Anthropology, Cognitive Psychology for IT Innovation

Do I need to understand psychology and anthropology to become an effective IT innovation office?  The answer is “YES” according to Gartner’s article titled “Meeting the Information Needs of the Chief
Innovation Officer in 2023 ”

I can think of these two areas that can be benefited from understanding psychology.

• User Experience (UX) Design: The principles of cognitive and behavioral psychology can be applied to design user interfaces that are intuitive and user-friendly. For instance, by understanding cognitive biases and decision-making processes, designers can create systems that help users make better decisions, avoid errors, and navigate more efficiently.
• Motivation and Engagement: Behavioral psychology can provide insights into what motivates people to engage with certain technologies. These principles can be used to develop systems and applications that increase user engagement, such as gamification elements in a productivity app.

Also, I believe anthropology provides a rich understanding of cultural dynamics, which is critical when designing technology for global use, such as:

• Understanding User Needs: By studying various cultures, companies can understand the different needs, preferences, and behaviors of users worldwide. This understanding can lead to the creation of more relevant and effective products and services.
• Cultural Sensitivity in Design: Anthropological research can ensure that technological innovations are culturally sensitive. For example, color choices in an application can have different connotations in different cultures. Thus, anthropological insights can help in making design decisions that are globally acceptable.

OK,  now we jump-start our innovation journey with useful tools. Then who is with us for the adventure? This article helps us to identify six different innovative people types:

Navigator(Strategist)  Navigators discern which technologies are crucial for the company, focusing on understanding current and future business processes and architectural plans.

Scholar(Pinoeer): Scholars tend to push the limits of new technology capabilities, often introducing fundamentally new solutions.

Responder(Validator): Responders ensure a technology’s maturity for deployment and support its first-time use within the company.

Counselor(Influencer): Counselors promote technology innovation through education and inspiration, primarily recommending technologies to top business and IT executives.

Conductor(Coordinator): Conductors coordinate and utilize the efforts of other groups within the company. They serve as centralized coordinators in decentralized organizations.

Pollinator(Mentor): Pollinators, or catalyst teams, stimulate distributed innovation within the organization, focusing more on coaching and mentoring nascent ideas.

If you ask me about the group I identify with, I would say, I embody 95% of the Scholar traits and 5% of the Navigator’s characteristics, residing in a world of endless possibilities next to Peter Pan’s Neverland.

Please visit me soon. I will order you a cup of “Rainbow Pixie Dust Parfait” 🙂

IT Strategic Planning: Chore or Creativity

IT strategic planning can be either an exciting intellectual adventure for a solution architect or it can transform into a routine, annual chore that merely involves patching up the gaps.

These three articles offer valuable perspectives to those making strategic decisions in the realm of IT strategic planning.

EA for IT decision-making: Enterprise architecture can be leveraged to strategically align business capabilities with technology investments. A key part of this is mapping key business capabilities to underlying technology applications and infrastructure services, enabling the business to emphasize agility in relevant areas and cost reduction in less dynamic/non-core areas. It’s a cooperative effort between domain-specific solution architects and broadly focused enterprise architects to yield optimal business outcomes for technology investments.

ITIL for decision making: Without a clear IT operating model with well-defined processes, roles, and accountability, the strategic guidance from enterprise architecture might not be consistently applied, leading to underperforming technology investments. This is where ITIL (Information Technology Infrastructure Library) and ITSM (IT Service Management) come into play. These methodologies can help to ensure that all IT activities are aligned, consistent, and predictable.

Data Analytic for Decision Making:

1. Increase Transparency: The “black box” nature of many analytics processes can contribute to mistrust. By making these processes more transparent and understandable, the CIO can help to build trust. This could involve explaining how models work, what data is used, and how decisions are made.
2. Build Ecosystems: By providing a 360-degree view of data and insights, the CIO can help stakeholders to understand and trust the analytics process. This might involve developing dashboards, reports, or other tools that make data and insights easily accessible and understandable.
3. Stimulate Innovation: Encouraging new ideas and maintaining a competitive stance can help to keep the organization at the forefront of D&A practice. This might involve setting up an analytics R&D function, encouraging innovation, and staying updated on latest trends and technologies.

