How to Create a Data Strategy Roadmap

Leading a business is demanding. Large enterprises have many stakeholders, high demands, variable and inconsistent market pressures, and layers of complexity. Small to mid-sized businesses may work with less complexity, but the pressures of growth and balancing the strategic and tactical are significant. Then there’s your data. You know there is more available than you’re getting. The promise of a “Data-driven Organization” is further on the horizon. You know that data and its strategic use are crucial for long-term success. 

Suddenly, data management questions and confusion pile up to the point that they can’t be ignored. Instead of quickly sifting through questions and providing a resolution, the business owner realizes they don’t have the answers their team needs about data security and organization, and they’re not sure anyone else does either. 

To make matters worse, this business will realize they have spent years sitting on valuable data instead of using it to leverage effective business growth.

While we hope you have never experienced this situation, the reality is all too common. The best way to prevent this nightmare scenario is to develop a data strategy.

What is a Data Strategy? 

A data strategy is a comprehensive plan that outlines exactly how data should be used and stored, who data can be used by, and what rules are non-negotiable for your company’s data interactions. 

While a data strategy is your company’s source of truth for guidance through data management, developing your company’s data strategy is the most important first step. 

If your business is revamping its existing data strategy or is building one for the first time, keep reading for tips on how to develop a data strategy roadmap that will guide your team through each phase. 

Why Data Strategy Roadmaps Are Important

A company without a clear data strategy risks internal misalignment and confusion, missed opportunities for growth, and potential operational inefficiencies. These challenges can hinder both day-to-day performance and broader strategic goals.

Establishing a data strategy roadmap creates alignment across teams, enabling smarter decision-making, improved efficiency, and a competitive edge. By fully leveraging both internal and external data, your business can build a more comprehensive and resilient foundation for growth.

Steps to Creating a Data Strategy Roadmap

There are several foundational steps involved in creating a data strategy roadmap, and it's important to recognize that this foundation will evolve over time. While it may be tempting to move quickly, taking the time to get each step right is essential. A thoughtful, well-structured roadmap sets the stage for long-term success and ensures your data strategy can grow with your organization’s needs.

Step 1: Define Clear Goals

It is critical that you involve all the necessary stakeholders and implement a system for them to provide feedback and share insights throughout the process. It is important to ensure that the stakeholders are aligned on the following types of questions:

Step 2: Assess Your Current Data Landscape

Now that these foundational questions have been answered, it's time to bring data experts into the conversation—if they weren’t a part of it already—and gain clarity on these questions: 

Step 3: Prioritize Your Initiatives

It may be tempting to dive into all the needs identified during the assessment phase, but a methodical approach—aligned with the high-value initiatives you uncovered—will drive greater alignment and long-term success for your team.

Start by establishing your data strategy roadmap priorities by breaking the effort into key value-driven phases. For example:

Clarifying these priorities helps align your team around meaningful outcomes, supports accurate budgeting, and highlights the technologies needed to deliver long-term impact.

As your project planning progresses, it may become clear that some of these initiatives will require outside assistance. This is a good time to begin conversations about whether your company would benefit from keeping the project in-house or working with an outside consultant for assistance in areas that require extra support. 

While there is value in keeping your data strategy project internal, an unbiased and expert third party can bring unique expertise and insights into potential blind spots. For example, Kenway’s team of data experts has data strategy experience in a wide variety of industries and can share their knowledge surrounding best practices and common pitfalls that your team may be unaware of.

Step 4: Develop the Roadmap

Crucial elements of your data strategy roadmap development include setting rules for data collection, storage, and analytics. You will also need to create a data governance roadmap, which will define who is allowed to access certain datasets, how data should be classified, best practices, and what standards should be followed to ensure consistent data compliance. 

Step 5: Implement and Monitor the Roadmap

Your team will need to monitor the performance of each phase of the data strategy to quickly identify and resolve issues before they escalate.

