Top Enterprise IT Trends for 2021

Between the global pandemic and the resulting economic upheaval, it’s fair to say many businesses spent 2020 in survival mode. Now, as we turn the page to 2021, we wonder what life will look like in this new normalcy. Whether it is employees working from home, the shift from brick and mortar to online sales and delivery, or the need to accelerate digital transformation efforts to remain competitive, 2021 will be a year of re-invention for most companies.

How might the new normal impact your company? Here are five of the top technology trends we predict will drive change in 2021:

1. The pace of cloud migration will accelerate

Most companies, by now, have started the journey to the public cloud or to a hybrid cloud environment. The events of 2020 have added fuel to the fire, creating an urgency to maximize cloud usage within companies that now understand that the speed, resilience, security and universal access provided by cloud services is vital to the success of the organization.

“By the end of 2021, based on lessons learned in the pandemic, most enterprises will put a mechanism in place to accelerate their shift to cloud-centric digital infrastructure and application services twice as fast as before the pandemic,” says Rick Villars, group vice president, worldwide research at IDC. “Spending on cloud services, the hardware and software underpinning cloud services, and professional and managed services opportunities around cloud services will surpass $1 trillion in 2024,”

The progression for most companies will be to ensure customer-facing applications take priority. In the next phase of cloud migration, back-end functionality embodied in ERP-type applications will move to the cloud. The easiest and fastest way to move applications to the cloud is the simple lift-and-shift, where applications remain essentially unchanged. Companies looking to improve and optimize business processes, though, will most likely refactor, containerize, or completely re-write applications. They will turn to “cloud native” approaches to their applications.

2. Artificial intelligence (AI) and machine learning (ML) will deliver business insight

Faced with the need to boost revenue, cut waste, and squeeze out more profits during a period of economic and competitive upheaval, companies will continue turning to AI and machine learning to extract business insight from the vast trove of data most collect routinely, but don’t always take advantage of.

According to a recent PwC survey of more than 1,000 executives, 25% of companies reported widespread adoption of AI in 2020, up from 18% in 2019. Another 54% are moving quickly toward AI. Either they have started implementing limited use cases or they are in the proof-of-concept phase and are looking to scale up. Companies report the deployment of AI is proving to be an effective response to the challenges posed by the pandemic.

Ramping up AI and ML capabilities in-house can be a daunting task, but the major hyperscale cloud providers have platforms that enable companies to perform AI and ML in the cloud. Examples include Amazon’s SageMaker, Microsoft’s Azure AI and Google’s Cloud AI.

Edge computing will take on greater importance

For companies that can’t move to the cloud because of regulatory or data security concerns, edge computing is emerging as an attractive option. With edge computing, data processing is performed where the data is generated, which reduces latency and provides actionable intelligence in real time. Common use cases include manufacturing facilities, utilities, transportation, oil and gas, healthcare, retail and hospitality.

The global edge computing market is expected to reach $43.4 billion by 2027, fueled by an annual growth rate of nearly 40%, according to a report from Grand View Research.

The underpinning of edge computing is IoT, the instrumentation of devices (everything from autonomous vehicles to machines on the factory floor to a coffee machine in a fast-food restaurant) and the connectivity between the IoT sensor and the analytics platform. IoT platforms generate a vast amount of real-time data, which must be processed at the edge because it would too expensive and impractical to transmit that data to the cloud.

Cloud services providers recognize this reality and are now bringing forth specific managed service offerings for edge computing scenarios, such as Amazon’s new IoT Greengrass service that extends cloud capabilities to local devices, or Microsoft’s Azure IoT Edge.

4. Platform-as-a-Service will take on added urgency

To increase the speed of business, companies are shifting to cloud platforms for application development, rather than developing apps in-house. PaaS offers a variety of benefits, including the ability to take advantage of serverless computing delivering scalability, flexibility and quicker time to develop and release new apps. Popular serverless platforms include Amazon Lambda and Microsoft’s Azure Functions.

5. IT Automation will increase

Automating processes across the entire organization is a key trend for 2021, with companies prioritizing and allocating money for this effort. Automation can cut costs and increase efficiency in a variety of areas – everything from Robotics Process Automation (RPA) to automate low-level business processes, to the automation of security procedures such as anomaly detection or incident response, to automating software development functions with new DevOps tools.

