How Banks Can Utilize Data Analytics

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How Banks Can Utilize Data Analytics

Take30 with a Data Scientist – 12/16/21

This session will discuss how the banking industry can utilize advanced data analytics to help their business. Our experts will look into these use cases: identifying profitable customers and products, forecasting branch performance, and new site selections. This webinar will also touch on the types of external data we can leverage in addition to banks’ internal data.

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Protecting your Cloud Data, hosted by KeepIt

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Protecting your Cloud Data, hosted by KeepIt

Take30 with a Data Advisor – 12/9/21

Did you know that Microsoft Office 365 and many other SaaS applications are not backing up your data? The fine print in EULA’s states that you are responsible for your data… not the SaaS vendor. Come learn why you should care and how you can solve this problem.

– The most common reasons for data loss in SaaS application
– 7 reasons why you should protect your SaaS data via active backups
– Why you should not have backup data stored in the same cloud as your primary dataset
– Q&A with industry experts.

Your time is valuable, and the problem is simple, so this will be a 30-minute program on backup for SaaS applications.

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Exploring Microsoft PowerBI Dataset Refresh Options and Automation

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Exploring Microsoft PowerBI Dataset Refresh Options and Automation

Take30 with a BI Expert – 12/2/21

In this session, Ironside experts explore real-world use cases with multiple options to efficiently refresh and automate Power BI datasets for better performance, increased security and reliability and lower maintenance over time.

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Susan Ma | Technical Manager

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John Fehlner | Senior Consultant

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AscentData: Making Cloud Analytics Easy

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AscentData: Making Cloud Analytics Easy

Take30 with a Data Advisor – 10/7/21

Ironside’s AscentData offering allows your organization to create new cloud analytics workloads and modernize existing ones at a cost that makes sense for you.

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Geoff Speare | Practice Lead, Information Management

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AscentAI: Assessing your AI Viability in under 3 weeks

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AscentAI: Assessing your AI Viability in under 3 weeks

Take30 with a Data Scientist – 9/23/21

Start with a small investment and give us 2-3 weeks. We’ll come back with model prototypes that give you hard data on the gains you can expect to make as your analytics capabilities improve.

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Drive Business Value With Azure Analytics and Power BI

/by

Drive Business Value With Azure Analytics and Power BI – Ironside & Microsoft

Take30 with a BI Expert – 8/24/21

Today, more than ever before, organizations like yours must evolve to keep up. Customers are more demanding than ever. Worldwide, businesses in every industry are seeking to drive social and economic progress in more efficient ways while growing their own profits. Data analytics is the path to accelerating your success.

Our team at Ironside, a Microsoft Gold Data Analytics Partner, held a webinar on Aug. 24 with Microsoft, sharing how you can tap into data analytics technology to start making better, data-based business moves.

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Michael Vollmer | Senior Consultant

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Accelerating Data Science for Customer Insights

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Accelerating Data Science for Customer Insights

Take30 with a Data Scientist – 7/30/21

“A use case driven approach allows you to focus on the data related to the problem you’re trying to solve.”

Among the thousands of decisions being made at your organization every day, there are golden opportunities. Some are small but have an outsized impact on profitability. Others are big enough to reshape your company’s future. So, prioritizing where you apply AI and understanding the potential impact on your business, the best opportunities stand out, and so do the best ways to act on them.

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,

Data Science as a Service

/by

PLATFORM | EBOOK

Data Science as a Service

Data Science as a Service: Leverage Expertise and Data Enrichment to Optimize AI Value



Reduce risk, lower costs and realize business value faster with a stepwise approach to data-enriched AI.

This eBook looks at how to discover the potential AI use cases for your business and alternatives for their implementation. It describes a flexible, customized data science as a service approach, developed by Ironside, that reduces the risk of getting started with AI and addresses the role that data enrichment can play in delivering more value from your AI investments.

Download this 10 page eBook to learn more about:

  • The importance of starting with the right use case
  • How to choose the right AI approach
  • The value of Ironside’s AscentAI — a flexible, customized
    data science as-a-service, progressive 4-Step solution
  • How to increase the business value of AI investments by adding
    feature-rich location-based data to your internal datasets
  • Advantages of Ironside & Precisely’s approach for AI data enrichment


Fill in the form below to get your copy of Data Science as a Service.

Why Ironside and AscentAI

Ironside has years of proven experience helping organizations successfully launch their AI journey. Our highly skilled data science team understands the common pitfalls and how to pivot around them. You can depend on Ironside to consistently offer a valuable point of view on AI/ML technology and infrastructure options and to understand that many organizations can’t jump in feet first and need a way to prove value quickly and easily with rapid cost-effective sprints before they begin to think about hiring and/or making large technology purchases. That is why we created AscentAI.

To learn more about AscentAI, visit IronsideGroup.com or you can email us at HelloAI@Ironside.com.

About Ironside

Ironside was founded in 1999 as an enterprise data and analytics solution provider and system integrator. Our clients hire us to acquire, enrich and measure their data so they can make smarter, better decisions about their business. No matter your industry or specific business challenges, Ironside has the experience, perspective and agility to help transform your analytic environment.

