The modern data mindset is your key to unlocking data integration

Command and control your data with our new ultimate guide.
November 16, 2022

The modern data stack has been around for a while, but surprisingly few organizations have fully embraced its technical benefits, let alone the organizational and cultural change that comes with it. Our new ebook The ultimate guide to data integration is a detailed guide to both technical implementation as well as cultural integration and change management, i.e. a modern data mindset. 

Organizations that adopt the modern data stack and a modern data mindset get the best of both technology and teamwork. On the technology side, organizations are able to leverage automation and dramatically improved performance, resilience and adaptability. On the teamwork side, expensive technical and analytical talent is relieved of repetitive chores and able to focus on valuable, intellectually challenging tasks that only a human can perform. 

There are three main pillars to the modern data mindset:

  1. The data hierarchy of needs is a hierarchical progression from solid data infrastructure to analytics and predictive modeling. 
  2. Modern technologies enable a change in data integration architecture from ETL to ELT, which has significant knock-on benefits to data engineering workflows.
  3. Even so, data engineering is vastly complicated and many data operations are well worth outsourcing and automating for the sake of preserving engineering hours, money and morale.

To build a successful data team, you must execute around these three pillars. Our ebook discusses these points in detail, but in the meantime, here is a taste of what it has to offer.

The data hierarchy of needs

The modern data mindset begins with a keen understanding of the data hierarchy of needs. Climbing this hierarchy involves systematically selecting tools, building infrastructure and instilling organizational habits and processes.

The hierarchy consists of the following steps:

  1. Data extraction and loading. This requires setting up a modern data stack, including a data pipeline, a central destination, transformation tool and a business intelligence platform.
  2. Data modeling and transformation. Once data is gathered in a destination, analysts can transform it into data models to support reports and dashboards. As your organization grows, you will need more analysts.
  3. Visualization and decision support. Data models enable visualizations, reports and dashboards to support important decisions.
  4. Data activation. Once you have solid data integration, consider routing data back into operational systems for real-time visibility and automated business processes.
  5. AI and machine learning. Predictive modeling, commonly described as artificial intelligence or machine learning, represents the pinnacle of data science and requires specialists such as data scientists and machine learning engineers.

The hierarchy is an essential model for building a mature data organization. Skipping steps in this progression, such as prematurely hiring data scientists, can be deeply problematic and is a common reason that data projects fail. First, you must put together a modern data stack. Then, build a solid team of analysts for reporting. Afterward, you can think about business process automation and data science. Our ebook covers the necessary steps to this process in detail.

ETL vs. ELT

The second pillar to the modern data mindset is the understanding that extract, transform, load (ETL), for a long time the prevailing data architecture for data integration and often used synonymously with data integration, has been superseded by extract, load, transform (ELT)

The central element to both architectures is the complexity of transformation, which is required to turn raw data into usable data models for analysis. ETL tightly couples extraction and transformation, resulting in pipelines that must be custom-built and carefully orchestrated to properly handle dependencies between intermediate data models, and rebuilt whenever either upstream schemas or downstream data models change.

By contrast, ELT separates extraction and transformation, loading raw data directly to a destination. This shifts transformations from the pipeline to the destination. Since pipelines tend to be written in general-purpose programming languages while destinations (especially data warehouses) use query languages, this also changes data modeling and transformation from an engineering-centric activity to an analyst-centric one.

Your data team should look for suites of data integration tools that follow the ELT architecture. The ultimate guide to data integration offers a detailed case for ELT over ETL if you (or a colleague) need convincing.

Outsourcing and automation

Despite the inherent advantages of ELT as an architecture, data integration is fundamentally a complicated undertaking that involves a wide range of difficult engineering and design choices. Building a DIY data integration system carries explicit monetary costs, consumes valuable engineering hours and is a failure-prone process that often results in stoppages and downtime.

A modern data stack is best served by outsourcing and automation whenever possible. The modern data mindset makes sense from a straightforward budget and efficiency perspective too. Our interactive landing page even contains a simple calculator to help jumpstart discussions around savings this new approach can bring.

Perhaps more importantly, outsourcing and automation can be essential to the morale of data professionals. The average tenure of a data scientist or analyst is less than three years. Employee morale and motivation is something all leaders must ultimately tackle, and is where the efficiencies of machines can directly impact the happiness of humans. The modern data mindset means harnessing technology to remove laborious and repetitive tasks which from the workflows of data professionals. 

This means that, as you evaluate the elements of your data stack, you should judge technologies first and foremost on their labor-saving attributes. Other considerations matter, too, and The ultimate guide includes a longer such list. 

Read the book

Our new ebook is an essential read as you plan and execute your organization’s data integration efforts. Beyond the three pillars of the data mindset, you will also gain practical ideas for evaluating and choosing data integration technologies and equipping your teams to make the most of them.

The keys to unlocking organizational and technological change and a vast array of benefits are within reach. Download The ultimate guide to data integration to learn more.

The ultimate guide to data integration is here!

Read it!

Start for free

Join the thousands of companies using Fivetran to centralize and transform their data.