Gartner predicts that, through 2024, enhancements in analytics and automatic remediation capabilities will refocus 30% of IT operations efforts from support to continuous engineering. And by 2023, 40% of product and platform teams will use AIOps for automated change risk analysis in DevOps pipelines, reducing unplanned downtime by 20%.

Tying it all together

These trends are not occurring in isolation.  They’re all part of the larger digital transformation effort that is occurring as companies pursue a multi-cloud strategy encompassing public cloud, private cloud and edge environments. Regardless of where the applications live or where the processing takes place, organizations are seeking ways to use AI and machine learning to optimize processes, conduct predictive maintenance and gain critical business insight as they try to rebound from the events of 2020 and re-invent themselves for 2021 and beyond.

Where will 2021 take you? Contact us for guidance on how you can take hold of these technology trends to maximize your business results and reach new goals.

-Mir Ali, Field CTO


3 Steps to Implementing a Machine Learning Project

Once you understand the benefits and structure of data science and machine learning (ML), it’s time to start implementation. While it’s not an overly complicated process, planning change management from implementation through replication can help mitigate potential pitfalls. We recommend following this 3-step process.

Step 1: Find Your Purpose

It can be fun to tinker around with shiny, new technology toys, but without specific goals, the organization suffers. Time and resources are wasted, and without proof of value-added, the buy-in necessary from leadership won’t happen. Why are you implementing this solution, and what do you hope to get out of the data you put in?

ML projects can produce several outcomes contributing to decisions fueled by data and gaining insights into customer buying behavior, which can be used to optimize the sales cycle with new marketing campaigns. Other uses could include utilizing predictive search to improve user experience, streamlining warehouse inventory with image processing, real-time fraud detection, predictive maintenance, or elevating customer service with voice to text speech recognition.

ML projects are typically led by a data scientist who is responsible for understanding the business requirements and who leverages data to train a computer model to learn patterns in very large volumes of data to predict outcomes while also improving the outcomes over time.

Successful ML solutions can generate 4-5% higher profit margins, so identify benchmarks, set growth goals, and integrate regular progress measurements to make sure you’re always on track with your purpose in mind.

Step 2: Apply Machine Learning

The revolutionary appeal for ML is that it does not require an explicit computer program to deliver analytics and predictions, it leverages a computer model that can be trained to predict and improve the outcomes. After the data scientist’s analysis defines the business requirements, they wrangle the necessary data to train the ML model by leveraging an algorithm, which is the engine that turns the data into a model.

Data Wrangling

Data preparation is critical to the success of the ML project because it is the foundation of everything that follows. Garbage in equals garbage out, but value in produces more value.

Raw data can be tempting, but data that isn’t clean, governed, and appropriate for business use corrupts the model and invalidates the outcome. Data needs to be prepared and ready, meaning it has been reviewed for accuracy, and it’s available and accessible to all users. Data is typically stored in a cloud data warehouse or data lake and it must be maintained with ongoing governance.

A common mistake organizations make is relying on data scientists to clean the data. Studies have found that data scientists spend 70% of their time wrangling data and only 30% of the time implementing the solution and delivering business value. These highly paid and skilled professionals are scarce resources trained for innovation and analyzing data, not cleaning data. Only after the data is clean should data scientists start their analysis.

ML Models

The data scientist’s core expertise is in selecting the appropriate algorithm to process and analyze the data. The science in ML is figuring out which algorithm to use and how to optimize it to deliver accurate and reliable results.

Thankfully, ML algorithms are available today in all the major service provider platforms, and many Python and R libraries. The general use cases within reach include:

  • Classification (is this a cat or is this not a cat) using anomaly detection, marketing segmentation, and recommendation engines.
  • NLP (natural language progression) using autocomplete, sentiment, and understanding (i.e., chatbots).
  • Timeseries using forecasting.

Algorithms are either supervised or unsupervised. Supervised learning algorithms start with training data and correct answers. Labeled data trains the model using the algorithm and feedback. Think texting and autocorrect – the algorithm is always learning new words based on your interaction with autocorrect. That feedback is delivered to the live model for updates and the feedback loop never ends.