,

Establish a Collaborative Data Governance Strategy

/by

DATA | CASE STUDY

Establish a Collaborative Data Governance Strategy

COMPANY OVERVIEW


A medical professional liability insurance company with clients and services spanning the U.S. that specializes in underwriting policies for physicians, dentists, advanced practices, clinics, and hospitals.

BUSINESS NEED


The client recognized a need for Advanced Analytics and Business Intelligence capabilities to create an information edge over the competition. In order to enable these analytics teams to develop as quickly and efficiently as possible, the client sought to enhance their existing data governance process. This process would ensure that analytic data would be trustworthy, consistent, and accurate, which would allow the analytics teams to focus all of their energy on problem solving, not data cleansing.

SOLUTION


Ironside identified the aforementioned opportunities in a previous technology strategy engagement as being foundational prerequisites for the company to realize its analytics goals. Ironside recommended  the formation of an enterprise data governance group. The group would consist of all claims data constituents from multiple subsidiaries and would be able to discuss the state of the data quality and implement improvements. The engagement was made up of the following phases:


Identify Who Will Govern


  • Identified key data domain constituents

  • Appointed a representative from each department to the Governance team

  • Appointed a data steward to monitor the data


Identify What to Govern


  • Conducted workshops with all representatives

  • Identified and recorded critical business data fields and their downstream usage


Teach How to Govern


  • Assigned a repeatable process to the data steward

  • Developed a standardized process for reporting, mitigating, and solving data issues

  • Coached the Governance team through a number of Governance meetings to rank data quality fields according to business value and effort


Establish Accountability


  • Business and IT partnered to establish responsibility and points of accountability

  • Executive Leadership made a commitment to hold the relevant parties responsible for data quality issues

  • Created dashboards and profiling processes to monitor the quality of governed data


KEYS TO SUCCESS


With Ironside’s help, the client enjoyed many benefits, which enabled them to:

  • Know changes to the data in advance and widely broadcast to the business

  • Have a clear point of contact, method for resolution, and timeline for solving data problems when one arises

  • Decrease the time Analysts spend verifying or questioning the validity of the data

  • Decrease the time to develop reports and analytics

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,

Build an Enterprise Data Warehouse for Self-Service Analytics

/by

DATA | CASE STUDY

Build an Enterprise Data Warehouse for Self-Service Analytics

COMPANY OVERVIEW


A multinational personal care company operating in 120 countries and regions around the world, producing cosmetics, fragrances, and hair & skin care products.

BUSINESS NEED


With the existing architecture and multiple siloed data marts, the client was not able to provide complete, accurate and timely business insights to stakeholders across functional areas. They were also dealing with multiple reporting platforms inherited due to acquisitions, making it tough for the IT team to provide timely support. The client needed to design a high performing central data repository that would be open to future integrations and drive self-service analytics through an enterprise-level analytics platform.

SOLUTION


The Ironside Information Management team designed and implemented an enterprise data warehouse from multiple source systems to integrate cross functional and brand data across the Americas region. The solution included:


Assess the Architecture


  • Reviewed existing set up, provided solution options, and recommended architecture along with optimal configuration (environments, number of servers, sizing) needed to sustain both current and future needs

  • Recommended database and analytics platform driven by business use cases, anticipated data growth over the next 5 years, and conducted performance testing on client data set


Discovery & Analysis


  • Discovered multiple hierarchies at division levels impacting regional reporting; recommended a uniform Item and Customer ‘America’s Hierarchy’ for consistent reporting across all business units

  • Identified master data quality issues and lack of MDM processes; recommended and helped set up a Data Governance & Stewardship counsel to define and maintain cleansed data to drive maximize benefit from the data warehouse initiative


Design & Develop


  • Consumed and homogenized data from multiple ERPs and other source systems into a Star Schema (dimensional data model) and also provisioned an ODS for operational reporting needs

  • Used an iterative and phased approach to deliver individual subject areas as part of the data warehouse implementation, ensuring business value being delivered at regular intervals

  • Designed and built business layer on top of the data layer for efficient reporting


Mentor & Collaborate


  • Used a flexible development approach and project planning by sharing ETL build effort with the client team to manage budget controls while ensuring best practices are enforced

  • Mentored client resources along the way to enable them to support the warehouse and enhance it with newer subject areas post project completion


KEYS TO SUCCESS


With the new data warehouse implementation, the client achieved the following benefits:

  • Provided regional reporting capability from a central data warehouse for subject areas like Manufacturing, Warehouse Management, Inventory, Procurement, Sales and Finance across master data like Product, Customer and Chart of Accounts

  • Drove user adoption of the enterprise reporting platform and helped change culture to be an empowered self-service reporting organization – distinct daily user logon count doubled from 40 to 80 and interactive report runs scaled up from 100 to 800 per month

  • Ability to turn off 49 cubes (across 7 divisions); No need to build and maintain cubes, thus freeing up the IT team to focus on delivering enhancements and integrating additional data sources

  • Smooth transition by retiring legacy BI systems and using the Enterprise reporting tool

  • Average performance improved by 5-15 times

“We can easily write automated reports without any help from IT teams, and we trust the data too.”

“Earlier my month end report took over 3 hours, but with the new data warehouse it runs in under 15 minutes.”

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