ben brown

June 16, 2023

Business

Redefining Enterprise Architecture: Responding to Tech Demands

ben brown

June 16, 2023

Business

When you think about major corporations and their approach to sustaining their business model and brand long-term, how often is information technology the first thing you think about? For most people it’s staying relevant through crafty marketing and branding initiatives, or big partnerships, all of which are important factors but none of which solely can sustain a business today.

As we know, technology permeates every aspect of business operations. Digital transformation starts with a solid architecture, one that leverages various technological components, such as infrastructure, networks, databases, software applications, and security measures. To be clear, the exact definition for “enterprise architecture” according to Gartner is “a discipline for proactively and holistically leading enterprise responses to disruptive forces by identifying and analyzing the execution of change toward desired business vision and outcomes”.

Embracing a Proactive Approach

The key takeaway here is that technology is not a reactive measure; it’s something that needs to be a proactive and integral part of an organization's approach to long-term sustainability. The key term is “proactive” because the last thing an enterprise can afford is to, again, be reactive in situations where they’re caught off guard by advancements and disruptions.

The world lucked out with AI in the sense that there’s been time for companies to explore its potential and experiment with its capabilities. AI offers transformative opportunities for corporations today who can now redefine their business models and align with the demands of the digital era.

How We’ve Always Known Enterprise Architecture (EA)

EAs have been that guidance that creates, integrates, and manages data and technology to align IT capabilities with the business's goals. The focus for enterprises is now on the tech aspect more so than project delivery or strategizing because that stuff is less comprehensive than the role technology plays in the business landscape today. What we’re outlining in this section is the elimination of the need to balance competing priorities and resources within EA.

The Key Technologies and Business Functions That EA Teams Focus on Today

In 2020, a report from Gartner estimated that 60% of organizations in 2023 would rely on EA to lead their approach to digital innovation. While we don’t have exact figures for that to compare against today, we do have an understanding of the technological and business functions that are a focus for EA teams which include:

Application Architecture
Data recovery
Governance, risk, and compliance
Cloud management
Mobile device management
Intelligent automation
Cybersecurity

Think about two manufacturing companies:

One relies heavily on innovation and strategic thinking, so they establish the following:

Dedicated space for R&D: They allocate a specific area or facility to experiment, prototype, and test concepts before integrating them into the EA.

Agile methodologies: This could involve methodologies such as Scrum or Kanban, to promote flexibility in their development process. This is what’s going to be key for them to respond quickly to market changes and customer demands.

Collaborate with other companies: Typically for IT operations, data governance, and business strategy. This is going to leverage expertise and resources that will contribute to innovation and consistently meet objectives.

Investments in new technologies: This includes exploring emerging technologies relevant to the industry and leveraging them to enhance their manufacturing processes, product development, and overall operational efficiency.

Data-driven Decision Making: They prioritize the collection, analysis, and utilization of data in their decision-making processes. This helps them identify opportunities and inefficiencies which further contributes to consistently meeting goals.

The other company is very project-driven so their focus is on the following:

Project management: This company will emphasize strong project management, with dedicated teams and resources for each project. They have well-defined plans, timelines, and milestones to ensure execution is efficient.

Resource allocation: They prioritize allocating resources based on the specific requirements of each project. This includes assigning personnel, budgeting, and managing project dependencies.

Stakeholder collaboration: The company emphasizes collaboration and communication with stakeholders, both internal and external, to ensure alignment on project goals, requirements, and expectations.

Risk management: This company would likely have robust risk management processes in place to identify, assess, and mitigate potential risks and issues that could impact the success of the project.

The whole point of this comparison is for companies to understand the importance of their EA team focusing on one aspect of projects and strategy either on the side of the business or technology. The best EA teams maximize one area before moving to the next and they never skip steps.

Actionable Recommendations

Closely align your priorities with your business's goals when defining your focus. Gain as much expertise and capabilities as possible in that area, collaborate with stakeholders, and consistently monitor how much you progress.

You always want to make the value of your EA known to decision-makers by demonstrating how it helps meet objectives. Showcase tangible outcomes and demonstrate the ROI of EA initiatives.

Written By Ben Brown

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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

ben brown

June 14, 2023

Business

Top 10 Python Libraries for Data Scientists

ben brown

June 14, 2023

Business

Machine learning and big data applications have seen a surge in usage over recent years. This is due to many factors but the most prominent can be attributed to the demand for businesses to possess data-driven insights. Inevitably this has forced data scientists to find the most efficient methods when building applications and machine learning models that can manage data in this way.