While it will be exciting to launch the data strategy you’ve worked so hard to develop, it's important to remain open to the reality that refinement and changes to your process will be necessary to maintain a robust and effective strategy. In addition to making adjustments based on internal business needs, industry best practices also evolve quickly. Remind your stakeholders (and yourself) that it's important to remain flexible and revise your strategy as needed in order to stay relevant for your customers. 

Challenges and Common Pitfalls of Data Strategy Roadmaps

The potential of developing and implementing a data strategy roadmap is exciting, especially considering the long-term benefits of data health for your company. But this process doesn’t come without its challenges. The best way to experience a smooth data strategy execution is to be aware of these common pitfalls and  effectively address them from the start: 

While data strategy does involve technology, the humans in your company are ultimately the ones who will make or break your data strategy goals. Attempting to craft a strategy without buy-in from essential team members (especially executives) can result in disagreements and a lack of alignment during implementation. 

The best way to avoid this pitfall is to involve all relevant stakeholders in the strategy development and conversations from the beginning. While this may slow the initial process down, it will save your team significant pain points in the future.

If your team doesn’t establish the most important goals and initiatives throughout this process, the strategy can quickly become overcomplicated due to conflicting priorities. Avoid overloading your team by aligning on realistic expectations that fit within existing responsibilities and timelines. 

Don’t forget to monitor your data strategy roadmap once it’s been established. As mentioned above, your industry and business needs will constantly evolve, which means your strategy should also be flexible enough to evolve. 

Encourage your team to be aware of the importance of improving your roadmap, and empower them to suggest optimizations. The more empowered your team feels, the easier it will be for them to recognize areas for improvement and provide effective suggestions.

Roadmaps for Business Success 

Creating a data strategy roadmap is a key milestone for any maturing, growth-oriented business. It provides a structured path to reduce technical debt, improve efficiency, and advance data maturity over time.

While it may be tempting to rush the process or make decisions in isolation, a thoughtful, collaborative approach lays a stronger foundation—ensuring your strategy aligns with both short-term priorities and long-term goals.

Whether your organization is in the thick of the data strategy process or just starting out, Kenway is here to provide expert guidance and insights for the smoothest data strategy implementation possible. Reach out today to find out how we could serve your organization! 

FAQs

Why do you need a data strategy roadmap? 

Data strategy roadmaps ensure that business and data objectives work together efficiently to drive business success. 

What does a data strategy roadmap look like?

A data strategy roadmap is a structured plan that outlines how your organization will transform its data into a strategic asset. It typically includes a clear vision, measurable goals, an assessment of current capabilities, and a prioritized sequence of initiatives across areas like data governance, architecture, tooling, and talent. The roadmap also defines timelines, ownership, and success metrics—ensuring all efforts are aligned with business objectives and scalable for future growth.

How do you build a data strategy roadmap?

Building a data strategy roadmap starts with aligning key stakeholders around a shared vision for how data will support business goals. From there, assess your current data landscape to identify strengths, gaps, and opportunities. Define high-impact initiatives and group them into clear, value-driven phases—such as improving data quality, modernizing architecture, or implementing governance. Finally, map these initiatives to realistic timelines and resource needs, while remaining flexible to adapt as your organization evolves.

Unified Analytics Leveraging Kafka, Databricks, and Snowflake

The Problem

A forward-thinking enterprise, deeply dependent on real-time contact center data, found itself constrained by a fragmented and outdated technology stack. Despite their ambition to become a data-driven organization, critical limitations in their legacy systems stood in the way of innovation:

The organization recognized the need for a foundational shift—one that would not only modernize their data environment but also unlock their ability to act on insights in real time.

How Kenway Helped

Kenway partnered with the client to lead a strategic transformation—bridging the gap between legacy constraints and the client’s vision for real-time, data-driven operations. Drawing on deep technical expertise and a collaborative approach, Kenway designed and implemented a modern cloud data solution centered on Kafka Connect, Databricks, and Snowflake.

Our Approach:

Results

The modernization effort fundamentally reshaped the client’s data landscape—replacing legacy systems with a scalable, cloud-based architecture that supports both strategic agility and operational efficiency.

