Steinert Analytics

Elevating your brand’s story.

Serving businesses in Central Ohio and beyond with a focus in healthcare & roofing.

Skills: Keboola, Azure Data Factory, Etleap, Python 3, ELT, APIs, JSON, SQL, LookML, Snowflake, GCP/BigQuery, Amazon Redshift, Looker, Looker Studio, Tableau, Databox, Power BI

Steinert Analytics transforms the data of Central Ohio businesses into profit driving insights. 

- Currently working with small to large-sized businesses in the $10M - $2B revenue range with a focus on mid-sized companies.

Founder & Analytics Consultant

Lead Data Architect

Data & Impact - a Passion2Knowledge Experience

Voicing the power of data with business domain experts.

- Building this brand from the ground up, it has given me my first taste of entrepreneurship, branding, and self-starting 
- Between 300-700+ total unique organic listeners per month
- Develop social media marketing content with Canva
- Obtaining actionable insights from Libsyn's analytics dashboard
- Serves as a platform that has enabled me to tap into a best-in-class Columbus, OH Area business network & beyond
- 50+ episodes released

https://podcasts.apple.com/us/podcast/data-impact-a-passion2knowledge-experience/id1431897021

https://open.spotify.com/show/1iu8YvxEiEXG4Lnr9WI5jj?si=eljC2-RBQzO01FkSyHvzhw&utm_source=sms

Podcast Host

LaSalle Investment Management

JLL Technologies | LaSalle Business Technology Group

- Serving as lead data architect and SME for LaSalle's $80 B Global AUM data pipeline & model
- Spearheading project management, data leadership & strategy, data management and technical expertise for tool migration and cost reductions in 2023
- Presented LaSalle's self-service BI strategy road map to Digital Product and Engineering exec leadership that leverages Power BI Premium and Workspaces which served as the catalyst for successful implementation in early 2023
- Actively upskilling in Power BI, Azure Data Factory & Databricks

Senior Data Engineer

Skills: Looker, Power BI, SQL, ETL, Snowflake, Linux/DevOps, HTML, Liquid LookML (like Ruby), Talend, Azure, Databricks

- Managing LookML transformations for $82 B in Global AUM asset data, responsible for migrating the entire project & Looker data model to Snowflake for performance increases that tap into Snowflake's virtual warehousing and parallel processing
- Writing advanced SQL views for operational finance data in AM and Corporate Functions asset data for query optimization
   - Continually enhancing Global AUM reporting through reimagined data visualizations and automated the company's financial roll forward analysis utilizing advanced LookML modeling & dimension/measure code
- Troubleshooting semi-additive facts from raw data as it relates to quarterly AUM totals on a specific end of quarter date
- Implementing a standardized process for user security & management through documentation of Looker groups, roles, and model sets as they relate to JLL's AD groups connected up via SAML
- Driving a majority of design decisions on LaSalle's raw transactional data by extracting field meaning, table relationships, and join keys to utilize the construction of a data model in Microsoft SQL Server Management Studio
- Working with LaSalle's world-class Liferay app developers to ensure proper design considerations are discussed and implemented in respect to Looker's iframe & API embed capabilities for multiple portal application projects as part of LaSalle's Corporate Functions 
- Managing Looker project upkeep in a highly complex multi-stage development framework (3 instances Dev-Test-Production) 
   - project version control, connection configuration, access grants in Snowflake, and Github repository set-up w/ JLL-T
- Responsible for Looker on-prem server back ups, restarts, upgrades & patching using Linux command line and LaSalle's VPN via RDC
- Ensure ETL loads aligned across 3 databases Dev-Test-Prod

Data Engineer | Lead Developer

Updox

*Updox acquired by EverCommerce in Dec. 2020*

Updox, an EverCommerce company, is simplifying the business of healthcare to help providers, patients and partners have a happier, more satisfying experience.