Among the various strategies, my investment would be focused on data and analytics tools. These tools are not only vital for analyzing existing customers but also instrumental in discovering new customer bases and developing innovative products and services. By fostering a culture that encourages new ideas and emphasizes continuous development in data and analytics practices, we can ensure that our organization remains at the forefront of the industry, maintaining a competitive edge.

I like Gartner’s Toolkits. They have everything that I need and are well organized.

Gartner’s  EA toolkit typically refers to a set of tools, methodologies, or software used to create, communicate, and manage the production of these artifacts. The toolkit aids in modeling, designing, and visualizing the architecture. It could include software tools for diagramming, modeling, architecture repository management, decision tracking, and more. I was glad that I used some of the tools in that toolkit and they were very useful.

A roadmap is my preferred tool as a solution architect, even though it’s not mentioned in the toolkit. Importantly, it should have an appealing and compelling visual presentation.

Missing Pieces  Found

I knew something was missing and had to go back to double-check to make sure I was doing the right things. The first time when reading Gartner’s articles about Business Outcome Driven EA(BODEA), it really made sense to me, then started to ask what then drives the business and how it should be defined. These two articles are a prequel to BODEA. All the things that drive business should be captured and documented in the form of ‘business context’ so that business leaders are informed and agree upon strategic decisions.

This business context becomes a vital part of the architectural process as it serves as a reference point(the missing piece) for all strategic and tactical decisions. By creating a comprehensive business context(BC), business and IT leaders can gain a unified understanding of the business’ direction and be aligned with the path forward.

This process ensures that the EA’s efforts are directly contributing to the achievement of business objectives, thereby demonstrating the business value of the EA. It promotes improved collaboration, communication, and alignment between the business and IT, making the EA a strategic partner in driving business outcomes.

Two practical & personal reasons why BODEA should be based on BC

1. Promotes Skill Development: A business context-driven approach encourages enterprise architects to develop business engagement skills. Allowing EA team members to interface directly with the business context work promotes a deeper understanding of business drivers and facilitates better collaboration.
2. Prevents Incomplete Missions: Distinguishing between business context and enterprise business architecture (EBA) prevents misalignment between business and IT. Without this distinction, there’s a risk that business context work might be mistaken for a complete business architecture, leaving crucial “architecting the business” work unsupported and unaddressed.

Being unable to demonstrate business aptitude is the last thing I want as a solution architect.

Last year, I was looking for a new job. Since I have been CISSP & HCISPP certified with ISC2.org, the security architect position was to my attention. I found most of the JD shared similar requirements. For instance,

In Florida, Solution Engineer JD shares the same requirements.

According to this article, to hire the best security architect, I will have to

1. Define the Role Flexibly: Be adaptable in defining the roles and responsibilities of the security architect position. This can help me attract a wider range of candidates.
2. Prioritize Requirements: Distinguish between must-have, nice-to-have, and wish-to-have skills, competencies, knowledge, and experience.
   • Must-Have: It includes baseline competencies and knowledge needed to fulfill the security architect’s primary responsibilities.
   • Nice-to-Have: These are desirable but not necessary qualifications. They often involve additional years of experience or certain certifications.
   • Wish-to-Have: These are special qualifications that may justify a higher compensation package. It might include specific industry expertise, knowledge of certain regulations, or hands-on experience with specific security tools.
3. Rethink Requirements: Avoid overly ambitious requirements that might be hard or impossible to meet.

In a conversation with a CISO of a healthcare service provider, he highlighted the difficulty in finding individuals with the courage to testify before FBI officials. The most challenging aspect isn’t always the threat itself, but ensuring the security and confidence of the people we protect, particularly when they start to feel insecure.

In my opinion, all threats are uncertain.  There is no telling when or where they will arrive.  According to this article, they are :

1. Nascent: These threats are usually associated with emerging technologies that are not yet in widespread use in production environments.
2. Hyped: Hyped threats suddenly gain a lot of attention, often based on anecdotal evidence. They might be real, but the individual attack examples can distract security leaders from the underlying and longer-term work.
3. Emerging: As business practices and technologies evolve, security teams trail behind lacking strong preventative controls and detection and response capabilities for these new technology threats.
4. Latent: These threats are under the radar for most organizations, under the assumption that attackers will exploit easier approaches. They are categorized further into:

Particularly, I was interested in “Latent Threats(LT)”. They are hard to predict since they are non-technical and have their own stories. Two of the  most common  LTs are:

1. Nontechnology threats: These are threats that arise from assets, events, and behaviors that the organization has no direct control over. The article mentions the shift in work practice as a change that might impact how these threats affect enterprises.
2. Employee Activism: This might initially seem irrelevant, but the internal dynamics of employee and customer activism can seed improved or malicious employee activity.

