Tag Archive for: AWS

AWS Summit New York 2024: Transforming Cloud Innovation and AI Solutions

/by

The AWS Summit New York 2024 was an exhilarating event to showcase cloud innovation, AI advancements, and industry best practices. At this action-packed day hosted at the Jacob K. Javits Convention Center, this year’s Summit brought together thousands of professionals, technology enthusiasts, and AWS experts to explore how cutting-edge AWS technologies can be used to revolutionize industries and empower businesses.

At this year’s Summit, over 170 sessions were offered covering a wide range of topics and technical depth, ranging from level 100 (foundational), level 200 (intermediate), level 300 (advanced), and level 400 (expert). Within these sessions, many AWS experts, builders, customers, and partners shared their insights on numerous topics such as generative AI, analytics, machine learning, industry specific solutions, and many more. Individuals were able to customize their own agenda ahead of time and choose from lecture-style presentations, peer-led discussions, and explore the Expo to learn about the numerous advancements of AWS technologies and deepen understanding of best practices. Dr. Matt Wood, VP for AI Products, AWS, hosted the keynote session to unveil the latest launches and technical innovations from AWS and demonstrate products and real-world success stories from AWS customers.

Below is a detailed look at some of my key takeaways and trends that summarizes this year’s Summit:

1. Amazon Bedrock

Stemming from the heavy emphasis on generative AI and its capabilities, one of the most exciting announcements from the Summit was the introduction of new capabilities in Amazon Bedrock. Amazon Bedrock is AWS’s relatively new service designed to simplify the creation of AI applications. The service provides access to pre-trained foundation models from leading AI providers, and enables businesses to build, deploy, and scale AI-driven solutions without deep expertise and extensive effort. In addition, the many key features of Amazon Bedrock allow users and businesses to build innovative AI solutions effectively and efficiently while ensuring scalability and compliance. The fundamental idea of this service is to revolutionize how companies develop and deploy generative AI applications, making it easier to integrate cutting-edge technology into existing workflow while significantly reducing computational costs. 

At this year’s Summit, additional features of Amazon Bedrock were introduced to enhance company knowledge bases with new Amazon Bedrock connectors for Confluence, Salesforce, SharePoint, and web domains. In doing so, companies can empower RAG models with contextual data for more accurate and relevant responses. 

Lastly, Guard Rails and Guard Rails API were introduced for Amazon Bedrock to contribute to the following:

  • Bring a consistent level of AI safety across all applications
  • Block undesirable topics in generative AI applications
  • Filter harmful content based on responsible AI policies
  • Redact sensitive information (PII) to protect privacy
  • Block inappropriate content with a custom word filter
  • Detect hallucinations in model responses using contextual grounding checks

Businesses and customers can apply safeguards to generative AI applications even if those models are hosted outside of AWS infrastructure. It is estimated that up to 85% of harmful content can be reduced with custom Guardrails.

2. Fannie Mae’s Data Science Platform

One of the first sessions that I attended was Fannie Mae’s presentation on their data science platform. The focus was on how Fannie Mae overcame traditional data management challenges through innovative solutions. Data scientists at Fannie Mae were responsible for exploring internal and external datasets, including sensitive data to develop and train models, create reports and new datasets, deploy models, and share insights. Before the utilization of AI, Fannie Mae’s data scientists struggled with data access (mostly personally identifiable information), governance, and operationalization. In addition, underwriting analysts spent significant time extracting structured data from unstructured documents. On average, each analyst spent 5 hours on every document, with over 8,000 underwriting documents per year. The challenge of inefficient manual document analysis was also resolved by the utilization of AI.

By leveraging Large Language Models (LLMs) and ontologies, Fannie Mae developed a knowledge extraction system that significantly reduced manual effort. Tools like Amazon Bedrock, Claude 3 Sonnet, Amazon Neptune, LangChain, and Amazon OpenSearch Service played a crucial role in this transformation. The use of AI has generated a potential savings of over 32,000 hours annually and improvements in accuracy, compliance, and scalability of underwriting analysis for Fannie Mae.

