Business<\/h5>\nMeet Your Problem Solver<\/strong><\/h3>\n![\"Mark](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2023\/05\/Mark-Paulson-Headshot-150x150.jpg\")
<\/p>\n
Mark Paulson | Senior Manager, Chicago Team | People & Change<\/a><\/h3>\nTHE BUSINESS PROBLEM<\/h4>\n
A common problem I see across the clients I work with is a challenge to understand all the different components that make up their operating models (process, structure, performance metrics, information, technology, people) and how they work together. When clients make a change to any one of those operating model components, they often do not realize there are resulting impacts to other components they need to account for.<\/p>\n
For example, an IT services client of mine recently decided they wanted to change their entire operating model from product-focused to functionally-focused. I had to help them understand that outside of defining the new structure and teams, they also needed to define the business processes<\/a> required to enable the new structure.<\/strong><\/p>\nOUR INSIGHT<\/h4>\n
To combat this challenge, I have a discussion with my client early on to identify potential effects the project will have on other operating model components. This conversation typically serves as a valuable education session for my client and helps firm up scope implications and watch-out areas on the project.<\/p>\n
My clients often find these up-front discussions extremely helpful as they not only educate but also help them think more broadly about implications for their organization they may not have initially considered.<\/p>\n
Technology<\/h5>\nMeet Your Problem Solver<\/strong><\/h3>\n![\"Suhail](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2023\/05\/Suhail-Ali-Headshot-150x150.jpg\")
<\/a><\/h3>\nSuhail Ali | Senior Architect, Chicago Team | Data & Analytics<\/a><\/h3>\nTHE TECH PROBLEM<\/h4>\n
A poor or non-existent master data management strategy is the root cause of most client data issues I see. Often, organizations will try to solve this by addressing the symptoms (building a data warehouse, purchasing the latest tool, and so on) and not looking at their data strategy wholistically.<\/p>\n
The size and complexity of data sources are also growing exponentially, and I see many clients fighting a losing battle relying on ensuring data quality through manual testing. They need to consider using machine learning<\/a> data observability tools to augment their data quality efforts to build a high level of organizational trust in the data.<\/p>\nOUR INSIGHT<\/h4>\n
For my current client, we introduced Microsoft Purview as part of building a data hub solution to create a business glossary and data catalog. This hub allowed them to grasp the benefits of data governance and have a strategic conversation about data governance and broader organizational change in the future.<\/strong><\/p>\nI’ve also helped current and past clients pursue the adoption of data observability tools such as Monte Carlo and Metaplane to help automate the process of identifying data quality issues. These tools can automatically identify data quality issues in production proactively and prevent downstream systems from using the data.<\/p>\n
Industry<\/h5>\nMeet Your Problem Solver<\/strong><\/h3>\n![\"Jeff](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2023\/05\/Jeff-Ehman-Headshot-150x150.jpg\")
<\/a><\/h3>\nJeff Ehman | Senior Manager, Chicago | Enterprise Portfolio and Program Management<\/a>, Consumer Packaged Goods (CPG) Industry<\/h3>\nTHE INDUSTRY PROBLEM<\/h4>\n
<\/p>\n
Mark Paulson | Senior Manager, Chicago Team | People & Change<\/a><\/h3>\nTHE BUSINESS PROBLEM<\/h4>\n
A common problem I see across the clients I work with is a challenge to understand all the different components that make up their operating models (process, structure, performance metrics, information, technology, people) and how they work together. When clients make a change to any one of those operating model components, they often do not realize there are resulting impacts to other components they need to account for.<\/p>\n
For example, an IT services client of mine recently decided they wanted to change their entire operating model from product-focused to functionally-focused. I had to help them understand that outside of defining the new structure and teams, they also needed to define the business processes<\/a> required to enable the new structure.<\/strong><\/p>\nOUR INSIGHT<\/h4>\n
To combat this challenge, I have a discussion with my client early on to identify potential effects the project will have on other operating model components. This conversation typically serves as a valuable education session for my client and helps firm up scope implications and watch-out areas on the project.<\/p>\n
My clients often find these up-front discussions extremely helpful as they not only educate but also help them think more broadly about implications for their organization they may not have initially considered.<\/p>\n
Technology<\/h5>\nMeet Your Problem Solver<\/strong><\/h3>\n<\/a><\/h3>\nSuhail Ali | Senior Architect, Chicago Team | Data & Analytics<\/a><\/h3>\nTHE TECH PROBLEM<\/h4>\n
A poor or non-existent master data management strategy is the root cause of most client data issues I see. Often, organizations will try to solve this by addressing the symptoms (building a data warehouse, purchasing the latest tool, and so on) and not looking at their data strategy wholistically.<\/p>\n
The size and complexity of data sources are also growing exponentially, and I see many clients fighting a losing battle relying on ensuring data quality through manual testing. They need to consider using machine learning<\/a> data observability tools to augment their data quality efforts to build a high level of organizational trust in the data.<\/p>\nOUR INSIGHT<\/h4>\n