- Working in SFDC, Zuora, Firetower, AWS, Matillion (ELT), Snowflake, Looker, Excel and Python 3 (Jupyter Notebook)
- Q1 2021 co-leading Updox-Databricks churn propensity & product usage predictive analysis using Python 3, Spark and SQL (facilitating/building machine learning models) 
- Performing ELT in Matillion to execute relevant data transformations for sales budgeting
- Descriptive, predictive & prescriptive data analysis on monthly financial/sales reports
- Writing LookML (SQL(DQL)) views and explores to provide better field optimization for data accuracy in visualizations through database relationship type assignment and performing left & full outer joins
- Coding advanced SnowSQL queries to generate persistent derived tables (PDT) for data accuracy and customized visualizations
- Conduct sales reporting breakdowns & reconciliation w/ Excel Pivot Tables, advanced formulas & VBA to slice and dice/format & match the data accordingly
- Data validation by crossing Snowflake DW with Looker visualizations using SQL (w/LookML) 
- Automating enterprise & channel sales reports using Looker and SQL 
- Defining business KPIs through internal (sales stakeholders) & external research[Gitlab] (ideal sales KPIs & telehealth industry standard) to create socialized/cross-functional/documented (Google Sheet Data Glossary) context/relevance when using Looker dashboards
- Q2 2020 I am project management lead (using Smartsheet Gantt chart) for the implementation of Updox Analytics product for customer enabled self-service analytics & reporting (partnered w/ Looker, a Google Cloud Platform) 
- Taking an agile methodology approach to SQL development and LookML for this embedded integration while co-developing with Looker/Google Team

Lead Business Intelligence Analyst 

Sigma Pi Fraternity, International

In April 2019 I joined the Sigma Pi Columbus Alumni Club to expand my involvement in the fraternity to give back and assist them in any projects they need me for. 
- Currently working on updating & optimizing sigmapicolumbus.org's site UI and upgrading to an HTTPS secure connection so we can streamline due payments for members directly on the website.

Member of the Columbus Alumni Club

Vertiv

- Perform MDF, Co-Op, and Rebate status updates, claim approvals, and budget tracking for Vertiv’s value-added partners using supply chain enterprise software (E2Open)
- Create, plan and manage marketing events with Vertiv’s diamond partners to ensure a strong brand presence, relevant promotions & product mixes, and value-added marketing collateral are present to showcase the differentiation of Vertiv’s critical infrastructure offerings
- Deploy through-channel marketing by updating and maintaining Vertiv’s enterprise channel management system (Zift) and reporting on adoption & performance of through-channel digital marketing campaigns
- Execute To-Partner digital marketing activities for on domain advertising with diamond partner CDW
- Utilizing Gantt Charts & other project management trackers in Excel & Wrike to streamline tracking & real time status updates for projects to create performance-based visibility in the channel marketing department

Channel Marketing Specialist

- Responsible for A+ content management, syndication (via web templates & HTML), and B2B digital optimization in efforts to take the U.S. Channel from $250 million to $1 billion in annual revenue
- Conduct web/content audits and situation analyses to give VERTIV a better understanding of the competitive digital landscape and how to improve our content
- Collaborate with the VERTIV Sales Analytics/Data Science department to build out an automated digital content audit using ScrapeHero and Microsoft SQL Server DW
- Work with digital B2B resellers such as Amazon, CDW, Graybar, & Anixter to better understand Search Engine Optimization potential on these mediums
- Inventory, sales (YoY deltas, run rate forecasting, and sales segment breakdowns), & pricing data analysis on all North American VERTIV SKUs through Microsoft Excel & Power BI
- Utilize digital marketing analytics to track performance, measure marketing ROI, obtain customer churn rate, and gain insight regarding how to best optimize and improve all VERTIV digital content
- Manage entire Vertiv Amazon Ads Campaigns & Digital Marketing Analytics tracking
- On the channel's Skunkworks agile team executing ad hoc digital content syndication & primary channel promotion management under c-suite executives and CEO Rob Johnson's lead 
- Currently conducting a market research basket analysis with the data science team on VERTIV product bundle sales using Power BI analytics and advanced statistical modeling in R (Apriori algorithm). 
- Coordinate, organize, and push out purchase orders for creative items such as VERTIV-branded magnets, flyers, and other promotional items
- Innovate & create channel marketing event plans for partner trade shows and training conferences to help VERTIV maximize brand presence, product knowledge deployment, and relationship building with partners and end customers

