Enabling Scalable and Rapid Data Product Delivery for Global Financial Services 10 Apr 2025, 2:38 pm

The Problem​

A large financial services organization, serving a global network of high-net-worth clients, faced challenges in efficiently delivering data products across the enterprise. The existing setup hindered rapid data product delivery and limited the ability to effectively scale data initiatives across the business. The organization needed a scalable solution that would:​

• Centralize data from diverse sources while minimizing the effort required for data engineering​
• Standardize data into a unified layer, while fueling innovation in a sandbox environment​
• Empower various teams, such as data scientists and BI dashboard developers, to independently prepare and model data without interfering with each other's workflows​

​How Kenway Helped​

Kenway implemented a Delta Lakehouse data platform on Azure Synapse, utilizing a medallion architecture (Bronze, Silver, and Gold layers). This platform enabled the organization to centralize, standardize, and govern data, while keeping data engineering complexity low and facilitating data access for multiple teams.​

1. Metadata-Driven Ingestion - We developed a metadata-driven ingestion framework processes using data contracts for different source types (flat files, databases, and application events). This minimized the complexity of onboarding new data sources, which now only required configuration updates to integrate additional data rather than code changes.
2. Data Exploration Layer - A Silver Data Lake layer, accessible via serverless cloud technologies, enabled easy data exploration through familiar tools like SQL Server Management Studio and Power BI. This ensured teams could quickly analyze data without needing highly-specialized skills.​
3. Project Centric Databases - Serverless databases were provisioned for specific projects, on top of the Silver Data Lake Layer. Teams could tailor and use data as needed while ensuring governance through Source Control backed data contracts. These data contracts enabled change control with familiar practices like branching, versioning, and code reviews.​

Results​

This solution significantly reduced the effort needed to onboard new data sources and simplified data engineering. Teams across the organization could independently access and model data, speeding up data product delivery without disrupting each other's workflows. Governance and control over shared data assets were enhanced through established version control processes, ensuring consistency and compliance across the data lifecycle.​

The post Enabling Scalable and Rapid Data Product Delivery for Global Financial Services appeared first on Kenway Consulting: Management & Technology Consulting.

How AI and Predictive Analytics Are Transforming Patient Care at Healthcare Organizations 7 Apr 2025, 2:29 pm

Understanding AI Predictive Analytics in Healthcare

Over the past decade, artificial intelligence (AI) has significantly influenced nearly every industry, and healthcare is no exception. AI-powered tools now assist in analyzing medical test results, tracking patient data, and even performing robotic-assisted surgeries. Among the most impactful applications of AI in healthcare is its role in optimizing predictive analytics.

Predictive analytics, as defined by Harvard Business School, is "the use of data to predict future trends and events." Predictive analytics has been used in healthcare since the early 2000s to anticipate health trends, increase the accuracy of patient diagnoses, and improve internal healthcare systems and processes. 

Today, AI and predictive analytics are converging, allowing machine learning to automate and enhance predictive analytics capabilities. This transition from manual analysis to AI-powered insights is revolutionizing patient care, leading to more accurate diagnoses, improved treatment plans, and streamlined clinical operations.

The Role of Data in Healthcare Innovation

The explosive growth of data in healthcare presents new opportunities and challenges. While vast amounts of patient data can drive more informed decision-making, many healthcare organizations struggle with outdated data infrastructure, making it difficult to extract meaningful insights. Healthcare companies also have a particular responsibility to maintain the integrity of their data, much of which contains private and highly-sensitive patient information. 

For predictive analytics and AI to reach their full potential in healthcare, organizations must address three key challenges:

Unstructured Data

Healthcare institutions store massive amounts of data in large, unstructured repositories known as data lakes. Without a strategic approach to organization and classification, this data becomes difficult to access, analyze, and utilize effectively.

Security Concerns

Given the sensitive nature of healthcare data, security is paramount. The Health Insurance Portability and Accountability Act (HIPAA) imposes strict compliance requirements, yet many organizations struggle to implement secure, efficient data management strategies.

Visibility Issues

Without properly trained data professionals, healthcare organizations often lack visibility into their own data assets. This lack of clarity makes it difficult to leverage predictive analytics for proactive decision-making.

Overcoming these challenges presents an opportunity for healthcare organizations to unlock the full power of AI-driven predictive analytics, paving the way for more effective patient care and operational efficiency.

The Benefits of AI Predictive Analytics in Healthcare

While these challenges are real, effectively organizing this data leads to the ability to use AI predictive analytics in healthcare, which will:

Enhance Diagnostic Accuracy

While a doctor’s trained eye remains irreplaceable, AI-driven algorithms complement physician knowledge by identifying patterns in medical imaging, lab results, and patient histories. AI can detect early signs of diseases such as cancer, heart disease, and neurological conditions, leading to earlier interventions and improved patient outcomes.

Streamline Clinical Workflows 

AI can automate routine administrative tasks like patient triage, scheduling, and medical record management. Reducing administrative burdens allows healthcare providers to focus on direct patient care, improving efficiency and reducing burnout among medical professionals.

Enable Personalized Treatment Plans 

AI analysis of genetic data and patient history creates the opportunity for tailored treatment plans that would otherwise take doctors hours of time and guesswork to accomplish. 

This personalized approach enhances patient outcomes by ensuring that interventions are based on an individual's unique health profile rather than generalized protocols.

While these examples of predictive analytics in healthcare present an ideal of the potential for your practice, it is important to consider the current state of your particular organization or healthcare practice before diving fully into a predictive analytics approach. 

Using AI throughout your organization requires a comprehensive understanding of the challenges outlined above, as well as an understanding of data infrastructure, technological capabilities, and your team’s capacity to employ AI. 

The Future of Predictive Analytics in Healthcare 

Proactive Care Management

Predictive analytics not only support diagnostics but also enhance ongoing patient care. AI models can predict patient deterioration, optimize chronic disease management, and even prevent hospital readmissions by recommending proactive interventions.

Importance of Data Governance 

With the continuous influx of healthcare data, organizations need to prioritize robust data governance strategies. Effective data governance ensures:

• Security: Protection of sensitive patient data through compliance with regulations like HIPAA.
• Reliability: The ability to trust data for decision-making.
• Accessibility: Ensuring authorized personnel can efficiently retrieve and use data.
• Value: Maximizing the usefulness of data for clinical and operational improvements.

