Have you ever cooked in a messy kitchen where all your ingredients, crockery, and cutlery are scattered everywhere? Finding anything is endless; even cooking a simple porridge bowl becomes a huge task. Now, think of a neatly organized kitchen — grouped ingredients, crockery is in the right place, and your cutlery is to hand — you definitely will cook faster and be in a good mood. That’s what a data ecosystem does for a company’s data.

What are data ecosystems?

Data ecosystems are like perfectly organized kitchens but for a company’s data. They combine all the tools and technology needed to handle data — crunching numbers, running AI programs, or analyzing customer behavior — into one easy-to-manage system.

Who needs data ecosystems?

Any business that relies on data (data is everything nowadays) can benefit from a data ecosystem. Without data, a business cannot detect helpful information on how to reduce costs, improve customer services, or scale itself. Whether it’s a bank analyzing financial transactions, a hospital managing patient information, or an online store tracking customer purchases, data ecosystems help companies work smarter, not harder.

As you can easily guess, for the 8 years of our work and more than 200 clients, we built a lot of different types of data ecosystems and data management systems. Our experts are deep down into the fintech, healthcare, biotech, renewable energy, and ecommerce industries, which really require accurate, organized, safeguard work with big data.

Why do data ecosystems matter?

Traditionally, companies had to piece together tons of separate hardware and software to handle their data, which was messy, expensive, and hard to manage. Data ecosystems simplify this by providing an all-in-one platform, making everything work together smoothly, kind of like how a smart kitchen makes cooking simple and efficient.

First, data ecosystems increase efficiency and streamline operations. A well-designed data ecosystem automates tasks, reduces errors, and improves communication between crucial business chains and links.

Second, decision-making becomes less blind, easier, and more efficient. Data ecosystems give organizations a comprehensive view of data with numerous nuances — from customers’ locations and typical purchase times to actual empty warehouse spaces. According to a recent McKinsey study, companies that use data-driven decision-making are 26% more profitable than others.

Third, potent data management leads to enhanced customer experience. As data ecosystems collect and analyze customer data, businesses have an opportunity to improve their products and services to better meet customers’ expectations. According to the same McKinsey study, 80% of customers will buy something when a company takes a personalized approach. 

Last but not least, data management provides a competitive advantage. Data ecosystems provide businesses with insights that their competitors may still not get. According to a study by Harvard Business Review, 84% of C-levels believe that accurate data gives a competitive advantage. Besides, data ecosystems foster innovation as a side effect of scaling insights.

Data ecosystem examples

Since data ecosystems are networks of connected tools working together to collect, process, analyze, and share data, people usually divide them by usage niches. Fintech, healthcare, government systems, edtech, business development, and social media holdings typically concentrate the most massive amounts of data. Here, we discuss only several examples to better understand their implementation possibilities.

Business data ecosystems

All large enterprises need a unified data management system for every department and partner. A typical example is a CRM, which collects and analyzes customer data to enhance sales and marketing. Due to unique business processes, no perfect CRM exists. KITRUM handles tasks like creating CRMs from scratch or optimizing existing ones. Other examples of data analytics ecosystems also exist.

  • Enterprise resource planning systems. ERPs connect business processes like finance, human resources, sales, and supply chain management. They create a data ecosystem within an entire organization. As popular ERPs, you could hear about SAP, Oracle E-Business Suite, and Microsoft Dynamics 365.
  • Supply chain management systems. SCM systems track goods and services  movements along a supply chain. SCM systems include suppliers, manufacturers, distributors, and retailers into tracking. Some examples are JDA Software Group, SAP Supply Chain Management, Oracle Supply Chain Management.
  • Online marketplaces and retailers. You definitely know Amazon, eBay, and Alibaba. Platforms like them collect data on customer behavior, sales, and market trends. They analyze the data and get insights into improving their global and tiny business processes, etc.

Financial data ecosystems

Financial and common fund data ecosystems are crucial for collecting, processing, analyzing, and distributing financial data. Every bank and government organization must establish these ecosystems, from local micro-funding to global payments, to operate legally and effectively. They must also comply with regional and international regulations. Here are some examples of financial and common fund data ecosystems:

  • Capital markets. These ecosystems collect data on financial markets, including stock exchanges, banks, etc. As an example, you can remind literally every bank you know.
  • Payment processing systems. These systems process payments and include merchants, banks, and payment processors. They offer solutions from Payoneer to PayPall, including Stripe, led by the Collison Brothers.
  • Fund administration systems. Fund administration data ecosystems support the operations of investment funds. Global leaders such as Advent, SS&C Technologies, Northern Trust, and many others manage funds’ day-to-day activities, ensure compliance with regulations, and provide accurate information to investors, etc.

Healthcare data ecosystems

The modern healthcare industry relies on vast amounts of data, from personal patient information to global research. Safely storing and managing this data is essential, making the creation of a proper data ecosystem crucial for healthcare organizations. Examples of healthcare data ecosystems include:

  • Electronic Health Records (EHRs). Epic Systems, Cerner, Allscripts, and many others store patient medical information and allow healthcare providers to exchange data. 
  • Health Information Exchanges (HIEs). They help to exchange health information between different healthcare organizations. In the USA, these are CareConnect, HealthShare Exchange, California HealthConnect, etc.
  • Public health surveillance systems. These data ecosystem systems collect and analyze data on disease outbreaks and public health threats, such as CDC WONDER, FoodNet, and the National Electronic Disease Surveillance System (NEDSS), etc.