Such efficiency and savings generated by the use of LLMs and ontologies is simply fascinating. This is a great reflection on how companies of all sectors can utilize the diverse capabilities of AI and customizable machine learning models to generate value.

3. IBM WatsonX & AWS: Scale Gen AI Impact with Trusted Data

Generative AI was a major theme at the Summit, and IBM WatsonX and AWS highlighted their collaborative efforts to expand the impact of this technology. The WatsonX suite offers tools like Watsonx.ai for model development, Watsonx.data for scaling AI workloads, and Watsonx.governance for ensuring responsible AI practices. This partnership brings a shift towards more open, targeted, and cost-effective generative AI solutions, while offering superior price-performance at less than 60% of the traditional costs.

4. Advancing AI and Cloud Solutions

Another key topic of the Summit was Innovating with Generative AI on AWS. This topic highlights how businesses can focus on performance, cost-efficiency, and ethical responsibilities in AI development. Many strategies were discussed for creating new customer experiences, boosting productivity, and optimizing business processes through generative AI.

Some of the key techniques included Retrieval Augmented Generation (RAG) for combining new and existing information, fine-tuning of AI models, and pre-training to enhance AI capabilities. The session emphasized the importance of accessible and high-quality data as the foundation for AI success, so that businesses can utilize generative AI to its maximum potential to drive innovation and create value. By using services designed to enable innovation and scale, businesses are able to measure and track value and ROI while optimizing for cost, latency, and accuracy needs. In addition, businesses can manage risk, maintain trust, and build with compliance and governance.

5. Boosting Employee Productivity with AI Agents

Another highlight was the exploration of AI agents powered by Amazon Q. With Amazon Q, businesses can design these AI agents to integrate seamlessly with tools like Slack, Microsoft Teams,  and other AWS-supported data sources to enhance employee productivity. These AI agents can improve efficiency across teams and organizations by streamlining data interactions and automating repetitive tasks. A demo of how to connect the Slack instance to Amazon Q and deploy it into the Slack workspace showed the simplicity of the whole process and how quick Amazon Q can generate value for an organization.

6. Building a Strong Data Foundation for Generative AI

A central theme at the Summit was the importance of a solid data foundation for successful generative AI initiatives. AWS demonstrated how businesses can harness structured and unstructured data through various tools and services. Key components of this foundation include:

  • Data Storage: Managing structured and unstructured data using SQL, NoSQL, and graph databases
  • Data Analytics: Utilizing data lakes for search, streaming, and interactive analytics
  • Vector Embeddings: Tokenizing and storing data for semantic similarity searches
  • Data Integration: Combining data from different sources using tools like AWS Glue and Amazon DataZone.

7. Governance and Compliance in the Cloud

Governance and compliance were also significant topics, with AWS highlighting how organizations can manage data securely and efficiently. Enterprise customers look for democratized data tools with built-in governance to discover, understand, and access data across organizations, with the ability for multiple personas to collaborate on the same data problems. In addition, easy-to-use and easy-to-access analytics and BI tools are crucial for value creation. The Summit showcased services like AWS IAM, Amazon Cognito, AWS Lake Formation, and Amazon S3 for data management, access control, and auditing. These tools help ensure that cloud operations are compliant with regulations and best practices

8. The Future of Generative AI

Lastly, the Summit concluded with a discussion on the future of generative AI. The evolution of AI agents such as Ninjatech.AI, multimodal models, and new regulations were some of the topics that were discussed. The session also explored the balance between value and feasibility in AI projects. It is crucial to identify the value generated from productivity, experience, and revenue, but also focus on the need for innovation that is both effective and sustainable.

The AWS Summit New York 2024 highlighted the latest advancements in cloud technology and AI. One of the major releases, Amazon Bedrock, allows businesses to build, deploy, and scale AI-driven solutions without extensive expertise and effort. This promotes businesses to focus more on performance, cost, and ethical responsibilities with gen AI.

