,

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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link='portfolio_entries,1123' wc_prod_visible='' prod_order_by='' prod_order='' sort='no' items='3' columns='3' paginate='none' query_orderby='date' query_order='DESC' size='fixed' orientation='av-orientation-square' gap='large' overlay_fx='active' id='' caption_elements='title' caption_styling='overlay' caption_display='always' color='' custom_bg='' av-medium-columns='' av-small-columns='' av-mini-columns='' av_uid='av-2ibtovw' custom_class=''] [/av_tab_sub_section][av_tab_sub_section tab_title='Analytics' vertical_align='middle' icon_select='no' icon='ue800' font='entypo-fontello' tab_image='' tab_image_style='' color='' background_color='' src='' attachment='' attachment_size='' background_attachment='scroll' background_position='top left' background_repeat='no-repeat' custom_class='' av_uid='av-1ztjhss'] [av_masonry_entries link='portfolio_entries,1124' wc_prod_visible='' prod_order_by='' prod_order='' sort='no' items='3' columns='3' paginate='none' query_orderby='date' query_order='DESC' size='fixed' orientation='av-orientation-square' gap='large' overlay_fx='active' id='' caption_elements='title' caption_styling='overlay' caption_display='always' color='' custom_bg='' av-medium-columns='' av-small-columns='' av-mini-columns='' av_uid='av-1satp18' custom_class=''] [/av_tab_sub_section][av_tab_sub_section tab_title='Platform' vertical_align='middle' icon_select='no' icon='ue800' font='entypo-fontello' tab_image='' tab_image_style='' color='' background_color='' src='' attachment='' attachment_size='' background_attachment='scroll' background_position='top left' background_repeat='no-repeat' custom_class='' av_uid='av-14gy7h8'] [av_masonry_entries link='portfolio_entries,1125' wc_prod_visible='' prod_order_by='' prod_order='' sort='no' items='3' columns='3' paginate='none' query_orderby='date' query_order='DESC' size='fixed' orientation='av-orientation-square' gap='large' overlay_fx='active' id='' caption_elements='title' caption_styling='overlay' 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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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link='portfolio_entries,1123' wc_prod_visible='' prod_order_by='' prod_order='' sort='no' items='3' columns='3' paginate='none' query_orderby='date' query_order='DESC' size='fixed' orientation='av-orientation-square' gap='large' overlay_fx='active' id='' caption_elements='title' caption_styling='overlay' caption_display='always' color='' custom_bg='' av-medium-columns='' av-small-columns='' av-mini-columns='' av_uid='av-2ibtovw' custom_class=''] [/av_tab_sub_section][av_tab_sub_section tab_title='Analytics' vertical_align='middle' icon_select='no' icon='ue800' font='entypo-fontello' tab_image='' tab_image_style='' color='' background_color='' src='' attachment='' attachment_size='' background_attachment='scroll' background_position='top left' background_repeat='no-repeat' custom_class='' av_uid='av-1ztjhss'] [av_masonry_entries link='portfolio_entries,1124' wc_prod_visible='' prod_order_by='' prod_order='' sort='no' items='3' columns='3' paginate='none' query_orderby='date' query_order='DESC' size='fixed' orientation='av-orientation-square' gap='large' overlay_fx='active' id='' caption_elements='title' caption_styling='overlay' caption_display='always' color='' custom_bg='' av-medium-columns='' av-small-columns='' av-mini-columns='' av_uid='av-1satp18' custom_class=''] [/av_tab_sub_section][av_tab_sub_section tab_title='Platform' vertical_align='middle' icon_select='no' icon='ue800' font='entypo-fontello' tab_image='' tab_image_style='' color='' background_color='' src='' attachment='' attachment_size='' background_attachment='scroll' background_position='top left' background_repeat='no-repeat' custom_class='' av_uid='av-14gy7h8'] [av_masonry_entries link='portfolio_entries,1125' wc_prod_visible='' prod_order_by='' prod_order='' sort='no' items='3' columns='3' paginate='none' query_orderby='date' query_order='DESC' size='fixed' orientation='av-orientation-square' gap='large' overlay_fx='active' id='' caption_elements='title' caption_styling='overlay' 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,

Implement a Single View of the Customer

/by

DATA | CASE STUDY

Implement a Single View of the Customer

COMPANY OVERVIEW


A manufacturer of analog, mixed-signal and digital signal processing integrated circuits used to convert, condition and process real-world phenomena, such as light, sound, temperature, motion, and pressure into electrical signals.

