Tag Archive for: artificial intelligence

Ironside’s smart, time-bound, and ROI-focused approach to AI

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Be sure to check out our Take30 webinar this Thursday around the approach and features of Ironside’s AscentAI.

Artificial Intelligence, at its core, is a wide ranging tool that enables us to think differently on how to integrate information, analyze data, and use the resulting insights to improve decision making.

With the current shift to digitization (which has been accelerated by the pandemic), customer behavior has changed significantly, along with the expectation around accuracy of AI-based predictions. We all are used to “What to Watch Next” recommendations from streaming channels like Netflix or “Suggested Products” to buy from Amazon, but now with most businesses offering the expected “Wait Time” before your a haircut, or pickup time for the food you ordered online, it is critical to manage the queue to ensure timely service that begets customer satisfaction & retention.

Many organizations want to leverage AI but are unable to mainly due to following reasons

  • High cost & Time to Market
  • Complexity & Lack of expertise
  • Uncertainty of success of the AI outcomes

Because one of our goals is to help our customers benefit from the AI revolution, derive real business value,  improve their, all in a timely fashion we at Ironside have introduced a product-style approach to Data Science called AscentAI: 

As with any project, the first steps are to identify and prioritize the business use case, define the objective, and clearly specify the goals. Next, we offer a rapid viability assessment (RVA),  which ensures the model provides sufficient signal to justify productionizing the machine learning model in under three weeks.

The activities during RVA involve:

  • Data collection & preparation
    • The quality and quantity of the data dictates the accuracy of the model
    • Split the data into two distinct datasets for training and evaluation
  • Feature engineering
    • Identify & define features for the models
  • Model training & evaluation
    • Choose different models and identify the best one for the defined requirements
  • Make & validate predictions
    • Measure prediction accuracy against real data sets

RVA is a decision phase, where if the AI provides more noise than actual signal, then it would not provide value in developing production-ready machine learning models. This gate-based approach ensures the customer has clear visibility into expected outcomes, and can take an informed decision on either pursuing the current use case or moving on to the next one.

If the RVA provides meaningful insights, then part of the next step is to productionize the best model, integrating it into existing business processes to start consuming the predictions.

The final step includes a performance monitoring dashboard, which is provided to monitor the performance of the models, identify the need to tune, and optimize the model due to the naturally expected skew over time. Finally, we strongly recommend model “retraining” over time at a predefined frequency to ensure the AI consistently delivers on the expected ROI.

Below is a snapshot of a real implementation of AscentAI for a customer with 1800+ stores to predict accurate “wait times” in real time in a very high volume setting on AWS cloud platform.

Top 4 Data Science Sessions of 2020: Part 3 of Take30’s One-Year Anniversary

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Ironside’s Take30 with a Data Scientist series was typically targeted towards business leaders, with topics focused on strategy, including use-case development advice, de-risking AI with Data Science-as-a-Service, and ways to overcome common barriers to AI adoption. We also covered technical concepts like Model Evaluation and Feature Store Development. On top of that, we took several deep dives into technology partners including IBM Watson Auto AI, AWS Sagemaker Studio, Snowflake and DataRobot. Finally, we had a couple industry spotlights where we explored common use cases in Higher Education and Insurance.

Several attendees have shared that these sessions bridge the gap between the technical world of Machine Learning and that of their business, which in turn has helped them to know how to bridge that gap within their own organizations. For technicians, it has helped them to understand how to talk to the business and draw out use cases and help the business adopt solutions. For the business leaders, it’s helped them know what to ask of the data science team or what to look for in building a team. 

Overcoming the Most Common Barriers to AI Adoption (2/25/21)

Because so many organizations are in the early stages of AI Adoption, this is likely the most important topic to CIOs and business leaders in the Data Science series. This session discusses the challenges with people, infrastructure, and data that every organization faces and offers sound advice on how to overcome them.

Is Data Science-as-a-Service Right for your Organization? (5/19/20)

AscendAI, Ironside’s Data Science-as-a-Service, provides many benefits to organizations that are in the early or mid-stages of AI Adoption. Learn more about Ironside’s offering and how it could reduce your time to ROI to as little as 12 weeks.

How Snowflake Breaks the Chains Holding Your Data Science Team Back (9/10/20)

We hosted a number of Technology related secession with Partners such as Snowflake. This session dove a bit deeper than Data Science Best Practices: Feature Stores. Other Technology related sessions include Watson Studio, AWS Sagemaker, and a data enrichment session with Precisely, titled More Data, More Insight: The Value of Data Enrichment for Analytics.

Data Science work requires infrastructure that is scalable, cost-effective, and with easy access to multiple data sources. Snowflake provides this and much more to a data science tech stack. It also integrates easily with other machine learning platforms like DataRobot, AWS, and Azure. Snowflake is particularly valuable for data sharing with external data sources.

Leveraging Data for Predicting Outcomes in Higher Ed (6/30/20)


We hosted an industry-related session sharing how Higher Education is leveraging machine learning in very creative ways; this ended up being one of our top attended sessions for the Take30 series. In this webinar, we reviewed some of the ways that higher ed is using machine learning such as enrollment management, space planning and student retention. We also discussed some of the use cases that are helping universities cope with the challenges and nuance of COVID-19. We also hosted another industry specific session on Insurance.