The Summit offered valuable insights and tools for businesses looking to leverage cloud computing for innovation and efficiency. Many case studies were showcased to further support the adoption of generative AI in businesses of all sectors and instances where generative AI can create value for all aspects of the business. The sense of urgency to adopt gen AI has doubled since last year, and the emphasis to build a solid data foundation for successful generative AI initiatives has never been greater. The many new innovations simplifies the process for businesses to leverage data to create and differentiate generative AI applications, and create new value for customers and the business. The phrase “Your data is the differentiator” should be remembered as businesses navigate through the AI journey. 

Overall, the AWS Summit provided a comprehensive look at how AWS is shaping the future of technology. With a strong emphasis on AI and machine learning advancements, security enhancements, and sustainability efforts, the future has never looked so bright for businesses, developers, and consumers. 

Success in Fitness Franchising: A Comprehensive Technical Case Study on AWS Enablement

/by

Burn Boot Camp, a woman-owned, 400+-unit franchise, has been disrupting the fitness industry since 2012. With an emphasis on personalized fitness and nutritional programs, the brand relies heavily on technology to interface with members. The potential value of data gathered was not being fully realized due to the disparate systems, apps and sources that had been created as result of the brand’s explosive growth. 

So Burn Boot Camp leaders made yet another bold move, envisioning the gathering and analysis of data from numerous systems, and the delivery of actionable insights via a franchisee portal. Eager to make large strides on their cloud computing journey, they chose AWS and Ironside as their partners.

Integrating data that fuels valuable insights.

Ironside helped integrate data from Burn Boot Camp’s accounting system, CRM, POS, FranConnect™ and more, and leverage automation to streamline operations. Franchisees have welcomed more automated, streamlined processes that require less manual effort, along with new insights into their own performance versus that of other franchisees, and actionable insights that can lead to greater success. 

Both franchisees and Burn Boot Camp leadership benefit from insights into everything from membership and retail sales to comparative performance data. 

Data helps strike the balance between franchise independence and brand identity.

It’s the challenge every franchise faces — capitalizing on the strengths of individual franchisees while maintaining a strong brand. Hard data that demonstrates how supporting brand principles promotes success and where franchisee contributions are promoting strong membership adds up to insights that everyone can rally around. 

Ironside has helped put these kinds of insights within reach of all Burn Boot Camp franchise owners, by helping the company leverage a dynamic suite of cloud services.

  • AWS Lambdas: Engineered to perfection in Python, these 80+ serverless functions (and counting) seamlessly executed specific tasks in response to API requests. Meticulously crafted, each task was designed to comprehensively fulfill the gathered requirements, ensuring a tailored and responsive user experience.
  • AWS API Gateway: The gatekeeper facilitating a harmonious flow of requests across web platforms, phones, TVs and various fitness devices. It served as the connective tissue, ensuring a unified experience for Burn Boot Camp’s diverse community.
  • CloudFormation: The architect behind the scenes, deploying the entire infrastructure with precision. This AWS service provided a standardized and efficient way to manage and provision resources.
  • AWS CodePipelines: The conductor of the symphony, orchestrating Continuous Integration and Continuous Deployment (CI/CD) pipelines. It ensured the smooth maintenance and evolution of the fitness revolution.
  • AWS DynamoDB: The dynamic caching and storage system enhancing data accessibility and speed. DynamoDB played a pivotal role in optimizing Burn Boot Camp’s data management, providing a scalable and efficient solution to meet the demands of a thriving fitness community.
  • CloudWatch: The vigilant guardian overseeing logging and maintenance. With CloudWatch, Burn Boot Camp maintained a watchful eye on system performance, ensuring optimal operation and proactively addressing any issues the moment they are detected.

Integration and migration are the keys for a smooth transition to the cloud.

In parallel, seamless integration with legacy systems and a meticulous data migration strategy ensured a smooth transition for Burn Boot Camp.. The fitness ecosystem expanded its horizons with integrations like Snowflake, Shopify, Loopspark, Sweatbase, and more. This interoperability showcased the versatility of AWS, allowing Burn Boot Camp to leverage a diverse range of external tools and platforms, enriching the overall fitness experience. This bridging of old and new systems showcases the adaptability of the AWS ecosystem and its ability to seamlessly blend with established platforms, all under one (AWS) roof. 

