Analysis to Anticipate
3. Develop Dynamic Automated Pricing Models
4. AR and VR in Retail
5. Subscription-based Business Models
6. Integrate AI & ML

These capabilities can only be achieved when your data is combined into a single, unified source of truth. To stay ahead, many retailers seek the assistance of data analytics consultants (such as those provided by Beinex) to create and implement the necessary modern data infrastructure.

1. Develop Individualized Experiences for a Single Customer

A survey was conducted on 1,000 individuals aged 18-64 to assist brands in improving relationships and building customer loyalty. The research found that 80% of respondents are likelier to engage with a company that provides personalised experiences. This demand for personalisation has grown even further since then. In a 2020 article on creating differentiation in retail, McKinsey suggests that the most effective retail experiences "involve the customer in the conversation and use data to establish tailored personalisation."

Of course, accomplishing this requires a Customer 360 perspective, which entails a comprehensive understanding of customer data across all brand interactions. These interactions may include transactional data, customer feedback, shopping preferences, website and mobile app activity, and more.

2. Utilise Predictive Analysis to Anticipate

Retailers are utilising sophisticated analytics that employs machine learning algorithms to make predictions based on patterns identified in customer data. These advanced algorithmic models allow retailers to determine how much of a particular product or service customers will likely buy during a specific timeframe.

Business executives use demand forecasting to encourage their most profitable customers to return to the store by providing timely notifications and valuable offers on relevant items. As a result, retailers can ensure that they are timing their shipments to ensure that the products their customers desire are available on store shelves while enhancing their supply chain.

3. Develop Dynamic Automated Pricing Models

Retailers frequently need to maintain a portion of their prices at a superficial level to remain competitive. These low-priced items, also known as doorbusters and key-value items (KVIs), are often the top sellers and traffic generators that shape a retailer's pricing reputation. KVIs can account for up to 80% of revenue but only half of a retail company's profit. To compensate for the low margin on KVIs, retailers tend to increase the prices of their higher-margin items and position them strategically alongside doorbusters and KVIs in creative ways to encourage shoppers to add higher-margin products to their shopping carts.

Retailers use dynamic pricing algorithms to adjust their product prices according to market demand and inventory levels, optimising their profit margins. These algorithms provide automated recommendations for pricing that enable retailers to make timely, informed decisions to improve their financial performance. To achieve maximum effectiveness, it is recommended that retailers work with a data analytics consulting firm to develop a tailored solution that aligns with their specific business objectives, operational processes, and customer needs.

4. AR and VR in Retail

The use of augmented reality (AR) and virtual reality (VR) in the retail industry is becoming more common, offering new and creative ways for customers to shop and experience products. By allowing customers to virtually try on products and creating immersive shopping experiences, AR and VR are changing how retailers interact with their customers. As per current retail technology trends, more and more retailers are integrating AR and VR into their strategies to improve customer experiences and boost sales.

5. Subscription-based Business Models

Subscription-based business models are gaining popularity as consumers seek convenient and personalised experiences, especially in industries like beauty and fashion. These models offer a unique opportunity for retailers to build a loyal customer base by regularly providing curated and customised products. Moreover, subscription models ensure a predictable revenue stream for businesses and reduce customer churn. As the subscription economy evolves, retailers must develop innovative ways to differentiate themselves and provide added value to their customers.

6. Integrate AI & ML

Artificial Intelligence (AI) and Machine Learning (ML) in retail is becoming more common. Retailers can use AI and ML for chatbots, personalisation, and predictive analytics to enhance the shopping experience for customers. By analysing large amounts of data, retailers can predict consumer behaviour and offer customised recommendations and promotions, resulting in better customer engagement and loyalty. As AI and ML technologies continue to advance, their potential impact on the retail industry is anticipated to be significant.

Summing Up

Retailers must prioritise adopting a customer-centric data and analytics approach to remain competitive against online and offline rivals. As technology advances and AI models become more sophisticated through advanced machine learning algorithms, retailers must utilise retail analytics to uncover valuable insights that can lead to novel methods of enhancing customer loyalty.

Advanced Analytics services from Beinex explain the why and how of change in your enterprise – the top line, bottom line behaviours and everything in between, from your organisational data. With minimal human intervention, it gives decision-makers the ability to have a firm grip on credible but previously hidden insights. As a decision-maker, you can employ data-driven insights and execute insights-led planning for your enterprise with telling and far-reaching effects.

Let’s introduce one such tool.

Beinex has a set of tools to automate the migration testing process, and the devices can also process syntax changes between the legacy database and Snowflake. Well, Syntax Migrator is one tool developed by our experts that follows a structured methodology that helps minimise migration risks. It is an error-free, timesaving, automated migratory tool that converts SQL syntax into Snowflake queries.

How does it work?

With the help of a user-friendly tool like Syntax Migrator, we can quickly convert SQL syntax into Snowflake queries by entering the SQL syntax in the console and then pressing the convert button. It is handy, and even persons with no technical expertise can easily use it.

Syntax Migration Platform can aid in:

1. Automatically translating DDL and DML
2. Selection of best possible data type
3. Intelligent usage of Temp and Transient Tables

Automatically translate DDL and DML.

• Creation of different tables views, and procedures can be quickly changed into Snowflake-compatible queries without any help from Snowflake syntax.
• No matter how the complex procedure is, it converts the syntax to Snowflake and maintains the logic and structure of the procedure.

Selection of the best possible data type

1. Syntax migrator selects the best data type available in Snowflake concerning the source data source even if the datatype is disparate.
2. No need to worry about any mismatch in the data while converting to the compatible datatype. All the data properties will be preserved during conversion.

Intelligent usage of Temp and Transient Tables:

About Snowflake

The cloud data platform from Snowflake enables a variety of data workloads, including data warehousing and data lakes, as well as data engineering, data science, and data application development across numerous cloud providers and geographies from any location inside the company.

Due to Snowflake's distinctive architecture, almost any concurrent user in the Data Cloud can benefit from near-infinite storage and real-time processing.

The Many Benefits of Migrating to Snowflake Data Cloud

Even though migrating from an on-premises solution to a cloud can be a tedious process, with Snowflake Data Cloud, it is not laborious, and it can reap benefits like the following:

1. Infinite elasticity
2. Highly concurrent
3. Exponential cost saving
4. Superior data security
5. Modern data cloud platform with your data
6. Reduced maintenance overhead
7. Continue to use on-premises transactional platforms
8. Move to pay for what you use instead of heavy AMCs
9. Conversion of queries into a best Snowflake-compatible format