I will have to continuously update threat intelligence and internal controls to mitigate both uncertain and latent threats, with a proactive, adaptable approach ensuring resilience amidst evolving threat landscapes.

In other words, my adaptability is the key to opening the door to look inside the room of uncertainty.

This article presents nine trending strategies for 2023. Out of the nine trends, I am interested in four areas and hope to use them to further our competitive edge in the coming year.

1.  Digital Immune System: A strong digital immune system can pave the way for a less vulnerable digital infrastructure, promoting customer trust by proactively identifying and addressing threats. This will not only safeguard any operations but also enhance customer trust in the brand name
2. Applied Observability: The application of observability in the organization’s systems and infrastructure can enhance understanding and enables proactive issue resolution. This, in turn, will help in improving the overall customer experience in online shopping.
3. Platform Engineering: The organization can realize the benefits of Platform Engineering through the creation of scalable and sustainable digital platforms. This has the potential to enhance customer interaction, streamline operations, and foster innovation.
4. Adaptive AI:

The retail organization stands to benefit from the implementation of Adaptive AI systems. These systems can offer highly personalized shopping experiences and improved data-driven decision-making, enhancing business operations.

=====================================================

I have heard many times of “Platform Engineering (PE)” and was not sure where it stood between Software Engineering (SWE), DevOps Engineering (DevOps), and Full Stack SWE. This Youtuber provided a historical background of why PE was imperative to developing the application architecture.

Platform engineers are like builders who work behind the wall to meet “nonfunctional requirements”. This role appeals to me because I can set standards, such as what tools, technologies, and protocols that other engineers should follow.

At least no one will tell me “what to do”.

Ambition is expensive, very much.

I am always learning new concepts and this time it was the “Digital Ambition“.  It was not very clear at first, and it seems that it refers to an organization’s strategic vision and goals related to leveraging digital technologies and capabilities to drive significant improvements, growth, and innovation within the business.

The same article discusses three levels of ripple effects when the value proposition is changed.

1. Creating a New Value Proposition:
   • Requires a new business model and capabilities to deliver a changed value proposition, a new operating model and resources aligned towards service delivery,  and a shift in the financial model to balance short-term profitability and long-term scalability.
2. Expanding the Value Proposition:
   • Necessitates changes to the business and operating models to accommodate an expanded value proposition, development of new capabilities and resources to deliver the expanded proposition, and adjustment of the financial model and incentives to align with the new value proposition.
3. Improving the Value Proposition:
   • Focuses on enhancing operational efficiency without changing the business model, introduction of new capabilities, such as automation and intelligence, and  improvement on the operating model and processes to support the enhanced value proposition

I am excited about digital transformation, but it will also require some adjustments to the current value proposition. I will be very cautious because even improving it will cause a huge wave that would shake up the core business model and capabilities.

It is essential to have ambition, but at the same time, I must also open my heart and listen to advise to prevent people from being swept away and perish by the waves.

Data, Pattern, and Meaning (of life)

I’ve learned a few exciting nuances of data analytics through my experience with Python Pandas and Jupyter Notebook.  Suddenly, a bunch of numbers transformed into a story of past, present, and future.  In this article, over ten different roles are discussed in relation to successful data-driven business architecture.

Some of the noticeable roles are

Data/AI Engineer: Data/AI Engineers build and manage data pipelines, ensuring the availability of relevant data for various organizational roles. They are responsible for curating data sets, operationalizing data delivery, and maintaining data governance and security compliance.