Associate Channel Marketing Analyst

- Produce semesterly budget in Excel to assure it results in a minimum 5% savings allocation
- Manage entire chapter funds and submit monthly reports to the international Executive Office
- Communicate weekly with account manager to set up billing cycles for 30 plus members with individual accounts and manage accounts receivables
- Maintain the chapter's OmegaFi billing management system through the roster and billing cycle adjustments
- Responsible for tracking expenses and writing checks to designated committees 
- File 990-N tax forms for the chapter's non-profit status
- Communicate with the Executive Office through monthly financial reporting and national bill payments
- Attended the fraternity's international Mid-Year Leadership Conference in St. Louis, MO to develop financial & organizational leadership skills. 
- Managing over $10,000 for the Spring & Fall 2017 semesters

Treasurer of the Gamma-Mu Chapter

- Managed and organized the chapter's member roster, workbook, and important forms
- Recorded detailed and accurate minutes during all weekly chapter meetings
- Submitted monthly, bi-annual, and annual reports to the Executive Office of Sigma Pi Fraternity regarding chapter status, committee progress, housing information, and improvements
- Took attendance at all meetings, philanthropy events, and Greek-related activities
- Coordinated service projects and annual chapter events
- Acted properly in difficult situations and decision making for the good of the chapter, while maintaining a high degree of leadership and rationality
- Attended the fraternity's international Mid-Year Leadership Conference in St. Louis, MO to develop presidential & organizational leadership skills. 
- Due to my organization and efficiency, I assisted the chapter in winning the UW Oshkosh Fraternity & Sorority Life's Chapter of Excellence and Most Improved Chapter of the Year for 2016 awards.

Secretary of the Gamma-Mu Chapter

Kohler Co.

Marketing analyst intern for the product side of Global Faucets - Performance Showering during summer 2017
- Execute comprehensive competitor research and pricing analysis to gain product insights 
- Analyze sales projections and product launch propositions to ensure marketing efforts are in line with company budget and KOHLER's target market
- In-depth data analysis via Excel & SAP studying sales trends, margins, and product specifications
- Presented a plan encompassing product marketing and supply chain that streamlines the direct distributive approach with Amazon, thus expanding KOHLER's Ecommerce initiatives while pushing social media marketing
- Constructed presentations on Microsoft PowerPoint illustrating the cutting-edge new features for the DTV+ Digital Showering interface
- Attended two intent workshops analyzing the Next Gen Rite-Temp and low flow showering systems to see how to strategically develop, market, and sell these industry leading products

Product Market Analyst Intern

University of Wisconsin Oshkosh

Phonathon caller at the UW Oshkosh Alumni Welcome and Conference Center

- Cold call, start a conversation, and build rapport with alumni of the university
- Sell the UW Oshkosh Foundation by delivering value-added propositions to successful alumni about the benefits of donating to the university
- Maintain accuracy and high attention to detail when recording alumni information and entering payment data

Sales Representative

Ansay & Associates

- Transferred client calls to personal and commercial lines advisors
- Effectively performed tasks in Applied EPIC (ERP) software
- Analyzed Excel documents containing reporting data for commercial lines agents' performance and sales projections. 
- Executed underwriting workshop exercises with a mock commercial lines sales meeting at CNA Insurance
- Created a presentation implementing Tableau, a BI software tool, that enhances the company's production and efficiency by emphasizing visual best practices in the workplace
  - 2016 Ansay Intern Business Challenge winner
- Sorted mail, printed labels, and scanned documents to other offices
- Created marketing items such as cover and pre-renewal letters, proposals, and carrier submissions
- Received first person experience with Ansay's sales process by riding along with and observing agents
- Created and saved MVR reports about individual driving history

Business Development Intern

A communicative analyst by nature. 📊

Founder at Steinert Analytics | steinertanalytics.com

Elevating your brand's story.

