On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Anaconda, Cloudera, Databricks, Dataiku, Datawatch, Domino, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta. Continue reading Data Science Platforms News Roundup, June 2018

I am constantly asked the question “What does one have to do to implement DevOps”, or some variant.  Most people who ask this question say how they have spent time searching for an answer. The pat answers they encounter typically is either technology-based (“buy these products and achieve DevOps magic”) or a management one such as “create a DevOps culture.” Both are vague, flippant, and decidedly unhelpful.

 

My response is twofold. First, technology and tools follow management and culture. Tools do not make culture and a technology solution without management change is a waste. So, change the culture and management first. Unfortunately, that’s the hard part. When companies talk about changing culture for DevOps they often mean implementing multifunction teams or something less than that. Throwing disparate disciplines into an unregulated melting pot doesn’t help. These teams can end up as dysfunctional as with any other management or project structure. Team members will bicker over implementation and try to protect their hard-won territory.

 

As the old adage goes, “everything old is new again” and so-called DevOps culture is no different. Multi-functional teams are just a flavor of matrix management which has been tried over and over for years. They suffer from the same problems. Team members have to serve two masters and managers act like a group of dogs with one tree among them. Trying to please both the project leader and their functional management creates inherent conflicts.

 

Another view of creating DevOps culture is, what I think of as, the “CEO Buy-in Approach”. Whenever there is new thinking in IT there always seems to be advocacy for a top-down approach that starts with the CEO or CIO “buying in” to the concept. After that magic happens and everyone holds hands and sings together. Except that they don’t. This approach is heavy-handed and an unrealistic view of how companies, especially large companies, operate. If simply ordering people to work well together was all it took, there would be no dysfunctional companies or departments.

 

A variation on this theme advocates picking a leader (or two if you have two-in-the-box leadership) to make everyone work together happily. Setting aside the fact that finding people with broad enough experience to lead multi-disciplinary teams, this leads to what I have always called “The Product Manager Problem.”

 

The problem that all new product managers face is the realization that they have all the responsibility and none of the power to accomplish their mission.

 

That’s because responsibility for the product concentrates in one person, the product manager, and all other managers can diffuse their responsibility across many products or functions.

 

Having a single leader responsible for making multi-functional teams work creates a lack of individual accountability. The leader, not the team, is held accountable for the project while the individual team members are still accountable to their own managers. This may work when the managers and project team leaders all have great working relationships. In that case, you don’t need a special DevOps structure. Instead, a model that creates a separate project team leader or leaders enables team dysfunction and the ability to maintain silos through lack of direct accountability. You see this when you have a Scrum Master, Product Owner, or Release Manager who has all the responsibility for a project.

 

The typical response to this criticism of multi-functional teams (and the no-power Product Manager) is that leaders should be able to influence and cajole the team, despite having no real authority. This is ridiculous and refuses to accept that individual managers and the people that work for them are motivated to maintain their own power. Making the boss look good works well when the boss is signing your evaluation and deciding on your raise. Sure, project and team leaders can be made part of the evaluation process but, really who has the real power here? The functional manager in control of many people and resources or the leader of one small team?

 

One potential to the DevOps cultural conundrum is collective responsibility. In this scheme, all team members benefit or are hurt by the success of the project. Think of this as the combined arms combat team model. In the Army, a multi-functional combined arms teams are put together for specific missions. The team is held responsible for the overall mission. They are responsible collectively and individually. While the upper echelons hold the combined arms combat team responsible for the mission, the team leader has the ability to hold individuals accountable.

 

Can anyone imagine an Army or Marine leader being let off the hook for mission failure because one of their people didn’t perform? Of course not, but they also have mechanisms for holding individual soldiers accountable for their performance.

 

In this model, DevOps teams collectively would be held responsible for on-time completion of the entire project as would the entire management chain. Individual team members would have much of their evaluation based on this and the team leader would have the power to remediate nonperformance including removing a team member who is not doing their job (i.e. fire them). They would have to have the ability to train up and fill the role of one type of function with another if a person performing a role wasn’t up to snuff or had to be removed. It would still be up to the “chain of command” to provide a reasonable mission with appropriate resources.

 

Ultimately, anyone in the team could rise up and lead this or another team no matter their speciality. There would be nothing holding back an operations specialist from becoming the Scrum Master. If they could learn the job, they could get it.