Key takeaways from Burn Boot Camp’s AWS journey.

Navigating through Burn Boot Camp’s transformative journey with Amazon Web Services (AWS), has revealed key insights into  this fitness revolution.These key takeaways encapsulate the invaluable lessons learned, the strides made, and the blueprint for future innovation. 

  • Navigating Franchise Business Model with AWS: AWS not only streamlined Burn Boot Camp’s operations but also facilitated the intricate dance of managing a franchise business model. AWS played a crucial role in deploying standardized infrastructure, and navigating the complexities of the franchise model while maintaining efficiency and scalability.
  • Maintaining Brand and Standard Across Franchise Business Model with AWS: AWS provided Burn Boot Camp with the tools to uphold brand consistency and operational standards across its diverse franchise network. Through AWS Lambdas and API Gateway, a unified fitness experience was crafted, ensuring that each location seamlessly adheres to the brand’s identity and service quality.
  • Precise Requirement Gathering and System Design: The success of AWS integration relied on meticulous requirement gathering, ensuring that each AWS Lambda function was tailored to address specific needs. Burn Boot Camp’s commitment to precise requirement gathering became the cornerstone for the flawless execution of cloud code, providing a personalized and responsive fitness experience.
  • Testing and Maintenance – Keys to Flawless Cloud Code: Regular testing and proactive maintenance ensured the reliability and performance of the cloud code, creating a robust foundation for the entire fitness ecosystem emerging as a pivotal element in Burn Boot Camp’s strategy for continuous testing and maintenance.
  • Effective Communication as the Linchpin: Throughout the AWS enablement journey, effective communication emerged as the linchpin for success. Clear and open communication channels facilitated collaboration between Burn Boot Camp and AWS, ensuring that the implemented solutions aligned seamlessly with the overarching vision of the fitness franchise.

Forging franchising futures with AWS and Ironside.

Burn Boot Camp’s transformation is not just a case study; it’s a roadmap for leveraging AWS to drive innovation, resilience, and technological prowess and gain a significant competitive edge. If you’re considering your own cloud transformation journey, talk with the Ironside team about the best way to get started. 

Bridging the Gap Between Legacy and Modern BI Platforms – Amazon Quicksight

/by

Today, Amazon QuickSight Announced Paginated Reporting; Cited Ironside as a QuickSight Delivery Partner

Connecting the Past to the Future

The past decade has seen a tremendous shift in how we consume analytics – from enterprise, templated, and paginated reporting to interactive, embedded dashboards with ML-augmented capabilities. It’s no surprise organizations have been eager to put these new tools in the hands of their employees. Unfortunately, they quickly realized a lift-and-shift approach for BI platforms requires extensive planning and training because of the fundamental differences between how legacy and modern BI tools address reporting needs.

Enterprise reporting has been the standard for decades. It’s what many business leaders and users alike are used to – and for good reason. Consumers could receive reports tailored to their individual needs and in various formats (PDF, CSV) on a scheduled cadence that contained all of their KPIs and performance metrics. Within the legacy BI space, organizations have been able to scale this extremely custom and robust reporting solution to their hundreds of users with great success for many years.

But in the age of big data, enterprises needed to approach data discovery and analysis differently. Data analysts became a highly valued and growing community within organizations. Companies rightly prioritized empowering these analysts to better leverage their technical skills and business acumen to drive meaningful impact. This meant migrating to modern BI platforms that favored interactive dashboards over reports numbering in the tens-of-hundreds of pages.

Among the many challenges of migrating from legacy to modern platforms was the reality that legacy users could no longer access reports with the same look and feel they’d grown accustomed to for years. Companies found that even with robust migration strategies, careful execution, and exhaustive change-management programs, they were left with reporting needs that neither a legacy system or modern BI tool could meet on its own. Instead, they had to maintain multiple systems to meet analysts’ needs for powerful dashboards and legacy users’ needs for robust operational reports.