Ethical Considerations and Patient Privacy

AI is often described as the "wild west" of modern technology due to its rapid evolution and lack of standardized regulations. One of the primary challenges in healthcare is ensuring that AI solutions comply with ethical standards and patient privacy laws.

Healthcare organizations must develop comprehensive AI governance frameworks to:

• Ensure responsible data access and prevent unauthorized use of sensitive information.
• Maintain transparency in AI-driven decision-making processes.
• Address biases that may exist in AI models to promote equitable patient care.

Proactively addressing these concerns enables healthcare organizations to integrate AI responsibly so that technology serves to enhance—rather than compromise—patient care.

Adopting AI-Driven Solutions for Better Patient Care 

AI-powered predictive analytics is transforming healthcare by improving diagnostics, optimizing workflows, and enabling personalized treatment plans. However, successful implementation requires:

• A well-defined data governance strategy.
• A clear understanding of organizational readiness for AI adoption.
• Collaboration between technology teams, medical professionals, and leadership to ensure seamless integration.

If your organization is exploring AI and predictive analytics, Kenway’s team of experts can help you navigate the complexities of data governance and AI implementation. Contact us today to learn how AI-driven solutions can elevate patient care at your organization.

FAQs

What is AI predictive analytics in healthcare?

AI predictive analytics in healthcare refers to the use of artificial intelligence and machine learning to analyze medical data and predict future health outcomes. 

What is an example of AI predictive analytics in healthcare?

One example of this tool is identifying patients who may be at high risk for developing an infection after a surgery. AI predictive analytics can evaluate patient vitals, lab results, and historical patient data to alert medical teams of early infection warning signs, which allows for quicker intervention and improved patient outcomes. 

How is AI being used in healthcare?

From improving operational processes for administrative healthcare teams to aiding physicians in diagnostic readings, AI is highly useful in a variety of healthcare settings. 

What are the benefits of AI predictive analytics in healthcare?

AI predictive analytics in healthcare provide benefits for patients who may receive early diagnoses and intervention treatments, and for doctors who benefit from reduced manual workloads.

The post How AI and Predictive Analytics Are Transforming Patient Care at Healthcare Organizations appeared first on Kenway Consulting: Management & Technology Consulting.

Healthcare System Integration: Overcoming Challenges & Improving Interoperability 3 Apr 2025, 6:34 pm

In healthcare, the business of treating patients, preventing disease, and educating the public is supported by an intricate web of systems, processes, and people. At the heart of this complex framework lies technology, which plays a critical role in facilitating healthcare delivery. However, while technology is indispensable in today’s healthcare landscape, it is not the primary focus of healthcare organizations. The primary business is care – technology merely serves as the vehicle for delivering it. And this is where the challenge lies: healthcare organizations often find themselves ill-equipped to manage the complexity of system integration and the seamless flow of data that is crucial for effective care delivery. 

System integration in healthcare is not just a technological challenge but a multifaceted problem that involves data interoperability, privacy concerns, organizational resistance, and data quality issues. To address these challenges, healthcare organizations must recognize the nuances of the healthcare ecosystem and the role that data and technology play in it. Only then can they begin to leverage technology to its full potential. 

The Complexities of Healthcare System Integration 

The complexity of healthcare systems cannot be overstated. Healthcare is a multi-faceted industry where various stakeholders—pharmacies, labs, urgent care centers, medical providers, and inpatient care units—are all involved in the patient care continuum. Each of these entities operates its own systems that collect and store patient data, but these systems often do not communicate seamlessly with one another. The result is a fragmented, siloed view of patient health, which can impede clinical decision-making and impact patient outcomes. 

To complicate matters, healthcare organizations are often dealing with legacy systems, outdated technology, and pre-built systems from multiple vendors. While these off-the-shelf solutions are designed to provide a comprehensive suite of features, they are not always compatible with one another. Data interoperability standards like HL7 or FHIR  exist, but their adoption and interpretation vary across systems, creating significant hurdles in achieving seamless integration. The inability to connect disparate systems is a challenge that healthcare organizations must confront if they are to unlock the full potential of their data. 

Data Privacy and Regulatory Concerns 

Another significant challenge that healthcare organizations face in system integration is the regulatory landscape. Data privacy regulations like HIPAA (Health Insurance Portability and Accountability Act) in the U.S. and GDPR (General Data Protection Regulation) in Europe have stringent requirements for the storage, handling, and sharing of patient data. These regulations are designed to protect patient confidentiality and ensure the ethical use of sensitive healthcare information. 

However, the very regulations that are meant to safeguard patient data create an additional layer of complexity in system integration. Privacy concerns often lead to the siloing of data across various healthcare departments. For example, patient information may be stored separately in pharmacy, lab, urgent care, and inpatient care systems. This fragmentation of data directly conflicts with the need to unify patient records for a more comprehensive and accurate view of their health. This siloing of data prevents healthcare organizations from achieving the data integration that is necessary for better decision-making and improved patient care. 

Resistance to Change and Adoption Challenges 

In healthcare, the human element is just as important as the technological one. Healthcare professionals are at the forefront of patient care, and any change to their workflow can create significant resistance. The fast-paced environment of healthcare, where patient flow and efficiency are critical, often leads to reluctance in adopting new systems or processes. This resistance to change can delay or even derail healthcare system integration initiatives. 

In addition, healthcare organizations face the challenge of training staff to use new technologies effectively. This training is essential, as even the most sophisticated systems are only as effective as the people using them. The pressure to move patients efficiently through care, coupled with concerns about new systems disrupting established workflows, can make healthcare users suspicious of new technologies. Overcoming this resistance requires a strong focus on change management and the involvement of healthcare professionals in the decision-making process. 

Data Quality and Governance 

Perhaps one of the most significant challenges in healthcare system integration is ensuring the quality and consistency of data. Healthcare data is critical in driving clinical insights and informing treatment decisions, but it is often incomplete, inconsistent, or inaccurate. The fast-paced nature of patient care makes it difficult to ensure that all data is accurately and consistently documented. This lack of standardization can result in fragmented patient records, which complicates the integration process. 