 

The very idea of a specialist would lose power, allowing team members to develop talents no matter their job title.

 

I worked in this model years ago and it was successful and rewarding. Everyone helped everyone else and had a stake in the outcome. People learned each other’s jobs, so they could help out when necessary, learning new skills in the process. It wasn’t called DevOps but it’s how it operated. It’s not a radical idea but there is a hitch – silo managers would either lose power or even cease to exist. There would be no Development Manager or Security Manager. Team members would win, the company would win, but not everyone would feel like this model works for them.

 

This doesn’t mean that all silos would go away. There will still be operations and security functions that maintain and monitor systems. The security and ops people who work on development projects just wouldn’t report into them. They would only be responsible to the development team but with full power (and resources) to make changes in production systems.

 

Without collective responsibility, free of influence from functional managers, DevOps teams will never be more than a fresh coat of paint on rotting wood. It will look pretty but underneath, it’s crumbling.

Today in CMSWire, Amalgam Insights Research Fellow Tom Petrocelli advises the developer community to support chaos engineering on CMSWire.

As it becomes increasingly important for organizations reliable IT infrastructure, traditional resilience testing methods fall short in tech ecosystems where root-cause troubleshooting is increasingly difficult to manage, control, and fix. Rather than focusing purely on the lineage of tracing catastrophic issues, Petrocelli advises testing abrupt failures that mimic real-world issues.

To learn more about the approach of chaos engineering to managing scale-out, highly distributed, and varied infrastructure environments, read Tom’s full thoughts on CMSWire.

Today, Onalytica, a leader in influencer marketing, published an interview on Hyoun Park as a key influencer in the Business Intelligence and Analytics markets. In this interview, Hyoun provides guidance on:

• How he became an expert in the Analytics and BI space based on over 20 years of experience
• The 4 Key Tipping Points for BI that Hyoun is excited about
• Hyoun’s favorite BI influencers, including Jen Underwood, Doug Henschen, John Myers, Claudia Imhoff, and Howard Dresner
• And then the 4 trends that will drive the BI industry over the next 12 months.

To read the interview and see what inspires this influencer, read the interview on the Onalytica website.

This summer, my Amalgam Insights colleague Hyoun Park and I will be teaming up to address that question. When it comes to data science platforms, there’s no such thing as “one size fits all.” We are writing this landscape because understanding the processes of scaling data science beyond individual experiments and integrating it into your business is difficult. By breaking down the key characteristics of the data science platform market, this landscape will help potential buyers choose the appropriate platform for your organizational needs. We will examine the following questions that serve as key differentiators to determine appropriate data science platform purchasing solutions to figure out which characteristics, functionalities, and policies differentiate platforms supporting introductory data science workflows from those supporting scaled-up enterprise-grade workflows.

Continue reading What Data Science Platform Suits Your Organization’s Needs?

On June 20, 2018 Degreed acquired Pathgather. The terms of the acquisition were not disclosed. All Pathgather employees are joining Degreed, creating a team of nearly 250 employees. This represents the merger of two companies present at the birth of the now-booming Learning Experience Platform (LEP) industry. Degreed and Pathgather have been direct competitors since the start. As a single entity, they are formidable with a client base of more than 200 organizations, with over 4 million licensed users and nearly $100 million in funding. From a learning science perspective, the marriage of psychology and brain science—Degreed is now stronger as well. Continue reading The Learning Science Perspective: Why Degreed Acquired Pathgather to Rapidly Grow the Learning Experience Platform Market

Amalgam has just posted a new report: The Roadmap to Multi-Million Dollar Machine Learning Value with DataRobot. I’m especially excited about this report for a couple of reasons.

First, this report documents multiple clear value propositions for machine learning that led to the documented annual value of over a million dollars. This is an important metric to demonstrate at a time when many enterprises are still asking why they should be putting money into machine learning.

Second, Amalgam introduces a straightforward map for understanding how to construct machine learning products that are designed to create multi-million dollar value. Rather than simply hope and wish for a good financial outcome, companies can actually model if their project is likely to justify the cost of machine learning (especially the specialized mathematical and programming skills needed to make this work.)