Key results included:

Ready to Modernize with Confidence?

If your organization is navigating similar challenges or considering how to evolve its data infrastructure, Kenway is here to help—offering strategic guidance and hands-on support every step of the way.
Let’s talk.

Automated Data Governance: Why Data Contracts Are Key

Implementing and upholding data governance strategies requires focused attention. With one-fifth of enterprises drawing data from 1,000 or more sources, governance is an investment that impacts your entire company.

Despite attempts to streamline data governance workflows, many companies still rely on reactive processes and non-linear data sets that require manual effort. These manual governance roadblocks are highly complex, costly, and often deliver less than desirable results. 

Moving from manual efforts to increasingly automated data governance can aid companies in keeping up with the pace of data ingestion and growth. When applied correctly, automated elements of data governance reduce the time spent on manual data management, as well as complex initiatives. 

Before introducing automation, it’s important to identify and address the root causes of your data governance challenges for a thorough understanding of how automation can best aid your business.

Here’s what to consider as you explore governance automation—and why data contracts are essential to success.

The Approach to Automation

Automated data governance involves creating and implementing data policies for a truly comprehensive data governance strategy that considers people, processes, and technology:

Answering these foundational questions builds alignment across IT and business stakeholders, leading to stronger governance strategies early in the data lifecycle. 

Moving governance activities “upstream” (also known as shifting left) emphasizes data definition and quality management before ingestion. This intuitive process is a welcome change from the traditional approach, which looks at large amounts of data and retroactively tries to define proper guidelines for use.

Evaluation and planning at the data source allows organizations to avoid the chaos of “top down” governance and realize the benefits of better analytics capabilities, data quality, and collaboration, while reducing the risk of non-compliance. 

Data Contracts and Data Governance

A shift-left data approach is a powerful tool, but buy-in across your company is the key to true success. Data contracts make this possible. 

A data contract is an agreement between a data producer and a data consumer that defines the data at a high level, including: 

By establishing these agreements early, organizations create a metadata-driven ingestion framework. Data contracts ensure that data is documented, structured, and understood from the moment it's ingested — not as an afterthought.

Key Benefits of Data Contracts: 

Data Contracts Set the Foundation for Automated Data Governance

Data contracts can also be used to program automation platforms. The benefits of automated data governance include: consistent monitoring of incoming data against contract terms, reporting continuous audits, supporting security and compliance requirements, and flagging regulated data for proper handling. 

Automating these tasks allows for more streamlined workflows as data moves through its lifecycle and frees up governance teams to focus on strategic initiatives rather than endless cleanup projects. 

Data Contracts Are for Every Company

Data contracts aren’t only for a small sector of companies. Every organization needs governance, whether it uses streaming, batch, or manually entered data. Data contracts provide a foundational solution by: 

Another perk is that data contracts don’t require you to hire new resources or learn a new programming language. If you have development capabilities in-house, you can create data contracts within your existing tech stack.

Implement Automated Data Governance Effectively with Kenway

While there’s no instant solution for improving data quality, data governance automation tools and resources can significantly lighten the load.

Data contracts create a strong foundation for platform automation and bridge the gap between IT and business departments. 

If your company needs additional resources to implement data contracts, Kenway can help. We coordinate across stakeholders, guide framework creation, and deploy technology solutions tailored to your industry or platform-specific needs—including regulated sectors like healthcare and finance, and support in Salesforce data governance.

For guidance on implementing data contracts and automating data governance, reach out to our experts today! 

FAQs

What is automated data governance?

Automated data governance is the process of automating data policies, business rules, and procedures. It’s not a replacement for a comprehensive data governance strategy, but it can help operationalize some aspects.

How can I automate data governance?

Implement metadata-driven ingestion, use data contracts, and leverage automation platforms to monitor and enforce governance policies. 

What is a data contract?

A data contract is an agreement between a data producer and data consumer that defines the data at a high level. It includes key metadata, such as how a piece of data is structured, when it’s available, and who owns it. 