Furthermore, data governance—an essential component of ensuring data quality—is often not a priority in healthcare organizations. Without proper data governance policies, departments within an organization may pursue their own agendas, leading to miscommunication and the creation of data silos. The lack of centralized oversight results in poor data quality, which ultimately undermines the effectiveness of system integration. 

Effective data governance is crucial to the success of healthcare system integration. It involves creating clear guidelines for data management, including how data should be collected, stored, shared, and protected. A robust data governance framework ensures that data is of high quality and that it can be effectively used to improve patient care. Healthcare organizations must make data governance a priority if they want to overcome integration challenges and realize the benefits of seamless data flow. 

Leveraging Technology to Improve System Integration 

Despite the challenges, there are opportunities to leverage technology to improve system integration in healthcare. One approach is to use pre-built systems from vendors that specialize in healthcare technology. These off-the-shelf solutions are designed to deliver the essential capabilities that healthcare organizations need, such as electronic health records (EHR), pharmacy management, and lab information systems. However, as mentioned earlier, these systems may not always be interoperable with one another, so it is important to select solutions that adhere to common data standards and are designed with integration in mind. 

Another way to improve integration is through the adoption of modern technologies such as cloud-based solutions, application programming interfaces (APIs), and middleware platforms. These technologies allow for greater flexibility in connecting disparate systems and improving data sharing across the healthcare ecosystem. By adopting these tools, healthcare organizations can create a more interconnected environment where data flows seamlessly across departments and between systems. 

Moreover, healthcare organizations must recognize that integration is not a one-time project but an ongoing process. As new technologies emerge and data grows in volume and complexity, system integration will require continuous monitoring, adaptation, and improvement. A commitment to innovation and a proactive approach to system integration will help healthcare organizations stay ahead of the curve and provide better care to patients. 

Conclusion 

System integration in healthcare is a complex and challenging endeavor that requires overcoming significant technological, regulatory, and organizational hurdles. Healthcare organizations must address issues such as data interoperability, privacy concerns, resistance to change, and data quality to deliver healthcare with excellence. By leveraging modern technologies and integration patterns, implementing effective data governance practices, fostering collaboration between stakeholders and vendors, and intentionally managing change with staff, healthcare organizations can improve the flow of critical information and ultimately enhance patient care. 

Our experience within healthcare systems design, implementation, and integration can help you overcome these challenges and elevate the efficiency and effectiveness of your care delivery. Connect with the experts at Kenway to discuss more. 

FAQs: 

1. What is healthcare system integration, and why is it important? 

Healthcare system integration is the process of connecting different healthcare technologies, departments, and data sources to enable seamless information sharing. It’s essential for improving patient outcomes, reducing redundancies, and delivering more coordinated care. 

2. What are the biggest challenges in healthcare system integration? 

The main challenges include data interoperability issues, outdated or incompatible systems, regulatory and privacy concerns (like HIPAA compliance), resistance to workflow changes, and poor data quality or governance. 

3. How does data interoperability impact patient care? 

Without interoperability, systems can’t communicate effectively, leading to incomplete or fragmented patient records. This can delay care, increase errors, and hinder clinical decision-making. 

4. What are the best practices for ensuring HIPAA compliance in system integration? 

Best practices include implementing access controls, encrypting data, conducting regular audits, clearly defining data governance policies, and ensuring vendors follow HIPAA-aligned standards during integration. 

5. How can APIs and cloud-based solutions improve healthcare data integration? 

APIs and cloud solutions enable flexible, scalable, and secure connections between disparate systems. They support real-time data sharing and reduce silos, making integration faster and more adaptable to evolving healthcare needs.

The post Healthcare System Integration: Overcoming Challenges & Improving Interoperability appeared first on Kenway Consulting: Management & Technology Consulting.

Manufacturing Cloud vs Core Salesforce - Which is Right for you 27 Mar 2025, 1:30 pm

Choosing the Right Salesforce Solution: Sales/Service Cloud vs. Manufacturing Cloud 

As a manufacturer navigating Salesforce solutions, one of the key decisions is whether to implement core Salesforce product (Sales / Service Cloud) or Manufacturing Cloud. This choice can significantly impact your business operations, cost structure, and long-term scalability. Below, we explore different scenarios to help guide your decision-making process. 

Finding the Best Fit for Your Business 

Scenario 1: Using Manufacturing Cloud as Both an ERP and CRM 

For small to mid-sized businesses (SMB) or growth-stage companies, Manufacturing Cloud has the potential to serve as both an Enterprise Resource Planning (ERP) system and a Customer Relationship Management (CRM) platform. This can result in cost savings by eliminating the need for separate systems, reducing integration complexity, and lowering overall IT maintenance efforts. 

However, this approach is typically viable only for companies with simpler operational needs. If your business requires deep ERP functionality, this may not be a sustainable long-term solution.  Understanding the depth of your operational needs will help determine if this approach is right for you. 

Scenario 2: Integrating Manufacturing Cloud with an ERP 

For most manufacturers, an ERP system remains a core part of operations. In this case, the decision becomes whether Manufacturing Cloud is a necessary addition to your ERP. Here are 3 key indicators that Manufacturing Cloud might be the right fit: 

1. Complex Partner Relationships and High-Touch Communication 

If your business model involves frequent interactions with partners, distributors, or suppliers, Manufacturing Cloud can enhance visibility and improve collaboration with advanced tools. Some factors to consider: 

• High volume of partner communications and transactions (such as managing warranties/claims and asset tracking) 

• Collaborative selling and account planning 

• Need for sharing business insights, demand signals, and supply chain data 

2. Expecting Significant Custom Development 

Not all business processes are the same. Some require significant customization, automation, and logic. If you’re anticipating heavy customization to your CRM, Manufacturing Cloud’s robust suite of automation tools (OmniStudio, Business Rules Engine) can save you tens of thousands of dollars in development and maintenance. This can look like: 

• Partner portals requiring advanced collaboration features or UI/UX requirements 

• Complex workflows and automation 

• Need for an intricate rule engine to manage pricing, quoting, or supply chain logic 

3. Heavy Reliance on Sales Forecasting 

Some organizations leverage statistical models to predict supply/demand. But for many manufacturers, this is only half the story. Many organizations rely on their sales teams and partners to extend their demand signal and provide meaningful inputs into their planning process. These individuals and entities have invaluable knowledge and insights that need to be considered. Manufacturing Cloud offers robust forecasting tools, specifically designed for manufacturers, that differ from traditional statistical forecasts. If your business relies heavily on sales-based forecasting to drive production and inventory planning, Manufacturing Cloud provides a more tailored, complete, and user-friendly solution than what can be accomplished with an ERP + Sales Cloud.  

