Assess Your Data Science and Machine Learning Capabilities

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Despite substantial investments in AI, many organizations aren’t seeing the payoff. Data science and machine learning (ML) capabilities can play an essential role – they connect people, data, and technology to bring AI to life and help it deliver value. This comprehensive framework will help you assess your organization’s data science and ML capabilities to close the gap between raw data and real organizational value.

A cautious culture, variable data quality, skills gaps, and unreliable analytics present major roadblocks to maximizing the potential of the data science and ML function. By assessing the maturity of their data science capabilities, data leaders can expose and address the relevant gaps, then strike the right balance between off-the-shelf tools and purpose-built solutions to build a foundation for AI that can deliver continuous innovation.

1. Build only what you need.

Not every data science and ML capability needs to be at the highest level of maturity to deliver value. Instead, focus on getting the fundamentals right: solid technical foundations, strong leadership, and a data culture that supports smart, scalable use.

2. Solve the right problem.

Teams often focus too much on symptoms or rush into technical solutions that miss the real problems. Progress relies on clearly defining the problems with an eye on strategic goals and ensuring your data teams are in sync with organizational needs.

3. Culture is the great accelerator.

Even the best data science and ML strategy will stall without the right mindsets and change readiness in place. High maturity in organizational culture and change management accelerates progress in all other capabilities.

Use this step-by-step blueprint to unlock the full potential of data science and machine learning

Our research offers comprehensive guidance and a simple-to-use assessment to help you understand and close the gaps in your data science and ML capabilities. Use this step-by-step framework to develop scalable, value-driving capabilities.

Formulate well-defined problems aligned to functional objectives to be maximized or minimized.
Assess data science and ML maturity level to determine the minimal essential maturity level of each capability needed to meet ROI success indicators.
Determine target state maturity and activities that will improve the effectiveness of each data science and ML capability.

Assess Your Data Science and Machine Learning Capabilities Research & Tools

1. Assess Your Data Science and Machine Learning Capabilities and Effectiveness – A comprehensive approach to building high-performance data science and ML capabilities.

Use this blueprint to assess your current state, determine the right tools, and involve the right people in optimizing the effectiveness of your data science and ML function.

Understand the challenges, obstacles, and recommended approaches to assessing your data science and machine learning capabilities.
Encounter actionable insights to inform your approach to identifying and resolving problems.
Benefit from multi-stage guidance on determining your target state and making a plan to achieve it.

2. Data Science and Machine Learning Maturity Assessment Tool – A practical tool to measure readiness and point the way to improvement.

Use this tool to evaluate your current data science and ML practices and systematically develop a plan to reach your target state.

Define the context of your organization’s strategic initiatives.
Assess your current maturity across nine key capabilities.
Generate a clear maturity assessment report that shows the way to your target state.

Assess Your Data Science and Machine Learning Capabilities and Effectiveness

Unlock the full potential of AI and data.

Analyst perspective

Bridging AI hype and real value

Data science and machine learning (ML) have incredible potential, but for many organizations they're still more hype than results. Investments in AI are high, yet few companies are truly getting game-changing value. It's no longer a question of "if" but "how" to make data and AI work - those who figure it out will surge ahead, as AI becomes increasingly core to products, decisions, and competitive advantage.

You don't need to reinvent every wheel to succeed with AI. The fastest movers blend bought solutions with in-house talent. In practice, this means adopting proven data science platforms and pre-built tools to accelerate your efforts while your team focuses on tailoring insights to the business. In short, buy the capabilities you can and build what's uniquely yours - this pro-buy stance gets you to value quicker and avoids wasted time on building things that others have already perfected.

Technology alone won't transform your business - people and process will. That's why you need a dedicated data science and ML function in the organization: a team with the skills and mandate to turn data into insight and insight into action. Equally important is assessing your analytics maturity with clear eyes. Understanding your gaps - be it data quality, skills, or culture - lets you target improvements that deliver real value. By combining the right tools (often acquired) with the right internal capabilities (people, process, and leadership support), you create a powerful engine for continuous innovation. The bottom line: success with AI comes from a savvy mix of external solutions and internal strength, all guided by a plan to steadily mature your data science practice and drive tangible business outcomes.

Ibrahim Abdel-Kader

Ibrahim Abdel-Kader
Senior Research Analyst, Data, Analytics, and AI
Info-Tech Research Group

Executive summary

Your Challenge

Common Obstacles

Info-Tech's Approach

Despite heavy investments, organizations struggle to harness innovation and agility through AI/ML:

Many risk falling behind more advanced rivals who leverage AI to reinvent products and operations.
Customers and investors now expect faster, smarter decisions powered by data.
Data abundance often creates noise instead of insight and has paralyzed decision-making.

Decision-makers lack timely, actionable insights and need to translate raw data into strategic clarity.

Even when leaders are committed to data science and ML, systemic and cultural hurdles derail progress:

Traditional decision-making processes and a cautious culture can stifle innovation.
Data volume is high, but data fitness is low, creating a gap between potential and actual results.
There is a shortage of analytics-savvy talent and trust in the insights produced.

