When it comes to unleashing the power of data, you need to master the art of data discovery. This process has been gaining popularity as more and more businesses are relying on it. Data discovery has helped organizations and businesses generate more leads by predicting market trends and providing useful insights.
If you are looking forward to understanding about data discovery, you have landed on the right page. Here, we have comprehensively discussed data discovery and various other aspects associated with it.
So, let’s dive in!
A Simple Explanation of Data Discovery
To put it simply, data discovery refers to a process involving data collection. The data is gathered via multiple data sources. Apart from being collected, the data is also classified, cataloged, evaluated, and analyzed.
Data discovery allows businesses and organizations to use high-quality data to find prospects and leads. Analyzing the data helps uncover valuable insights, which are essential for successful lead generation strategies.
How Data Discovery Works?
The process of data discovery might involve different types of steps depending on the context and industry. First, a business's data requirements are identified. The data is then discovered using a variety of channels or data sources. Next, the raw data is cleaned from inconsistent or incorrect information.
Once the data is prepared the next step is analysis. Due to these steps, the data is made extremely valuable. Since data discovery is important for almost every business, it’s imperative to improve its authenticity and offer relevant data insights.
Major Steps of the Data Discovery Process
Data discovery is similar to data aggregation, as the later is an organization-based analysis process. Data discovery is a continuous process that involves a wide range of steps. However, three of the most prominent steps associated with data discovery are:
- Preparation
- Visualization
- Data Analysis
1. Preparation
This is the most essential step that ensures the quality of the data discovered. During the preparation phase, the data is rearranged. This makes collected data highly suitable for the next two processes, visualization and analysis.
Without this step, the data would be so complex that it would fail to unfold the key business insight. Data preparation identifies the correct information and removes unwanted data. This is referred to as the data cleaning.
At present, numerous types of tools and software make the data preparation process a lot easier. You can also take advantage of some other classification and discovery tools.
By using these tools, you can remove the outliers and identify a unified data format for data discovery. This helps to locate null values and set a particular standard for exceptional data quality.
2. Visualization
Once the data is cleaned and all its elements have been formatted for better consistency and readability, the next step is visualization. Visual data discovery, also known as data mapping, prepares the data for visual formats, including graphs, charts, and maps.
As a result, marketing experts can obtain broader insights through the visualized data. Taking advantage of these visual guides takes less than anticipated time to identify primary market trends. Hence, data visualization tends to be the most essential step of any data discovery process.
3. Analysis
After data visualization, it’s time to analyze the data. This process involves analyzing the data to make it more concise and organized. In other words, this process converts the data into a readable format. For business users, data analysis serves as a way to unleash the true potential of collected data.
In some cases, the analysis process creates descriptions to summarize the data sets. These descriptions aren’t complete sentences or phrases. On the other hand, the analysis offers descriptive statistics, making data easy to read.
How is Data Discovery Used?
The most prominent use cases for data discovery include data completeness, social media analysis, accessibility, business relationship insights, compliance, and lead generation.
Here are a few of the most notable use cases for data discovery:
Lead Generation
Through visual mapping of the collected data, it is possible to find valuable insights. Usually, it won’t be easy for sales and marketing teams to identify such insights. That’s why most businesses rely on data discovery for lead generation.
You can also combine data sourced from different platforms and create required insights. With this process, you can get the most out of your lead generation campaigns.
Investment Signals
Data discovery is quite essential for businesses and investors. This is so, as it allows them to look for investment opportunities. For this purpose, businesses and companies use the data discovery process to find new and promising startups.
Investors can rely on both the manual and auto-discovery processes. They can also use advanced solutions to generate investment signals. For instance, firmographic data serves as a powerful source of information, allowing investors to find new emerging companies.
With this process, you can search for a wide range of data points, including company name, employee count, location, revenue, and industry. Data discovery also helps marketers filter unwanted companies' data.
This helps identify companies that fall within the specific parameters and offer new investment opportunities. With data discovery, you can relieve yourself from the hassle of manually searching for companies that fall within your criteria.
Data Completeness
Another benefit of data discovery is that it is highly suitable for investors. Data completeness is achieved at the preparation and analysis stages of data discovery. It ensures that the data is free from inaccurate information and missing values.
Data completeness is the key to overall data quality. If the data is incomplete, it could lead to inaccurate results. As a result, you could end up making flawed decisions.
Compliance
Since the data discovery process involves broad analysis, businesses use this process to ensure their data is GDPR compliant.
Data Discovery Methods
The data discovery process has evolved significantly in the previous few years. This was made possible by the collaboration between data discovery and artificial intelligence. Besides, automated tools have also played a role.
Here are two of the most popular methods used for data discovery processes.