In addition to these 3 key indicators, more generally speaking, a major portion of your business depends on a capability that Manufacturing Cloud provides out-of-the-box, it may be worth adopting early.  

When to Adopt Manufacturing Cloud 

If Manufacturing Cloud is a strong fit and your organization has an immediate need for its capabilities, the best time to adopt it is from the beginning. Waiting too long can lead to unnecessary custom development, technical debt, and costly rework. 

A frequent scenario involves organizations starting with Sales and Service Cloud, only to later realize they’ve custom-built many of the features that Manufacturing Cloud already provides. While this phased approach can work, it can also lead to inefficiencies and redundant development efforts. 

A Purposeful Approach to Staged Adoption 

If Manufacturing Cloud is a fit but you don’t need its full capabilities immediately, one strategy is to start with Sales Cloud licenses and upgrade when ready. This approach allows users to: 

• Get comfortable with Salesforce core functionality (Accounts, Contacts, Leads, Opportunities) 

• Reduce change management strain 

• Defer costs until necessary 

However, this method requires discipline. It’s easy to get started with Sales Cloud and never look back. If you choose this path, work with your Salesforce Account Executive to schedule a structured upgrade plan in advance. 

Making the Right Investment for the Future 

If you’re uncertain about which path to take, pause and assess your business needs against Manufacturing Cloud’s capabilities. Understanding your long-term goals, current processes, and required functionality will help ensure the right investment for your business’s future. Kenway is here to help. Reach out to one of our trusted experts today. 

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Kenway Consulting Announces New Partnership with Databricks 20 Mar 2025, 2:00 pm

CHICAGO, Thursday, March 20, 2025 – Kenway Consulting today announced a new partnership with Databricks, the data and AI company, that will help deliver cutting-edge data engineering, machine learning (ML), and real-time analytics solutions to clients.

As a Registered Databricks Partner, Kenway will leverage Databricks’ Data Intelligence Platform to optimize data ecosystems – from strategy and architecture, to engineering and modeling – allowing clients to unlock deeper insights and maximize the value of their data.

“Our partnership with Databricks enhances our ability to help organizations optimize their data to align with their business goals and strategy,” said Byron Leonsins, Data and Analytics Practice Co-Lead, Kenway Consulting. “Databricks’ best-in-class approach to unifying data, AI, and analytics allows us to more effectively help clients eliminate silos, optimize governance, and accelerate digital transformation.”

The Databricks Data Intelligence Platform democratizes access to analytics and intelligent applications by marrying customers’ data with powerful AI models tuned to their business's unique characteristics. The platform is built on a lakehouse foundation of open data formats and open governance to ensure that all data is completely within the customers’ control.

Kenway's Data and Analytics practice offers comprehensive services to navigate the entire data lifecycle. The company designs optimal data strategies, implements effective architectures and data engineering patterns, and ensures quality and governance to deliver business value through data products like dashboards, reports, integrations, and ML models.

To learn more about Kenway’s Data & Analytics practice,
visit https://www.kenwayconsulting.com/data-and-analytics

Kenway Consulting Announces New Partnership with Databricks

# # #

About Kenway Consulting

Kenway Consulting is a management and technology consulting firm whose entire reason for existence is to help companies and its employees. Founded in 2004 on the principles of being good and being truthful, Kenway’s set of Guiding Principles steers each employee’s decision-making process and centers on integrity, quality, value and respect. The company focuses on the means and not the outcomes, always in line with these Guiding Principles, and always with integrity as its cornerstone. Kenway strives to provide all clients with unmatched quality and service, and specializes in customized business solutions using its Application Development, Artificial Intelligence, Contact Center Solutions, Data and Analytics, Data Compliance and Privacy, Program and Product Management, and Salesforce practices.

The post Kenway Consulting Announces New Partnership with Databricks appeared first on Kenway Consulting: Management & Technology Consulting.

Kenway Consulting Named to Forbes' 2025 List of America's Best Management Consulting Firms 18 Mar 2025, 7:12 pm

CHICAGO, Tuesday, March 18, 2025 – Kenway Consulting today announced that it has been named to the 2025 Forbes list of America’s Best Management Consulting Firms. This is the third year in a row Kenway has been honored with this prestigious award presented by Forbes and Statista Inc., the world’s leading statistics portal and industry ranking provider.

The list was determined through a two-step, online methodology of both industry executives and clients. Step one involved a peer-to-peer survey of more than 1,200 partners and executives at management consulting firms who recommended consultancies in 16 client sectors and 17 functional areas. Step two involved a survey of more than 1,150 clients who were prompted to evaluate management consultancies they worked with in the last four years.

Respondents were asked with which industries and functional areas they had collaborated, and were presented with a unique shortlist per industry or functional area based on last year’s results and the recommendations collected from the expert survey. Self-recommendations were not accepted or included in the results. The final list highlights 191 companies.

“Kenway was founded to help people, and our goal since the beginning has been to offer all businesses a customized, white-glove experience and best-value solution that works for their unique needs. We are honored that our clients and industry peers recognize this, and have once again named us to this prestigious list,” said Matt Kueker, CEO, Kenway Consulting. 

Kenway Consulting Named to Forbes' 2025 List of America's Best Management Consulting Firms

# # #

About Kenway Consulting

Kenway Consulting is a management and technology consulting firm whose entire reason for existence is to help companies and its employees. Founded in 2004 on the principles of being good and being truthful, Kenway’s set of Guiding Principles steers each employee’s decision-making process and centers on integrity, quality, value and respect. The company focuses on the means and not the outcomes, always in line with these Guiding Principles, and always with integrity as its cornerstone. Kenway strives to provide all clients with unmatched quality and service, and specializes in the areas of Technology Solution Delivery, Enterprise Program Leadership, and Information Insight.

The post Kenway Consulting Named to Forbes' 2025 List of America's Best Management Consulting Firms appeared first on Kenway Consulting: Management & Technology Consulting.