Unsupervised learning algorithms start with unlabeled data. The algorithm divides the data into meaningful clusters used to make inferences about the records. These algorithms are useful for segmentation of click stream data or email lists.

Some popular algorithms include CNN (convolutional neuro network), a deep learning algorithm, K Means Clustering, PCA, Support Vector Machine, Decision Trees, and Logistic Regression.

Model Quality

With everything in place, it’s time to see if the model is doing what you need it to do. When evaluating model quality, consider bias and variance. Bias quantifies the algorithm’s limited flexibility to learn the pattern. Variance quantifies the algorithm’s sensitivity to specific sets of training.

Three things can happen when optimizing the model:

  1. Over-fitting: Low bias + high variance. The model is too tightly fitted to the training data, and it won’t generalize data it hasn’t seen before.
  2. Under-fitting: High bias + low variance. The model is new and hasn’t reached a point of accuracy. Get to over-fitting first, then back up and reiterate until the model fits.
  3. Limiting/preventing under/over-fitting: There are too many features in the model (i.e. data points used to build the model), and you need to either reduce them, or create new features from existing features.

Before unleashing your ML project on customers, experiment first with employees. Solutions like virtual assistance and chat bots that are customer-facing can jeopardize your reputation if they don’t add value to interactions with customers. Because ML influences decision-making, accuracy is a must before real-world implementation.

Step 3: Experiment and Push into Production

With software projects, it either works or it crashes. With data science projects, you have to see, touch, and feel the results to know if it’s working. Reach out to users for feedback and to ensure any changes to user experience are positive. Luckily, with the cloud, the cost of experimentation is low, so don’t be afraid to beta test before a full launch.

Once the model fits and you’ve pushed the project into production, make noise about it around the organization. Promote that you’re implementing something new and garner the attention of executive leadership. Unfortunately, 70% of data projects fail because they don’t have an executive champion.

Share your learnings internally using data, charts, results, and emphasizing company-wide impact. You’re not going to get buy in on day one, but as you move up the chain of command, earning more and more supporters, your budget will allow for more machine learning solutions. Utilize buzzwords and visual representations of the project – remember data science needs to be seen, touched, and felt.

Ensure ML and data science success with best practices for introducing, completing, and repeating implementation. 2nd Watch Data and Analytic Solutions help your organization realize the power of ML with proper data cleaning, the right algorithm selection, and quality model deployment. Contact Us to see how you can do more with the data you have.

-Sam Tawfik, Sr Marketing Manager, Data & Analytics


Understanding Data Science, Artificial Intelligence, and Machine Learning

Amazing possibilities are available in data science with artificial intelligence (AI) and machine learning (ML). Large sets of data, inexpensive storage options, and cloud processing capabilities are enabling computers to make human-like decisions. Across industries, businesses are leveraging these algorithm-based models to save time, reduce costs, enable users, and grow profits.

What’s the difference between Data Science, Artificial Intelligence, and Machine Learning

Data science, AI, and ML can get lumped together, but there are some distinctions to understand. Simply put, AI is a computer doing things that typically would require human scrutiny or reasoning. ML is the application of statistical learning techniques to automatically learn patterns in data. These patterns are used to develop a model to make more accurate predictions about the world. And both terms utilize data science to accomplish outcomes.

With these central terms defined, we recommend using ‘machine learning’ or ‘ML’ to describe data science projects internally because there is sometimes an aura of fear around AI that “the robots are going to take my job.” Although joking (a bit), buy-in from executives is critical to a successful data project, so ML is recommended over AI.

Utilizing Machine Learning for Profit Growth

A recent study showed 78% of companies have already deployed ML, and 90% of them have made more money as a result. Manufacturing and supply-chain management are experiencing the largest average cost decrease, and marketing, sales, product and service development are reaching the highest average revenue gains. Additionally, a McKinsey survey revealed that organizations with a high diffusion of ML had 4-5% higher profit margins than their peers with no ML. Not only can ML reduce your overall costs, but it also enables you to grow your bottom line. If your organization is not utilizing ML, now is the time to start.

