Data Ecosystem

What is Data Ecosystem?

The term data ecosystem refers to the programming languages, packages, algorithms, cloud-computing services, and general infrastructure an organization uses to collect, store, analyze, and leverage data.

No two organizations leverage the same data in the same way. As such, each organization has a unique data ecosystem. These ecosystems may overlap in some cases, particularly when data is pulled or scraped from a public source, or when third-party providers are leveraged (for example, cloud storage providers).

Components of a Data Ecosystem

Sensing

Sensing is about finding data sources for your project. It involves checking the quality of the data to see if it’s valuable. To do this, you ask questions like:

1. Is the data accurate?
2. Is the data recent and up to date?
3. Is the data complete?
4. Is the data valid? Can it be trusted?
5. Data can be sourced from internal sources, such as databases, spreadsheets, CRMs, and other software. It can also be sourced from external sources, such as websites or third-party data aggregators.

Key pieces of the data ecosystem leveraged in this stage include:

1. Internal data sources: Proprietary databases, spreadsheets, and other resources that originate from within your organization
2. External data sources: Databases, spreadsheets, websites, and other data sources that originate from outside your organization
3. Software: Custom software that exists for the sole purpose of data sensing
4. Algorithms: A set of steps or rules that automates the process of evaluating data for accuracy and completion before it’s used

Collection

Once a potential data source has been identified, data must be collected.

Data collection can be done manually or automatically. However, manually collecting a large amount of data is usually not practical. That’s why data scientists use programming languages to write software that can automate the data collection process.

For instance, they can write a code called a web scraper that collects relevant information from websites. They can also create an application programming interface (API) that directly extracts specific data from a database or interacts with a web application. These automated processes make data collection more efficient and save time.

Key pieces of the data ecosystem leveraged in this stage include:

1. Various programming languages: These include R, Python, SQL, and JavaScript
2. Code packages and libraries: Existing code that’s been written and tested and allows data scientists to generate programs more quickly and efficiently
3. APIs: Software programs designed to interact with other applications and extract data

Wrangling

Data wrangling is a set of steps that make raw data easier to use. It involves tasks like combining different datasets, filling in missing data, removing unnecessary or wrong data, and organizing data for analysis in the future.

Data wrangling can be done by hand or with the help of automation. When the dataset is small, doing it manually can work fine. But for bigger data projects, there is so much data that it’s more efficient to use automation.

Key pieces of the data ecosystem leveraged in this stage include:

1. Algorithms: A series of steps or rules to be followed to solve a problem (in this case, the evaluation and manipulation of data)
2. Various programming languages: These include R, Python, SQL, and JavaScript, and can be used to write algorithms
3. Data wrangling tools: A variety of data wrangling tools can be purchased or sourced for free to perform parts of the data wrangling process. OpenRefine, DataWrangler, and CSVKit are all examples.

Analysis

After raw data has been inspected and transformed into a usable form, it can be analyzed. The type of analysis depends on the specific problem you are trying to solve with your data project. It can be diagnostic, which helps understand what happened in the past, descriptive, which describes the current situation, predictive, which predicts future outcomes, or prescriptive, which suggests actions to take.

Although each type of analysis is different, they all use similar processes and tools. Typically, analysis starts with automated processes, especially when dealing with large datasets. Once the automation is done, data analysts use their expertise to gain further insights.

Key pieces of the data ecosystem leveraged in this stage include:

1. Algorithms: A series of steps or rules to be followed to solve a problem (in this case, the analysis of various data points)
2. Statistical models: Mathematical models used to investigate and interpret data
3. Data visualization tools: These include Tableau, Microsoft BI, and Google Charts, which can generate graphical representations of data. Data visualization software may also have other functionality you can leverage.

Storage

At every stage of the data life cycle, it is important to store data securely and make it easily accessible. The specific method of storage depends on the data governance procedures of your organization.

Key pieces of the data ecosystem leveraged in this stage include:

1. Cloud-based storage solutions: These allow an organization to store data off-site and access it remotely
2. On-site servers: These give organizations a greater sense of control over how data is stored and used
3. Other storage media: These include hard drives, USB devices, CD-ROMs, and floppy disks

The Importance of the Data Ecosystem

Every component of the data ecosystem interacts with and affects other parts, which means that if a business isn’t careful, it can face threats to data integrity, privacy, and security.

A good example of this is the recent SolarWinds hack, which is considered one of the worst security breaches ever. SolarWinds is a company that makes software used by over 30,000 client companies and organizations to manage their networks. This software is an important part of their data ecosystems.

In early 2020, hackers inserted harmful code into SolarWinds’ software, which was then sent out to clients as updates. As a result, thousands of companies and organizations had their data exposed to hackers, including government agencies like NASA and the Federal Aviation Administration.