Amalgam provides the following starting point for designing Multi-Million dollar machine learning value:

Stage One is discovering the initial need for machine learning, which may sound tautological. “To start machine learning, find the need for machine learning…” More specifically, look for opportunities to analyze hundreds of variables that may be related to a specific outcome, but where relationships cannot be quickly analyzed by gut feel or basic business intelligence. And look for opportunities where employees already have gut feelings that a new variable may be related to a good business outcome, such as better credit risk scoring or higher quality supply chain management. Start with your top revenue-creating or value-creating department and then deeply explore.

Stage Two is about financial analysis and moving to production. Ideally, your organization will find a use case involving over $100 million in value. This does not mean that your organization is making $100 million in revenue, as activities such as financial loans, talent recruiting, and preventative maintenance can potentially lead to billions of dollars in capital or value being created even if the vendor only collects a small percentage as a finder’s fee, interest, or maintenance fee. Once the opportunity exists, move on it. Start small and get value.

Then finally, take those lessons learned and start building an internal Machine Learning Best Practices or Center of Excellence organization. Again, start small and focus on documenting what works within your organization, including the team of employees needed to get up and running, the financial justification needed to move forward, and the technical resources needed to operationalize machine learning on a scalable and predictable basis. Drive the cost of Machine Learning down internally so that your organization can tackle smaller problems without being labor, cost, and time-prohibitive.

This blog is just a starting point for the discussion of machine learning value Amalgam covers in The Roadmap to Multi-Million Dollar Machine Learning Value with DataRobot. Please check out the rest of the report as we discuss the Six Stages of moving from BI to AI.

This report also defines a financial ROI model associated with a business-based approach to machine learning.

If you have any questions about this blog, the report, or how to engage Amalgam Insights in providing strategy and vendor recommendations for your data science and machine learning initiatives, please feel free to contact us at info@amalgaminsights.com.

In my recent Market Milestone, Calero Acquires Veropath to Bolster its Global Role in Technology Expense Management, I made a quick comment about Veropath as an “accretive acquisition target.” But then I realized that I hadn’t explained what that meant from an Amalgam perspective.

From Amalgam’s perspective, the Technology Expense Market (aka Telecom Expense Management, although these solutions now regularly manage a wide variety of IT assets, services, and subscriptions) roughly breaks out into companies of five sizes, each with capabilities that could be considered “accretive” to larger organizations. I should add that there are a number of additional TEM companies that are at these sizes, but do not fit these profiles. Outlying companies might be very profitable, stable, and good providers, but are not typically considered great acquisition targets.

The first size is those of 1 – 10 employees. These are companies that are usually good at a specific task or have a single product that is custom-suited to managing a specific capability, such as automated invoice processing or rate plan optimization or network data management. The companies in this space tend to have a combination of specialization and subject matter expertise from a technical perspective, but lack the support staff to manage a large number of clients. These are a combination of technology acquisitions and acquihires.

The second size is 10 – 30 employees. These Technology Expense Management companies have found a specific geographical, market, or service niche and tend to have some combination of technology and services. There is a long tail of TEM companies in this category that lack the scale to go national, but may have built strong geographic, technical, or process management capabilities. However, these companies typically lack the sales and marketing engine to expand beyond their current size, meaning that further growth will often require outside capital and additional investment in revenue-creating activities.

The third size is roughly between 30 and 75 employees. At this size, the TEM vendor has found a strong go-to-market message and is supporting both mid-market and enterprise vendors regularly. These vendors have built their own platform, have a significant internal support team, and typically have a strong sales leader who is either the CEO or a VP of Sales. At this point, Amalgam notes that the biggest challenge for these vendors is creating a management team empowered to make good decisions and in letting go of decisions as a CEO. This management challenge is quite difficult to surpass, both because it adds a lot of complexity to the business with very little immediate benefit to the CEO or the firm’s employees. However, at this scale, TEM businesses are also a good target for acquisition as they have built out every business function needed to be a successful and stable long-term business. Roughly speaking, these companies tend to have about $100 million to $500 million in spend under management and run as stable, profitable businesses. There are a number of strong TEM vendors in this space including, but not limited to, Avotus, Ezwim, ICOMM, Mobile Solutions, Network Control, SaaSwedo, SmartBill, Tellennium, Valicom, vCom, VoicePlus, and Wireless Analytics