Assemble your SWAT Team to face Market Volatility

Market volatility is back. To survive and outperform, organizations must act quickly to build internal shock absorbers starting with a Pricing SWAT Team: a cross-functional group empowered to react quickly, make informed decisions, and minimize the impact of volatility on revenue, margin, and customer trust. If you haven’t started already, start small and iterate to deliver impact quickly.

What a High-Impact SWAT Team Does

A mature Pricing Team drive value through optimal pricing. These teams are agile, informed, and engaged with all parts of the business. Their core responsibilities are:

This team becomes your organization's shock absorber, helping you respond to volatility instead of being steamrolled by it.

The SWAT Team Maturity Curve: Where Do You Stand?

Our clients land across the maturity spectrum. Here’s how we define the four stages of evolution:

Level 0: Least Mature

Your business is flying blind—and every shock hits you at full force.

Level 1: Foundational

You’re beginning to reduce friction, but execution is still inconsistent.

Level 2: Structured

You now have a real shock absorber—capable of withstanding moderate volatility.

Level 3: Strategic

You’re not just absorbing shocks—you’re turning them into strategic wins.

Kenway Can Help You Get Ready — Now

We help clients:

Data Governance in Salesforce: Best Practices for Data Stewardship

As the most robust and widely used customer relationship management (CRM) platform on the market, Salesforce has evolved far beyond just a customer-facing system. Nearly two-thirds of executives say that they rely on CRM data for every stage of the customer lifecycle. From website visits to customer org charts, Salesforce creates a trusted 360-degree view of your customers. 

This powerful view is what drives customer relationships, improves the customer experience, and allows your organization to glean more actionable insights to guide your business strategy. 

However, realizing the full breadth of Salesforce’s capabilities often comes with the major hurdle of unreliable, incomplete, and poorly integrated data. One survey found that 44% of CRM users said their company loses 10% of annual revenue due to poor data quality. 

So, how can you best position your organization to get ahead of these potential pitfalls? One solution to these challenges is to implement a data governance plan that includes a resilient data stewardship framework within the platform.

Defining Data Stewardship vs. Data Governance in Salesforce

Data governance and data stewardship are often confused with one another. The terms are related, but there are distinct differences.

Data Governance

Data governance is a collection of clearly defined policies, procedures, standards, processes, roles, and responsibilities that ensure the effective and efficient use of data in enabling an organization to achieve its goals. Data governance policies and procedures ensure that the combination of data across clouds, objects, and profiles can be leveraged optimally for users.

In this era of digital transformation, platforms like Salesforce are likely consuming massive amounts of data from other platforms across an enterprise’s tech stack, and often share data with those platforms. In order to realize the true power of the data that Salesforce consumes and generates, it’s essential to have clear policies and procedures related to object creation, field values, business processes, and rules within the platform. Your Salesforce data governance strategy will inform how your unique data sets are allowed to be accessed and changed, ensuring streamlined data use and secure access at every touchpoint.

Data Stewardship

Data stewardship is the practical oversight and management of an organization’s data assets. Data stewards are responsible for implementing the tactical aspects of a plan for CRM data governance. If data governance consists of the policies and procedures for how data is captured and managed, data stewardship is responsible for the active implementation of those rules and procedures. 

For organizations that use Salesforce, data stewards ensure that the front-line user enters data correctly so that the business can leverage that data in-platform for tasks like forecasting, reporting, advanced analytics, and investment strategy planning. 

Why Salesforce Data Governance and Data Stewardship Matter

An effective data governance plan in Salesforce and data stewardship framework enable you to mitigate risk, empower people with the right data at the right time, and drive business growth.

Improve Data Quality

As with any system, the old adage “garbage in, garbage out” applies to Salesforce. No matter how well-designed, user-friendly, and integrated your Salesforce org is, its output is dependent on the quality of the data in the system. Without a data steward maintaining data integrity throughout its lifecycle, the value of your Salesforce investment can decline dramatically.