Finding Your Perfect Consulting Partner: When "Better" Isn't Better 17 Mar 2025, 2:26 pm

The Consulting Selection Challenge

You've been tasked with finding a consulting firm for your organization's critical initiative. As you research potential partners, every firm claims to be "the best" or "better" than their competitors. Yet after countless meetings and proposals, you're left wondering: Why is it so difficult to find the right match for our specific needs?

The challenge isn't finding a "better" consulting firm—it's finding the right one. Choosing the wrong partner, even one with an impressive reputation, can lead to misaligned expectations, wasted resources, and solutions that don't address your actual problems.

A Different Approach to Selecting Your Consulting Partner

At Kenway Consulting, we understand this challenge because we've guided countless organizations through it. We've seen firsthand how the traditional approach to selecting consulting partners often leads to frustration and suboptimal results.

We believe effective consulting relationships are built on alignment and trust, not superficial comparisons or generic excellence claims. When you work with us, we serve as trusted advisors who:

• Prioritize understanding before solution-building
• Provide honest assessments, even when it means recommending someone else
• Focus on long-term partnership value over short-term contracts

Your Path to Finding the Right Consulting Fit

Finding your ideal consulting partner requires looking beyond marketing claims and surface-level capabilities. Here's a practical framework to guide your selection process:

1. Assess Deep Understanding: Does the firm take time to comprehend your challenges beyond symptoms to address root causes?
2. Evaluate Transparency: Are they honest about their strengths and limitations rather than claiming to excel at everything?
3. Check Cultural Alignment: Do their values and working style complement yours? This often determines success more than technical expertise alone.
4. Confirm Long-term Focus: Are they invested in your sustainable success or just securing the next contract?

How Kenway Creates the Right Partnership

Our approach is guided by our Guiding Principles that ensure we create the right fit with clients:

1. Integrity

We view every engagement through the lens of "what's best for the client"—even when that means recommending another firm. One financial services organization approached us for a specialized technical implementation outside our wheelhouse. Rather than forcing a fit, we connected them with a more suitable partner, building trust that later led to collaboration in areas where our expertise was the perfect match.

2. Treat Each Client Uniquely

Every organization has its own culture, strengths, and challenges. Our tailored approach means we adapt to your environment rather than forcing you into our methodologies. We become an extension of your team, offering guidance that respects your organization's unique character while providing the external perspective needed to overcome obstacles.

3. Value and Quality

We deliver premium consulting and focus on efficient, effective solutions. By emphasizing financial stewardship, we ensure that our clients receive exceptional value without unnecessary costs. Every decision is scrutinized to maximize return on investment, ensuring that our solutions are both effective and sustainable. Every engagement includes a clear knowledge transition plan, ensuring your team gains the skills and insights to maintain momentum long after our involvement ends.

What Success Looks Like

Organizations that find the right consulting fit experience:

• Solutions that address root causes, not just symptoms
• Smoother implementation with less resistance
• Sustainable results that continue delivering value
• Teams empowered with new capabilities and confidence

Conversely, those who select based solely on reputation or general "better than" claims often face:

• Generic solutions that don't fit their specific context
• Higher costs with lower return on investment
• Implementation challenges due to cultural misalignment
• Dependency rather than empowerment

Explore our success stories to see how finding the right fit has transformed challenges into opportunities for organizations like yours.

Take the First Step Toward the Right Partnership

The best way to determine if Kenway is the right fit for your organization is through conversation. Let's discuss your challenges and goals with no obligation—just an honest assessment of how we might help.

Schedule a consultation to start the conversation about your specific needs and discover if we're the right partner for your journey.

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At Kenway, we're not focused on being the biggest or claiming to be the best—we aim to be the right partner for organizations that align with our principles of integrity, customization, and value.

The post Finding Your Perfect Consulting Partner: When "Better" Isn't Better appeared first on Kenway Consulting: Management & Technology Consulting.

Understanding the Modern Data Stack: Architecture, Tools, and Benefits 11 Mar 2025, 2:11 pm

Data is the cornerstone of today’s digital enterprises, enabling solutions to complex challenges and driving innovation. Yet, many organizations struggle to fully capitalize on their data. A staggering 73% of data goes unused, hindered by silos, poor quality, and outdated systems. To address these challenges, businesses are leveraging a revised, modern data stack. 

The modern data stack is an ecosystem of cutting-edge tools designed to manage the growing volume, velocity, and variety of data while meeting the increasing demands for scalability, flexibility, and actionable insights. By leveraging a modern data stack, organizations can align people, processes, and technology to collect and store data, analyze it, automate workflows, and scale to support the growing number of analytics use cases over time.

In this guide, we explore the architecture, tools, and benefits of the modern data stack, highlighting how it outperforms traditional approaches.

What Sets the Modern Data Stack Apart?

The transition from traditional to modern data stacks represents a paradigm shift in how data is managed and leveraged. Traditional data stacks, built for static and siloed environments, are ill-equipped to handle today’s complex data landscapes. Here’s how modern data stacks stand out:

Key Differentiators Between Traditional and Modern Data Stacks:

Traditional data stacks were built for an outmoded approach to data. They were highly customized and required extensive in-house resources to maintain. Because of these limitations, traditional data stacks could not handle the complexity or volume of data needed to support modern data and analytics.

Enter the modern data stack. Modern data stacks set themselves apart by being built for today’s data challenges and future data demands. There are several factors that separate traditional and modern data stacks

1. Cloud-Native Architecture:
   • Traditional stacks often rely on on-premise infrastructure, leading to inefficiencies and scalability issues. Modern data stack architecture leverages cloud platforms, offering cost efficiency, flexibility, and near-limitless scalability.
2. Decentralized Data Ownership:
   • Within the traditional approach to data, centralized teams often controlled all aspects of the data lifecycle from sourcing, transformation, and distribution. This approach often led to bottlenecks, preventing users from obtaining their core data assets efficiently. 
   • Modern data teams, however, are focused on curating high-quality, standardized data outputs which are readily available to business users. Business users will work in conjunction with the data team to produce high-value data products
3. Streamlined Integration:
   • Modern data stacks incorporate sophisticated ETL/ELT tools with pre-built connectors, simplifying data ingestion and reducing the need for costly, time-consuming development.
4. Advanced Analytics and AI Integration:
   • While traditional stacks struggle with advanced technologies, modern data stacks are purpose-built to support machine learning, AI, and predictive analytics.
5. Proactive Data Governance:
   • Modern data stacks embed governance at every stage, ensuring data integrity, compliance, and security from the ground up.
6. Storage
   • Modern data stacks leverage data lakes and/or lake houses, which provide greater flexibility in data storage and analysis. Data lakes allow you to store raw data, and are built to provide flexible, scalable storage as your data volumes grow.
7. Security and Compliance
   • Modern stacks often include advanced, integrated tools for data access, security, and compliance.