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As we continue our Take30 with a Data Scientist series, we’ll continue to partner with experts in Machine Learning technology to offer demos and successful solutions as well as strategic sessions for business leaders. We also hope to spotlight some of our clients this year and the exciting AI driven applications we are developing for them in Retail, Insurance, Higher Ed, and Manufacturing. Coming up on May 20th, we will be hosting an industry focus for Banking. 

We’d love to have 1-on-1 conversations to discuss any challenges you may be facing with AI adoption. Please feel free to sign up for a spot with Pam Askar, our Director of Data Science.

Getting Started with AI: Four Things to Consider

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So you’re thinking that 2021 is the year to infuse Artificial Intelligence/Machine Learning (AI/ML) into your business. You’ve read about the difference it’s making in other organizations. You want to beat — or keep pace with — your competitors. But where should you begin? 

Should you license AI/ML software? How do you find the right business problem to solve? And if you’re like most organizations, your data is imperfect. Should you focus there first? 

Ironside can help. We’re a data and analytics consulting firm with a track record of helping companies get started with AI.

Let’s start with four things we think every organization should consider on their AI journey. They’re not the only four things you need to know, but we know your time is valuable, so let’s start here:

  1. Develop an AI Use Case Catalog – One of the first places you’ll start is to develop a list  of possible business problems, opportunities or challenges that AI might improve. We assist our customers in building that list by talking to executives and functional area leaders and understanding the organization’s strategic goals, how they are measured and then considering challenges/pain and what information is missing that would improve decision making. The catalog of use cases should be enhanced with information from a thorough data analysis. Is there the data to support the use case? Is there enough of it? What’s the data quality? Can you forecast improvement of an important metric? What’s the return on investment? 
  2. Involve a variety of stakeholders. Executive sponsorship in some form is critical in funding and executing an AI project. But building a culture of AI across an organization starts by involving as many stakeholders as possible across functional areas. Even if the use cases surfaced by some stakeholders are not immediately pursued, people want to be included and to have a voice. Broader involvement will avoid roadblocks and seed a culture of AI. Organizational success will grow over time. 
  3. Start small – Rank the use case catalog and find one or two to test. Identify the relevant business sponsor and data and prepare a limited set of data, or features. Build a simple machine learning algorithm to see if there are results that show that AI could improve a desired outcome. If not, move on to the next use case in the catalog (see, that’s why we need a catalog). If the early results are promising, move on to building a more advanced machine learning model.  
  4. Limit your investment – For as low a cost as possible, get a model deployed and start using it in the business to begin to get the benefit. You’ll inevitably iterate on that model but expediting that process and limiting the investment — and the risk — is the goal. Now here’s where we answer the questions about hiring a data scientist or buying software. 

Sometimes the answer is yes but for many organizations the answer is “no.” They’re just not sophisticated enough. And big costly failures could sour your organization on pursuing AI and set you back years

Ascend AI 

So what should you do? One option to get started is Ascend AI, a data science as-a-service solution that Ironside developed. Ascend AI lowers the risk out of diving into AI on your own. It is  underpinned by a custom configured and scripted cloud-based architecture as well as our highly skilled data scientists. 

We bring the data scientists and engineers and the technology. You provide the data and the business problems. 

We start with your leading use cases or help you develop them in a use case catalog. Then we perform rapid viability assessments on the leading use cases selected and if signs are good we would then build out full machine learning algorithms. Finally, we could deploy and host and manage the algorithms. At any point, depending on customer preference and maturity, we would hand the IP back to our customers and help them develop AI competency in house. We’re not a black box. 

Of course there’s more to getting started with AI than these four points and data science as a service might not always be the answer. The thing to remember is that AI should be consumed in bite sized-chunks and is attainable to even the most technologically immature organizations.

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Enabling Clients to Be Self-Sufficient with AI

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LEXINGTON, MA, May 10, 2019 – Ironside, an enterprise data and analytics firm, was featured in a Wall Street Journal article about AI consultants that enable their clients to be self-sufficient with AI and not have to rely on their consulting counterparts to manage the model.

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Ironside Recognized in Wall Street Journal as AI for Analytics Consultant

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LEXINGTON, MA, May 3, 2019 – Ironside, an enterprise data and analytics firm, was recognized in the Wall Street Journal this morning for AI work being done at one of our clients, Coverys, a Boston-based provider of medical professional liability insurance.

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Five Traits of Highly Effective Data Science Teams

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2019 is the year that data science, machine learning and artificial intelligence for business will become ubiquitous. Most organizations large and small, across all industries, have recognized the benefits and competitive advantage that these capabilities bring to bear. If you have not already begun the journey, chances are this will be the year you begin to develop this competency. Whether you’re about to take your first step, you’re a team of one looking to scale, or even a more mature organization that is always seeking self-improvement, consider the following traits to maximize your chances of success with data science.

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The Reinvention of BI Leadership in the AI Enterprise

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At least weekly, I am granted the opportunity to meet and work alongside experienced professionals who serve in a corporate business intelligence (BI) leadership function. When they describe their role upon introduction, there is a common thread to the scope of influence and control which usually intersects one or more of these domains: Read more