For my current client, we introduced Microsoft Purview as part of building a data hub solution to create a business glossary and data catalog. This hub allowed them to grasp the benefits of data governance and have a strategic conversation about data governance and broader organizational change in the future.<\/strong><\/p>\nI’ve also helped current and past clients pursue the adoption of data observability tools such as Monte Carlo and Metaplane to help automate the process of identifying data quality issues. These tools can automatically identify data quality issues in production proactively and prevent downstream systems from using the data.<\/p>\n
Industry<\/h5>\nMeet Your Problem Solver<\/strong><\/h3>\n<\/a><\/h3>\nJeff Ehman | Senior Manager, Chicago | Enterprise Portfolio and Program Management<\/a>, Consumer Packaged Goods (CPG) Industry<\/h3>\nTHE INDUSTRY PROBLEM<\/h4>\n
OUR INSIGHT<\/h4>\n
To combat this challenge, I have a discussion with my client early on to identify potential effects the project will have on other operating model components. This conversation typically serves as a valuable education session for my client and helps firm up scope implications and watch-out areas on the project.<\/p>\n
My clients often find these up-front discussions extremely helpful as they not only educate but also help them think more broadly about implications for their organization they may not have initially considered.<\/p>\n
Technology<\/h5>\nMeet Your Problem Solver<\/strong><\/h3>\n<\/a><\/h3>\nSuhail Ali | Senior Architect, Chicago Team | Data & Analytics<\/a><\/h3>\nTHE TECH PROBLEM<\/h4>\n
A poor or non-existent master data management strategy is the root cause of most client data issues I see. Often, organizations will try to solve this by addressing the symptoms (building a data warehouse, purchasing the latest tool, and so on) and not looking at their data strategy wholistically.<\/p>\n
The size and complexity of data sources are also growing exponentially, and I see many clients fighting a losing battle relying on ensuring data quality through manual testing. They need to consider using machine learning<\/a> data observability tools to augment their data quality efforts to build a high level of organizational trust in the data.<\/p>\nOUR INSIGHT<\/h4>\n
For my current client, we introduced Microsoft Purview as part of building a data hub solution to create a business glossary and data catalog. This hub allowed them to grasp the benefits of data governance and have a strategic conversation about data governance and broader organizational change in the future.<\/strong><\/p>\nI’ve also helped current and past clients pursue the adoption of data observability tools such as Monte Carlo and Metaplane to help automate the process of identifying data quality issues. These tools can automatically identify data quality issues in production proactively and prevent downstream systems from using the data.<\/p>\n
Industry<\/h5>\nMeet Your Problem Solver<\/strong><\/h3>\n<\/a><\/h3>\nJeff Ehman | Senior Manager, Chicago | Enterprise Portfolio and Program Management<\/a>, Consumer Packaged Goods (CPG) Industry<\/h3>\nTHE INDUSTRY PROBLEM<\/h4>\n
<\/a><\/h3>\nSuhail Ali | Senior Architect, Chicago Team | Data & Analytics<\/a><\/h3>\nTHE TECH PROBLEM<\/h4>\n
A poor or non-existent master data management strategy is the root cause of most client data issues I see. Often, organizations will try to solve this by addressing the symptoms (building a data warehouse, purchasing the latest tool, and so on) and not looking at their data strategy wholistically.<\/p>\n
The size and complexity of data sources are also growing exponentially, and I see many clients fighting a losing battle relying on ensuring data quality through manual testing. They need to consider using machine learning<\/a> data observability tools to augment their data quality efforts to build a high level of organizational trust in the data.<\/p>\nOUR INSIGHT<\/h4>\n
For my current client, we introduced Microsoft Purview as part of building a data hub solution to create a business glossary and data catalog. This hub allowed them to grasp the benefits of data governance and have a strategic conversation about data governance and broader organizational change in the future.<\/strong><\/p>\nI’ve also helped current and past clients pursue the adoption of data observability tools such as Monte Carlo and Metaplane to help automate the process of identifying data quality issues. These tools can automatically identify data quality issues in production proactively and prevent downstream systems from using the data.<\/p>\n
Industry<\/h5>\nMeet Your Problem Solver<\/strong><\/h3>\n<\/a><\/h3>\nJeff Ehman | Senior Manager, Chicago | Enterprise Portfolio and Program Management<\/a>, Consumer Packaged Goods (CPG) Industry<\/h3>\nTHE INDUSTRY PROBLEM<\/h4>\n
THE TECH PROBLEM<\/h4>\n
A poor or non-existent master data management strategy is the root cause of most client data issues I see. Often, organizations will try to solve this by addressing the symptoms (building a data warehouse, purchasing the latest tool, and so on) and not looking at their data strategy wholistically.<\/p>\n
The size and complexity of data sources are also growing exponentially, and I see many clients fighting a losing battle relying on ensuring data quality through manual testing. They need to consider using machine learning<\/a> data observability tools to augment their data quality efforts to build a high level of organizational trust in the data.<\/p>\n For my current client, we introduced Microsoft Purview as part of building a data hub solution to create a business glossary and data catalog. This hub allowed them to grasp the benefits of data governance and have a strategic conversation about data governance and broader organizational change in the future.<\/strong><\/p>\n I’ve also helped current and past clients pursue the adoption of data observability tools such as Monte Carlo and Metaplane to help automate the process of identifying data quality issues. These tools can automatically identify data quality issues in production proactively and prevent downstream systems from using the data.<\/p>\nOUR INSIGHT<\/h4>\n
Industry<\/h5>\n
Meet Your Problem Solver<\/strong><\/h3>\n
<\/a><\/h3>\nJeff Ehman | Senior Manager, Chicago | Enterprise Portfolio and Program Management<\/a>, Consumer Packaged Goods (CPG) Industry<\/h3>\n
THE INDUSTRY PROBLEM<\/h4>\n