Benefits of the Modern Data Stack

The modern data stack enables businesses to keep up with the velocity and scale of modern data analytics while also reducing costs and the burden on internal resources.

1. Enhanced Data Capabilities: Modern data analytics requires real-time insights. With modern data stacks, users can make timely, on-the-spot decisions with real-time data. In situations where speed and accuracy are critical, such as customer-facing interactions, these capabilities can be a key competitive differentiator.

As companies incorporate advanced analytics tools like machine learning and artificial intelligence, modern data analytics enables them to implement and use those tools effectively. This supports more accurate predictions, better understanding of customer behaviors, and enhanced operational efficiency.

2. Refined Processes: Modern data stacks incorporate data governance and support compliance measures to ensure that data is accessible, accurate, consistent, and reliable. These practices also reduce risk by supporting processes to ensure compliance with regulations like HIPAA and General Data Protection Regulations (GDPR).

3. Empowered Data-Driven Processes: A modern data analytics stack can support self-service tools so users can self-serve without needing to rely heavily on IT teams or waste valuable time collecting and normalizing data.

Data-driven collaboration is also increasingly important for compliance. With the Securities Exchange Commission (SEC) and the European Union making progress to enact standards for Environmental, Sustainability, and Governance (ESG) compliance, companies need to ensure that they have the right data to plan, execute, and report on a wide variety of activities. Modern data analytics tools incorporate governance practices that allow companies to make data-driven decisions and share their results with stakeholders and regulators with confidence.

Unlock the Potential of Your Modern Data Stack

As data continues to grow in importance, adopting a modern data stack is no longer optional—it’s essential. The right modern data stack architecture and tools empower organizations to innovate, scale, and stay competitive in a data-driven world.

At Kenway, we specialize in helping businesses design and implement modern data stacks tailored to their unique needs. From optimizing data pipelines to enhancing governance and enabling advanced analytics, we ensure your data ecosystem is ready for today’s challenges and tomorrow’s opportunities.

Overcome data silos and inefficiencies. See how we helped an asset management firm overcome data silos and inefficiencies to achieve a 360-degree customer view. With our expertise, the firm built a modern data warehouse and platform that delivers real-time insights and seamless access to critical information. Today, the firm’s employees can easily access all the information they need on prospective and current clients from a single source.

Ready to transform your data strategy? Contact us today to explore how a modern data stack can revolutionize your business.

Modern Data Stack FAQs

What is the modern data stack?

The modern data stack is a cloud-based ecosystem of tools designed to simplify data integration, storage, governance, and analysis. It enables businesses to manage data efficiently, scale seamlessly, and extract meaningful insights to drive decision-making

How to build a modern data stack

Building a modern data stack involves:

1. Defining Goals: Identify your business objectives and data needs.
2. Selecting Tools: Choose scalable, cloud-based tools for ingestion (ETL/ELT), storage (data lakes/warehouses), and analytics.
3. Integrating Governance: Incorporate data governance, security, and compliance from the start.
4. Enabling Collaboration: Use self-service analytics tools to empower teams.
5. Testing & Scaling: Continuously test performance and scale as your needs grow.

What should I look for in each component of the modern data stack?

Here’s what to prioritize for each component of the modern data stack:

1. Data Sources: Broad compatibility with structured and unstructured data formats.
2. Data Ingestion Tools: Pre-built connectors, scalability, and support for ETL/ELT processes.
3. Data Storage: Flexible, cloud-based solutions (e.g., data lakes/warehouses) with scalability.
4. Governance & Security Tools: Built-in access controls, data lineage, and compliance features.
5. Analytics Tools: User-friendly, self-service options for advanced analytics and real-time insights.

What is the modern data stack architecture?

Modern data stack architecture leverages cloud-native platforms to ensure scalability, flexibility, and efficiency. It incorporates tools for data ingestion, storage, governance, and analytics, enabling seamless end-to-end workflows.

What are modern data stack tools?

Modern data stack tools include ETL/ELT solutions (e.g., Fivetran), cloud data warehouses (e.g., Snowflake), BI tools (e.g., Looker), and governance platforms (e.g., Alation).

How does a modern data warehouse fit into the stack?

A modern data warehouse serves as the central repository, enabling fast, scalable analytics and real-time data accessibility. It supports structured and semi-structured data, making it a cornerstone of the modern data stack.

What challenges arise when building a modern data stack?

Common challenges include selecting the right tools from a crowded market, ensuring interoperability, and aligning people and processes for maximum efficiency.

How can AI and ML enhance a modern data stack?

AI and ML tools automate analytics, generate insights, and enable predictive modeling, enhancing decision-making capabilities and operational efficiency.

The post Understanding the Modern Data Stack: Architecture, Tools, and Benefits appeared first on Kenway Consulting: Management & Technology Consulting.

Contact Center AI: What You Need to Know 4 Mar 2025, 3:35 pm

AI has made huge strides in nearly every industry, and this impact has been keenly felt in contact centers. Contact center artificial intelligence (also known as CCAI) is one of the industries where AI has made significant waves and is revolutionizing productivity and customer understanding and satisfaction.

What is Contact Center AI (CCAI)? 

Contact Center AI is the use of artificial intelligence to automate and streamline contact center operations for increased efficiency and cost savings. 

Contact Centers are complex operations and the backbone of how many companies keep a pulse on their customers’ satisfaction levels and pain points. A contact center that runs smoothly is an indicator of the overall health of your business. 

Contact centers are crucial for so many organizations, but a traditional contact center model requires extensive staffing and availability for 24/7 call center or chat responses. What results from these extensive needs is a contact center solution that puts your company in touch with its customers, but that depletes resources at the same time. 