Strategize and Compete More Effectively

Improving operational effectiveness, decision-making, strategy development, and execution all depend on data quality. Having a well-defined data strategy enables a strong Salesforce data governance that empowers data stewards and positively impacts everyone in the enterprise with the information they need to plan and act more effectively.

Protect Your Reputation and Brand Image

Poor data stewardship and data governance in Salesforce can diminish trust in the data utilized across your organization, leading to internal friction and lost productivity. It can also cause reputational risk in the eyes of your customers, partners, and vendors who need assurance that you can maintain data accuracy and privacy. Effective CRM data governance and stewardship protect you from these risks.

The Challenges of Creating an Effective Data Governance and Data Stewardship Framework

Because of the power and flexibility of the platform, Salesforce data stewards can have a significant impact on the end-user experience without having to code. For example, a single admin can implement and police a mid-sized enterprise’s CRM data governance through “clicks” and straightforward platform configuration.

Your Salesforce platform is likely touched by a plethora of your enterprise’s technical and functional resources. These resources may differ in the way they manage data, document their work, and communicate with one another. These discrepancies further highlight the need to have a clearly articulated and understood data management strategy that includes strict governance protocols.

Data volume is another challenge, since Salesforce holds an enormous amount of information. Addressing these concerns only grows more important as volumes of data grow larger and more complex over time.

41% of business leaders say data complexity is a major hurdle to understanding their data. The more data you have, the more important it is to implement Salesforce data storage best practices.

Best Practices for Establishing a Salesforce Data Stewardship Framework within Your Data Governance Model

A solid Salesforce data governance framework guides your data stewardship practices and sets your team up for success. The following Salesforce data management best practices are important to consider as you develop your data stewardship framework:

1. Strict Data Governance

Ensure Salesforce users are only seeing the records they need to see. Leverage profiles, roles, and permission sets to mitigate oversharing. Utilize validation rules, minimize the use of free-form text fields, and leverage other field types to ensure that users are entering data in a consistent manner.

2. Consider Future Data Use

Architect and design your Salesforce solution to accommodate how data will be used in the short and long term. For example, adding validation rules to keep out erroneous data will help in creating a standard data model that allows admins to act as first-line data stewards.

3. Optimize Your Data Model

Ensure good data literacy. Data stewards should have proper data literacy, which includes an understanding of the relationships between objects used across your Salesforce clouds. Set up a data model that can cleanly organize data so that it’s easy to sort and analyze down the line. 

4. Only Keep the Fields You Need

Consistently assess field usage across objects and clouds. If fields aren’t being used or don’t need to be there, remove them to mitigate the presence of tech debt and clutter in your data storage.

5. Leadership Awareness and Alignment

It’s easy for leaders to buy into your data strategy when it aligns with your organization’s broader business strategy. It is important that your organization understands that data governance in Salesforce is done “with and for” them rather than “to” them.

6. Consider the Data Lifecycle

To prevent the endless cycle of low-quality data in your Salesforce instance, it’s important to maintain data quality throughout the lifecycle. If you’re integrating data into Salesforce, apply governance as far upstream as possible.

7. Quality Checks

Include policies and procedures around consistent data checks and updates during development activities and key project milestones.

8. Staffing Considerations

How you assign data stewardship tasks will depend on the size of your organization and your budget.

Here are some general rules of thumb to consider as you plan for staffing: 

Develop Confidence with Data Governance in Salesforce

It’s common for companies to struggle with Salesforce implementation, data quality, manual processes, inadequate reporting, and a lack of trust in their data. Additionally, mergers, acquisitions, and new software implementations can cause (and expose) data quality gaps. 

You may want to consider working with a Salesforce partner if you struggle with these challenges or want to avoid these pitfalls:

The right Salesforce partner can change the way you use the platform. If the partner has experience both inside and outside of Salesforce, they can design a holistic modern data governance and management solution that is absorbed into your Salesforce platform.

