Services | EntrePro Corporation

Strategy & leadership

Set the direction

Fractional or interim Chief Data Officer

Senior data leadership without a full-time hire. Dr. Mehovic joins your leadership team one to three days a week, owns the data agenda, and represents data at the top table, as he has done as Head of Data at SimplePractice and Druva and as CDO at Electric Cab North America.

Data vision, roadmap, and quarterly priorities
Ownership of architecture, platform, governance, and insights
Board and investor reporting on data and analytics
Hiring and coaching of the permanent team, including your eventual CDO

Proof point: defined the data strategy and led a 12-person team through SimplePractice's 2021 public listing.

Enterprise data strategy

A single, prioritized strategy that spans the whole data estate: enterprise architecture, data engineering, analytics, data science and AI, and governance. It starts from your business drivers and ends with an execution roadmap, performance metrics, and a staffing plan.

Current-state assessment through stakeholder interviews and platform review
Target architecture and operating model for the data function
Evolutionary, paced roadmap with cross-functional collaboration plan
Data KPIs: time to insight, coverage, adoption, integrity, user satisfaction

Proof point: delivered complete data strategies at SimplePractice (400 employees) and Druva (800 employees) within the first 90 days.

Building the data function

Design and stand up the organization that owns data: enterprise data architecture, the application database platform, governance, and insights. Covers org structure, role definitions, hiring, and the handover to a permanent leader.

Organization design for the data office and its four pillars
Job descriptions, interview loops, and candidate evaluation
Team coaching and process design: intake, prioritization, delivery
Managed teams of up to 40 across the US, Canada, India, and China

Proof point: reorganized HSBC Auto Finance's 40-person BI department into four groups and doubled its throughput within a year.

Architecture & engineering

Build the platform

Enterprise data architecture & data warehousing

Design of the end-to-end data landscape: operational stores, landing and staging, central warehouse or lake, data marts, and consumption layers. Fluent in both Kimball and Inmon, on-premises and cloud.

Enterprise and application data models; metadata-driven design
Warehouse and lakehouse design on Snowflake, BigQuery, Redshift, Athena, Databricks, or SQL Server
Massively parallel and multi-tenant architectures
Availability modeling and capacity planning

Proof point: architected a 19-node, 2 TB massively parallel warehouse for 1,000+ users at about a quarter of the cost of a comparable state system.

Data engineering & pipelines

Reliable ingestion and transformation from every source your business runs on, with the operational discipline to keep it running: monitoring, data quality checks, change management, and archiving.

ETL/ELT with Airflow, dbt, Fivetran, Airbyte, Glue, SSIS, or custom code
Automated code generation for repetitive pipelines and legacy formats
Multi-cloud consolidation: AWS, Azure, GCP, Oracle Cloud
Pipeline performance, scheduling, and cost control

Proof point: consolidated usage data from four clouds, thirteen environments, and hundreds of customers into one data lake at Qubole.

Application database design & data-access layer

The database under your product determines how fast you can build, how well it performs, and how clean your analytics will be. We design it properly, with a database-centric, metadata-driven approach that keeps logic where it belongs.

Logical and physical modeling; naming, integrity, and logic placement standards
Multi-tenant designs with strict per-customer data isolation
Data-access layer design (ADO.NET, ORMs, stored procedures)
Vendor and platform selection: PostgreSQL, MySQL, SQL Server, Oracle, DB2, Aurora

Proof point: designed the multi-tenant database and data layer for Verint's workforce-forecasting product used by major banks.

Data migration, integration & MDM

Architecture and program leadership for moving data between systems and keeping it consistent afterward, whether the cause is a new platform, an acquisition, or a legacy retirement.

Migration architecture, sequencing, and cut-over planning
Master data management for customer, product, pricing, and asset domains
Enterprise integration over bus or middleware architectures
Post-acquisition consolidation of disparate systems and stacks

Proof point: led a 30-person team through Equinix's migration across tens of enterprise applications, including MDM and real-time integration.

Analytics, AI & governance

Turn data into decisions

Business intelligence & executive analytics

Reporting that leadership trusts and actually uses: certified metrics, automated dashboards, and the processes that keep them accurate. For every function, from finance and billing to product, sales, and risk.