CCAI solves this problem by using AI to monitor calls, run reports, and automate chat or call interactions

Types of Contact Center AI Solutions

Contact center AI doesn’t refer to a one-size-fits-all solution, but rather a multifaceted set of capabilities that can be tailored to your organization’s specific needs. 

The main types of contact center AI solutions are: 

Reporting

Contact center reporting is used during customer interactions to gather data that is then analyzed and delivered to your organization for insights into customer trends and metrics like average call time, abandonment rate, and CSAT scores. 

Speech Analytics

While contact center reporting is focused on the hard data surrounding call center metrics, speech analytics is an AI capability that analyzes customer interactions through the lens of language patterns, common words and phrases, and even a customer’s emotional state at different points in the conversation. Speech analytics is a valuable tool that takes call center reporting a step further by understanding customer emotions and behaviors.

Intelligent Virtual Agent (IVA)

An IVA is a voice or chat service that uses AI technology to simulate conversations by mimicking human speech patterns to find solutions for customers in real time using internal knowledge. Many contact centers use this helpful service to free up time for customer service representatives and provide a streamlined experience for callers.

Testing 

While contact center AI provides an entire host of solutions, it also introduces new use cases that need quality assurance testing. This testing can be automated and used to simulate a variety of possible contact center customer interactions to ensure the AI is enabling a smooth process from start to finish of every call.

Contact Center AI Recommendations 

Contact Center AI solutions are necessary for contact centers to stay relevant and successful, and every variety of CCAI is useful in unique ways. 

Some of Kenway’s top tips for implementing or refreshing CCAI in your organization are:

• Consistent monitoring and training to prevent AI hallucination and ensure data governance. 
• Capitalizing on your CCAI reporting and speech analysis capabilities. The insights your CCAI solution provides for you will only be as useful as the changes you implement based on those reports. 
• Consider implementing conversational AI for your contact center solution if you haven’t already. This AI capability will help your organization keep customers happier while keeping costs down and is a great alternative to IVR models, which can often leave callers frustrated with a lack of specialized service.
• Automated testing like Cyara’s Botium uses AI to test AI. This ensures seamless performance without adding complicated manual testing to your to-do list.

Implementing AI Throughout Your Organization 

AI can feel overwhelming, especially if your organization has never used contact center AI. But the benefits of automated reporting, speech analytics, and increased productivity far outweigh the unknown that comes with implementing CCAI.

Focus on starting small, maintaining data security and governance throughout your implementation, and educating your team to use AI to their advantage. 

If your organization wants extra support in the process of implementing CCAI, the Kenway team would love to help. Reach out today to learn more about how your organization can unlock the potential of your contact center!

FAQs

How is artificial intelligence transforming contact centers? 

AI has transformed contact centers from call centers with capabilities limited by staff availability to dynamic contact centers that can field high call volume, answer customer questions, direct callers to proper experts, and provide expert insights all at once. 

What is CCAI?

CCAI is the abbreviated term for contact center artificial intelligence, which is the use of AI to aid in contact center reporting, data collection, and customer service. 

Another related but different term is Google CCAI, is a Google owned cloud-based platform that uses AI to help organizations with customer service and operational efficiency.

The post Contact Center AI: What You Need to Know appeared first on Kenway Consulting: Management & Technology Consulting.

ERP Modernization in Private Equity: A Value Creation Framework  27 Feb 2025, 6:22 pm

For lower middle market (LMM) private equity firms, technology modernization is more than an IT upgrade—it’s a direct path to operational efficiency, EBITDA growth, and higher exit valuations. Yet, many portfolio companies still rely on outdated ERP systems, fragmented data environments, and manual workflows that limit scalability, slow operations, and introduce avoidable risk. 

Despite its importance, technology due diligence in LMM deals often lacks rigor—focusing on surface-level IT assessments while overlooking how legacy systems and inefficient processes impact portfolio performance and resilience. This oversight can delay integration synergies, increase working capital inefficiencies, and create unforeseen risks that erode value post-acquisition. 

For PE firms that take a proactive approach, closing this technology gap represents an untapped opportunity to enhance portfolio performance, improve visibility into operations, and mitigate risks that could otherwise disrupt value creation plans. 

Recent research highlights the measurable impact of ERP modernization in driving operational efficiencies across PE-backed companies. According to  KPMG’s 2023 CEO Survey, of PE-backed companies that implemented modern ERP systems: 

• 95% reported tangible operational improvements 

• 40% achieved reductions in IT costs 

• 38% decreased their inventory levels 

• 35% shortened their cycle times 

At Kenway Consulting, we've developed a first principles framework for ERP modernization and digital transformation, designed to help PE firms systematically unlock value from technology investments. 

The Problem: Technical Debt and Its Hidden Costs 

When evaluating acquisitions or portfolio performance, private equity firms frequently uncover technology environments plagued by inefficiencies, including: 

• Disparate systems managing core business functions, leading to data inconsistencies and reporting gaps. 

• Reliance on tribal knowledge and manual workflows, increasing business continuity risk. 

• Legacy, on-premise ERP systems requiring specialized maintenance and costly workarounds that limit agility. 

• Excel-dependent processes for financial reporting, inventory management, and production planning—hindering scalability and automation. 

• Limited real-time visibility into operations, making it difficult to support data-driven decision-making. 

• Insufficient change management and user adoption programs, leading to resistance that undermines technology investments. 

These technical inefficiencies directly erode EBITDA and enterprise value by: 

• Driving up labor costs and slowing revenue realization due to operational inefficiencies. 

• Creating business continuity vulnerabilities through reliance on outdated or unsupported systems. 

• Restricting scalability—companies with fragmented ERP and data environments struggle to integrate acquisitions, optimize supply chains, and expand into new markets. 

These inefficiencies are often symptoms of accumulated technical debt—a silent killer of technology modernization efforts. According to McKinsey, technical debt accounts for 40% of the average IT balance sheet, absorbing capital that could otherwise fuel innovation and growth. Companies spend an additional 10% to 20% on top of project costs just to manage existing tech debt, further diverting resources from strategic priorities. Organizations with high levels of technical debt are also 40% more likely to experience failed or incomplete IT modernization initiatives, directly impacting scalability and competitiveness. 