Python is a data scientist's best friend largely because of its simplicity as well as its range of libraries and frameworks that are specifically designed to create applications and manage data. What separates a Python framework or library from a language like R, Java, or Julia for data science is mainly its simplicity and the range of libraries and frameworks available.

In the realm of data science, there’s so much variety when it comes to how you can approach app development. Python is highly flexible, which will always be a major draw for data scientists. With that said, it’s important to not just know your options but ultimately how to leverage them. Here are some of the top choices for data scientists when it comes to Python libraries:

TensorFlow

This is a great choice when integrating machine learning that allows data scientists to get a visual understanding of how data flows through neural networks or processing nodes. It’s an open-source software library that Google created for users to build and deploy machine learning models and then train them at scale.

Pandas

This tool is super powerful for data manipulation and analysis as it provides structures and functions that work with pre-existing data. It also allows data scientists to easily transform and preprocess data which in the long run allows them to extract more of those valuable insights that we mentioned were in demand. Pandas’ ability to handle large datasets and integrate them with other libraries makes it a fundamental tool in a data scientist's toolkit.

OpenCV

As the name implies, this is another open-source library used for real-time computer vision tasks. With OpenCV, data scientists can do things that contribute to a much broader ideal for Artificial Intelligence. This includes tasks such as object detection, facial recognition, image stitching, and video analysis.

Theano

This is one of the first open-source software libraries for deep-learning. It’s known for its speed (due to its ability to optimize) and efficiency when handling mathematical computations, especially those found in model development for machine learning. Of course, now TensorFlow is the renowned favorite when it comes to deep learning but the two collaborate well and offer unique advantages.

PyTorch

PyTorch is another popular deep-learning framework with dynamic computational graphs and highly productive GPU acceleration (Great for data-intensive apps). It provides an intuitive programming interface that’s flexible and has gained popularity because of how easy it is to use and the level of support it offers for research and prototyping deep learning models.

NumPY

This is an imperative library if you’re dealing with numerical and scientific computing in Python. Healthcare, Finance, Manufacturing, Research, and Education among other industries for example will all utilize NumPY to solve problems and manipulate large datasets unique to their needs.

Matplotlib

This library is your go-to for data visualization and analysis. It works with other Python libraries such as Pandas and NumPy which allows data to easily be manipulated and integrated. For app dev, this will help with the data-driven aspect through its range of features and plotting functionalities.

Seaborn

Seaborn is a library built on top of Matplotlib for data visualization. It provides a higher-level interface and a variety of statistical visualizations. It simplifies the process of creating visually appealing and informative plots, which makes it valuable for data exploration and sharing results.

Statsmodels

As the name implies, this is a library for statistical modeling and hypothesis testing. It offers a comprehensive set of tools for regression analysis, time series analysis, survival analysis, and other statistical techniques. Statsmodels are very widely used in fields such as economics, social sciences, and finance.

Scikit-learn

This is a widely used machine learning library that provides a range of algorithms and tools for classification, regression, clustering, and dimensionality reduction. It's known for its user-friendly API and comprehensive documentation, making it an excellent choice for both beginners and experienced data scientists.

Choosing What’s Best For You

There’s a lot for data scientists to consider when narrowing down what libraries and frameworks are best for the task at hand. When it comes to Python, the number of options is a huge benefit but it doesn’t come without its challenges.

If you don’t have expertise in particular libraries, it can be difficult to navigate integration, and learning them on the fly is not easy, nor is it efficient.

Some quick notes about what you’ll generally want to look for include the following:

Compatibility and integration: Ensure the library works well with your existing tools and frameworks.
Performance and efficiency: Look for libraries that are optimized for speed and that can handle large amounts of data efficiently.
Documentation and resources: Look for libraries with clear documentation that explains and provides examples of how to use it.
Community support: Choose libraries that have an active community of users.
Scalability and extensibility: If you anticipate your project growing or taking on larger datasets, choose libraries that can scale and work well with distributed computing.
Long-term viability: Choose libraries that are regularly maintained and updated. You’ll want to make sure the library will be compatible with newer versions of Python, that it receives bug fixes, and incorporates new features over time.

The Takeaway

In Canada alone, around 90,000 SME's (Small and Medium Enterprises) disappear annually. This is prior to the introduction of AI, which means that “Staying competitive” is going to take on a whole new meaning in the years to come. You can’t fight it, but you can plan for it by adopting the right approach and consulting with experts who know how to navigate this change.