BUSINESS NEED


The client wanted to move their externally managed customer master process to an in-house solution they could manage themselves. Their goal was to maintain their own single view of the customer solution mastering both account and contact entities and the relationship between the two. They needed greater control and flexibility around data quality, standardization and validation, along with the ability to better manage change and processing times. The client partnered with Ironside to develop the single view of the customer solution.

SOLUTION


Ironside’s Information Management practice was responsible for delivering the solution. The solution included building data cleansing, data standardization and data validation routines to prepare the data for matching and consolidation to ultimately master the data. This included validating and standardizing business and personal names, addresses, phone numbers, and email addresses. The data was enriched with different attributes to better define the address, and the account and contact entities.


Develop a Plan


  • Start with the requirements statement of work. During this phase, the software was installed, data profiling and a data quality assessment was completed which supported business requirements, and business and technical solution documents were generated

  • Completing the business and technical documents supported the creation of the statement of work and implementation plan for the build phase


Define the Data Sources


  • Enriched address data with the Spectrum Universal Address Module On-Demand web services. These services validated addresses making corrections, scoring, validation and adding attributes such as a residential indicator

  • Incorporated Google Translate into the process to translate Account Names in foreign languages. Keeping all Account Names in a common language increases the matching potential and helps to better define the “Golden Record”


Formalize the Approach


  • Spectrum allowed data sources to be added quickly and easily, allowing for greater flexibility than before. Created sub-processes which can be reused as needed, which reduced the initial development time along with future development

  • Used Spectrum to incorporate transliteration for personal name standardization and address validation and translation for account names


Ensure Future Success


  • Move from third party managed MDM solution to one managed internally

  • Decreased processing time, allowing for multiple processes per day and faster updates to target systems

  • Manage changes to the solution in a timely manner and no longer need to wait on the vendor to complete the change

  • Adapt to data inconsistencies in an easier manner, which allows for better data quality to help better align the sales force with their accounts and contacts


KEYS TO SUCCESS


By moving to an in-house master data management solution, the client is now able to:

  • Increase match rate from the previously managed master data management process by 2-3x

  • Better align sales force with client accounts

  • Quickly adapt to business rule changes, which was difficult to do with the previous MDM solution

  • Better address validation and data standardization allows accounts to be aligned to a sales person and not in a generic sales grouping

About Pitney Bowes

Pitney Bowes (NYSE:PBI) is a global technology company powering billions of transactions – physical and digital – in the connected and borderless world of commerce. Clients around the world, including 90 percent of the Fortune 500, rely on products, solutions, services and data from Pitney Bowes in the areas of customer information management, location intelligence, customer engagement, shipping, mailing, and global ecommerce. And with the innovative Pitney Bowes Commerce Cloud, clients can access the broad range of Pitney Bowes solutions, analytics, and APIs to drive commerce.

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,

Centralize End-to-End Enterprise Analytics for Fitness Club

/by

DATA | CASE STUDY

Centralize End-to-End Enterprise Analytics for Fitness Club

COMPANY OVERVIEW


Major fitness club operator and franchisor that’s been expanding at high speed due to a strong business model and broad appeal—now with more than 1,200 locations and approximately 8.5 million members.

BUSINESS NEED


The company had low visibility into why their rapid growth was happening due to a siloed data infrastructure and informal, manual reporting processes. They needed to maximize the technologies they’d already invested in, build a strategy across all analytics focus areas, and free their Information Systems team to provide analytics value instead of administration.

SOLUTION


The Ironside Analytics Advisory team was asked to help. In doing so, we focused on the client’s core goal was of getting custody over their data and using the wealth of information available in order to make data-driven decisions company-wide. There were four steps to the process:


Define the Focal Points


  • Determine which data has highest priority, how to gauge the impact of specific actions on the membership base, and how to integrate different departments’ data resources

  • Develop roadmap that features clear metrics, standardization of critical data sources, cloud-based agility, and parallel predictive analytics proof of concept


Prepare the Data


  • Map analytics goals to data resources—including identifying data formats, storage types and quality for each location

  • Tag sources with goals— specifically, CRM systems, POS, demographic data, web logs, spreadsheets, facilities data, marketing automation platforms and sales compensation