Bridging the Gap

So – how do organizations move to a platform that incorporates the modern analytics movement of cloud-based, self-service and augmented analytics, while also creating limited friction for users entrenched in legacy reporting models? Amazon QuickSight Paginated Reporting is beginning to bridge the gap.

This release is centered around paginated reporting, distribution and analysis – the core tenets of an enterprise reporting implementation. The disparity between platforms continues to shrink, allowing organizations to spend more time evolving their new ideas rather than reimagining existing ones. Lastly, this release addresses an important piece to a successful adoption – creating a smooth transition for the user community.

Enterprise Reporting in the Cloud

Enterprise reporting entails the delivery of insights in templated and tabular formats on a regular basis. Some users prefer fewer visualizations and more granular data, including pivot tables spanning multiple pages. Amazon QuickSight has new features that allow report authors to design, build and distribute presentation-ready formats from within the same platform.

  New report creation tools

  • Headers & Footers
    Gives the author the ability to add custom report information within dedicated sections to make reports easier to scan and absorb
  • Page Margins, Padding Controls & Guardrails
    New formatting tools allow authors more flexibility in customizing how reports appear
  • Repeating Content
    Allows authors to quickly build stories by taking different slices of a particular chart and recreate them within a report

New report distribution and analysis tools

  • Custom schedules with enhanced features
    Gives administrators the flexibility to address the wide variety of distribution requirements from the user community
  • Historical snapshots
    Allows administrators to audit report delivery and track usage to inform scheduling
  • PDF or CSV
    Provides two options so users can receive reports in the desired format for effective analysis

Amazon QuickSight customers can rely on ongoing innovations. Some AWS releases feature exciting new technology. Other releases are about incorporating existing legacy functionality to better meet user needs. The goal is to help companies envision a future within a modern BI platform – and make getting there easier. Paginated Reporting accomplishes both.

Questions?

If you have questions about migrating to Amazon QuickSight, and how Ironside can help, email us at AscentIQ@IronsideGroup.com

Homer leverages Amazon QuickSight to help solve marketing challenges attached to digital privacy

/by

Ironside is an Enterprise Data and Analytics firm and Advanced AWS Partner specializing in building innovative solutions leveraging AWS native analytics services. In a recent project, we worked with Homer Learning to build and launch a solution leveraging Amazon QuickSight to assist their marketing department gain greater visibility into the attribution and conversion of digital marketing spend. 

As a provider of digital education products to children via mobile and web, recent changes by the major industry ecosystem vendor data privacy terms & conditions (Apple & Google) have made tracking usage of Homer’s products very challenging. For the growth of their business, they needed to understand which digital advertising and marketing efforts were converting new customers and driving user consumption. 

Partnering with Homer’s data and analytics team, Ironside engaged to implement Amazon QuickSight Dashboards and Reports sourced from their data lake of advertising spend and user product usage information. The solution required close coordination with various business users within their marketing department and Homer analytics technical leadership to determine the effectiveness of advertising spend for both new user acquisition and user attention. 

Graphical user interface, chart Description automatically generated

Exhibit A:Homer Learning Marketing Attribution Amazon QuickSight Dashboard and Reporting

Ironside’s Practice Lead for Business Intelligence, Scott Misage, shared, “The Homer Learning solution is interesting as it brings the headlines in the newspaper to customers engagement with the requirements, with Homer leveraging AWS to house their data analytics platform, Amazon QuickSight ”

Understanding the data elements from their variety of advertising and product platforms is essential for Homer’s marketing decision makers and is what Amazon QuickSight delivers. Ironside worked closely with business users to understand how they were looking to consume the data and align that to traditional and advanced features within Amazon QuickSight. Jin Chung, Sr. Architect, Analytics Platform at Homer shared, “The Ironside team worked closely with our business stakeholders to understand how they have interacted with the data previously and put forward solutions that could enhance that experience with some of the new features in Amazon QuickSight.” 