Case Study: Achieving Regulatory Compliance with Salesforce

Meeting new regulatory requirements is always a challenge, especially at large organizations with legacy systems and processes. When one of the country’s largest banking institutions needed to update its processes in order to stay compliant with new regulations, it turned to Kenway—and we turned to Salesforce data governance to solve the problem.

The Kenway team approached this challenge by configuring custom workflows to automate the benchmarking analysis process and incorporated Salesforce data governance best practices into these processes. The combination of new processes backed by a solid CRM data governance plan resulted in the firm’s ability to increase productivity and reduce costs. 

Make Salesforce Work Better for You

At Kenway, we help you leverage holistic Salesforce capabilities by considering data strategy, management, governance, and stewardship across your technology ecosystem. Whether we are working solely on a Salesforce engagement or a broader technology initiative, these processes are integrated into every project.

If you want to seize the full range of opportunities Salesforce offers and get rid of data quality, integration, and accessibility issues once and for all, reach out to our experts today!

FAQs

What is data governance in Salesforce?

Data governance in Salesforce is a collection of clearly defined policies, procedures, standards, processes, roles, and responsibilities that ensure the effective and efficient use of data to enable an organization to achieve its goals.

What is the difference between data governance and data stewardship?

While data governance lays out the guardrails and guidelines for proper data management, data stewardship is the day-to-day oversight and management of that data. In other words, data stewardship activities are informed by the data governance plan. 

What are Salesforce data governance best practices and top tips?

What are the benefits of implementing data governance in Salesforce?

Prepare to React Quickly to Tariffs. Build out your ‘What-If’ Tools

Companies that were caught off guard by the spikes in inflation and tariffs a few years ago, lost market share and profit. Those who were prepared, were able to position themselves advantageously.

Uncertainty and rapid change are back. Companies need to be able to quickly respond and adjust.

For leaders managing complex product portfolios and pricing agreements, relying on manual analysis cobbled together from multiple sources will lead to slow and poor decision-making. The company will be at risk of margin erosion and competitive disadvantage.

What-If Analysis

To navigate this volatility successfully, organizations need processes and technology that enable swift, data-driven decision-making. The goal is to move beyond reactive adjustments towards proactive scenario planning, understanding potential impacts before they fully materialize. One highly effective approach is What-If Analysis.

What-if analysis is a data simulation technique used to understand how changes in a set of input variables will impact key business outcomes.

For example, a firm may need to understand the overall impact of different scenarios where country-specific tariffs, input costs, inflation, are all moving at once and unequally impact each of their SKUs. As they weigh the probability of different scenarios, they will need to know the potential impacts to things like product line margin, business unit profitability, inventory holding cost, competitor price positioning, etc.

What-if Analysis allows leaders to explore various potential future states based on different assumptions and external factors.

Real-World Example

Imagine a new 10% tariff, effective in 30 days, is unexpectedly announced on goods from Vietnam. A robust What-If model could be leveraged in the following way:

By modeling scenarios, you can quickly evaluate options and choose the strategy that best balances profitability, customer relationships, and market realities.

Building out Your What-If Dashboard

Several key steps are needed to build out a What-If Dashboard:

  1. Define the Needed Answers: Clearly identify the business questions related to tariffs, inflation, pricing, costs, and inventory that you expect to address.
  2. Identify Critical Data Inputs: Determine the essential data points which often include:
    • Product Master Data: SKU, Hierarchy, Finished Good and Component Countries of Origin
    • Costing Data: Raw Component Costs
    • Inventory & Procurement Data: On-hand/In-transit/Open POs; Tier 1 and Tier 2 Supplier Locations
    • Pricing & Sales Data: List Price, Average Sell Price, Special Pricing Agreements
    • Market & External Factors: Competitor pricing intelligence, inflation indices, FX rates, demand elasticity estimates.
  3. Consolidate & Prepare Data: Define the best approach to consolidate the data. More advanced companies will have already integrated their BI tools with sources of this critical data. Maturing companies may need to find scrappier, manual work arounds to quickly gain access to insights.
  4. Build & Validate the Model: Develop the analytical model incorporating the data and business logic. Validate outputs rigorously against historical data or known scenarios to build confidence in its accuracy.
  5. Select the Right Tool/Platform: Choose technology that offers the necessary flexibility. While sophisticated spreadsheets can work for simpler cases, BI platforms are better equipped to handle complex calculations.
  6. Integrate & Iterate: Embed the tool and its outputs into key business processes (e.g., Pricing SWAT/Committee reviews). Ensure cross-functional teams (Sales, Finance, Supply Chain, Procurement) understand how to use the insights. Continuously refine the model as business needs and data sources evolve.