Written By Ben Brown

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ben brown

June 12, 2023

Business

8 Things to Know When Building a Reactive Machine Learning System

ben brown

June 12, 2023

Business

Every day that a business isn’t working to differentiate itself from its competitors is a day it’s going backward in its industry. As sophisticated IT infrastructures become the minimum standard and with data-driven decision-making fueling innovation, businesses must be proactive about finding the technology that gives them a competitive edge.

One of the big topics right now when it comes to gaining this edge is integrating Reactive Machine Learning, which to say the least, can be a game-changer for the businesses who utilize it effectively.

What is a Reactive Machine Learning System?

Instead of telling you all the things a reactive system is, it’s better to tell you what it isn’t:

Reactive is the opposite of batch learning where a system takes one big dataset and then uses that to generate its insights and make predictions. Instead, it can process real-time data and respond immediately.

Reactive learning is not a deliberative agent which focuses on analysis and reasoning before taking action. A reactive system instead uses predetermined rules or patterns that are meant to make the system act quickly.
Reactive systems are not useful in complex decision-making processes such as long-term forecasting. Instead, something like fraud detection would benefit from a pre-determined system protocol.

This covers the basics. Machine Learning models are made from algorithms that analyze data, recognize patterns and outliers in that data, and then make predictions or decisions.

Where Does it Fit in a Business?

The evolution of the internet has exceeded comprehension looking back 20 years. With this, user standards have risen as well which has made integrating machine learning models essential for businesses to meet the standards of their industry.

3 Ways a Business Might Utilize Reactive ML:

Automating processes: Think about a chemical testing laboratory with a vast amount of highly sensitive data to be managed. Reactive ML can be used to prevent errors by automating the analysis aspect. As a result, the laboratory cuts down its processing time and increases the efficiency of instrumentation.
Energy consumption: Take a utility provider, for example. Reactive ML can optimize how much energy is consumed using real-time data to determine the appropriate adjustment. In addition to this, it can implement demand response programs by taking past data and identifying patterns to make recommendations on energy usage.
Personalizing recommended content: This is what streaming services like Netflix or Disney+ use in the “suggested” section, or social media platforms for the type of content someone is fed. In this case, ML algorithms will be used to analyze user data and recognize patterns that determine what they’re fed.

How to Build It

There’s a lot that goes into building a reactive ML system and the specifics will always vary just as with the construction of any complex IT platform. What businesses must do to carry it out effectively can be understood with these basics principles:

Gather data: Collect relevant data that you want to train and validate the reactive ML algorithms on. Make sure that the data is accurate and diverse, and that it fits in the problems domain. Then, clean and preprocess that data to remove noise and handle missing values.
Train the algorithms: Choose the ML algorithms that you think best fit the problem at hand. Train the algorithms using your gathered data, adjust hyperparameters and then evaluate performance. Consider using techniques like cross-validation to ensure the system is well-rounded and that you’ll avoid overfitting.
Integrate the system: Once you’ve developed the necessary software infrastructure to integrate the reactive ML system, connect components. This may involve building pipelines, creating ingestion and processing mechanisms, and implementing decision-making modules based on the trained data we mentioned previously.
Test and evaluate: This is an essential piece of this puzzle. Use the appropriate evaluation metrics to assess the accuracy and effectiveness of the system. Then fine-tune the system based on the results and make the necessary iterations as you go (which leads to the next point).
Monitor and maintain: Consistently monitor the performance of the reactive ML system in your production environment. In addition to this, update the model periodically as new data becomes available or when business requirements change. And lastly, regularly assess the system's impact on organizational outcomes and make adjustments as needed.

Again, these are very baseline as every project is going to have unique variables and every business is going to have unique goals. With that said, the most important part of digital transformation is what comes next, so with that in mind, consider this:

How scalable is the system? Whether you’re using a distributed computing framework, cloud services, or anything of the sort, the system needs to be designed while thinking about the volume of data and user requests it will need to handle.
What are your requirements for processing speed? If your reactive ML system needs to respond in real-time to user requests or traffic, processing speed becomes a major concern. To ensure it fits your ideal framework, you can optimize algorithms and hardware or use a distributed framework such as Apache Spark. Again, monitor the changes you make and keep looking for opportunities to refine.
How does it fit with current systems? When introducing reactive ML into your current systems, you have to consider how it will fit and interact with the infrastructure. APIs or connectors that enable data exchange are what you’re going to need if you want interoperability with existing systems.

The Takeaway

Finding components to build a framework that will support a business long-term is a never-ending quest. Doing what other companies are doing without an in-depth analysis of how the things you want to introduce will serve you long-term could set you back. It’s best to consult with an organization who’s overseen various projects

Written By Ben Brown

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