Formalize the Approach


  • Extract information from legacy system and move it to new data warehouse structure

  • Uncover data quality issues that need resolution and fill in additional data as needed—establishing a reliable data foundation for the new data warehouse


Implement Iteratively


  • Use roadmap and goals to prioritize data sources, and bring high-impact data into warehouse first to form a core of BI reporting and financial performance monitoring/ forecasting

  • Pilot projects in which advanced visualization and dashboarding help predict customer churn


KEYS TO SUCCESS


The client enjoyed many benefits, since Ironside enabled them to:

  • Accelerate time to analytics value—for example, Salesforce campaign success is now measured in minutes

  • Connect formerly siloed departments though a central data warehouse

  • Modernize outdated financial and BI processes—automated reports now trigger as ETL completes pushing out fresh data

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query_order='DESC' size='fixed' orientation='av-orientation-square' gap='large' overlay_fx='active' id='' caption_elements='title' caption_styling='overlay' caption_display='always' color='' custom_bg='' av-medium-columns='' av-small-columns='' av-mini-columns='' av_uid='av-1satp18' custom_class=''] [/av_tab_sub_section][av_tab_sub_section tab_title='Platform' vertical_align='middle' icon_select='no' icon='ue800' font='entypo-fontello' tab_image='' tab_image_style='' color='' background_color='' src='' attachment='' attachment_size='' background_attachment='scroll' background_position='top left' background_repeat='no-repeat' custom_class='' av_uid='av-14gy7h8'] [av_masonry_entries link='portfolio_entries,1125' wc_prod_visible='' prod_order_by='' prod_order='' sort='no' items='3' columns='3' paginate='none' query_orderby='date' query_order='DESC' size='fixed' orientation='av-orientation-square' gap='large' overlay_fx='active' id='' caption_elements='title' caption_styling='overlay' 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[/av_one_half][av_one_fourth min_height='' vertical_alignment='av-align-top' space='' margin='0px' margin_sync='true' padding='0px' padding_sync='true' border='' border_color='' radius='0px,0px,10px,0px' background_color='#ffffff' src='' attachment='' attachment_size='' background_position='top left' background_repeat='no-repeat' animation='' mobile_breaking='' mobile_display='' custom_class='' av_uid='av-2cum7d'] [/av_one_fourth] [/av_section]
,

Modernize the Data Warehouse for Finance IT

/by

DATA | CASE STUDY

Modernize the Data Warehouse for Finance IT

COMPANY OVERVIEW


Leading investment advisory firm with more than 10,000 employees and greater than $1 billion in revenue—a major player in the financial services industry.

BUSINESS NEED


The client’s IT group struggled to keep their governance and planning efforts moving at the pace of business. They’d attempted to centralize their data sources and generate more actionable reporting, but their outdated infrastructure couldn’t meet increased information demands or integrate effectively with their BI reporting. To solve these issues, the client was tasked with moving their data warehouse to a more modern solution.

SOLUTION


The Ironside Information Management team worked hand-in-hand with the client’s database engineers to move the warehousing infrastructure out of the current obsolete environment to a more modern data warehouse. Our solution required us to:


Develop a Migration Plan


  • Outline a plan that addressed pain points occurring between the data warehouse and the Cognos BI reporting layer, increased query efficiency, and enabled timely analytics

  • Include recommendations for storing historical point-in-time data for more accurate referencing of past events


Document and Test


  • Document existing warehouse logic, including extracts, transformations and schedules

  • Serve as the solution architect for a proof of concept of the data warehouse redesign

  • Test data handling and report output using POC system


Consolidate


  • Bring together approximately 20 tables from an array of data sources, including TM1, A-Track, Remedy, Oracle GL, and Excel, into an IBM PureData for Analytics (Netezza) implementation capable of meeting and exceeding client requirements


Reengineer


  • Reengineer both the ETL processes used to move and transform all the different information streams and the reporting layer making that information available for analysis


KEYS TO SUCCESS


The modernizing of the data warehouse has enabled the client to:

  • Improve ease of use and performance in Cognos reporting

  • Enable time comparison reporting, such as evaluating last month’s data against today’s data

  • Increase performance approximately 60 times faster than with the previous environment—reports that used to take two minutes to run are now taking two seconds

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