Without addressing these barriers, PE firms risk leaving value on the table. Poorly integrated legacy systems and inefficient processes reduce portfolio attractiveness at exit, hinder synergy realization, and slow down post-acquisition value creation. 

A First Principles Framework for Technology Modernization 

Rather than treating ERP modernization as a software implementation exercise, LMM private equity firms should approach it from first principles—breaking down the business’s core objectives, processes, and inefficiencies before making technology decisions. 

At Kenway Consulting, we guide PE-backed companies through a structured framework that ensures ERP modernization drives measurable business impact. 

1. Define Clear Business Objectives Before Selecting Technology 

Before evaluating ERP vendors or automation tools, companies must first articulate their business goals and value drivers: 

• Revenue growth targets—How can technology support a higher transaction volume or improved pricing strategy? 

• Operational efficiency benchmarks—What are the KPIs for reducing manual processes and overhead costs? 

• Customer satisfaction and service metrics—How can technology enable a seamless customer experience? 

• Scalability objectives—What capabilities are needed to integrate acquisitions or expand into new markets? 

2. Strip Away Non-Essential Complexity Before ERP Upgrades 

Many legacy ERP environments have accumulated unnecessary complexity over time, resulting in bloated processes and inefficiencies. Before modernizing, simplify: 

• Eliminate redundant approval workflows that slow down decision-making. 

• Automate financial reconciliation processes to reduce errors and manual labor. 

• Remove legacy procedures that exist only due to system limitations from outdated ERPs. 

• Establish clear data ownership by identifying who is responsible for cleaning and qualifying data—critical for successful ERP migration. 

3. Simplify Before Automating: A Common Pitfall in ERP Modernization 

One of the biggest mistakes PE-backed companies make is automating broken processes instead of fixing them first. ERP systems should not just digitize inefficiencies—they should optimize and streamline business operations before automation is applied. Our approach emphasizes: 

• Map current workflows to identify bottlenecks, redundancies, and inefficiencies before ERP implementation. 

• Redesign processes for scalability and efficiency—removing unnecessary manual interventions before introducing automation. 

• Ensure data integrity and governance to prevent garbage-in, garbage-out scenarios, where automation only exacerbates poor data quality. 

• Create an execution roadmap—defining the sequence of process improvements before ERP implementation ensures smooth adoption and better ROI. 

Beyond process simplification, LMM PE firms often face budget constraints and operational disruption concerns. We develop tailored implementation strategies that can identify high-impact, lower-cost improvements while minimizing business disruption risks based on each portfolio company's specific situation. 

4. Implement Right-Sized Solutions for PE Investment Horizons 

Not every portfolio company needs a full ERP replacement. Some benefit more from incremental improvements, such as custom application development to modernize key workflows, while others require a complete system overhaul. The right approach depends on: 

Scenario	Recommendation	Project Duration	Value Creation Focus
Performance-Constrained Legacy ERP	Targeted upgrades & optimizations	6-12 months	Target high-impact operational bottlenecks Address critical revenue leakage points Prioritize quick-win automation opportunities
Highly Customized, High-Risk Legacy System	Full ERP modernization with cloud-based solution	12-24 months	Build scalable platform for growth Enable seamless add-on integration Enhance data visibility and reporting Reduce operational risk exposure
Multiple Disparate Systems with Poor Integration	System consolidation & process standardization	3-9 months	Streamline system landscape Standardize core processes Improve operational visibility Create foundation for future growth

By aligning ERP modernization strategies with the firm’s investment timeline, PE firms can maximize returns without over-investing in unnecessary complexity. 

Case Study: Technology Modernization in Action 

A mid-market e-commerce wholesaler specializing in specialty products faced significant growth limitations due to antiquated technology and fragmented IT infrastructure. Kenway Consulting conducted a comprehensive IT Platform Assessment to identify modernization opportunities aligned with business objectives. 

Our solution included transitioning from single-resource IT partners to implementing a scalable cloud infrastructure centered around Dynamics 365 and Azure services. This transformation reduced dependency on specialized IT resources while establishing a scalable, extensible foundation for growth. The modernization delivered actionable operational insights that directly impacted margin optimization and topline growth—transforming technology from a back-office function into a strategic value driver. 
Read the full case study here. 

Next Steps: Unlocking Value Through ERP Modernization  

While ERP modernization is complex, PE firms can take immediate action: 

1. Conduct a Portfolio-Wide ERP Health Assessment – Identify which companies have scalable technology and which are at risk due to outdated systems. 

2. Look for Quick Wins in Process Optimization – Identify high-impact improvements that can enhance efficiency without major system overhauls. 

3. Prioritize Modernization Efforts Based on EBITDA Impact – Focus on high-ROI initiatives that improve margins, scalability, and operational resilience. 

Beyond ERP: A Structured Approach to PE Value Creation 
ERP modernization is just one lever for maximizing ROI in private equity. A structured technology strategy across the full investment lifecycle—from pre-acquisition due diligence to post-acquisition modernization—can further enhance EBITDA and portfolio-wide synergies. Learn more about optimizing technology strategies for PE investments here. 

Why Partner with Kenway? 

At Kenway Consulting, we work alongside private equity firms and their portfolio companies to transform ERP modernization from a challenge into a strategic advantage. Here's what sets our approach apart: 

• Vendor-Neutral Technology Expertise: Unlike software vendors that lead with product sales, Kenway provides unbiased guidance—recommending only what your portfolio companies truly need, whether that's a full ERP replacement or targeted enhancements to existing systems. 

• Cross-Practice Approach: Our integrated teams combine ERP implementation expertise with data strategy specialists who ensure proper data governance and migration—critical elements that often determine project success or failure. 

• Validated Selection Methodology: Our structured evaluation framework helps PE firms select technology solutions that align with both operational needs and investment timelines, avoiding costly over-implementation. 

• Proven Track Record in LMM: We understand the unique constraints and opportunities within lower middle market portfolio companies, having successfully guided numerous PE-backed businesses through technology transformations aligned with value creation timelines. 

If your firm is navigating ERP challenges or modernization roadblocks, let's connect. Our team can assess your portfolio's ERP landscape and develop a pragmatic roadmap tailored to your investment strategy. 

Contact us at info@kenwayconsulting.com to start the conversation. 

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