Digital + data + data science = vision to value, faster. That’s the message shared in this presentation hosted by DCO Canada and presented by Chief Data Scientist Michael Gonzales. The conversation starts with this quote from an article published through the Harvard Business Journal* – The worst mistake a company can make is to hire data scientists, give them access to data, and turn them loose expecting them to come up with something brilliant. – In fact, the role of a data science team is to bring business value, business impact. That can’t happen without business involvement. Dr. Gonzales shares valuable insight into marrying your digital and data to receive maximum results.

*Harvard Business Journal Publication – Are You Setting Up Your Data Scientists to Fail?

By Michael Gonzales, PhD

An Analytic Ecosystem Inventory (AEI) helps organizations document and quantify their current analytics landscape. It collects metrics about analytic applications, supporting technologies, and data sources — offering a clear view of how analytics is implemented and consumed across the business.

Unlike general assessments that capture opinions from analytic users, the AEI focuses on quantitative insights. It measures the technologies, applications, and user communities that shape your organization’s analytic maturity.

What the Analytic Ecosystem Inventory Includes

The AEI framework typically covers four major components:

• Antecedents: Documentation related to analytics, corporate objectives, and standards.
• Applications: Analytic applications, their user base, and life stage.
• Technologies: Tools and platforms supporting analytic operations.
• Data: Information about data sources, size, type, and frequency of use.

To ensure consistency, it’s best to use a structured instrument — such as a spreadsheet — for gathering data. This enables accurate, repeatable collection even when multiple teams are involved.

1. Understanding Antecedents

Antecedents are formal documents used to evaluate the maturity of analytics in an organization.
For example, a business strategy that references analytics demonstrates that the enterprise recognizes data as a competitive advantage.

Below are key artifacts assessment teams should review when determining analytic maturity:

• Business strategy documents referencing analytics (can be redacted if necessary)
• Analytic strategy documentation
• Organization charts for analytics and data governance teams
• Analytic development and implementation standards
• Example of a requirements document for the analytic environment
• Example of a test plan for an implemented analytic application
• Example of a Service Level Agreement (SLA) with user communities
• Education curriculum or training offered by the analytics team
• Course evaluation forms used post-training

Gathering these documents requires minimal resources. Teams can request documents during the kickoff session, follow up with examples via email, and confirm titles shared by internal stakeholders.

2. Technical, Data, and Application Examination

An Excel-based AEI spreadsheet helps structure and standardize information collection. It should include columns for:

• Technology licenses
• Data sources and size
• Supported user communities
• Applications built on each technology

Table 1: Architecture Inventory (not shown)

This format provides clarity when evaluating relationships among applications, technologies, and users.

3. Techniques for Analyzing the AEI

The AEI involves two primary areas of assessment:

1. Formal antecedent documentation
2. The inventory of analytic applications, supporting technology, and data

To gain accurate insights, teams should not analyze these areas in isolation. Instead, compare findings across information sources—such as surveys, interviews, and inventory data—to validate consistency.

For instance:

• If SMEs mention specific analytics technology standards, those should appear in the technology inventory.
• If SMEs claim there’s no formal training but the organization offers a structured curriculum, that discrepancy needs investigation.

Figure 1 – Overlapping Information Channels highlights how these data sources intersect.

4. Analyzing Antecedents

To evaluate antecedent documents effectively, teams can:

• Review each document and provide observations.
• Use a Likert Scale to rate maturity factors.

This structured approach enhances repeatability and transparency, compared to subjective review methods.
(Refer to Table 2 – Assessing Antecedents for sample evaluation criteria.)

Table 2 – Assessing Antecedents

5. Analyzing the Inventory

The AEI offers valuable insights based on quantitative patterns. Analysts can use these patterns to answer key questions such as:

• Support for Standards – How consistent are the technologies supporting analytics? Are multiple versions in use?
• Application Maturity – Are applications mostly new, expanding, mature, or legacy?
• User Communities – Do these applications support broad user groups or niche teams?
• Departmental Concentration – Are analytic applications centralized or spread across departments?
• Data Latency – Is the data consumed in batches, real time, or on demand?

These findings help organizations identify gaps, improve efficiency, and enhance governance across their analytics landscape.

Conclusion

Conducting an Analytic Ecosystem Inventory provides organizations with a comprehensive snapshot of their analytic maturity. By documenting antecedents, technologies, applications, and data sources, teams can uncover improvement opportunities, align analytics with business goals, and support future scalability.

About the Author

Michael L. Gonzales, Ph.D., is an IT industry veteran with over 30 years of experience as a Chief Architect and Senior Solutions Strategist. He specializes in leveraging business analytics for competitive advantage in global enterprises.

His research and presentations have been featured at leading international conferences such as:

• Decision Sciences Institute
• Americas Conference on Information Systems
• Hawaii International Conference on Systems Science

Dr. Gonzales holds a Ph.D. in Information and Decision Science from the University of Texas.
He currently serves as Managing Partner at dss42, LLC, and Senior Data Scientist at Prolifics.

• Download the Comprehensive Analytics Maturity Assessment (CAMA)
• Learn about the instruments that add value to your CAMA

Companies that know how to leverage their analytic and IT resources gain a business analytic-enabled competitive advantage (Porter, 1980; Sambamurthy, 2000), which is the basis of our research. For the purpose of this guide, the term analytics represents a comprehensive view that encompasses the 5 analytic areas listed below and related topics.

