Dark data is defined as information that organizations collect but don’t regularly use. It can be internal, like employee performance reviews, or external, like customer purchase history.
Despite its name, dark data is not necessarily bad. In fact, it can be very valuable to organizations if used correctly. Dark data can be used to improve business intelligence (BI) efforts by providing insights that would otherwise be hidden.
For example, dark data can be used to understand customer behavior, identify trends, and make predictions. It can also be used to improve marketing campaigns and target customers more effectively.
So how can you use dark data with your BI tool? In this article, we are going to break down the steps you need to take in order to collect and analyze dark data using your BI tool.
1. Use data that you already have but don’t regularly use
This could be internal data like employee performance reviews or external data like customer purchase history.
2. Collect new data that you don’t currently have but may be helpful in the future
This could be data from social media, sensors, or IoT devices.
No matter the method, collecting data effectively requires a plan. You need to know what data you want to collect, how you will collect it, and where you will store it.
Storing data effectively
Once you have collected your data, you need to store it effectively. This can be done in a number of ways:
1. Use a data warehouse
A data warehouse is a database designed to store and analyze large amounts of data. It is typically optimized for performance and scalability. Some BI tools offer a data warehouse as a feature of the BI system.
2. Use a NoSQL database
A NoSQL database is a non-relational database that can be used to store data of any type. It is typically more flexible and scalable than a relational database.
3. Utilize cloud storage
Cloud storage is a service that allows you to store data on remote servers. This can be done through services offered by cloud providers.
Once you have decided how to store your data, you need to think about how you will access it. This is where your BI tool comes in.
Accessing data with your BI tool
Business intelligence tools are software applications that allow you to access, visualize, and analyze data. They typically provide a range of features, such as dashboards, reporting, and data mining.
Most BI tools can connect to a variety of data sources, including data warehouses, NoSQL databases, and cloud storage. This allows you to access all of your dark data in one place.
Once you have connected your BI tool to your data sources, you can start visualizing and analyzing your data. This will allow you to uncover hidden insights that can improve your business.
For example, you could use your BI tool to:
1. Understand customer behavior
By analyzing customer purchase history, you can understand what they like and don’t like. This can help you improve your marketing campaigns and target customers more effectively.
2. Identify trends
By analyzing data over time, you can identify trends that would otherwise be hidden. This can be used to make predictions about the future and plan for changes accordingly.
3. Improve decision-making
By understanding the data, you can make better decisions that improve your business. For example, you could use dark data to choose a more profitable product to sell or identify which customers are most likely to churn.
4. Automate processes
By understanding the data, you can automate current manual processes. For example, you could use dark data to automatically generate marketing reports or identify which products need to be restocked.
5. Reduce costs
Understanding the data can reduce costs and improve efficiency. For example, you could use dark data to identify which employees are underperforming which products are selling at a loss.
Creating a dark data analytics strategy that works
To make the most of dark data, you need to have a clear strategy. This should be designed to help you achieve your specific business goals.
Your dark data strategy should consider the following:
1. What business goals do you want to achieve?
First, you need to identify what business goals you want to achieve with dark data. For example, do you want to improve customer retention or increase sales? How will you measure success?
2. What data do you need to achieve these goals?
Next, you need to identify what data you need to achieve your goals. For example, if you want to improve customer retention, you will need data on customer behavior. If you want to increase sales, you will need data on product popularity.
3. How will you collect the data?
Once you know what data you need, you need to think about how you’re going to collect it. There are many ways to collect dark data, including web scraping, customer surveys, and social media monitoring.
4. How will you access the data?
After you have collected the data, you need to think about how you will access it. This is where your BI tool comes in. Most BI tools can connect to multiple data sources.
5. How will you use your BI tool?
Consider how you will use your BI tool to achieve your business goals. Your BI tool has the capabilities to help you understand, visualize, and analyze your data via dashboards, reports, and data mining.
6. What are your IT requirements?
Finally, you need to think about your IT requirements. For example, do you need to purchase a new BI tool or upgrade your existing one? How will you deploy and manage your dark data strategy?
The unexpected challenges of dark data
While dark data can be valuable, it also comes with a number of challenges. Here are some of the most common challenges businesses face when dealing with dark data:
1. Lack of governance
Because dark data is often unstructured and unruly, it can be difficult to govern. This can lead to chaos and confusion, especially if multiple people are trying to access and use the data.
You need to establish clear governance policies and procedures to avoid these problems. For example, you need to decide who can access the data and how it can be used. You also need to establish a process for managing changes to the data.
2. Lack of security
Dark data can also pose security risks. This is because it often contains sensitive information, such as customer data or financial information. If this data falls into the wrong hands, it could be used for malicious purposes.
To protect your data, you need to implement security measures such as encryption and role-based access control. You also need to create a disaster recovery plan in case of a security breach.
3. Lack of storage
Dark data can also take up a lot of storage space. This is because it is often unstructured and generated in large quantities. If you don’t have enough storage space, you may not be able to keep all of your dark data.
To solve this problem, you need to think about how you’re going to store your dark data. For example, you may need to purchase additional storage or implement a cloud-based solution. Alternatively, you may need to identify dark data that isn’t useful and get rid of it.
4. Lack of processing power
Dark data can also be difficult to process. This is because it is often unstructured and generated in large quantities. If you don’t have enough processing power, you may not be able to analyze all of your dark data.
To solve this problem, you need to have a well-designed plan. For example, you may need to purchase additional processing resources or implement a cloud-based solution.
The bottom line
If you are going to use dark data, you need to be aware of the challenges it poses. By understanding these challenges, you can develop strategies to overcome them. This will help you get the most out of your dark data and use it to achieve your business goals.
If you are ready to discover new insights, consider using dark data. By understanding these challenges, you can develop strategies to overcome them. This will help you get the most out of your dark data and use it to achieve your business goals.
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