The fourth size is between 75 and 1,000 employees. These TEM companies are rarely acquired and start becoming the acquirers of other TEM companies because they have successfully built an organization that can scale and run multiple business units. At this size, TEM companies start to manage over a billion dollars a year in spend and tend to either be publicly traded or backed by private equity. And at this point, TEM companies start running into adjacent competitors in markets such as Managed Mobility Services, SaaS Vendor Management, Cloud Service Management, IT Asset Management, and other related IT management areas. This is an interesting area for TEM because, after several years of watching Tangoe acquire businesses at this scale in the early 2010s, multiple new vendors appeared at this scale in the mid-to-late 2010s. Currently, Amalgam considers Calero, Cass, Cimpl, Dimension Data, MDSL, Mobichord, Mer Telemanagement Systems (MTS), One Source Communications, Sakon, and TNX to be representative of vendors of this size of large TEM providers.

Currently, the fifth size of 1,000+ employees is a market of one: Tangoe. This company has grown both organically and acquisitively to manage over $38 billion in technology spend, making it roughly six times larger than its nearest competitor. At this size, Tangoe focuses on large enterprise and global management challenges and is positioned to start pursuing adjacent markets more aggressively. Amalgam believes that there is sufficient opportunity in this market for additional firms of this scale, however, and expects one or more of Calero, Cass Information Systems, MDSL, or Sakon to leap into this scale in the next three-to-five years.

So, when Amalgam refers to “accretive opportunities” in the TEM space from an acquisition perspective, this is the rough context that we use as a starting point. Of course, with the 100+ firms that we track in this market, any particular category has both nuance and personalization in describing individual firms. If you have any questions regarding this blog, please feel free to follow up by emailing info@amalgaminsights.com and if you’d like to learn more about what Calero has done with this acquistion of Veropath, one of the largest UK-headquarted TEM vendors, please download our Market Milestone available this week (or as supplies last) for free.

On May 22, Domino held its first Analyst Seminar in advance of its Rev conference for data science leaders. Domino provides an open data science platform to coordinate data science initiatives across enterprises, integrating data scientists, IT, and line of business.

At the Analyst Seminar, Domino introduced its Model Management framework: five pillars supporting a core belief that data science best practices involve data science not just being a siloed department or team, but that its resulting models should drive the business. For this to be possible,  all relevant stakeholders across the enterprise will need to buy into data science initiatives, as this will involve changes to existing business process in order to take advantage of the knowledge gained from data science projects.

Continue reading Domino Debuts Data Science Framework

Key Stakeholders: Chief Learning Officers, Chief Human Resource Officers, Learning and Development Directors and Managers, Corporate Trainers, Content and Learning Product Managers, Chief Executive Officer, C-suite, Managers

Top Takeaways:

• If you want high-quality leadership at all levels of your organization then you need to provide employees with effective broad-based leadership training and development solutions.
• Optimized leadership training and development are multi-faceted and involve multiple distinct learning systems in the brain that each have different processing characteristics. Thus, a tool that is effective for one aspect of leadership training, may be sub-optimal for another aspect. One-size-does-NOT-fit-all.
• Many vendors offer a broad suite of tools, with little guidance on what to use when.
• Learning science serves as a guide for optimally mapping tools onto training problems.
• Tools optimized for leadership training and development must be grounded in learning science – the marriage of psychology and brain science. In this report, I briefly outline the learning science behind leadership profile, people skills and situational awareness training and development.

Do you want leaders with a deep understanding of the “Leadership Profile” that I define with the following traits?

• Knowing the definitions and implications of unconscious bias, harassment and diversity
• Having a strong grasp of their organization’s vision and structure
• Leading with strong communication and people skills that show empathy and respect for others in every interaction, including those that are challenging (e.g., performance evaluations or conflict resolution)
• Leading with situational awareness an ability to “read” any situation, “think on one’s feet” and adjust, as well as instill confidence in others
• Leading individuals as well as their team through adversity, all in a calm and collected manner

If the answer to these questions is “yes”, then your organization wants leadership training and development tools that are grounded in learning science – the marriage of psychology and brain science. You want to find the vendors who have leveraged the $100’s of millions of dollars in psychological and brain science research (over $10 million of which was awarded to the author) by building a scientifically-grounded, optimized platform for leadership training and development. But how can you identify these vendors?

