Data Analytics

Drive Business Growth

Data can help drive business growth by providing valuable insights that inform key business decisions. By analyzing data and identifying patterns and trends, businesses can gain insights that enable them to make informed decisions about their operations, products, and services.

​Improve Decision Making

Data analytics can help improve decision-making by providing businesses with the insights they need to make informed decisions about their operations, customers, and market trends. By leveraging the power of data analytics, businesses can make more accurate and informed decisions, leading to improved efficiency, increased profitability, and long-term success.

​Enhance Operational Efficiency

Data analytics can help businesses optimize their operations by identifying areas where they can improve processes, optimize resources, and reduce costs, resulting in reduced costs, increased productivity, and improved customer satisfaction.​

Data Analytics Strategy

• A high-level plan that outlines how an organization will use data to achieve its business objectives. It helps organizations make better use of their data assets by providing a clear plan. ​
• By aligning data with the overall business strategy and objectives, a data strategy ensures that data is used to support decision-making and drive business value.

​Cloud Data Warehouse & BI​

• Turn data into actionable insights that drive business growth. ​
• Build out a modern data platform in the cloud. ​
• Design and implement custom dashboards that provide actionable views of key data and metrics. These dashboards are integrated and domain specific.

​Data Fabric & Data Virtualization

• Data fabric is the cutting-edge data technology to overcome complexity, so data teams spend time on business performance with data, integrating data as a single entity, regardless of where it is stored or how it is used.
• The main purpose of a data fabric is to simplify and streamline the process of accessing, integrating, and using data without moving the data from its source location.

​AI & Internet of Things (IoT)

• Harness the power of AI and other emerging technologies to drive competitive differentiation and automation. ​
• ​Help clients develop their AI enterprise strategy and bring to life their Intelligent Agents​
• Design and activate IoT strategies for our clients that bring to life the value of data, AI, and physical hardware

Data Governance MDM & Data Management​

• Define data as a strategic asset and develop a framework to optimize its value.​
• Define clear data ownership, establish standards for data quality, and identify key data requiring Master Data Management.​
• Utilize the organization’s resources, processes, and technology to transform data into a valuable business asset that drives value

​Robotics Process Automation

• Automate routine tasks to improve efficiency and productivity.​
• With Robots and Machine Learning software, automation orchestrates disparate applications and manual processes.​
• Leverage Azure Bot Service to design and build automations that cover a wide range of tasks like customer service, data entry, and cross-application work.

Partners include: Microsoft Azure and Promethium​

A Data Strategy is a comprehensive plan to leverage data as a strategic asset and drive business growth and optimization.​

5 Essential Components of a Data Strategy​

1. Business Objectives: Define the business goals of data initiatives and prioritize data initiatives based on their value and impact for the business​
2. ​Data Technology: Identify the data technology and infrastructure required to achieve the business goals of each data initiative​
3. Operating Model: Establish the operating model to define, design, and build data products​
4. ​Data Governance: Implement governance on data ownership, data discoverability, and data quality
5. Roadmap: Outline the data journey including milestones and goals​

Overall, the Data Strategy should align to the business strategy so that data supports and accelerates the business goals.​

Analyze the Operating Model

• People: roles and responsibilities; skills and expertise; data literacy and culture; and talent / recruiting strategy
• Process: innovation; prioritization and funding; transformation; governance; and knowledge management
• Technology: architecture and tech strategy; operations and maintenance; security, reliability, and continuity, access management; and sandboxing and experimentation

Define the Use Cases

• Specific ways the strategy is implemented: profitability analysis; data-driven marketing; management reporting; and customer engagement

Data Strategy

• Strategy
• Vision
• Mission

Assessment of the Current State

Comprehensive assessment of the following: data inventory; data quality; analytics and reporting; governance and security; IT infrastructure; and business processes

Identify Gaps Between Current and Future State

Identify potential improvements: data awareness and literacy; business optimization; data-driven decision making; and data governance

Design a Data Strategy Roadmap

Clearly define a plan that includes: goals and objectives; phased approach; timeline; data analytics capabilities; and data infrastructure blueprint

Enabling modern data platform for historical times series metrics and reporting

​An Enterprise Data Warehouse (EDW) is a centralized database that is designed to store and manage large volumes of structured data from multiple sources within an organization. The EDW is typically used to support data analytics, reporting, and business intelligence activities, and is designed to allow users to access and analyze data from a single, comprehensive source.​