Book an initial consultation: https://calendly.com/steinertanalytics/initial-analytics-consultation

I am a data-driven business strategist and marketer with a passion for both the operational and technical components of data-driven strategy, blended with tactical execution. I have a broad array of experience across various industries, including an aptitude for turning your marketing and sales systems into insight-rich sources to drive revenue and save your team money.

As a full time employee, I had in-depth involvement in critical areas of business success. When I was in channel, product and event marketing analysis and project management at multibillion dollar companies, I enhanced the brand presence of my employer and optimized successful B2B marketing campaigns for their channel sales efforts leveraging data analysis and insights.


When I moved into data & analytics engineering at an innovative telehealth SaaS start-up and $80 B AUM commercial real estate investment management firm, it gave me the opportunity to strengthen the relationship these companies have with their clients by providing information that attracted and retained customers.

Additionally, I am a creative and hungry entrepreneur driven by the desire to help my clients in Central Ohio succeed with turning data into profitable action. Please feel free to reach out on LinkedIn or via email (christian@steinertanalytics.com) if you are interested in connecting with me.

Christian Steinert

Leadership wanted self-service analytics.
 
(So Finance built their own "Net Revenue" metric in Power BI DAX.)
 
Here's what happened:
 
The data team built a beautiful self-service dashboard.
 
Gave Finance the ability to slice data however they wanted.
 
Filters. Drill-downs. Full control.
 
Leadership loved it.
 
Two months later: chaos.
 
𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺:
 
Finance created their own calculated measures.
 
Operations did the same.
 
Marketing built their version in Excel connected to the dashboard.
 
Now "Net Revenue" has 5 definitions across the organization.
 
Nobody trusts any of them.
 
𝗪𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴:
 
Self-service without governance is just chaos at scale.
 
You gave users tools without giving them governed data.
 
𝗛𝗼𝘄 𝘁𝗼 𝗱𝗼 𝘀𝗲𝗹𝗳-𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗿𝗶𝗴𝗵𝘁:
 
𝗗𝗲𝗳𝗶𝗻𝗲 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝗢𝗡𝗖𝗘 𝗶𝗻 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝘄𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲
 
Net Revenue gets calculated in the warehouse. Not in Power BI DAX. Not in Excel.
 
𝗟𝗼𝗰𝗸 𝗱𝗼𝘄𝗻 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀
 
Users can filter and slice. They can't create their own revenue definitions.
 
𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴
 
Data glossary. Metric definitions. Where it comes from. Who owns it.
 
𝗧𝗿𝗮𝗶𝗻 𝘂𝘀𝗲𝗿𝘀
 
Show them how to use the governed metrics. Answer their questions.
 
𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁:
 
Finance self-serves their analysis.
 
But they're using the same Net Revenue definition as Operations.
 
Trust increases. Ad-hoc requests drop 80%.
 
𝗧𝗟;𝗗𝗥:
 
Self-service BI is powerful.
 
But without governance, it creates more chaos than value.
 
Govern the metrics. Then give users the tools.
 
♻️ Share this if you've given users self-service tools and watched them create chaos.
 
Follow me for real talk on self-service BI that actually works.

I help healthcare data leaders with inherited chaos fix broken definitions and build AI-ready foundations they can finally trust. | Host @ The Healthcare Growth Cycle Podcast

Your first week as a data leader.
 
(You just inherited 50 reports with 3 definitions of "Net Revenue.")
 
𝗗𝗮𝘆 𝟭: Excitement. You're modernizing this place.
 
𝗗𝗮𝘆 𝟯: Confusion. Net Revenue is calculated three different ways—two stored procedures and a Power BI DAX formula.
 
