As businesses strive to obtain a competitive edge, they are increasingly turning to data collection in order to gain insights that were once unavailable. Data acquisition is the process of collecting data from a variety of sources, both structured and unstructured. Because of this it can be a challenge for businesses to find a way to make use of it all. However, by using big data analytics and artificial intelligence (AI), businesses can turn this data into valuable insights that can help them stay ahead of the competition.
Structured vs Unstructured Data
Structured data is information that is organized in a specific way. This data is typically found in databases and can be easily accessed and analyzed. Structured data is often used to track business performance or customer behavior. It can also be used to identify trends or patterns.
Unstructured data is information that is not organized in a specific way. This data includes things like social media posts, images, and video. While unstructured data can be difficult to analyze, it can provide valuable insights into customer behavior or trends.
Data Acquisition: How to collect data from structured and unstructured data sources
There are a number of ways to collect data from structured and unstructured data sources.
Here are some of the most common methods for collecting unstructured data sources:
- Collecting data from social media: Social media is a great source of unstructured data. Businesses can use social media monitoring tools to collect data from social media platforms such as Facebook, Twitter, and Instagram. These tools can help businesses track mentions of their brand, followers, and trends.
- Collecting data from the Internet of Things: The internet of things is composed of devices that are connected to the internet, such as smart thermostats and cars. These devices generate a lot of unstructured data that can be used to obtain insights into customer behavior and preferences. Businesses can use IoT analytics to collect and analyze this data in order to gain insights into how customers are using their products.
- Collecting data from other unstructured sources: There are a number of other unstructured sources of data that businesses can tap into in order to gain insights into customer behavior. This includes data collected from text files, emails, surveys, photo libraries, and call center logs. Businesses can use big data analytics and machine learning algorithms to extract insights from this data.
Structured Data Acquisition
There are a number of ways to collect data from structured data sources. Here are some of the most common methods:
- Collecting data from databases: Databases are a common source of structured data. Businesses can use database management tools to extract data from databases in order to analyze it. It’s also common for businesses to use Business Intelligence reporting tools as a means for understanding structured data.
- Collecting data from spreadsheets: Spreadsheets are another common source of structured data. Using spreadsheet software, businesses can inspect the data they obtain.
- Collecting data from ERP systems: ERP systems are a common source of enterprise-level structured data. Businesses use ERP software to pull data for analysis.
- Collecting data from CRM systems: CRM systems are a common source of customer-level structured data. CRM software allows data to be pulled and evaluated.
It is important for businesses to have the right tools and technologies in place in order to make use of this data. Big data analytics and artificial intelligence are two technologies that can be used to turn raw data into valuable insights.
Benefits of collecting data from structured and unstructured data sources
There are a number of benefits when collecting data from structured and unstructured data sources. Here are some of the most important benefits:
- Helps businesses obtain a competitive edge: By using the latest technologies, businesses can now collect data from social media, the internet of things, and other unstructured sources. This data can be used to obtain insights into customer behavior and preferences. This information can help businesses adapt their products and services to better meet the needs of their customers. As a result, businesses that are able to collect and use this data will have an advantage over those that don't.
- Helps businesses make better decisions: Data from structured and unstructured sources can be used to make better business decisions. For example, by using data from social media, businesses can determine which products and services are most popular with their customers. This information can then be used to make decisions about which products to promote and which ones to discontinue.
- Helps businesses save money: Collecting data from structured and unstructured sources can help businesses save money. For example, by using data from the internet of things, businesses can determine when equipment needs to be serviced or replaced. This information can help businesses avoid costly downtime and repair bills.
Challenges of collecting data
Despite the benefits of collecting data from multiple sources, there are a number of challenges. Here are some of the most common challenges.
- Ensuring data quality: One of the biggest challenges of collecting data from both structured and unstructured sources is ensuring that the data is of high quality. There is no guarantee that this data will be complete or without faults. As a result, businesses need to carefully check the data for accuracy before using it to make decisions. Data Scientists can be critical in understanding the data and helping create a data governance process.
- Cleansing data: Another challenge of collecting data from unstructured sources is that this data can be very difficult to turn into usable information. This data can contain a lot of errors and duplicates. This is where AI comes in. AI can actually be used for Data Cleansing as machine learning algorithms can learn how to label unstructured data and make it more structured.