​An EDW typically includes the following components:​

• ​Data model: The EDW uses a data model to define the structure and relationships between data elements, and to ensure that data is stored in a consistent and logical way.​
• ​Data integration: The EDW includes tools and processes for extracting, transforming, and loading data from multiple sources into the EDW. This can include data cleansing and transformation processes to ensure that data is consistent and of high quality.​
• ​Data storage: The EDW includes a database management system (DBMS) to store data in a structured, organized way, and to support fast querying and analysis.​
• ​Data access: The EDW includes tools and interfaces for accessing and analyzing data, such as SQL queries, reporting tools, and business intelligence software.​

​Overall, an EDW is a key component of an organization’s data infrastructure, and plays a crucial role in supporting data-driven decision-making and analysis.​

Data visualization is the process of presenting data in a visual format that is easy to understand and interpret. It is an essential tool for businesses to make better decisions and gain a competitive edge.​

• ​Communicate complex info: Visualizations help stakeholders understand data and make better decisions.​
• Identify patterns and trends: Spotting trends and identifying outliers are easier with visualizations.​
• Support collaboration: Visualizations can be shared and discussed by stakeholders.​
• Improve efficiency: By using visualizations, businesses can quickly identify areas where they can improve efficiency and optimize their operations.​
• Stay competitive: Data-driven businesses can stay competitive by identifying insights and opportunities with visualizations.​

​Data visualization is a crucial tool for businesses to make better decisions, improve efficiency, and stay competitive. With the right data visualization strategy, businesses can unlock the full potential of their data to drive growth, innovation, and success.​

Data fabric is a term used to describe an architecture for managing and integrating data across an enterprise. It provides a unified and flexible data management framework that enables organizations to manage their data as a single entity, regardless of where it is stored or how it is used. The main purpose of a data fabric is to simplify and streamline the process of accessing, integrating, and using data within an organization, while maintaining data governance and security.​

The Benefits of Data Fabric for Businesses​

• Data Integration: A data fabric enables organizations to integrate data from disparate sources, such as databases, cloud services, and applications, into a single, unified view. This helps organizations avoid data silos and enables them to make informed decisions based on a complete picture of their data.​
• Data Governance: A data fabric provides a centralized framework for managing and governing data, which helps organizations ensure the quality and accuracy of their data. This also helps organizations maintain compliance with regulations and data privacy laws.​
• Improved Decision Making: A data fabric enables organizations to access and analyze data from multiple sources, which helps them make informed decisions based on the most up-to-date information.​
• Increased Agility: A data fabric enables organizations to quickly and easily integrate new data sources, which helps them respond to changing business requirements and stay ahead of the competition.​

LBMC Data Insights team provides the full slate of AI/DS strategy, design, architecture, infrastructure, and managed services to our clients to solve complex business problems.

Applications of next-generation data techniques like artificial intelligence, cognitive computing, machine learning, deep learning, advanced analytics and optimization are used to improve efficiency, reduce cost, increase profit and provide competitive advantage for our clients. ​

Emerging Technologies utilize a holistic approach that optimizes the integration of intelligent, robotic and autonomous systems towards five objectives:​

• Reveal Insights – Reveal deeper insights from datasets and real time interactions​
• Optimize Performance – Optimize the performance of people, systems, processes and machines​
• Harness Automation – Harness automation for competitive advantage across the enterprise​
• Enhance Experience – Design experiences with predictive, ambient and conversational attributes​
• Sustain Trust – Build systems that establish and maintain trust, and promote compliance

LBMC Data Insights team brings a large alliance IoT ecosystem that enables us to complement our technical and sector expertise with insights and solutions. We provide end-to-end consulting services (from strategy to implementation to managed services) to unlock the insights for our clients from the huge amount of data generated by machines and devices in near real-time.​

• Architect – End-to-end consulting service for the CIO/CTO defining optimal operations in the production environment, covering all aspect of IoT/OT operations.​
• Asset Performance Management – Solution delivery using IoT/OT Data Integration and IoT/OT Advanced Modeling techniques to obtain real-time insight into production processes.​
• Production Process Performance Management – Design and Implement/support implementation of solutions (platforms, applications) to allow data collection and structuration as well as analytics for industrial processes.​
• New Value Creation – End-to-end consulting from ideation to implementation of smart environments, products, connectivity platforms, analytic models and new services with valuable insights from user experiences.​
• Connected User Experience – Design and Implement/support implementation of solutions (wearables, AR/VR platforms and applications) to create new customer value by collecting data and insights from wearables, proximity, motion and behavior of users.​

