What is a Data Ecosystem?

Let’s start with a short explanation of what a data system is and what the examples of data ecosystems are.

Data ecosystem definition

A data ecosystem is a combination of a company’s infrastructure and applications that is used to collect and analyze information. Data ecosystems enable businesses to better understand their customers and craft superior operations strategies. There are three key components data systems consist of:

  • The people who use it.
  • The technology that supports it.
  • The processes that facilitate it.

There are no two organizations that make use of the same data in the same way. Each business has a unique data system. Of course, data systems may overlap in some cases, especially when data is pulled or scraped from a public source. Below, you will find some real-life examples of how you can use data in your data system:

  • You can use economic data and forecasts, as well as data from suppliers to improve demand forecasting and reduce instances of ‘out of stock’.
  • Data from suppliers, social media data, and consumer data (such as purchase history and demographics) can be used by a telecommunication company to keep tabs on market changes and competition.
  • A transportation company can use geolocation data, traffic and routing data, and weather data to improve bus routing and ensure drivers arrive at stops on time.

Key Elements of Data Ecosystems

Data must first be ingested from sources. Then, it’s translated, stored, and analyzed by data scientists before the final presentation in an understandable format. The entire process is long and arduous, taking months to implement.

Source data

There are internal and external data sources. Internal sources are proprietary databases, spreadsheets, and other resources originating from your company. External data sources are sources that originate from outside your organization. While identifying data sources for your project, you should evaluate its quality and accuracy.

Data storage ETL (Extract, Transform, Load)

ETL is the process of preparing data for analysis. It’s a general term for the data preparation layers of a big data ecosystem. As there are different kinds of data such as structured and unstructured data, raw data, etc., you usually need different schemas and alignments to manage it properly.

Data warehouses

Once the data is extracted and transformed during the ETL phase, it should be stored in a data lake or warehouse and eventually processed. Many data science teams consider this phase the most important component of a big data ecosystem. It’s good to remember that storing data in lakes is different than storing it in warehouses. Lakes preserve the original raw data and data stored in a warehouse is much more focused on the specific task of analysis.

Data analysis infrastructure

Analysis is an important component of the data ecosystem where all the dirty work happens. The data, after being collected, ingested, and prepared, is crunched together. It passes through several tools that shape it into actionable insights. Depending on the particular project, data analysis can be diagnostic, descriptive, predictive, or prescriptive.

Data visualization

It matters how the data is visualized. To make sure it is quick to understand, the data should be visualized as clean, clear charts. Data visualization software helps users turn complex data into easy-to-follow charts and graphs. Implementation of data analytics software is a huge step toward data-driven, effective decision-making. Data visualization tools include Looker, Tableau, Microsoft BI, and many others.

Benefits of Using Modern Solutions in Data Science Ecosystems

The data ecosystem interacts with various business areas. Therefore, you should always aim at using as modern solutions as possible. This is the only way to grow and gain a competitive edge. Using modern solutions in data science ecosystems has many significant advantages.

Access to information

First and foremost, organized and visualized data provides you with access to necessary information whenever you want, wherever you are. When data is easily accessible across the organization, better decisions can be made. At the same time, a data ecosystem enhances security – when data is managed properly and centralized, it is much easier to identify and fix inconsistencies and vulnerabilities that arise in fragmented systems.

Improving decision distribution

An effective data ecosystem improves decision-making by centralizing and standardizing data from various sources. It integrates seamlessly with analytical hardware and software services to ensure data quality and enable organizations to derive insights efficiently. Efficiency is also improved as data silos between suppliers, partners, distributors, and other stakeholders are eliminated.

Organization’s data ecosystem for customer and market behavior

When you know how to collect, process, and interpret data, it is easier for you to understand your customers and market better. Data ecosystems allow companies to understand how customers interact with their businesses. According to Capgemini’s Data Sharing Masters report, companies that are part of data-sharing ecosystems improve customer satisfaction by an average of 15% and improve productivity and efficiency by 14% in 2-3 years.

Analytics Data & Assets in Various Departments – the Importance of Modern Data Ecosystems in a Business Environment

Every organization in every industry and every business field will benefit from an effective data ecosystem.

Data management

The data analytics handled in a modern data ecosystem involves the use of innovative technologies and algorithms. They analyze large data sets and uncover patterns, correlations, and trends. Analytics data is a great way for a company to gain a comprehensive understanding of their assets’ performance and lifecycle.

Data science ecosystem

A data science ecosystem is a complex set of tools and technologies that help businesses solve a multitude of problems. It revolves around data science and Machine Learning, transforming the future of organizations. The data science ecosystem consists of different people and different roles such as Data Scientist, Database Administrator, and Data Analyst.

Data analytics ecosystem

A data analytics ecosystem allows organizations to analyze raw data to make conclusions and make data-driven decisions. It provides companies with valuable insights into their supply chain management, customers, and market conditions. Finally, the techniques included in the data analytics ecosystem help businesses optimize their performance and maximize profit.

Conclusion – Why Should You Create a Data Ecosystem with STX Next?

A cloud data ecosystem is a type of system we’ve been working with for ages. This is how we build data ecosystems for our clients:

  1. We identify data types, locations, and standards so that your data ecosystem is clean and healthy.
  3. Once we collect such information, we choose the best technologies to enable data tracking, storage, and transfer.
  5. Based on our Data Engineering experience, we build a system for analyzing data so that you get the data you can trust.
  7. Time to define the data outputs and train your team to interact with the data ecosystem in the most efficient way.

Our Data Engineering process’s result is building a data pipeline. It’s a combination of tools and processes that move data from one system to another for further handling and storage. At STX Next, we use data pipelines for data migration, data integration, data processing, and data transformation.

Do you need more information about how our data scientists build effective data ecosystems? Contact us for a free consultation.