The Data Ecosystem

The Data Ecosystem Substack addresses the complexities of handling data, focusing on data modeling, quality, strategy, and frameworks. It breaks down data lifecycle, governance, and organizational challenges, providing actionable insights to improve data management and alignment with business goals.

Data Modeling Data Quality Data Strategy Data Frameworks Data Lifecycles Data Governance Organizational Data Structures Data Analytics AI Integration

The hottest Substack posts of The Data Ecosystem

And their main takeaways

Issue #14 - The Forgotten Guiding Role of Data Modelling

659 implied HN points • 14 Jul 24

Data modeling is like a blueprint for organizing information. It helps people and machines understand data, making it easier for businesses to make decisions.
There are different types of data models, including conceptual, logical, and physical models. Each type serves a specific purpose and helps bridge business needs with data organization.
Not having a structured data model can lead to confusion and problems. It's important for organizations to invest in good data modeling to improve data quality and business outcomes.

Issue #16 - The Data Quality Conundrum (Part 2 – Solving)

439 implied HN points • 28 Jul 24

🕹 Technology Data Quality Data Governance Data Management Data Tools Data Strategy Data Analytics

Data quality isn't just a simple fix; it's a complex issue that requires a deep understanding of the entire data landscape. You can't just throw money at it and expect it to get better.
It's crucial to identify and prioritize your most important data assets instead of trying to fix everything at once. Focusing on what truly matters will help you allocate resources effectively.
Implementing tools for data quality is important but should come after you've set clear standards and strategies. Just using technology won’t solve problems if you don’t understand your data and its needs.

Issue #17 - Unpacking the People, Process, Technology & Data Framework

339 implied HN points • 04 Aug 24

💼 Business Operations Management Strategy Data Technology

The People, Process, Technology framework helps organizations balance these three key areas but often misses the importance of data. Companies should not just focus on technology but also consider how people and processes interact.
A new framework that includes data is called People, Process, Technology & Data. This approach shows how these four components work together, helping organizations make better decisions and manage change more effectively.
Using structured questions and understanding the roles of each component can enhance planning and execution in businesses. It's essential to revisit these elements regularly to stay aligned with goals and adapt as needed.

Issue #15 – The Data Quality Conundrum (Part 1 – Root Causes)

399 implied HN points • 21 Jul 24

💼 Business Data Quality Management Analytics Governance Process Improvement

Poor data quality is a big problem for organizations, but it's often misunderstood. It's not just about fixing bad data; you need to figure out what's causing the issues.
Data quality has many aspects, like accuracy and completeness. Good data helps businesses make better decisions, while bad data can cost a lot of money.
To solve data quality issues, you need a complete approach that looks at different root causes. Simply fixing one part won't fix everything, and different sources might create new problems.

Issue #13 - Defining the Data Operating Model

359 implied HN points • 07 Jul 24

💼 Business Data Strategy Organizational Structure Team Dynamics Business Processes Collaboration

A Data Operating Model is key for turning data strategy into action. It outlines how the organization works to achieve its goals using data.
Without a proper Data Operating Model, companies face problems like data silos and short-term thinking. This impacts collaboration and the quality of data solutions.
Successful operating models need to adapt as teams grow and change. They should cover not just team structure but also day-to-day tasks, delivery methods, and oversight.

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Issue #12 – The Three Biggest Data Problems Companies Face

239 implied HN points • 30 Jun 24

💼 Business Data Management Organizational Structure Process Improvement Data Quality Technology Integration

Companies often struggle with a data operating model that doesn't connect well with their other teams. This leads to isolation among data specialists, making it hard to work effectively.
Data models, which are important for understanding and using data correctly, are often overlooked. When organizations don’t reference these models, they can drift further away from their goals.
Many data quality issues come from deeper problems within the organization, like poor data governance and inconsistent processes. Fixing just the visible data quality issues won't solve the bigger problems.

Issue #8 - Deliver on the Data Needs, not the Data Desires

199 implied HN points • 02 Jun 24

💼 Business Data Strategy Stakeholder engagement Analytics Data Governance Business Intelligence

It's important to focus on what the business truly needs from data, not just what they think they want. Conversations should help uncover real goals and challenges.
Data projects often fail because teams don't ask the right questions or fully understand the business context. Engaging stakeholders regularly is key to success.
A clear step-by-step process helps develop effective data solutions. Start with building a strong data foundation before moving on to more complex analytics.

Issue #10 - The Data Lifecycle

159 implied HN points • 16 Jun 24

🕹 Technology Data science Data Management Data security Data Analysis Data Engineering Data Visualization

The data lifecycle includes all the steps from when data is created until it is no longer needed. This helps organizations understand how to manage and use their data effectively.
Different people and companies might describe the data lifecycle in slightly different ways, which can be confusing. It's important to have a clear understanding of what each term means in context.
Properly managing data involves stages like storage, analysis, and even disposal or archiving. This ensures data remains useful and complies with regulations.

Issue #11 - Dispelling the AI Hype Train

139 implied HN points • 23 Jun 24

🕹 Technology AI Data science Business strategy Digital Transformation Organizational Culture

AI needs a proper plan and strategy to work well. Companies shouldn't think they can just jump in without understanding how it will fit into their overall goals and data.
Many AI projects fail because organizations overlook the importance of data quality and proper infrastructure. Good data practices are essential for AI to be effective.
It's important to get everyone in the company on board with AI. This means training employees and creating a culture that embraces the technology, rather than fearing it.