Data Scientist: Data Scientists model business problems, discovering insights through quantitative disciplines, and using visualization techniques to present findings. They contribute to the development of the organization’s data infrastructure and provide insights for decision-making processes, often through predictive and prescriptive analytics

Artificial Intelligence/Machine Learning Developer: AI/ML Developers infuse applications with AI capabilities, integrate, and deploy AI models developed by data scientists or provided by service providers. They are skilled in data collection and preparation for model training, API management, and containerization.

Insight, Insight, Insight

All three roles require insights on data to varying degrees, but I would say that the Data Scientist role is most intensively focused on gaining insights from data. Data Scientists use advanced statistical, algorithmic, and data mining techniques to model complex business problems, discover insights, and present these findings for decision-making processes. They specialize in transforming data into actionable knowledge, which often involves creating predictive or prescriptive models to guide business strategies.

This article talks about two different mindsets on data analytic practice.

Deductive reasoning is a top-down approach. Professionals using this approach typically start with a clear understanding of the organization’s objectives and strategy, formulate performance indicators, and then ask specific business questions. They design a data model structured to answer these questions, gather and load the necessary data into that model, and then use this model to answer the questions. This approach is rigorous and systematic, resulting in a solid strategy review that can validate if the organization is on the right track.

Inductive reasoning, on the other hand, is a bottom-up approach. It begins with sets of data, with the goal of discovering unknown insights within them. This approach does not start with a specific question or a rigid data model, but rather it explores the data using various types of analytics, looking for meaningful results. The results can include noise, obvious findings, and new insights, often emerging from the combination of different types of data. Data science and new styles of data management, such as data lakes, utilize this approach.

Becoming philosophical, all of a sudden.

During my college years, I was attracted to a particular branch of philosophy: Existentialism. It posits that existence precedes essence, suggesting that we exist first and only later define our essence or meaning through our actions.

I realized Inductive Reasoning aligns more closely with the existentialist viewpoint. Here, existence (data or evidence) precedes essence (conclusions or theories). Inductive reasoning starts with specific observations or data – raw, undefined existence. Through analysis of this data, patterns are observed, and conclusions are drawn, creating the “essence” or meaning. This reflects the existentialist view of forging our essence through our experiences or actions. We exist first, experience life (gather and analyze data), and from those experiences, derive our essence or meaning (conclusions or theories).

Insightful AI for Meaningful Interpretation

Essentially, we are looking for hidden meanings in data.  An insightful data scientist will notice the complex pattern and pull all evidence together to grant significance to it. Is AI capable of assigning meaning to data?, and to the extent, will it convey purpose to individual life once it collects the data from 5 billion people on Earth?

Would you mind sharing the answer with me?

Gravity in Data

Data Gravity: It was already there, and it has been there since creation. It just seemed too natural to notice its presence. In reading this article, I came across the concept of data gravity for the first time, and I saw why I had so many problems managing data, particularly data migration between different geographical locations, technologies, and cloud environments.

Just like how a planet’s gravitational pull increases as its mass increases, attracting more objects towards it, the same concept applies to data. As data accumulates and its “mass” grows within a particular environment (like a data center or cloud storage), it attracts more services and applications. This is due to the fact that these services and applications often perform better when they are close to the data they need to access, just like objects in space move faster as they get closer to the planet due to increasing gravitational pull.

The factors that accelerate this “gravitational pull” in a data context are latency and throughput. Latency refers to the delay before a transfer of data begins following an instruction for its transfer, and throughput is the amount of data transferred in a given amount of time. Lower latency and higher throughput result in faster data access and processing, making the services and applications more effective and efficient. This can be visualized as services and applications accelerating towards the data at an increasingly faster velocity as they get closer to the data.

As more data accumulates in a given environment, it becomes more difficult to move due to various factors.

Overcoming Gravity

This article gave me a few ideas on how to overcome the data gravity. A Data Hub approach can potentially counteract data gravity by decentralizing the data processing.

Instead of bringing all data to a central repository for processing (which can introduce latency and strain on network resources), the Data Hub architecture can bring the processing to the data, creating integrated views of the data where and when needed. This increases scalability and operability and aligns with the shift in data architectures from collecting data to connecting data.

Probably, it is not 100% feasible to construct a data gravity-free architecture.  The job of the data architect is to redirect the undeniable force toward stability and interconnection while minimizing the power that holds us back from “drifting” without hindrance.