Kenway Can Help

We've supported many of our clients build out What-If Analysis to support data-driven decision-making. Don’t lose margin or market share because you didn’t act fast enough to prepare for these volatile times. If you want to know how it would work in your organization, read more here or reach out to us today.

Building Shock Absorbers in a Volatile Environment

The current business landscape is marked by significant uncertainty. Rising costs, potential tariffs, and shifting supply chain dynamics create a volatile environment that demands proactive preparation. Companies that merely react to these pressure risk margin erosion, operational disruption, and damage to customer trust. Instead of being caught flat-footed, forward-thinking organizations are building internal functions that can act as "Shock Absorbers,” strategic capabilities designed to reduce the impact of macroeconomic volatility. These shock absorbing capabilities are not established overnight; safeguarding your company requires investment and planning.  

1. Acknowledge Volatility and Assemble a SWAT Team:

It must be recognized that navigating turbulence requires dedicated focus by a core, strategic, cross-functional SWAT team. This group, comprised of leaders from finance, operations, supply chain, sales, and data analytics, must be empowered to:

2. Implement Rapid, Decision-Grade Reporting:

Gut feel isn't enough in volatile times; quantitative analysis is critical. Your SWAT team needs timely, relevant data to make informed choices. This means moving beyond standard operational reports to:

3. Define Adaptive Business Processes:

Decisions are only effective if they can be implemented swiftly. You should review and refine your core business processes related to pricing, sourcing, inventory management, and production planning. The goal is to create workflows that are:

4. Master Change Management and Communication:

Volatility impacts everyone: employees, partners, and customers. Clear, consistent and reliable communications are critical to maintaining trust with your internal partners and customers.

Building Your Resilience with Kenway Consulting

Developing these internal shock absorbers requires expertise and focused execution. This is where Kenway Consulting can help.

Don't wait for the next economic jolt. Proactively building your business shock absorbers will not only help you weather the coming months but also position you for greater stability and competitive advantage in the long run. Contact us today to partner with you to transform your organization into a resilient enterprise that thrives amidst tariff uncertainties and supply chain volatility.

Data-Driven Healthcare: How Pharma Companies Are Leveraging Analytics for Smarter Decisions

The US pharmaceutical industry is valued at $602.19 billion (as of 2023) and only continues to grow. The breadth of the pharmaceutical industry comes with a heightened responsibility to deliver safe, effective medications to millions of people. This is not an issue that can be left up to chance—and with the advance of data analytics for pharma, it no longer has to be. 

From optimizing internal operations, reducing inefficiencies in clinical processes, and driving innovation through predictive analytics, pharma analytics is continually contributing to the betterment of the industry as a whole.

The Growing Role of Pharma Data Analytics

What is Pharma Analytics?

Pharma analytics is the use of data analytics to make improvements, increase positive clinical trial outcomes, and accelerate production. 

Pharmaceutical companies have long held vast amounts of data—from clinical trials, patient health records, and market insights. So, how can they properly leverage their data? Pharma analytics enables machine learning to forecast outcomes and respond more quickly to demand. The end result is an increased ability to enable both business optimization and patient outcomes. 

Pharma Analytics in Research & Development 

Pharma analytics is also useful for innovation before medications reach the market. From identifying potential drug candidates faster to optimizing trial design, pharma data analytics is reducing costs while increasing the chances of success.