What happens when infrastructure becomes unmanageable?

The unmanageable inclusion of big data in the data ecosystem causes various issues and consequences.

Increased downtime

As infrastructure expands without adequate oversight or automation, it becomes more vulnerable to failures, resulting in frequent outages. Increased downtime can lead to revenue loss, diminished customer trust, and harm the company’s reputation. This disruption of critical services can cause frustration among users and stakeholders.

Poor system performance

Unmaintained infrastructure usually indicates poorly optimized systems, leading to inefficient resource allocation. This degradation in system performance can result in slow response times, timeouts, or crashes, which frustrate both internal teams and customers.

Scaling challenges

When infrastructure is not managed correctly, scaling becomes complex and unpredictable. This inability to scale can hinder a company’s response to new opportunities, impede user growth, limit global expansion, and obstruct adding new features. As a result, innovation is stifled, leaving the business vulnerable to competitors.

Increased operational costs

Without effective infrastructure management, resources can be either over-provisioned or under-provisioned. Inefficient use of hardware, cloud resources, and staff time — often due to manual processes — can substantially increase operational costs. Rising infrastructure expenses can erode profitability, particularly in cloud services, where costs can escalate quickly if not monitored. As a result, businesses may spend heavily on maintaining systems, leaving less budget available for innovation and growth.

Security vulnerabilities

Unmaintainable infrastructure typically lacks regular security patching, monitoring, and updates, making it vulnerable to attacks and data breaches. Such breaches can result in losing sensitive data, regulatory penalties, and declining customer trust. Recovering from a significant security incident is often costly and time-consuming and can damage the company’s reputation.

Developer inefficiency and burnout

Poorly managed infrastructure hinders development teams from effectively deploying, testing, and maintaining applications. The constant need for firefighting and dealing with unreliable environments create bottlenecks in development workflows. As developers spend more time addressing infrastructure-related issues instead of building new features, their productivity declines. This results in slower time to market, increased employee frustration, and potential burnout, which can ultimately lead to higher turnover rates.

Stunted innovation

Innovation often falls by the wayside as teams become overwhelmed with troubleshooting and managing an unmanageable infrastructure. The time that could be devoted to developing new products or enhancing existing ones is wasted on operational challenges. This inability to innovate hampers growth and leaves the company vulnerable to competitors who can quickly adapt and introduce new solutions.

Tips to simplify the delivery of data management infrastructure

Utilize the advice from KITRUM’s Solution Architect to prevent errors in delivering data management infrastructure and streamline the processes.

Adopt Infrastructure-as-Code (IaC)

Infrastructure as Code (IaC) enables teams to define, provision, and manage infrastructure using machine-readable configuration files. This approach ensures consistency and repeatability. You can use tools like Terraform, Ansible, or AWS CloudFormation to automate the provisioning and management of data infrastructure components such as databases, storage, and networks. Version control is also important for these configurations to track changes and facilitate collaboration.

Leverage cloud-native solutions

Cloud services that provide fully managed data services — such as databases, analytics, and data lakes — significantly reduce the complexity associated with maintenance, scaling, and infrastructure setup. Consider using managed services like AWS RDS for relational databases, Google BigQuery for analytics, or Azure Data Lake Storage. These services offer scalability, built-in backups, security features, and automated patching, allowing your team to focus on higher-level tasks instead of low-level infrastructure management.

Use containers and orchestration

Containers, such as Docker, package applications along with their dependencies, making them portable and more accessible to deploy. Orchestration platforms like Kubernetes simplify the management of these containerized applications. You can use Docker to containerize essential data infrastructure components, including databases, data processing tools, and analytics platforms. Kubernetes can automate these containers’ deployment, scaling, and management, helping maintain consistency across different environments such as development, staging, and production.

Centralize monitoring and management

Centralized monitoring and management tools give teams visibility into the health and performance of data infrastructure, enabling them to detect and resolve issues more quickly. Tools like Prometheus, Grafana, and Datadog can be used to collect and visualize performance metrics. Similarly, centralized logging tools such as ELK (Elasticsearch, Logstash, Kibana) or Splunk offer a unified view of system logs. This simplifies troubleshooting and ensures that issues can be diagnosed and resolved swiftly.

Automate data pipelines and workflows

Utilize tools such as Apache Airflow, AWS Step Functions, or Google Cloud Dataflow to automate data ingestion, transformation, and loading processes. This ensures a smooth data flow from source to destination with minimal human intervention, reducing errors and delays.

Provide self-service tools

Empowering developers and data teams to manage their own infrastructure needs lessens the burden on centralized IT teams and speeds up delivery. By creating self-service portals or utilizing tools like Kubernetes Operators, Helm charts, or cloud platform dashboards, teams can provision resources on demand without needing extensive infrastructure expertise. This approach enhances agility and simplifies the process of infrastructure delivery.

Standardized and template infrastructure

Standardized infrastructure templates help reduce variability and complexity by offering predefined configurations that can be reused across different projects. Utilizing the Infrastructure as Code (IaC) template — such as Terraform modules, AWS CloudFormation stacks, or Kubernetes Helm charts — allows teams to standardize the deployment of essential infrastructure components. This approach enables teams to establish consistent and reliable environments quickly, minimizing the need for custom setups.