Manual Data Discovery
Before the introduction of automated data discovery, data governance or collection was done manually. Back then, data specialists had no choice but to spend hours discovering the data manually, which involved processes like preparing, mapping, and analyzing the data.
However, things are different today, as artificial intelligence and automated data discovery tools are working in close collaboration to speed up things. Talking about manual data discovery, this process involves monitoring data lineage and metadata to unveil trends.
This requires extensive knowledge about data linage and categorization to manually organize and map the data. Apart from being a time-consuming process, manual data discovery wasn’t that efficient and accurate.
Automated Data Discovery
Technological advancements have led to the rise of automated data discovery. AI has played a key role in making this process more efficient and impressive. With an intelligent data discovery process, businesses can get the most out of their marketing strategies.
Automated data discovery relies on a wide range of aspects including data mapping specifications, data matrices, and data flow diagrams. Now, AI is capable of mapping and visualizing data using machine learning algorithms. This wasn’t even thinkable a few years ago.
During this process, the AI analyzes the relationships between data and detects patterns. This helps in seeking valuable data-driven insights, which in turn boosts business growth. With this advancement, it’s easy to enhance the readability of the discovered content.
The data thus collected is business-oriented and works well for the sales and marketing teams. Through automation, sales teams, business users, and acquisition experts can access relevant data insights.
Is there any Difference between Data Classification and Data Discovery
Classification is a relatively more complex process, especially when compared with data discovery. During data classification, data experts rely on some additional steps that are not used during the data discovery process.
During this procedure, classification labels are assigned to data by using predetermined rules and keywords. In other words, data classification is a process of tagging data at a large scale. As a result, experts can make decisions on how to classify their data.
With predetermined tagging rules and keywords, you can classify data across different platforms. Using this process, companies and businesses can improve their data visibility and narrow scope.
Below are some of the most noteworthy use cases of data classification:
Data Visibility
Through data classification, you can realize the true potential of your datasets. With this process, it won’t take long to improve the data visibility. Companies can get better results for their lead generation and marketing campaigns. With data visibility, you can unlock the true potential of collected data points.
Security and Compliance
As mentioned earlier, compliance and security are of great importance for both businesses and data experts. Through data classification, you can bridge the compliance gaps by gathering data through regulated processes.
Since labeling and tagging are involved during the classification process, data scientists can use AI models to tag and remove non-compliant data. This could enhance the quality of data and improve the overall efficiency of your lead generation strategy.
Narrowing Scope
Data can be used in a variety of ways and it’s essential to narrow down its scope. This is an important component for unfolding the deeper meaning of the data collected through various sources.
Usually, data discovery offers a bigger picture of your data while classification serves as a way to limit the scope. This allows focusing on the most prominent aspects of your data. As a result, businesses can focus more on other important aspects like generating a promising marketing campaign.
What to Consider when Choosing the Right Data Discovery Tools?
Since data discovery involves multiple steps, there are different types of tasks associated with it. Also, you need to choose from a variety of approaches to process it. At present, a wide range of tools are used by data experts for data discovery.
Most of these tools are specifically designed to perform relevant tasks associated with data discovery. These tasks include gathering data from different sources, locating the right trends, and finding relevant patterns.
Listed below are a few of the helpful tips to consider when fiding the best data discovery tool.
Flexibility: This is the key to performing successful data discovery. Only settle for tools, which are capable of integrating with other data discovery tools and applications.
Ease of Use: The next tip is to look for a tool that is easy to use. For instance, look for a data discovery tool with a minimum learning curve. In other words, it should allow beginners or non-technical users to navigate through it effortlessly.
Scalability: This is another aspect to consider when it comes to the quality of data. Scalability and performance are two interlinked and crucial factors. As your reliance on data grows, you need a more powerful tool to discover more data.
Data Security: Look for tools with robust security features. This will help to protect sensitive data related to your business or company.
Data Discovery Best Practices
It’s imperative to implement the best and most useful data discovery practices. This enables your business or organization to get the full potential of collected data and leads to more insightful and effective decision-making.
Below are the three best practices to improve almost every data discovery process.
Focus on Data Quality: This is the area where completeness, relevancy, and accuracy play the key role. With high-quality data, it’s possible to find the most accurate and relevant data.
Automate the Process: Automated data discovery is beneficial in numerous ways. This renders the data discovery a quicker and more efficient process.
Enforce Data Governance: Governance is another best practice associated with discovering and analyzing data. You need to consider governance aspects like data ownership, defined policies, and usage rights.
Following these best practices not only protects your sensitive information, it will also lead to carving better data usage and compliance strategies.
Final Thoughts
Data discovery is referred to as a powerful data exploration, which lets you take advantage of all aspects of a dataset. With this, you can unlock the potential of your data and offer you with advanced analytics. Hence, data discovery is important for data experts, investors, and businesses alike.