The challenge, when creating an analytic-enabled business strategy, is to identify which activities to focus on. To that end, our research identifies factors of analytic-centric initiatives that significantly contribute to the overall maturity and success of a program (Gonzales, 2012). Building on this research, coupled with extensive practical application of maturity assessments for leading companies our Comprehensive Analytic Maturity Assessment (CAMA) creates an index that measures the analytic-enabled competitive maturity of an organization.

The Value of a Repeatable Analytic Maturity Assessment

While it’s important that companies invest in an unbiased measurement of their analytic maturity, it is only a fraction of the value. One key success factor is the ability to periodically conduct the same assessment to measure and monitor the progress of your analytic program(s). If you can demonstrate significant maturity increases, the results will support your argument for additional budget and resources.

Conducting the same assessment periodically means that you must retain the instruments used and methodology applied. Some assessment services will simply not comply.

Dr. Michael Gonzales, Chief Data Scientist with Prolifics, doesn’t recommend that you invest in any assessment that contains black-box processes. “Frankly, if you do not have an assessment that provides visibility to all aspects of how the maturity level is derived, then it’s not worth the price,” Gonzales explains. “Real value from these initiatives is derived when you can internalize the assessment instruments and processes to enable your organization to periodically conduct the assessment.”

The following video describes 4 of the instruments this author recommends.

Five Analytic Areas for CAMA

• Data Science (DS) – an inter-disciplinary field to unify statistics, machine learning, deep learning, big data, and data analysis.
• Machine Learning (ML) – the application of computer algorithms that improve automatically through experience. A sub-set of Artificial Intelligence.
• Business Intelligence (BI) – techniques and technologies used for data analysis of business information including the provision of historical and current views of operations.
• Big Data – a field focused on the analysis of data sets too large or complex to be dealt with by traditional data processing.
• Spatial Analysis – the application of statistical analysis and related techniques to data with a geographical dimension.

More questions about your company can gain and maintain a competitive advantage using advanced analytics? Our experts are here to help.

Embrace and leverage data science for innovative solutions. This presentation is designed to bring together business and data science members to achieve maximum outcomes.

https://youtu.be/Th-BXcfNO88

Data and analytics are drivers for success in every industry – that’s a fact. And the amount of data consumers create and businesses consume is exploding at rates unfathomable just a decade ago. So how do you keep up? Better yet, how do you harness the power of your data to get ahead and stay ahead of your competition. Three legends in the data and analytics space share innovative ways analytics is shaping the next set of industry disruptors. Don’t miss the conversation in the Innovation Sandbox.

Meet the experts!

By Michael L. Gonzales, PhD
Director of Research for Advanced Analytics, Prolifics
Written in collaboration with and for Eckerson Group.

Many believe that we’ve entered a new era of technical progress that forces companies on a path of continuous innovations. Cognitive computing is considered not only part of this new era, but potentially the core technical driver due to its ability to improve human decision-making through automation and augmentation. This paper will define cognitive computing, describe why cognitive computing is an IT-enabling competitive advantage for companies, and outline the necessary steps that IT must take if they hope to leverage the technology.

A Disruptive Climate

In the early 80’s Microsoft announced to the world that computing power was moving from mainframes to desktops and companies that want to be industry leaders must make that move.¹ Apple seems to be willing to continue a long running success of introducing disruptive innovations even at the expense of their own products. The iPhone cannibalized iPod sales and the iPad is eating away at iMac.² Google has been buying up companies in seemingly diverse industries: from smart thermostat to robotics to artificial intelligent companies. The objective is to corner the market on skilled resources in order to create the next generation of human-computer interaction.³

These are examples of disruptive strategies that companies execute with the goal to reshape or otherwise transform entire industries and the market places in which they compete. As companies redefine the market on their terms, they essentially throw the entire space into a period of disruption. Many analysts concede that technical progress is the catalyst for more innovations, which perpetuates disruption and a fierce competitive business climate.

So what do disruptive strategies have to do with Cognitive Computing? Futurists⁴, vendors, and leading companies believe that cognitive technology is a disruptive force in an information-intense age. Put simply: Cognitive Computing enables companies to identify and implement innovative, potentially game- changing products and services.

About the Author

Michael L. Gonzales, Ph.D., is an active practitioner in the IT space with over 30 years of industry experience serving in roles of chief architect and senior solutions strategist. He specializes in the formulation of business analytics for competitive advantage in global organizations. Recent engagements include companies in the top global 100.

Dr. Gonzales holds a Ph.D. from the University of Texas with a concentration in Information and Decision Science. He has presented and published his research at leading IT international conferences, including: Decision Sciences Institute, Americans Conference on Information Systems, and Hawaii International Conference on Systems Science. His research streams include analytics against extremely large data and success factors for IT-enabled competitive advantage.

Dr. Gonzales is a successful author, industry speaker and is currently the Director of Research for Advanced Analytics for a leading IT consulting firm, Prolifics, Inc.

About Eckerson Group

Eckerson Group is a research and consulting firm that helps business and analytics leaders use data and technology to drive better insights and actions. The firm helps companies develop strategies and roadmaps that maximize their investment in data and analytics. Its consultants and researchers each have more than 20 years of experience in the field and are uniquely qualified to help business and technical leaders succeed with business intelligence and analytics, big data management, data governance, performance management, and the internet of things.

¹ Hagel, John et al, Shaping Strategy in a World of Constant Disruption, HBR, October, 2008.
² Fox, Justin, Apple Versus the Strategy Professors, HBR, January, 2013.
³ Rowinski, Dan, Google’s Game of Moneyball In the Age of Artificial Intelligence, January, 2014.
⁴ Mayes, Randall, The Future, The Futurist, November-December, 2014.