Better decision making
Data and analytics to support actionable insights
With the right data structure, robust processes and analysis tools, your business identifies what needs to be improved and/or optimized and make much better informed decisions.
A successful data management strategy is built on three pillars.
What is a data-driven company?
A data-driven company values data as an asset that it uses as a central point in all its decision-making processes and understands data’s importance.
Businesses who prioritize data will nurture it, work with it and disseminate it throughout the organization in a regulated manner.
Data management in today´s digital transformation era
How can a company ensure all data is leveraged to become a digital enterprise?
Standardization of data
One platform to connect, maintain and enhance all enterprise data.
Data is the core of digital transformation, the ease of setup and maintenance of data held in the cloud without limitations is key.
Platform integration allows independence of your systems but centralization of all data across platforms in the cloud boosts the ability to make decisions across the business.
Cloud-based data management allows you to bring data assets together (both internal and external) to store, mix and use as necessary in a scalable way.
Democratization of data
Data at the fingertips of everybody in your organization to empower decision-making.
Data is the necessary component to make decisions and take action, and every person in an organization must have the data made available to them, wherever and whenever they need it.
In today´s AI era, fast decisions and suggested analytics automatically surfaced are becoming a key facet of democratizing the data you hold.
Solid insight for better performance
Instant detection of, and reaction to, enterprise challenges.
Placing trust in the decisions you make as a business is a risk factor, how much do you rely on “gut-feeling”?
By shifting organizational mindsets to a data-driven decision culture, the certainty factor increases as you can make decisions based on fact.
AI-driven decisions, predictions and algorithms are going further to help enterprises be more precise, faster.
Real-time data analysis covers a wide range of business areas and scenarios.
| Marketing | Sales | Operations | Human Resources | Finance |
|---|---|---|---|---|
| Improve campaign analysis, segmentation and planning | Identify better opportunities for upselling and cross-selling | Optimize production processes, identify any problem and reduce bottlenecks | Increase visibility into recruiting and retention to reduce employee turnover | By analyzing trends and patterns, teams can forecast future financial performance and control cashflow |
| Optimize paid media spend | Improve sales performance analysis | Enhance inventory planning and performance | Support workforce demand planning | Automated data collection and analysis minimize human errors, ensuring more accurate financial reporting |
| Improve website, social, and email analytics | Adapt personalized offers and pricing | Improve sales and operations planning and alignment | Monitor benefits, compensation and rewards | Automation of routine tasks such as data entry, reconciliation and reporting frees up time for more strategic activities |
| Support customer journey analytics at granular touchpoint levels | Get recommendations on next best actions for customers and prospects | Predict production outputs and consumption across the supply chain | Predict organization health trends | Continuous monitoring can detect unusual transactions or patterns that may indicate fraud |
| Capture the purchase behavior trends of consumers to improve customer experience and optimize offers and supply chain | Streamline fulfilment and distribution across the supply chain |
How Prodware can support your data strategy
Data visualization with tools such as Microsoft Power BI is the tip of the iceberg.
Prodware Data & Analysis experts can support you with a 360-degree approach to cover 100% of your data journey with central data management.
Core data management technologies from storage to analysis
AZURE DATA LAKE
Azure Data Lake is a scalable and secure data storage and analytics service provided by Microsoft Azure. It is designed to handle large volumes of data from various sources and make it accessible for analysis and processing. Azure Data Lake supports your data warehousing, machine learning and big data analytics requirements.
- Azure Data Lake can store petabytes of data, making it suitable for big data applications
- It can scale up or down based on your need, ensuring you only pay for what you use
- It integrates well with other Azure services such as Azure Data Factory, Azure Databricks and Azure Synapse Analytics, providing a comprehensive data solution
- It supports structured, semi-structured, and unstructured data, including JSON, CSV and Parquet
- Data is encrypted both at rest and in transit, ensuring high levels of security
- It allows you to manage who can access and manipulate data
- You can run complex analytics and machine learning models on the data stored in Azure Data Lake
- It supports real-time data processing, enabling timely insights and decision-making
MICROSOFT FABRIC
Microsoft Fabric is a comprehensive, end-to-end analytics and data platform designed to unify various data-related tasks and services for simplified data management, processing and visualization. It operates on a convenient SaaS model for predictable and simplified cost control.
- This unified platform combines services such as Power BI, Azure Synapse Analytics and Azure Data Factory into a single environment
- Microsoft Fabric includes Data Engineering, Data Factory, Data Science, Real-Time Analytics, Data Warehouse and Databases
- It uses OneLake for centralized data storage, for seamless data management across different analytics tools
- It embeds AI capabilities to streamline data processing and analysis
- Fabric supports real-time data ingestion and processing
- Centralized administration and governance supports your data security and compliance needs
- Tailored experiences for different roles such data engineers, scientists, and business users makes Microsoft Fabric easy to use
Microsoft Power BI
Power BI is a cloud-based business analytics service that provides interactive data visualizations and business intelligence capabilities with an interface simple enough for end users to create and share your own reports and dashboards.
- Create visually appealing and interactive dashboards that can be shared across your organization
- Your users can take a wide range of built-in visuals or create custom ones
- Power BI connects to various data sources, including Excel, SQL Server, Azure etc.
- Access real-time data to make timely and informed decisions.
- Build reports and dashboards with ease by using drag and drop functionality
- AI and machine learning capabilities for deeper and automated insights, even use natural language to ask questions about your data
- Access your reports and dashboards wherever you are with Power BI mobile apps
- Implement data governance policies to ensure data security and compliance and control access to data and reports based on user roles
Practical steps in data management
Step one
Create the foundations
Assessment on the Data Assets, Tools and Processes.
Define Business Goals and Insights.
Create the Data Infrastructure Set-Up (Storage, Processing, Integration, Security).
Step two
Build the data engine
Assure Data Acquisition and the right integration: (data sources, processes of data extractions, data transformation, data loading in a centralized System.
Data Analysis and Insights Generation: Set data analysis capabilities of the data integrated in the centralized system.
Set the right Data Visualization for every data consumer in the organization.
Step three
Establish data governance
Ensure that data is managed effectively and securely within an organization:
Define data governance framework and scope.
Define processes and metrics for data quality management for standardization and completeness.
Ensure data integrity with audit trails, lineage and traceability.
Implement data security measures and access controls.
Establish data governance and change management processes.
Provide training and awareness programs to employees on data governance policies and best practices.
Continuously monitor data quality, integrity and security with a feedback loop.
Step four
Become a data driven organization powered by AI
Implement Advanced Analytics and Predictive Modelling (Machine Learning, Forecasting, etc.).
Integrate innovative Artificial Intelligence applications and technologies in your business software such as Microsoft Copilot and Prodware AI apps to surface insights automatically in daily work.
Transforming your decision making process – Big Data and AI
Innovative storage solutions such as cloud computing, combined with software that uses sophisticated computer algorithms, enable large volumes of digital data to be analyzed.
These tools are designed to capture and correlate relevant information to surface patterns, correlations and insights.
Data Mining, i.e., the discovery and analysis of collected data via statistical, mathematical, and computational techniques, uses tools to process and better classify, visualize and analyze the flows of data.
Discover more about Predictive Analytics, IoT, Machine Learning and AI technologies in our dedicated page on Big Data.
Why Prodware for better decision making
We have been around for 30 years and have acquired the expertise and experience in helping businesses drive transformation projects. Prodware’s holistic approach to data management ensures secure, integrated and scalable solutions. Our expertise and guidance in disruptive technologies and innovation help us provide the solutions and services you need to meet the challenges of managing your data and deriving the greatest insights while gaining that sustainable competitive edge for your business.