From Data to Model

Machine learning is already a staple in many of the functions we utilize daily. Predictive search in Google and within catalogues, fraud detection on suspicious credit card purchases, near-instant credit approval, social network suggestions via mutual connections, and voice recognition are all common today. Behind these intelligent decisions is a model that acts as a function or program. The model is trained on sample data using a machine learning algorithm to learn patterns. Based on the information learned about the sample data, the model is applied to inputs it may or may not have seen before and predicts an outcome.

Traditional programming depends on the written program and the input data it’s fed. The computer runs the program against the data, and you get an output directly tied to the logic or function of the program. Only the data that can be processed by the program gets analyzed, and outliers are removed.

In ML, the computer is still given input data. For example, what you know about your customer – time stamps, demographics, spend, etc. – but it doesn’t have a written program. Instead, it’s given the output you desire. For example, you might want to know which customers churn. Then you build a model by training programmed algorithms to analyze input data and predict an output. Essentially, the model recognizes the correlation between the output results and the input data. Here, the model utilizes algorithms to identify patterns in data that that heavily influence the customer churn score.

Understanding Data Science, Artificial Intelligence, and Machine Learning

In this example, an organization might discover that most customers stop doing business with them after a certain promo ends, or a high percentage of customers who come in through a specific lead gen pipeline don’t stay for long. Using this information, the organization can make informed and specific decisions about how to reduce churn based on known patterns.

All relevant data is taken into account in ML to deliver a more comprehensive story about why things are happening in your organization. Machine learning can quickly affirm or discredit intuition and allow organizations to fail faster, and in the right direction, to meet overall goals more efficiently.

A best practices approach is necessary to streamline the process of introducing, completing, and repeating a data science project. With 2nd Watch Data and Analytics Services, you realize the power of machine learning with the right algorithm selection and model deployment. Contact Us to see how machine learning can positively impact your organization or download our eBook, “Artificial Intelligence and Machine Learning: 3 Steps to Set the Table for Data Science in 2021” to learn about the 3 steps necessary to producing valid and applicable results from your data science project.

– Rob Whelan, Practice Director, Data & Analytics


7 Trends Influencing DevSecOps & DevOps Adoption

Companies worldwide have been increasing DevOps adoption and DevSecOps adoption into their regular workflows at an exponential rate. Whether following Agile methodologies or creating independent workflows stemming from DevOps, companies have been leveraging the faster manufacturing rate with superior quality that DevSecOps provides.

However, the increasing development in autonomous technologies such as AI or ML is idealizing a work cycle where the system operates independently of humans. It aims to provide faster, reliable, and better products – shifting from DevOps to NoOps.

A set of practices coupling software development (Dev) and information technology operations (Ops), DevOps is the combination of employees, methods, and products to allow for perpetual, seamless delivery of quality and value. Adding security to a set of DevOps practices, a DevSecOps approach provides multiple layers of security and reliability by integrating highly secure, robust, and dependable processes and tools into the work cycle and the final product.

This desirable outcome of integrating DevOps and DevSecOps into corporations has made it a trendy work cycle in the market. However, with a growing focus on automation and development in Artificial Intelligence and Machine Learning, we could be heading into a NoOps scenario, where self-learning and self-healing systems govern the work processes.

NoOps is a work cycle wherein the technologies used by a company are so autonomous and intelligent that DevOps and DevSecOps do not need to be exclusively implemented to maintain a continuous outflow of quality and value.

What are the trends that truly influence DevOps and DevSecOps adoptions in countless tech businesses – small and large – all across the globe? Download our 7 Trends Influencing DevOps/DevSecOps Adoption to find out.

-Mir Ali, Field CTO


Cloud Crunch Podcast: 5 Strategic IT Business Drivers CXOs are Contemplating Now

What is the new normal for life and business after COVID-19, and how does that impact IT? We dive into the 5 strategic IT business drivers CXOs are contemplating now and the motivation behind those drivers. Read the corresponding blog article at We’d love to hear from you! Email us at with comments, questions and ideas. Listen now on Spotify, iTunes, iHeart Radio, Stitcher, or wherever you get your podcasts.