Executive and investor dashboards; automated board packs
KPI definition, single source of truth, and metric certification
Looker, Tableau, Power BI, Sigma, Cognos, MicroStrategy
Self-serve and in-product analytics for your customers

Proof point: built the automated executive dashboard at Overjet and real-time fraud and risk reporting at SimplePractice.

Data science, machine learning & AI readiness

Practical applied modeling grounded in statistics and performance theory, plus the data foundation that AI initiatives need before they can succeed: clean, well-modeled, governed data.

Predictive models, anomaly detection, and recommendation logic
Record linkage and entity resolution for deduplication and matching
Benchmarking and simulation of system performance
AI-readiness assessment of your data estate

Proof point: a record-matching engine three times more accurate than the leading commercial tool; a patented recommendation system at Facebook.

Data governance, quality & protection

The disciplines that make data trustworthy: stewardship, quality measurement, access control, and protection of sensitive information, designed to fit the organization rather than slow it down.

Data stewardship model and operating procedures
Data quality programs and information-integrity KPIs
HIPAA, PII, and PHI protection; access controls; disaster recovery
Tooling such as QualityStage, Informatica, or Atlan where it adds value

Proof point: set up a dedicated Data Quality group at HSBC Auto Finance and governed PHI across clinical SaaS products.

Performance & troubleshooting

Make it fast and keep it running

Database & data platform performance

Systematic performance improvement grounded in queueing theory and thirty years of practice. Most slow systems are slow for reasons that are fixable without new hardware: logic in the wrong place, poor indexing, oversized fields, and SQL that fights the optimizer.

Performance assessment with measured baselines and targets
Query, index, schema, and storage optimization
Capacity planning and scalability modeling
Platform configuration and physical design

Proof point: 10× on a 3-billion-row system; several orders of magnitude on ResMed's flagship application; multifold capacity gains at Verint within a year.

Troubleshooting & rescue engagements

When a data system is failing, a delivery is stuck, or a vendor's product isn't performing, we step in quickly, find the root cause, and fix it, then leave the team with the knowledge to prevent a repeat.

Root-cause analysis of slow, unstable, or inaccurate systems
Recovery of stalled data warehouse and migration projects
Augmenting an in-house team with senior hands for a defined period
Knowledge transfer and documentation on exit

Proof point: revived a voice-mail broadcasting company's platform with a combined team of 30, enabling it to compete for Fortune 500 clients.

Technical evaluation & due diligence

An independent, expert assessment of a database, data platform, application, or vendor, for executives who need a straight answer before they invest, acquire, or sign.

Design reviews of third-party or in-house applications and databases
Vendor and platform evaluation and selection
Data and technology due diligence for M&A and investment
Architecture reviews against performance, scalability, and compliance criteria

Proof point: evaluated a third-party application for ResMed and evaluated database vendors for Concerro ahead of its acquisition.

Training, workshops & executive education

Dr. Mehovic has taught database and data warehousing courses as an adjunct professor at three universities. The same material, adapted to your team: from SQL and data modeling fundamentals to how executives should think about the data function.

Data modeling and database design workshops for engineers
Data warehousing and analytics fundamentals
Executive briefings on data strategy, AI readiness, and the CDO role
Guest lectures and conference talks

Proof point: adjunct professor at San Diego State University, the University of Dallas, and Dallas County Community College.

How we work together

Engagement models

Assessment

2 to 4 weeks

A focused review of one area, such as a database, a platform, a strategy, or a vendor, ending in a written findings-and-recommendations report and a working session with your leadership.

Project

1 to 12 months

Defined scope and deliverables: an architecture, a migration, a warehouse, a performance program. Dr. Mehovic leads, hands-on, with your team or a team he assembles.

Fractional leadership

Ongoing retainer

One to three days a week as your CDO or Head of Data, owning the data agenda and building the permanent function. Typical engagements run six months to several years.

Advisory

A few hours a month

A standing relationship with your CEO, CTO, or data leader: a sounding board for decisions, a reviewer for designs, and a first call when something goes wrong.

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Everything between the database and the board meeting