In this era of digital transformation, where organizations rely increasingly on cross-functional and deeply collaborative teams, leadership is becoming more distributed and employees are taking on leadership roles much earlier in their careers. Combine this with some of the recent corporate crises (#metoo, unconscious bias, discrimination) and effective leadership training becomes even more important. The work of thought leaders such as Jim Collins who identify world-class leaders who are humble, clear, and fair show the business value of leaders who understand people.

In this report, I briefly review the psychology and brain science of learning, then map this learning science onto three critical aspects of optimized leadership training and development:

• Hard Skills of Leadership – The hard skills of leadership training and development provide the leader with all of the knowledge and facts associated with strong leadership. This includes learning the rules, regulations, and compliance requirements, but also includes learning the “hard” skills” of people skills such as the ability to identify unconscious biases, sexual harassment, and discriminatory behavior. Knowledge of verbal and non-verbal communication skills and team dynamics are also critical.
• “People” Skills Training – The goal is to provide the leader with the people skills necessary to communicate effectively with verbal and non-verbal cues. This includes eliminating any action from the leader’s behavioral repertoire that expresses bias, harassment or discrimination. These people skills must be trained effectively, and across a broad range of typical and atypical situations (e.g., during conflict resolution, performance evaluation, or under time or social pressure).
• “Situational Awareness” – The best leader can “read” any individual, group or situation, can “think on their feet” and can adjust their strategy and behavior effectively. This involves a rich suite of cognitive, behavioral, but most importantly emotional skills. One must develop the ability to “walk a mile in someone else’s shoes” to understand and “read” another’s state of mind, as well as to understand how one’s own behavior is interpreted by others. This requires training across a broad range of situations. High situational awareness is key to knowing “what to do when”.

Distinct Learning Systems in the Brain (the “What”, the “How”, the “Feel”)

As I have elaborated in detail in other research reports, there are distinct learning systems in the brain. Each system is “optimally” tuned to specific types of learning, and critically, the training tools that most effectively recruit each learning system are different.

The figure below provides an overview of the three main learning systems in the brain, along with the relevant psychological processes, and a schematic of the relevant brain regions.

 

Cognitive Skills Learning (The “What”): The cognitive skills learning system has evolved to store information and learn facts. This system mediates hard skills learning and I refer to this as the “what” system. Cognitive skill learning relies heavily on working memory and attention and is mediated by the prefrontal cortex in the brain. Processing in this system is optimized when information comes in brief, coherent chunks (often referred to as microlearning), is delivered spaced over time, and is tested periodically to ensure storage of the information in long-term memory that resides in the hippocampus and medial temporal lobe structures. Mental repetitions are key to long-term memory storage. I refer to these procedures as those that “Train for Retention”.

Behavioral Skills Learning (The “How”): The behavioral skills learning system has evolved to learn behaviors. This system mediates people (aka soft) skills learning, and I refer to this as the “how” system. Behavioral skill learning does not rely on working memory and attention, in fact, I have shown that “overthinking it” hinders behavioral skills learning. Behaviors are learned through gradual, incremental, dopamine-mediated reward/punishment learning in the basal ganglia of the brain. Processing in this system is optimized when behavior is interactive and is followed in real-time (literally within milliseconds) by corrective feedback. If a behavior is elicited that is rewarded, dopamine will be released into the basal ganglia, the neural connections that drove that behavior will be strengthened, and the likelihood that behavior will be elicited again will increase. If a behavior is elicited that is punished, dopamine will not be released, the neural connections that drive that behavior will be weakened, and the likelihood that behavior will be elicited again will decrease. Physical repetitions are key to long-term behavior change.

Emotional Learning (The “Feel”): The emotional learning system has evolved to facilitate the development of empathy and understanding of our and others’ behaviors, and to “read” nuance in each situation. This system is critical to situational awareness, affects processing in both the cognitive and behavioral skills learning systems in the brain, and is referred to as the “feel” system. Emotional learning affects how one processes and links hard skills information and facts to specific situations, and what people skills are engaged in specific situations. Emotional learning can be instilled by “walking a mile in someone else’s shoes” and learning to “read” individual and group personality. Emotional learning recruits the amygdala and other limbic structures.