𝗗𝗮𝘆 𝟱: Concern. Nobody knows which is "correct." The DBA who built them left 2 years ago. Zero documentation.
 
𝗗𝗮𝘆 𝟳: Panic. Meeting with CFO and VP of Revenue Cycle.
 
CFO: "Net Revenue for Q3 was $23.4M."
 
VP: "I'm showing $22.1M."
 
Both pulling from reports your team built.
 
𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗶𝗻𝗵𝗲𝗿𝗶𝘁𝗲𝗱.
 
Not a modern platform. Not AI-ready infrastructure.
 
Chaos disguised as reporting.
 
𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗻 𝗺𝗶𝗱-𝗺𝗮𝗿𝗸𝗲𝘁 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲:
 
Healthcare moves slow. Decisions are political.
 
Finance built their Net Revenue 5 years ago. Operations built theirs 3 years ago with different logic.
 
IT inherited both. Never questioned them.
 
Nobody had time to align. Nobody had authority to force it.
 
Chaos compounded.
 
𝗬𝗼𝘂𝗿 𝗷𝗼𝗯 𝗻𝗼𝘄:
 
You can't deploy AI on this foundation.
 
You can't build models when Finance and Operations disagree on basic metrics.
 
Fix the definitions before doing anything strategic.
 
𝗪𝗵𝗲𝗿𝗲 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁:
 
Pick ONE critical metric causing the most pain.
 
Get Finance, Operations, and IT in a room. Force the conversation. Document the agreed definition.
 
Update all reports to use it.
 
Build trust. One metric at a time.
 
𝗧𝗟;𝗗𝗥:
 
You inherited chaos. Fix definitions. Build trust. Then modernize.
 
That's the path to an AI-ready foundation.
 
♻️ Share this if you've inherited data chaos.
 
Follow me for real talk on cleaning up healthcare data.

Net Revenue is screwed up in 3 ways.
 
(The VP and CEO started arguing on the call.)
 
Last week I had coffee with a senior data architect at a Columbus insurance company.
 
20 years in data. Built their entire governance infrastructure from scratch.
 
He told me something that goes against the data mesh hype:
 
𝗖𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝘄𝗶𝗻𝘀. 𝗘𝘃𝗲𝗿𝘆 𝘁𝗶𝗺𝗲.
 
Here's why:
 
You're the first data engineer at a mid-sized healthcare company.
 
You find Net Revenue defined 3 different ways: 

→ Stored procedure #1 
→ Stored procedure #2 
→ Power BI DAX formula
 
You call the VP of Revenue Cycle and CEO: "How do we define Net Revenue?"
 
VP: "Deductions include write-offs."
 
CEO: "Deductions do NOT include write-offs."
 
They debate. You mediate.
 
𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲.
 
𝗪𝗵𝘆 𝗱𝗲𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗳𝗮𝗶𝗹𝘀:
 
Finance defines metrics one way. Operations another. IT a third.
 
Nobody agrees. Nobody trusts the data. Decisions stall.
 
Data mesh sounds great: "Give control to teams closest to the data."
 
In healthcare? It creates chaos.
 
𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗖𝗼𝗹𝘂𝗺𝗯𝘂𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗱𝗶𝗱:
 
Centralized governance to the data team.
 
Built data dictionaries and glossaries.
 
Documented every metric with full lineage.
 
Got business sign-off on definitions.
 
Result? 20 years of trusted metrics and faster decisions.
 
𝗥𝗲𝗱 𝗳𝗹𝗮𝗴𝘀 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲:
 
→ 3+ definitions for the same KPI 
→ Execs arguing about metrics 
→ No agreed "source of truth"
 
𝗧𝗟;𝗗𝗥:
 
Data mesh doesn't work for mid-market healthcare.
 
Data team owns definitions. Business signs off. Everyone sees the same numbers.
 
That's trust.
 
P.S. - Full breakdown in newsletter. Link in comments. 👇
 
♻️ Share this if execs ever argued about a metric.
 
Follow me for governance that works.

My data consulting predictions for 2026.