Pharma Analytics Benefits

Optimizing the Pharmaceutical Supply Chain

Disruptions to today’s supply chains are often swift, delaying life-saving treatments from reaching patients in need. Predictive analytics in the pharmaceutical industry, however, enables companies to anticipate possible supply chain risks, ideally resulting in better planning and supply-loss prevention. 

Enhancing Salesforce Efficiency

CRM platforms and pharmaceuticals don’t go hand-in-hand at first glance, but this connection is actually highly important for pharma data analytics.

A platform like Salesforce allows companies to employ data governance protocols, track provider engagement, segment customers, and identify future opportunities. Salesforce creates data visibility, which in turn enables pharmaceutical companies to make better informed, holistic decisions for their company at large. 

Improving Clinical Trial Recruitment & Success

Clinical trials are crucial for advancing the development of critical treatments and medicine, but they are also complex, expensive, and often struggle to recruit eligible patients. 

Pharma analytics enables companies to identify suitable and willing patients more quickly, which leads to a smoother and more efficient process for everyone involved. 

The Future of Pharma Predictive Analytics

As predictive analytics continues to grow, a few areas making rapid advances are: 

Drug discovery and development

Machine learning models aid early and often in the drug research and development cycle, reducing the time spent on trial and error testing. 

Market forecasting and risk management

Great visibility into market trends and patient needs is allowing companies to anticipate demand and manage drug-lifecycle predictions. While this is advantageous from a go-to-market strategy perspective, it is also advantageous for patients. A better market system will make it easier for patients to consistently and quickly access the medications they need. 

Risk Management

Predictive analytics can help companies proactively identify and address compliance and data governance risks, supply chain vulnerabilities, and potential room for improvement in trials.

While the benefits of pharma analytics are numerous, challenges do exist. Maintaining patient data security and confidentiality is paramount, and becomes increasingly difficult when multiple parties are using information for analytics. Practices like maintaining ethical standards and focusing on well-trained AI models are just as (if not more) important than enjoying the benefits listed above. 

Data Governance & Compliance

Your pharma analytics program will only be as strong as your healthcare data foundation. Data governance is not just a nice-to-have in healthcare—it is a full necessity. From HIPPA to FDA guidelines, ensuring accurate and secure data at all times will make every step in pharma analytics smoother and more trustworthy. 

Once your organization has laid a robust data governance and compliance foundation, it is time to develop a strong pharma analytics strategy. This strategy will depend largely on the size and specific goals of your organization, but every good analytics strategy will include: 

Increasing Positive Outcomes in Pharma Analytics 

Pharma analytics is not a trend—it is a crucial element and driver of current outcomes in the healthcare industry. Embracing data science and predictive analytics enables pharmaceutical companies to improve their operations, increase clinical trial success, and ensure regulatory compliance. 

Kenway Consulting’s team of data governance, data analysis, and Salesforce experts is well-versed in the healthcare industry. Contact us today if your organization is ready to explore all that pharma analytics has to offer.

FAQs 

What is pharma analytics?

Pharma analytics refers to the use of data analysis and generative tools to predict and inform pharmaceutical professionals on future market trends and possible patient outcomes.

What role does data science play in the pharmaceutical industry?

Data science opens up deeper insights from complex data so that pharma professionals can make faster, more informed decisions.

What are the benefits of big data analytics in pharmaceutical research?

Benefits include faster drug development, clinical trial success, and more robust data governance frameworks.

How do pharmaceutical companies ensure data security and compliance in analytics?

Adherence to regulations like HIPPA safeguards pharmaceutical companies from governance breaches. Additionally, developing a company-specific governance framework greatly aids teams in their ability to easily follow governance best practices and ensure regulatory compliance.

Enabling Scalable and Rapid Data Product Delivery for Global Financial Services

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:​

​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.​

Healthcare System Integration: Overcoming Challenges & Improving Interoperability

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. 

  1. 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. 

  1. 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. 

  1. 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. 

  1. 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.