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The modern data mindset is your key to unlocking data integration

November 16, 2022
The modern data mindset is your key to unlocking data integration
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Command and control your data with our new ultimate guide.

The modern data stack has been around for a while, but surprisingly few organizations have fully embraced its technical benefits, let alone the organizational and cultural change that comes with it. Our new ebook The ultimate guide to data integration is a detailed guide to both technical implementation as well as cultural integration and change management, i.e. a modern data mindset. 

Organizations that adopt the modern data stack and a modern data mindset get the best of both technology and teamwork. On the technology side, organizations are able to leverage automation and dramatically improved performance, resilience and adaptability. On the teamwork side, expensive technical and analytical talent is relieved of repetitive chores and able to focus on valuable, intellectually challenging tasks that only a human can perform. 

There are three main pillars to the modern data mindset:

  1. The data hierarchy of needs is a hierarchical progression from solid data infrastructure to analytics and predictive modeling. 
  2. Modern technologies enable a change in data integration architecture from ETL to ELT, which has significant knock-on benefits to data engineering workflows.
  3. Even so, data engineering is vastly complicated and many data operations are well worth outsourcing and automating for the sake of preserving engineering hours, money and morale.

To build a successful data team, you must execute around these three pillars. Our ebook discusses these points in detail, but in the meantime, here is a taste of what it has to offer.

The data hierarchy of needs

The modern data mindset begins with a keen understanding of the data hierarchy of needs. Climbing this hierarchy involves systematically selecting tools, building infrastructure and instilling organizational habits and processes.

The hierarchy consists of the following steps:

  1. Data extraction and loading. This requires setting up a modern data stack, including a data pipeline, a central destination, transformation tool and a business intelligence platform.
  2. Data modeling and transformation. Once data is gathered in a destination, analysts can transform it into data models to support reports and dashboards. As your organization grows, you will need more analysts.
  3. Visualization and decision support. Data models enable visualizations, reports and dashboards to support important decisions.
  4. Data activation. Once you have solid data integration, consider routing data back into operational systems for real-time visibility and automated business processes.
  5. AI and machine learning. Predictive modeling, commonly described as artificial intelligence or machine learning, represents the pinnacle of data science and requires specialists such as data scientists and machine learning engineers.

The hierarchy is an essential model for building a mature data organization. Skipping steps in this progression, such as prematurely hiring data scientists, can be deeply problematic and is a common reason that data projects fail. First, you must put together a modern data stack. Then, build a solid team of analysts for reporting. Afterward, you can think about business process automation and data science. Our ebook covers the necessary steps to this process in detail.

ETL vs. ELT

The second pillar to the modern data mindset is the understanding that extract, transform, load (ETL), for a long time the prevailing data architecture for data integration and often used synonymously with data integration, has been superseded by extract, load, transform (ELT)

The central element to both architectures is the complexity of transformation, which is required to turn raw data into usable data models for analysis. ETL tightly couples extraction and transformation, resulting in pipelines that must be custom-built and carefully orchestrated to properly handle dependencies between intermediate data models, and rebuilt whenever either upstream schemas or downstream data models change.

By contrast, ELT separates extraction and transformation, loading raw data directly to a destination. This shifts transformations from the pipeline to the destination. Since pipelines tend to be written in general-purpose programming languages while destinations (especially data warehouses) use query languages, this also changes data modeling and transformation from an engineering-centric activity to an analyst-centric one.

Your data team should look for suites of data integration tools that follow the ELT architecture. The ultimate guide to data integration offers a detailed case for ELT over ETL if you (or a colleague) need convincing.

Outsourcing and automation

Despite the inherent advantages of ELT as an architecture, data integration is fundamentally a complicated undertaking that involves a wide range of difficult engineering and design choices. Building a DIY data integration system carries explicit monetary costs, consumes valuable engineering hours and is a failure-prone process that often results in stoppages and downtime.

A modern data stack is best served by outsourcing and automation whenever possible. The modern data mindset makes sense from a straightforward budget and efficiency perspective too. Our interactive landing page even contains a simple calculator to help jumpstart discussions around savings this new approach can bring.

Perhaps more importantly, outsourcing and automation can be essential to the morale of data professionals. The average tenure of a data scientist or analyst is less than three years. Employee morale and motivation is something all leaders must ultimately tackle, and is where the efficiencies of machines can directly impact the happiness of humans. The modern data mindset means harnessing technology to remove laborious and repetitive tasks which from the workflows of data professionals. 

This means that, as you evaluate the elements of your data stack, you should judge technologies first and foremost on their labor-saving attributes. Other considerations matter, too, and The ultimate guide includes a longer such list. 

Read the book

Our new ebook is an essential read as you plan and execute your organization’s data integration efforts. Beyond the three pillars of the data mindset, you will also gain practical ideas for evaluating and choosing data integration technologies and equipping your teams to make the most of them.

The keys to unlocking organizational and technological change and a vast array of benefits are within reach. Download The ultimate guide to data integration to learn more.

The ultimate guide to data integration is here!

Read it!
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