The Homer Amazon QuickSight environment is integrated to many other AWS analytics and management platform services that provide data processing and security capabilities. A key component of the platform is the aggregation of 3rd party data delivered to Homer via AWS S3 and blended in Databricks Delta Lake.  Ironside worked to create a secure and functional solution that integrated QuickSight to the Delta Lake with AWS Athena. 

About Ironside

Ironside helps companies translate business goals and challenges into technology solutions that enable insightful analysis, data-driven decision making and continued success. We help you structure, integrate and augment your data, while transforming your analytic environment and improving governance.

About Homer

The journey of parenthood begins without a map. As parents, we want the best for our kids. We want them to grow up to be confident lifelong learners who are ready to take on the world. At HOMER, our purpose is to give kids the best start to their learning journey during the window of opportunity—before the age of 6—where 85% of brain development takes place. We guide and champion children through this pivotal time as they build their skills and deepen their love of learning, and we partner with parents to provide the support that all kids need.


Precisely with AWS: Ironside creates a User Experience Through Their Combined Value

/by

Ironside, an Enterprise Data and Analytics firm, was featured in a Wall Street Journal article highlighting 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. A key part of Ironside’s strategy is having a broad portfolio of technology partners to see how they fit together to provide more value to our mutual customers. Two partners that fit together well are Precisely, an industry-leading Data Provider, and AWS, an Industry cloud leader. Precisely and AWS are technology partners — Precisely Data is offered through the AWS Marketplace and AWS Data Exchange. Ironside saw an additional opportunity for them to work together and Precisely was very interested to move forward. 

Through Ironside’s combined expertise, in both Amazon QuickSight and Precisely’s expertly curated location data, they designed an enhanced business user experience for Amazon QuickSight customers.  In this solution, Ironside leveraged Amazon QuickSight’s ability to ingest multiple data sources (like Precisely Points of Interest data stored in S3/Redshift/Snowflake) in addition to customer data repositories to create enriched Insights. 

Exhibit A: Precisely Points of Interest Data providing Context to Amazon QuickSight Bike Sharing Ridership Dashboard

Ironside’s Practice Lead for Business Intelligence, Scott Misage, shared, “As the diversity and volume of data increase, organizations need to find ways to harness this data explosion and find pathways to bringing additional insights and intelligence to visualizations and dashboards. By leveraging the capabilities of these two complementary partners, it’s easier than ever for organizations to accelerate time to value with analytics.”

For Precisely’s Sales and Consulting teams, their Amazon QuickSight environment, created by Ironside, will provide them a User Experience to demonstrate how their data can enhance customers’ AWS data platform strategy. Matt Reaves, Vice President, Channel Sales at Precisely shared, “Ironside’s vision and expertise in execution has given our customers and sales teams great tools to showcase how Precisely Data can increase the value of their adoption of AWS. Ironside’s work with the QuickSight platform helps to demonstrate context for our multiple datasets.” 

Precisely’s Amazon QuickSight environment is supported by Ironside’s Managed Service team with additional expertise from their Business Intelligence Practice. As new features in Amazon QuickSight are made available, Ironside works to incorporate them into the environment and assists the Precisely teams with understanding how these releases impact a customer’s use of Precisely Data for enriched Insights.

ABOUT IRONSIDE
Ironside helps companies translate business goals and challenges into technology solutions that enable insightful analysis, data-driven decision making and continued success. We help you structure, integrate and augment your data, while transforming your analytic environment and improving governance.

ABOUT PRECISELY
Precisely is a new company with a remarkable heritage. We were formed when Syncsort and Pitney Bowes Software & Data combined, bringing together decades of experience and expertise in handling, processing and transforming data. Precisely data integration, data quality, location intelligence, and data enrichment products power better business decisions to create better outcomes.

ABOUT AWS

Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud platform for 14 years. AWS offers over 175 fully featured services for compute, storage, databases, networking, analytics, robotics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 77 Availability Zones (AZs) within 24 geographic regions, with announced plans for nine more Availability Zones and three more AWS Regions in Indonesia, Japan, and Spain. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs.

