For many years, data models have plagued data scientists and analysts with inefficiency that eroded the usefulness of their organizational data. While solutions such as data lakes and data warehouses create a central repository for organizational data, they often lack the agility to deliver the complex data insights required to power a modern enterprise.
In the current age of data-driven business, the need to utilize a labyrinth of technology, processes, and personas is ever increasing. Data is now required to support many use cases at speed, and as such the environment in which we operate – internal and external to the organization – becomes more complex.
To be successful, organizations must utilize the full potential of their data by democratically designing data strategies with agile capabilities for cross-functional teams.
A data mesh does just that by creating a flexible and agile solution for modern data strategy.
With a data mesh in place, organizations can fully leverage their data assets for strategic decision-making — without the concern that changes will necessitate a costly system overhaul in the future.
The opportunity cost presents an even greater risk, if companies are not able to take advantage or pursue and opportunity because it could not be identified or executed.
The following article will explore the data mesh architecture and how it improves upon antiquated data strategies in four key ways: trust, accessibility, culture, and architecture.
For data to be useful, the organization must confirm that the data it relies on can be trusted. Trust in data is created when the organization can ensure the data's accuracy, integrity, completeness, and quality. The data must also encompass the added dimensions of ease of use and flexibility in access, which empower business leaders to make rapid data-driven decisions.
A data mesh architecture treats data as a product. It leverages a business domain-oriented structure and a federated governance model for constant improvement. As the model matures, these components synergistically grow stronger together, enhancing trust.
To maximize the utility of data assets, an organization needs to make data products easily accessible. Data products should be created with the end-user in mind to make it effortless to access the data and insights they need.
Modern enterprises have a wide range of end-users. Cross-functional teams are commonplace, and personnel at all levels will frequently make data-driven decisions. To meet this requirement, data platform must cater to the needs of end-users in a self-service capacity.
By creating systems that allow team members to access data products that are governed intelligently — with a federated architecture, and lightweight centralized guardrails — the organization ensures data democratization.
A data mesh architecture fulfills this essential need by allowing the platform to readily share and connect with consumers of data while also managing the necessary dimensions of their data products.
This method, in turn, allows organizations to generate more value. With the new architecture, every team member has access to all the data they require, creating value faster due to greater speed to insight.
Creating a culture in which data is democratized, enablement exists, and individuals can discover, learn, and make faster data-driven decisions. In this environment, everyone is linked together and primed for success.
A data mesh methodology achieves these goals by automating many of the traditionally encumbered data strategy processes. It allows business leaders to leverage data rather than manage it. What results is an enterprise-wide positivity towards data, where it can be seen as a powerful tool rather than a hindrance.
When it comes to building a data system, selecting the best tool for the job is crucial. It is important to pick the appropriate technology for the task.
Organizations must avoid relying on the centralization of data in one technology that services all personas and use cases. There is a need for data to be distributed and centralized in a combination depending on the use case, need, and persona. Hence, flexibility required in an architecture.
Endless data transformation projects consume human resources, potentially damaging the company financially. The challenge of current data architectures is that they require highly specialized skills. These skills are limited in availability, hard to train, and expensive which impacts cost, agility, and the ability to deliver.
A data mesh architecture is superior in this capacity because it can grow with organizational needs while being distributed and highly accessible. Data mesh doesn’t require a complexity of skill sets, making it easier to democratize data to more people across an organization.
It is not unusual for companies to inquire about significant roadblocks in executing a data roadmap and how to make the most of a data strategy.
We also see many discussions in the industry regarding the substantial number of data projects that fail to deliver as promised.
Industry experts tend to break this issue into five root causes:
Computational bottlenecks are becoming common. These occur when the architecture cannot handle the volume, variety, and velocity of the data.
Architectures that were designed for a specific purpose are now being asked to fulfill many additional purposes, both human and machine, yet they remain inflexible in structure.
How can the data be protected once it begins to fragment? The risks of data drift and lost control, as well as growing concerns over access control have caused centralized data governance teams and capabilities to be overwhelmed by the burdens.
Diverse and multiplying data types and the increasing data speed are putting current architectures under pressure.
As a result of these challenges, we see higher operational costs, longer delivery times, and increased overhead in hyper skills, architecture requirements, and shadow analytics developments.
Organizations searching for a digital transformation tend to fall into the dangerous trap of assuming new technology will fix their current issues. They believe that all will be resolved if they move the data to a new platform or technology via a transformation program.
This notion only contributes to the strikingly high number of data projects that fail to deliver. It is unfortunate that regardless of the technology used throughout the last few decades, the underlying issues have not been addressed.
These underlying issues at the core of common data strategy are often the result of two challenges:
The first problem is the extract, transfer, and load (ETL) process. If you examine a data warehouse project, architecture, or team carefully, you will find that 70% of the workload is some form of data engineering.
Essentially, this job is moving data from one place to another. Moving data around is expensive and time-consuming. You will also find that, in most modern architectures, 80% or more of the ETL work is not necessary.
In the age of Big Data, ETL jobs have gotten more complex. Data engineering has evolved in less-developed platforms that demand significant expertise. These platforms tend to be tough to discover, hard for personnel to learn, and expensive to use.
The old enterprise data models still have certain applications in today's organization. Still, if implemented with a broad brush to all data products, they will stifle innovation and restrict access to data.
Flexibility is the watchword for data models in modern organizations. Businesses today require many diverse data models, most of which have different lifespans. Some relevant data models will only be useful for days, while others can be leveraged for years. Because of these changing requirements, data architectures need to be agile, inclusive, and flexible.
The challenges listed above are change management challenges. They reflect an era when many believed that the route to success was through the tight management and compartmentalization of data.
The data mesh architecture helps your company avoid the common problems of outdated data strategy. You may use the talents and tools you currently have, with no need for a major overhaul in the near or distant future.
Application Modernization at Speed and Scale Enterprises are pursuing greater application scalability, cost efficiency, and standardization with containerization and virtualization platforms. So, what’s the difference? Containers are a type of virtualization technology that allows users to run multiple operating systems inside a single instance of an OS. They are lightweight and portable, making them ideal for running applications across different platforms.Explore
Container Orchestration or Compute Service? Amazon Web Services (AWS) offers a range of cloud computing services to meet enterprise needs. Included in its service offering is the elastic compute service (ECS) and elastic compute cloud (EC2). Choosing between these two services can be difficult, as one focuses on virtualization while the other manages containerization. In the following article, we will explore the differences between Amazon ECS and EC2 to help you better understand which service is right for your use case.Explore
What is Application Modernization? Application modernization is the process of converting, rewriting, or porting legacy software packages to operate more efficiently with a modern infrastructure. This can involve migrating to the cloud, creating apps with a serverless architecture, containerizing services, or overhauling data pipelines using a modern DevOps model.Explore
What are the Differences? Though often used interchangeably, data pipelines and ETL are two different methodologies for managing and structuring data. ETL tools are used for data extraction, transformation, and loading. Whereas data pipelines encompass the entire set of processes applied to data as it moves from one system to another. Sometimes data pipelines involve transformation, and sometimes they do not.Explore
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore
The Rise in Big Data Analytics According to Internet World Stats, global internet usage increased by 1,339.6% between 2000-2021. With nearly thirteen times as many people using the internet, this has resulted in a massive increase in the amount of data being processed daily. Our increased sharing and consumption of digital media also compounds this increased usage to create an enormous pool of data for big data analytics firms to process.Explore