Thought Piece: What unified data actually unlocks

Hi there,

Most executives have heard the phrase "data is the new oil" countless times in boardrooms and conferences. Having data isn't enough though. Most enterprises are drowning in data while starving for insights.

I recently encountered an American automotive manufacturing company that perfectly illustrates this challenge. They had invested millions in digital tools, but their AI initiatives weren't delivering results. Their data was trapped in dozens of disconnected systems:

• Marketing data lived in HubSpot

• Sales data in Salesforce

• Operations data in SAP

• Customer service data in 5 different tools

Each department had its own "source of truth," and none of them agreed with each other. This fragmentation kills transformation efforts. When we finally unified their data fabric, their AI models suddenly became dramatically more effective.

The harsh reality of data fragmentation

Most organizations have no idea what data they actually have. Data gets scattered across:

• Hundreds of SaaS tools (average company uses 150-200)

• Legacy systems

• Spreadsheets

• Paper records

A Fortune 500 firm couldn't even answer how many customers they had. Different systems gave different counts, and nobody knew which one was right.

Disconnected systems create serious problems:

• Scheduling tools don't sync with payroll systems

• Customer service can't access real-time production data

• Problems escalate unnecessarily

• Integration becomes a requirement for sustainable transformation

Rethinking data quality versus relevance

You can have the cleanest, most pristine data in the world and it might still be completely useless. We've been obsessing over data quality while missing something more important: relevance.

Smart organizations are making two massive shifts: 

1. Generating relevant data

• No more settling for whatever numbers are easy to collect

• Asking tough questions about what data actually matters

• Pharma companies now pour resources into human-specific studies

2. Auditing historical data

• Not just checking if numbers add up

• Asking whether preserved datasets matter anymore

• Companies maintain decades of pristine records only to discover those datasets are irrelevant

You can polish a lump of coal all day long and it's never going to become a diamond. But start with a rough diamond (relevant but messy data), and AI can help cut it into something spectacular.

Stop asking "Is this data clean?" Start asking "Does this data actually tell us something useful?"

Understanding your data architecture options

Every business leader should understand these solutions to discern when the right or wrong ones are being suggested by tech teams.

Data warehouse

• Like a library where every book must be properly cataloged first

• Highly structured, organized data in relational tables

• Ideal for pre-defined queries and reporting

Data lake

• Like a smart storage closet

• Toss in everything structured or unstructured

• No meticulous labeling upfront required

• Intelligent systems organize behind the scenes

• Stores unstructured, raw data in original format

Data lakehouse

• Combines raw flexibility of data lakes with warehouse-grade structure

• No need to pre-define how data is stored (schema-on-write)

• Structure it only when you use it (schema-on-read)

• Can store both structured and unstructured data

Current adoption shows the trend:

• 55% of organizations use lakehouses for analytics

• Projected to reach 67% in three years

• 85% of adopters power their AI initiatives through these platforms

How data lakehouses create real business value

Think of it as a sophisticated assembly line for your data, where each stage transforms raw information into business gold.

Ingestion layer

• Acts like a powerful digital vacuum

• Pulls in operational data, financial transactions, customer interactions

• Includes IoT sensor readings and social media sentiment

• Keeps data untouched in native format 

Distillation and processing layers

• Refine raw data into structured, usable formats

• Align fragmented information

• Customer records match transaction histories

• Support tickets link to usage data

• Marketing campaigns connect to actual outcomes

Processing layer

• Runs complex queries

• Powers AI models

• Translates numbers into insights

• Reveals behavioral patterns and inefficiencies

Insights and unified operations layers

• Deliver actionable intelligence via dashboards

• Monitor performance and manage workflows

• Prevent your lake from becoming a data swamp

Building the infrastructure without boiling the ocean

Always start small and think big. Begin with your most valuable data sources and gradually expand.

Core principles

1. Make it accessible - Your data lake should be easy to query by both technical and non-technical users. Data is only valuable if people can actually use it.

2. Keep it clean - Implement strong data governance from day one. Bad data is worse than no data because it leads to false confidence.

3. Think real-time - In today's world, week-old data might as well be ancient history. Design your infrastructure for real-time processing from the start.

4. Plan for scale - Your data needs will grow exponentially. Choose solutions that can grow with you without requiring a complete rebuild.

Security considerations

• Centralizing data creates opportunities and risks

• Explore robust security measures

• Don't let security become an excuse for data silos

• Modern data lakes offer granular access controls, encryption, and audit trails

Avoiding the predictable pitfalls

Watch out for these common mistakes:

• "Build it and they will come" fallacy - Show clear value and make it easy to access

• "Perfect data" trap - Start with what you have and improve as you go •

• "Technology first" mistake - Focus on solving business problems, not implementing technology

• Data quality issues - Garbage in, garbage out requires governance from day one

• Governance problems - Establish clear ownership and policies with cross-department representation

• Siloed data ownership - Data must flow across teams, not stop at departmental borders

Your immediate action plan

Tomorrow, start here:

• Confirm an executive sponsor

• Map your tech ecosystem and AI readiness with brutal honesty

• Use proven frameworks to locate opportunities within six weeks

Month 1: Know where you stand

• Map every system in your organization

• Identify your champions (rarely the ones with fancy titles)

• Look for people building workarounds because existing systems don't cut it

Month 2: Build your strategy

• Turn insights into action with specific targets

• Scale gradually and communicate how this changes daily work

• Document hours spent on tasks that could be automated 

Month 3: Lay the foundation

• Clean up your most critical software

• Focus on tools and datasets that drive highest-value decisions

• Target those that are relatively quick to update

One final thought

Start small, move fast, learn constantly. Start now and do not stop. Budgets don't determine which organizations win. The ones who get ahead learn and adapt the fastest.

Tool consolidation reduces chaos. Fewer tools, tighter integration, better results. The best organizations simplify workflows, unify their systems, and operate from the same source of truth.

When you've unified your data fabric and built the right foundation, you're ready for whatever comes next.

Much Love,
Matt

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