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Ironside is pleased to announce the release of a new packaged service “Ascend AI.” For nearly 10 years, Ironside has offered data science expertise and advisory services to organizations who seek to establish AI within their enterprise. Now Ironside offers a powerful new service for organizations who are earlier in their AI journey.

AI is becoming more of a necessity for businesses to retain their competitive edge, keep internal costs low and manage risk. But getting started can be overwhelming. What technologies should we invest in? Should we hire data scientists and how many? What use cases should they work on? Where would they get started? How much will this cost us?  Can our current infrastructure support this? Is our data mature enough? Is our organization ready?

Ironside’s strong history of helping organizations get started on their AI journey allows us to understand common pitfalls and how to pivot around them, have a valuable point of view on AI/ML technology and infrastructure options, and provide a highly skilled data science team. We understand that many organizations can’t jump in feet first and need a way to quickly and easily prove value with rapid cost-effective sprints before they begin to think about hiring or large technology purchases. That is why we created Ascend AI.

What is Ascend AI?

Ascend AI is a packaged service, delivered in progressive modules to allow you to scale up at your own pace.

Ascend AI's Progressive 4-Step Solution

Ironside provides the data science team, including solution architects, developers, experience designers, data engineers and of course experienced data scientists, and leverages their own infrastructure and AI IP that they’ve developed over the years. You provide your data and business subject matter experts who work closely with the Ironside team to develop a customized AI solution that delivers measurable results.

We bring the technology and expertise so you can focus on putting the results to work for your organization.

Who is Ascend AI for?

Ascend AI is right for any organization that says:

  • We need to test out AI use cases before investing in technology and people.
  • We want to build a business case to gain executive support for further AI funding.
  • We want to become an AI driven organization, but without investing in capital expenses or building an internal center of excellence.
  • We need a trusted AI partner, not an off-the-shelf solution.
  • We have unique business problems and use cases that don’t fit any solution on the market.

Get started, today!

If you want all the benefits of implementing artificial intelligence to analyze, action and manage your data, without any of the hassles and headaches, Ascend AI delivers.

In today’s “Big Data” era, a lot of data, in volume and variety, is being continuously generated across various channels within an enterprise and in the Cloud. To drive exploratory analysis and make accurate predictions, we need to connect, collate, and consume all of this data to make clean, consistent data easily and quickly available to analysts and data scientists.

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For players in the biopharmaceutical space, it is becoming increasingly clear that advanced analytics can be of enormous assistance in solving many of the unique challenges the industry faces. To understand the extent of the impact that advanced analytics can make, it’s first necessary to examine how healthcare in the US has undergone a major transformation over the past decade.

First, there’s the presence of managed care. It puts pressure on pharmaceutical companies to provide stronger evidence of efficacy and safety, reduce costs of drug development and healthcare in general, and provide personalized care by targeting patient groups that are most likely to benefit from treatments and least likely to suffer adverse events. Read more

LEXINGTON, Mass., Dec. 12, 2016 /PRNewswire/ — Ironside, a Boston-based data and analytics firm, just released an authoritative list of questions that they recommend business leaders try to answer now in planning for their analytics initiatives.

Today, it’s safe to say that the majority of businesses understand the value of analytics. They get that their data is important and that using it in the right way can have significant benefits. The problem, though, is that even though the business community as a whole acknowledges these facts, there’s still a lot of magical thinking around analytics tools and platforms out there. Read more

According to Dave Chaffey’s 2016 global social media research summary, over 2.3 billion people actively use websites like Facebook, Snapchat, Twitter, and LinkedIn to view content or engage with other users . This massive audience represents a huge opportunity for organizations to understand what trends they can connect with, who their ideal customers are, and what the sentiment is around their brand in the marketplace. These insights all become possible through social media analytics. Read more

Ironside experts Dan Gouveia and Chi Shu recently got the chance to share knowledge with our local analytics community at the Scalable R Analytics Meetup in Cambridge, MA. Presenting to a packed room, Chi and Dan dove into several ways that R’s powerful data science capabilities could be scaled to apply to much larger, enterprise-level data sets. They demonstrated how to achieve this scalability both with dashDB, a cloud data warehouse, and Spark, a big data-oriented parallel processing framework. Read more

The immense amount of data being collected today, in any industry, expands the reality of advanced analytics and data science. In concept, it creates an explosion of opportunities and expands what can be accomplished. In reality, we are often limited in scope by our data processing systems which may not be able to handle the complexity and quantity of data available to us. The introduction of Spark has offered a solution to this issue with a cluster computing platform that outperforms Hadoop. Its Resilient Distributed Dataset (RDD) allows for parallel processing on a distributed collection of objects enhancing the speed of data processing. For this reason, Spark has received a lot of interest and promotion in the world of big data and advanced analytics, and for good reason. Read more

Suppose you’re trying to build a model to predict respondents, and in your data set, about 3% of the population will respond (target = 1). Without applying any specific analysis techniques, your prediction results will likely be that every record is predicted as a non responder (predicted target = 0), making the prediction result insufficiently informative. This is due to the nature of this kind of information, which we call highly imbalanced data. Read more

Do you ever wonder how Netflix makes recommendations for you? Or how the drug store decides which coupons to offer you when you make a purchase? Behind the scenes they have a data scientist conducting what is called market basket analysis, which searches through vast amounts of purchase history information to find patterns in people’s purchases, web searches, or Netflix viewing preferences. The data mining technique used for market basket analysis is called Association Rules (AR). This is the actual algorithm designed to detect probabilistic if- then statements, such as “If you watched Breaking Bad and House of Cards, then you are also likely to enjoy Mad Men.” Read more

This year at IBM Insight 2015, Ironside had the unique opportunity to tell one of our most compelling recent customer stories: how Manchester PD used Ironside’s IronShield predictive policing platform to implement some highly effective crime reduction strategies. Greg Bonnette, our VP of Strategy & Innovation, delivered the presentation on behalf of Manchester PD, who IBM had selected as a presenter, and highlighted the components of IronShield and the results that Manchester PD is getting out of it. Read more

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