, , ,

Analytics Assurance Services: Delivering Confidence During Uncertain Times

/by

The world has changed dramatically over the course of a single month, and companies are struggling even more with things that have historically challenged them:

  • Finding the best people to run, build and innovate on their analytics tools and data
  • Making these environments accessible to employees in a work-at-home model

In this Forbes article, Louis Columbus cites a recent Dresner survey that shows up to 89% of companies are seeing a hit to their BI and Analytics budgets due to COVID-19. The survey includes these two recommendations:

Recommendation #1

Invest in business intelligence (BI) and analytics as a means of understanding and executing with the change landscape.

Recommendation #2

Consider moving BI and analytical applications to third-party cloud infrastructure to accommodate employees working from home.

89% of companies are seeing a hit to their BI and Analytics budgets due to COVID-19.

We’re here to help you explore your options.

Now that the role of analytics is more important than ever to a company’s success, analytics leaders are again being asked to do much more with much less — all while companies are experiencing staff reductions, navigating the complexities of moving to a work-from-home model, and struggling to onboard permanent hires.

To address these short-term shortages (and potentially longer-term budget impacts), companies are naturally evaluating whether leveraging a managed-service approach — wholly or even just in part— can help them fill their skills gap while also reducing their overall spend.

As they weigh this decision, cost, technical expertise, market uncertainty and the effectiveness of going to a remote-work model are all top-of-mind. Here’s how these factors might affect your plans going forward:

Factor 1: Cost

As the Dresner number showed, most analytics teams need to reduce spend. Doing this mid-year is never easy, and usually comes at the expense of delayed or canceled projects, delayed or cancelled hiring, and possibly even staff reductions. All of these decrease a company’s analytics capabilities, which in turn decreases its ability to make the right business decisions at a critical time. A managed services approach to meeting critical analytics needs, even just to address a short-term skills gap, can provide valuable resources in a highly flexible way, while saving companies significant money over hiring staff and traditional consulting models.

Factor 2: Technical Expertise

A decade ago, your options for analytics tools and platforms were limited to a handful of popular technologies. Today even small departments use many different tools. We have seen organizations utilizing AWS, Azure, and private datacenters. Oracle, SQL Server, Redshift all at the same company? Yes, we have seen that as well. Some of our customers maintain more than five BI tools. At some point you have to ask: Can we hire and support the expertise necessary to run all these tools effectively? Can we find and hire a jack-of-all trades?

In a managed services model, companies can leverage true experts across a wide range of technology while varying the extent to which they use those resources at any particular time. As a result, companies get the benefit of a pool of resources in a way that a traditional hiring approach simply cannot practically provide.

Factor 3: Effectiveness of Remote Work Engagement

If you weren’t working remotely before, you probably are now. Companies are working to rapidly improve their processes and technologies to adjust to a new normal while maintaining productivity.

Managed service resourcing models have been delivering value remotely for years, using tools and processes that ensure productivity. Current events have not affected these models, therefore making them an ideal solution for companies  trying to figure out the best way to work at home.

Times are changing. We’re ready!

Ironside has traditionally offered Managed Services, to care for and maintain customer platforms and applications, and consulting services, to assist in BI and Analytics development.

Companies can leverage our Analytics Assurance Services temporarily, for a longer period of time to address specific skills gaps, or to establish a cloud environment to support remote analytic processes.

With Ironside, you can improve your data analytics within your new constraints, while reducing your costs. We’d love to show you how.

Contact us today at: Here2Help@IronsideGroup.com

, ,

New Capabilities Announced for AWS QuickSight

/by

Earlier today the AWS team unveiled two new capabilities for QuickSight, Amazon’s signature Business Intelligence tool. Speaking live from the AWS re:Invent conference at the Venetian in Las Vegas, the four hosts announced the ability for users to easily embed QuickSight dashboards in applications and previewed new native Machine Learning capabilities. Read more