AI initiatives continue to stall out as programs struggle to move from pilot stage to production and profit.

Our approach moves organizations from ad hoc experiments to scalable, value-driving capabilities:

Determine how data science and ML can propel your specific business objectives.
Perform an honest maturity assessment of your organization's data science and ML capabilities.
Embed data science and ML where it drives outcomes and monitor results.

Institutionalize data science and machine learning as a continual business discipline.

Info-Tech Insight

Data science and machine learning unlock hidden value by turning raw data into smarter, context-rooted decisions; but success doesn't come from pushing every capability to "Level 5" maturity. It comes from having the right technical foundations (quality data, proper tools and platforms) and organizational enablers (engaged leadership, skilled people, and a supportive data culture) in place.

Organizations are struggling to compete in the era of AI and data

Organizations today struggle to harness innovation and agility through AI/ML. Despite heavy investments, relatively few are seeing real returns.
Many companies risk falling behind more advanced rivals who leverage AI to reinvent products and operations.
At the same time, customers and investors now expect faster, smarter decisions powered by data. Business leaders are inundated with information.
Counterintuitively, this data abundance often creates noise instead of insight. The sheer volume of data, and uncertainty over which data to trust, has paralyzed decision-making and even halted decisions altogether because the data was overwhelming.
The challenge for data and tech leaders is to translate raw data into strategic clarity. Executives are making more decisions than ever, under high scrutiny, and they feel the pressure to get them right.
Bridging that gap, and doing so faster than the competition, is the core challenge in the era where AI is the new currency of competitive advantage.

Companies Gaining AI Value

Only 26% have moved projects beyond pilots to achieve tangible AI/ML value.

Source: Boston Consulting Group (BCG), 2024

Data Overload

72% of leaders have stalled decisions due to overwhelming data.

Source: Oracle, 2023

CEOs Betting on AI

75% of CEOs say advanced AI will determine competitive advantage.

Source: IBM Institute for Business Value, 2023

Progress is stalling despite best intentions

Traditional decision-making processes and a cautious culture can stifle innovation. Many companies continue to rely on legacy workflows and intuition.
Employees stick to familiar habits, and management may be reluctant to trust AI-driven insights for critical decisions. This inertia means organizations fail to act on the data they have.
Fragmented, poor-quality data is a major bottleneck. Enterprises typically have data locked in departmental silos or incompatible systems, with patchy governance. Without unified, clean data, even the best algorithms underperform.
The misalignment between talent gaps and adoption resistance manifests as hesitation on the ground. Moreover, many employees fear AI could disrupt their jobs or decision-making authority. Without strong change management, people may resist or ignore new DS/ML tools.
Companies have invested in data platforms and data science talent, but process and people issues are blocking ROI. Siloed data and siloed thinking go hand in hand: if data isn't shared and trusted across the enterprise, and if leaders aren't empowering teams to use it, then fancy algorithms won't matter.

Culture and Process Hurdles

Cultural resistance and outdated processes are cited as the greatest barrier to getting value from data initiatives. 78% of organizations remain stuck in legacy mindsets despite new technology.

Source: Wavestone, 2024

Talent and Adoption Gaps

The fluidity and tenuous nature of the CDO/CDAO role is reflected in the 51% of organizations that view the role as being successful and well-established, while the rest are struggling with turnover or view the role as a failure.

Source: Wavestone, 2024

Info-Tech's approach:

From experimentation to enterprise impact

To overcome these hurdles, Info-Tech recommends treating data science and machine learning not as isolated tech projects but as an integrated business discipline. Our approach is designed to move organizations from ad hoc experiments to scalable, value-driving capabilities:

The Info-Tech difference:

Start With the Why - Rather than doing AI for AI's sake, begin by determining how DS/ML can propel your specific business objectives.
Know Your Starting Point - Next, perform an honest maturity assessment of your organization's data science and ML capabilities across people, process, data, and technology.
Less Is More - With gaps understood, the next step is to identify a targeted portfolio of initiatives to close those gaps and achieve quick wins.

Assess Your Data Science and ML Capabilities Project Overview

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	1. Assess data science and ML capabilities and effectiveness	Outcome
Best-Practice Toolkit	Formulate well-defined problems Assess data science and ML maturity level Determine target-state maturity and activities	Development of data science and machine learning problem statements aligned to functional objectives to be maximized or minimized. Definition of successful indicators that contribute to the positive ROI of functional objectives. Current-state maturity assessment of data science and machine learning capabilities. Determination of the minimal essential maturity level of each data science and machine learning capability needed to meet ROI success indicators. Identified activities to improve the effectiveness of each data science and machine learning capability.
Guided Implementations	Call 1 - Identify functional objectives to maximize or minimize Call 2 - Develop data science and ML problem statements Call 3 - Define ROI success indicators Call 4 - Assess current-state maturity Call 5 -Determine target-state maturity Call 6 - Identify activities to close maturity gap

1. Assess data science and ML capabilities and effectiveness

Outcome

Best-Practice Toolkit

Formulate well-defined problems
Assess data science and ML maturity level
Determine target-state maturity and activities

Development of data science and machine learning problem statements aligned to functional objectives to be maximized or minimized.
Definition of successful indicators that contribute to the positive ROI of functional objectives.
Current-state maturity assessment of data science and machine learning capabilities.
Determination of the minimal essential maturity level of each data science and machine learning capability needed to meet ROI success indicators.
Identified activities to improve the effectiveness of each data science and machine learning capability.