- Processing data: Unstructured data is often in the form of text, images, or videos, which makes it hard for computers to understand. In order to make use of unstructured data, businesses need to use special tools, like BOSS AI, and algorithms that are designed for this purpose.
- Storing data: Collecting and storing data can be expensive and challenging. Many businesses have invested considerable time and money in creating large data warehouses or data lakes. Federated Machine Learning is actually making it possible to collect data and train a machine learning model at the source, where the data resides.
Despite these challenges, businesses cannot afford to ignore unstructured data. The insights that can be gleaned from unstructured data sources can be invaluable, and businesses that are able to make use of this data will be at a major advantage over their competitors. By using the latest technologies and techniques, businesses can overcome the challenges posed by unstructured data and extract the value that lies within it.
Best practices for collecting data from structured and unstructured data sources
There are a number of best practices that businesses should follow when collecting data from structured and unstructured sources. Here are some of the most important best practices:
- Define the goals and objectives: Before starting to collect data, businesses need to define the goals and objectives of the data collection effort. This will help businesses determine which data to collect and how to best use this data.
- Use the right tools: Collecting data from unstructured sources can be challenging. As a result, businesses need to use the right tools to collect this data. Some of the most popular tools for collecting data from unstructured sources include social media listening platforms, web scraping tools, and text analytics tools.
- Clean and process data: As mentioned above, collecting data from unstructured sources can generate disorganized data. As a result, businesses need to spend time cleaning and processing this data before it can be used to make decisions.
- Store data securely: Collecting data from unstructured sources can generate a lot of data. This data needs to be securely stored somewhere so that it can be accessed and analyzed when needed.
Using big data analytics and artificial intelligence tools
Big Data Analytics
The field of big data analytics has exploded in recent years, as businesses have scrambled to find a way to make use of the vast amounts of data being generated every day. The key to success in big data lies in the ability to collect data from a variety of sources, both structured and unstructured.
Until recently, businesses have had to rely mainly on structured data for their analytics needs. However, with the advent of big data technologies, businesses are now able to collect and make use of unstructured data as well. This has led to a new era of insights.
Big data analytics has become so important because it allows businesses to identify sequences that would be otherwise invisible. By analyzing both structured and unstructured data, businesses can gain a much deeper understanding of their customers and their markets. This can help them make better decisions about what products or services to offer, how to market them, and where to focus their efforts.
Big data analytics tools use sophisticated software and machine learning algorithms to analyze data in order to find correlations. The ability to collect and analyze unstructured data has also led to the development of new forms of analytics such as text analytics and image analytics. Text analytics extracts meaningful information from text documents, while image analytics extracts insights from images and videos. These new forms of analytics are proving to be very valuable for businesses in a wide range of industries.
The bottom line is that big data analytics is essential for businesses today. By using the latest technologies to collect data from all sources, businesses can gain a competitive edge.
Artificial intelligence is a field of computer science that deals with the development of intelligent computer systems. These systems are able to learn and evolve on their own, making them capable of extracting insights from data on their own. Artificial intelligence can be used to analyze both structured and unstructured data.
Structured data is one of the most important sources of information for artificial intelligence (AI) systems. This data is typically organized in a specific format, which makes it easy for computers to process.
One of the benefits of using structured data is that it can be used to train AI models. Machine learning algorithms rely on large amounts of data in order to learn how to identify trends. By using structured data, businesses can reduce the amount of time and effort needed to prepare this data for training.
Another advantage of using structured data is that it can be used to improve the accuracy of AI models. When data is structured correctly, it becomes easier for computers to identify patterns. This can lead to more accurate predictions and recommendations.
When working with unstructured data, one of the main advantages of using artificial intelligence for data analysis is that it can automatically identify tendencies that would otherwise be challenging to find. Additionally, artificial intelligence can be used to predict future trends based on past behavior. This makes it a valuable tool for businesses that want to make sense of unstructured data.
By using big data analytics and artificial intelligence, businesses can turn raw data into valuable insights that can help them make better decisions and obtain a competitive edge.
Structured data is data that is easy to access and organize, such as the data found in databases. Unstructured data, on the other hand, is data that is not easily organized and is often hidden within text or images. Collecting data from both sources is important for businesses because it allows them to gain insights that were once unavailable. By using the latest technologies, businesses can now collect data from multiple sources in order to gain a competitive edge.
Contact one of our DATA BOSSES to learn more about the advantages of collecting data from multiple sources and how to use that data to your advantage.