Enabling Consistent Data Management Through Data Assets Governance

Data governance is the set of policies, procedures, and standards that govern how data is collected, stored, and managed.​

Effective data governance is essential for businesses to gain valuable insights from their data and mitigate risks related to data quality, privacy, and security.​

• ​Data quality: Data quality is a key element of data governance that ensures that data is accurate, consistent, and of high quality. This involves establishing clear standards for data quality and ensuring that data is regularly monitored and maintained.​
• Data ownership: Data ownership is another important element of data governance that defines who is responsible for managing and maintaining data. This involves establishing clear roles and responsibilities for data ownership, and ensuring that data is properly secured and protected.​
• Master Data Management: Master data management is the process of managing the core data elements that are essential to an organization’s operations. ​
• Data discoverability: Data discoverability is the process of making data easily accessible and discoverable by stakeholders within an organization. ​

By effectively managing these core elements of data governance, businesses can ensure that their data is accurate, secure, and well-managed. This enables them to make informed decisions, mitigate risks, and drive growth and innovation.​

We leverage an Enterprise Data Management Framework to gain understanding around your current people, process, technology and data.

• Business can set benchmarks to simply measure the quality of their data.​
• These dimensions and metrics allow for business leaders to determine justifiable levels of data quality based on the most important business factors (data timeliness, missing data, incorrect data types).

Dimensions are categories of data quality issues with a shared reason why they are important and often have similar underlying causes.

• ​Why is the data in my warehouse not up-to-date?
• Why is the data in my operational tool six hours behind?
• Why does my dashboard take so long to refresh?

Metrics describe how specifically a dimension is measured, both quantitatively or qualitatively, and can be tracked over time.

• Difference between dashboard access time and latest refresh time of data
• Number of hours in which service level agreement was not met
• Average latency between ELT load and reverse ETL operationalization

Enabling Consistent Data Management Through Master Data Management​

Master data management (MDM) is the process of creating, maintaining, and utilizing master data across an organization. This process includes identifying and defining master data, creating and maintaining a central repository of master data, and ensuring data quality and consistency across all systems that use the master data. ​

• Data Consistency and Data Quality: MDM helps ensure that data is consistent and accurate across different systems and applications. It provides a single, authoritative source of key business data, which reduces the risk of data inconsistencies and errors.​
• Cost Savings: MDM can help reduce costs by eliminating data redundancies and improving data quality. This results in more efficient processes, fewer errors, and lower operational costs.​
• Regulatory Compliance: MDM can help organizations comply with industry-specific regulations by ensuring that data is accurate and complete. ​
• Unified View: MDM helps organizations create a unified view of their customers, patients, facilities…etc. allowing the organization to understand their from a comprehensive perspective and ultimately, better business outcomes.​

Overall, MDM is critical for organizations looking to establish a solid data foundation, reduce costs, comply with regulations, improve decision-making, and drive business success.

Robotic Process Automation (RPA) is a technology that uses software robots or “bots” to automate routine and repetitive tasks. RPA can be used to automate a variety of business processes, such as data entry, report generation, and customer service, among others. The benefits of RPA include increased efficiency, improved accuracy, and reduced costs.​

Benefits of Robotic Process Automation

• Increased Efficiency: RPA can perform repetitive tasks faster and more accurately than humans, leading to increased efficiency and productivity.​
• Improved Accuracy: RPA can reduce errors and inconsistencies, leading to more accurate data and fewer mistakes.​
• Reduced Costs: RPA can help organizations save time and money by automating repetitive tasks and reducing the need for manual labor.​
• Better Customer Experience: RPA can help organizations provide better customer service by automating routine tasks and providing faster response times.​
• Compliance: RPA can help organizations comply with regulations by automating compliance-related tasks and providing accurate and auditable records.​

Overall, RPA is a powerful technology that can help organizations streamline their operations, reduce costs, improve accuracy, and provide better customer service.

LBMC’s Data Insights Advisory Services team consists of highly experienced data professionals, with experience across on premise and cloud data platforms, data visualizations, and robotic process automation.

Our Mission

To see data insights create a significant impact inside organizations, and to be one of the best in the business at providing value inside the business.

At LBMC, we focus on building collaboration within our clients’ business and IT teams to facilitate the most effective use of data throughout their organization. We are solution-focused, delivering with quality and excellence, using an agile approach to ensure our clients receive value every step of the way. ​

If you are interested in more information, complete the form below to have an LBMC sales team member contact you.