Guided Implementations

Call 1 - Identify functional objectives to maximize or minimize

Call 2 - Develop data science and ML problem statements

Call 3 - Define ROI success indicators

Call 4 - Assess current-state maturity

Call 5 -Determine target-state maturity

Call 6 - Identify activities to close maturity gap

Insight summary

Overarching insight

Data science and machine learning unlock hidden value by turning raw data into smarter, context-rooted decisions - but success doesn't come from pushing every capability to "Level 5" maturity. It comes from having the right technical foundations (quality data, proper tools and platforms) and organizational enablers (engaged leadership, skilled people, and a supportive data culture) in place.

Step 1 insight

Functional owners frequently state a symptom to be the core problem or chase a fancy technical solution that doesn't address any problem at all.

It is imperative that problems are well defined and that there is collaboration between the data and functional teams to get there.

Step 2 insight

Culture is the silent multiplier. High maturity in culture and change management accelerates progress in all other capabilities.

Low maturity here creates friction. For example, resistance to DS/ML adoption may persist despite strong technology.

Step 3 insight

Achieving level 5 maturity should not be an automatic target to strive for, as obsessions over this can derail the potential ROI for data science and machine learning.

Determine the minimum maturity level needed to adequately address the problem and meet ROI success indicators.

Tactical insight

Capabilities mature at different speeds. For example, data readiness often lags behind leadership aspirations.

Tactical insight

Weaknesses in one capability (e.g. ethics) can derail progress in others (e.g. use cases).

Blueprint deliverable

This blueprint is accompanied by this key deliverable to help you accomplish your goals:

Data science and machine learning maturity assessment tool

Key deliverable:

Data Science and Machine Learning Maturity Assessment Tool

This tool will help you analyze your organization's current- and target-state maturity in data science and machine learning capabilities and systematically develop a plan for your target data science and practices.

Blueprint benefits

IT Benefits	Business Benefits
Unified Data and Analytics Platform: Integrated, well-governed data and ML pipeline reduces silos and manual work, giving IT a reliable single source of truth for insights. Higher Efficiency and Speed: Streamlined model development and MLOps practices shorten deployment cycles (models to production faster), improving IT's agility in delivering solutions. Upskilled Team and Sustainable AI Capability: IT staff gain advanced data science skills and frameworks, enabling a self-sufficient, analytics-savvy team that can continually support and scale AI initiatives.	Faster Data-Driven Decisions: Leaders make quicker, smarter decisions powered by trustworthy insights, increasing organizational agility and responsiveness to market changes (addressing the 72% who faced decision paralysis). Tangible ROI and Cost Savings: Focused DS/ML projects deliver measurable value (e.g. revenue uplift, efficiency gains). AI top performers achieve significantly higher ROI¹ and "1.5x faster revenue growth than peers by leveraging AI effectively.² Competitive Edge Through Innovation: By embedding AI in products and operations, the business can reimagine customer experiences and processes, driving innovation that differentiates it from competitors and secures long-term market leadership.

IT Benefits

Business Benefits

Unified Data and Analytics Platform: Integrated, well-governed data and ML pipeline reduces silos and manual work, giving IT a reliable single source of truth for insights.
Higher Efficiency and Speed: Streamlined model development and MLOps practices shorten deployment cycles (models to production faster), improving IT's agility in delivering solutions.
Upskilled Team and Sustainable AI Capability: IT staff gain advanced data science skills and frameworks, enabling a self-sufficient, analytics-savvy team that can continually support and scale AI initiatives.

Faster Data-Driven Decisions: Leaders make quicker, smarter decisions powered by trustworthy insights, increasing organizational agility and responsiveness to market changes (addressing the 72% who faced decision paralysis).
Tangible ROI and Cost Savings: Focused DS/ML projects deliver measurable value (e.g. revenue uplift, efficiency gains). AI top performers achieve significantly higher ROI¹ and "1.5x faster revenue growth than peers by leveraging AI effectively.²
Competitive Edge Through Innovation: By embedding AI in products and operations, the business can reimagine customer experiences and processes, driving innovation that differentiates it from competitors and secures long-term market leadership.

Source: 1. Accenture, 2024. 2.IDC, 2023

Unlock the full potential of AI and data.

About Info-Tech

Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.

We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

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A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.

Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.

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