Issue #9 - Clarifying Data Terminology

159 implied HN points • 09 Jun 24

🕹 Technology Data Management Data Analysis Data Governance Data Literacy Data science

Data can mean many things, from raw collections to curated evidence used in decisions. It's important to define what data means in each situation to avoid confusion.
Poorly defined data terms can lead to problems in data literacy, collection, and management. This can create issues for organizations trying to use data effectively.
Understanding different categories of data, like data types and processing stages, helps in managing and analyzing data better. Knowing these categories makes it easier to communicate and use data in an organization.

Issue #7 - The Business Strategy, Where The Data Journey Starts

179 implied HN points • 26 May 24

💼 Business Strategy Data Leadership Analytics Performance

A business strategy is the game plan for a company to reach its goals. It involves having a clear vision, mission, and set of goals to guide the organization.
Good business strategies have defined components that everyone in the company knows. This helps avoid confusion and keeps everyone focused on the same objectives.
Data plays a crucial role in shaping modern business strategies. Companies need to integrate data and analytics into their plans to make informed decisions and stay competitive.

Issue #1 - We Need to Rethink Data

259 implied HN points • 13 Apr 24

🕹 Technology Data Management Information Systems Data Strategy Business Intelligence Analytics

The data industry is really complicated and often misunderstood. People usually talk about symptoms, like bad data quality, instead of getting to the real problems underneath.
It's important to see the entire data ecosystem as connected, not just as separate parts. Understanding how these parts work together can help us find new opportunities and improve how we use data.
This newsletter aims to break down complex data topics into simple ideas. It's like a cheat sheet for everything related to data, helping readers understand what each part is and why it matters.

Issue #3 - Pulling Back the Data Ecosystem Curtain

219 implied HN points • 28 Apr 24

💼 Business Data Strategy Business Models Decision-making Data Governance

Data in a business starts with understanding its goals and needs. The success of data efforts relies on how well it aligns with what the business wants to achieve.
The data lifecycle turns business needs into actionable insights. It involves sourcing data, organizing it, and finally consuming it to gain meaningful insights that support decision-making.
Surrounding factors like market trends and organizational issues can impact how data is used. It's important to recognize these influences to address challenges and keep data initiatives on track.

Issue #6 - Hey Data, Show Me The Money!!!

119 implied HN points • 19 May 24

💼 Business Investment Strategy Data Analytics Finance

Investing in data is a strategic move, not just about spending money. It's important to align data efforts with business goals to see real value.
When pitching for data investment, focus on the benefits it will bring. Clear communication of value can help rebuild trust with leadership.
Measuring the success of data investments through defined KPIs is essential. This helps in making future improvement and investment decisions.

Issue #5 - Data’s Growth Problem: Losing the Audience

99 implied HN points • 12 May 24

🕹 Technology AI Tools Data Strategy Business Intelligence Data Governance

Data growth is huge but understanding it is lagging behind. Even though we generate tons of data daily, many people and businesses struggle to truly grasp what it means.
Organizations often rely too much on consultants and vendors for quick fixes instead of addressing the core issues of their data practices. This can lead to overspending and not solving the deeper problems.
To benefit from data, companies need to focus on building strong foundations like data governance and internal capabilities. It's important to think long-term instead of prioritizing quick solutions.

Issue #2 - The Data Ecosystem: Where do you even start?

119 implied HN points • 21 Apr 24

🕹 Technology Data science Machine Learning Data Engineering Data Governance Analytics

Data can be really complicated, and it's easy to miss how everything connects. People often focus on their own area and forget about the bigger picture of the data ecosystem.
Chief Data Officers (CDOs) are important but can only do so much to fix data issues. They deal with many challenges, including limited power, lack of experience, and politics within the organization.
To improve in the data field, we need to recognize the gaps in our knowledge, prioritize what to focus on, and continuously educate ourselves in both our own areas and related data domains.

Issue #4 - The Five Minute History of Data

59 implied HN points • 05 May 24

🕹 Technology Data science AI Analytics Data processing Cloud Computing

Data is generated and used everywhere now, thanks to smart devices and cheaper storage. This means businesses can use data for many purposes, but not all those uses are helpful.
Processing data has become much easier over the years. Small companies can now use tools to analyze data without needing a team of experts, although some guidance is still necessary.
Analytics has shifted from just looking at past data to predicting future trends. This helps companies make better decisions, and AI is starting to take over some of these tasks.

Issue #18 – Organisational Structures for Today's Data Realities

1 HN point • 11 Aug 24

💼 Business Organizational Structure Data Strategy Team Dynamics Leadership Collaboration

Organizing data teams is tricky because they need to work with different departments. Companies often struggle to define who owns data responsibilities since data is needed everywhere.
Data roles are changing fast, making it hard for teams to have clear structures. As new roles appear, it can get confusing to know where everyone should fit within the team.
Choosing the right structure for data teams is important and should align with a company's goals. There isn't a one-size-fits-all answer, and each company needs to find what works best for them.