Optimized Leadership Training and Development

Hard Skills of Leadership: The goal of executive training is to provide the leader-in-training with as much leadership-relevant information as possible. This includes information about the rules and regulations that govern the organization to ensure compliance. It also includes fact-based training on important psychological factors such as the definition of unconscious bias, harassment and discrimination. Information on how to identify and avoid inappropriate behaviors is also important. The amount of information is substantial, and the learning science is clear on how to impart this information effectively. Because this type of learning is mediated by the cognitive skills learning system in the brain (the “what”), which has substantive working memory and attentional constraints, brief bursts of compelling content should be utilized. Training should be spaced over time, and retention testing should be incorporated. Training content should be available 24/7 on any device. A number of vendors provide excellent tools for training the leadership profile and hard skills in general.

“People” Skills: The best leader is one who leads by example, says and does the right things in an ever-changing setting, and meets all of these requirements simultaneously. At its core, people skills are about behavior. They are about what we “do”, “how” we do it, and our “intent”. People skills are challenging, nuanced and difficult to master. In leadership, their importance is amplified because the goal of a leader is to maximize productivity and the ROI obtained from employees while simultaneously keeping employees positively engaged, satisfied with their workplace environment, and disinterested in leaving for another organization. Behavior change involves gradual, incremental, dopamine-mediated reward/punishment learning in the basal ganglia of the brain (the “how”) and extensive physical repetitions. People skills training requires in person or virtual role play with real-time interaction and corrective feedback. Ideally, and especially for leadership training and development, the role play should occur under a broad range of environmental settings with different ethnic and gender mixes, typical and atypical settings, and under extreme conditions. Unfortunately, many of the tools that optimize hard skills learning (e.g., the Leadership profile) are suboptimal for people skills training. These include spaced training, microlearning and knowledge testing.

For example, short, focused training on a single situation (microlearning) is ineffective for people skills training because behavioral skills are best learned with longer training sessions and broad variability in scenarios. From a learning science perspective, this is the area most in need of additional corporate offerings. No currently available corporate training platforms include a broad-based, real-time interactive offering that directly engages the behavioral skills learning system in the brain. That said, I fully expect immersive technologies, such as virtual reality, combined with high-end computer graphics and AI to drive the interactions will solve this problem in the near future.

“Situational Awareness”: The best leader is the one who adapts quickly and effectively to any situation—commonly referred to as situational awareness. This involves a deep cognitive (the “what”), behavioral (the “how”) and emotional (the “feel”) understanding. One who is strong in the trait of situational awareness can accurately read any situation, knows what to do in each situation, and has the behavioral repertoire to engage each situation with the appropriate set of behaviors. This involves a keen understanding of individual and group motivation and personality dynamics, empathy, and an ability to “walk a mile in one’s own or another’s shoes” to see all views of a situation. The optimal method for training situational awareness is to combine the cognitive skills associated with Leadership Profile training with the behavioral skills associated with broad-based people skills training through the lens of emotion, motivation and personality. As suggested above this may ultimately be solved by virtual reality technology and high-end AI to drive interactions. Some vendors address specific critical aspects of situational awareness directly (e.g., measuring and leveraging personality) whereas others rely on efforts such as the use of diverse scenario-based training and testing.

Conclusion and Call to Action

As this very brief primer suggests, leadership training and development optimized for brain functioning is critical to an organization’s success. Employees are taking leadership roles earlier in their careers than ever before, and the need for effective corporate leadership is on the rise. When leaders are effective, profits rise, employees are engaged and satisfied, and turnover is low. When leaders are ineffective, profits diminish, the workplace sours, employees leave and organizations can lose billions of dollars overnight (e.g., Facebook, Uber, etc). The best leadership training and development solutions are aligned with the learning science – the marriage of psychology and brain science – and optimally engage the “what”, “how”, and “feel” systems in the brain. Organizations can obtain a competitive advantage by leveraging broad-based leadership training and development solutions that empower employees with the leadership knowledge that they need, the people skills necessary to lead by example, and the awareness to read any situation and adjust effectively when needed. Learning and Training professions should work with the C-suite and management at all levels to evaluate specific leadership needs and gaps, then develop solutions and policies that address these challenges. This approach takes full advantage of learning science to build better leaders.

If you would like to speak with the author of this piece, W. Todd Maddox, Ph.D. to learn more about his 25+ years research in brain science supported by over $10 million in external funding and cited over 10,000 times by his peers, please contact us at info@amalgaminsights.com to set up a time to chat with Todd.