(These are 3 right from my head.)

𝟭. 𝗧𝗵𝗲 𝗺𝗶𝗱-𝗺𝗮𝗿𝗸𝗲𝘁 𝗹𝗲𝗮𝗻𝘀 𝗼𝗻 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁𝘀 𝘁𝗼 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝗺𝗼𝗿𝗲, 𝗳𝗮𝘀𝘁𝗲𝗿.

Gone are data teams with 5-6 hires. No one wants to hire right now. Instead, the mid-market is squeezing consultants to get what they need faster.

𝟮. 𝗗𝗮𝘁𝗮 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁𝘀 𝗮𝗿𝗲 𝘂𝗽 𝗮𝗴𝗮𝗶𝗻𝘀𝘁 𝗮 𝗹𝗼𝘁 𝗺𝗼𝗿𝗲 𝗿𝗶𝘀𝗸 𝘄𝗵𝗲𝗻 𝗶𝘁 𝗰𝗼𝗺𝗲𝘀 𝘁𝗼 𝘁𝗵𝗲𝗶𝗿 𝗼𝗳𝗳𝗲𝗿𝘀.

Be prepared for clients that expect a lot more promise out of the gate. Large deals will not happen without offers that promise big returns in short time periods. 

This drives up risk for the consultant, and puts extreme pressure to deliver the highest quality work. We need to be ready to grind.

𝟯. 𝗧𝗵𝗲 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝗰𝗿𝗲𝗮𝘁𝗲 𝘂𝗻𝗶𝗾𝘂𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲𝗶𝗿 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝘄𝗶𝗻.

I'm not just talking about innovative data products. I'm talking about the journey when building. Consultants need to lean into their saavy people skills to create an enjoyable working relationship. 

Take your clients out for drinks, crack jokes and post funny gifs in the Teams group chat. Talk about your love for comic books and movies. If you're all business, your clients won't thrive during the hard meetings too.

As we continue to provide our services in the Age of AI, the stakes are higher.

More efficiency, less time and intentional relationships are crucial.

♻️ Share with data leaders & consultants that are new or anxious about the new year.

Follow me for more insights on the realities of being a data consultant in 2026.

The CFO presented Q3 revenue to the board.
 
(The COO's numbers were $2M different. In the same meeting.)
 
Uncomfortable silence.
 
Board members looking at each other.
 
Someone finally asks: "Which number is correct?"
 
CFO: "Mine comes from the financial system."
 
COO: "Mine comes from operations reporting."
 
Both confident. Both wrong in different ways.
 
The CEO pulls you aside after: "We need to fix this. Now."
 
𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 𝘁𝗿𝘂𝘀𝘁 𝗱𝗶𝗲𝘀.
 
Not because the data is bad.
 
Because leadership realizes they've been making strategic decisions on inconsistent numbers.
 
𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝘀:
 
Finance defines revenue based on cash collected.
 
Operations defines it based on services delivered.
 
Both are technically "right" from their perspective.
 
But when they're both called "Q3 Revenue" in board presentations? Disaster.
 
𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗰𝗼𝘀𝘁:
 
It's not just embarrassment.
 
Strategic decisions get delayed. Budgets get questioned. Leadership loses confidence in the data program.
 
The board starts asking: "If we can't agree on revenue, what else is wrong?"
 
𝗛𝗼𝘄 𝘁𝗼 𝗳𝗶𝘅 𝗶𝘁:
 
Stop blaming the departments. They're not wrong—they're just measuring different things.
 
Get everyone in a room. Define what "Q3 Revenue" means for board reporting.
 
Document it. Get sign-off. Update every report.
 
Then enforce it.
 
𝗧𝗟;𝗗𝗥:
 
Misaligned definitions don't just create confusion.
 
They kill strategic decisions and erode trust in your entire data program.
 
Fix the definitions. Restore the trust.
 
♻️ Share this if you've watched executives present conflicting numbers in the same meeting.
 
Follow me for real talk on fixing healthcare data chaos.

taplio