Data is always an important aspect that contributes to the success of every business sector and its future insight. In the realm of dark data management, along with structured data, a huge amount of unstructured data also occupies the storage of every organization; starting from raw survey data to previous employees’ details or customer information, and so on.
Most companies store large amounts of unstructured or semi-structured data in log files or data archives for future use. This unprocessed or unanalyzed data, known as ‘dark data,’ remains in data repositories, waiting to be explored.
Is Dark Data useful?
The proper methodology and analysis tools allow companies to utilize valuable dark data. When harnessed effectively, dark data generates business insights. There are several ways to utilize dark data.
Generally, organizations analyze the dark data to develop a greater context. Analyzing dark data helps reveal trends, patterns, and relationships that normal business intelligence and analytics activities often miss. Dark data analysis could give your better understanding of the customers’ requirements and helps in reframe your business insights.
Benefits of utilizing dark data
Analyzing ark data can be helpful to the organizations to discover deeper business trends, understand customer expectations, and make strategic decisions for betterment. Apart from this, there are also other benefits of dark data utilization, such as:
Organizations can address storage space issues and save costs by utilizing the unstructured data that accumulates over time. When they make use of this large, unutilized data, they recover storage space and achieve financial savings.
Systematically storing and using collected data allows organizations to automatically strengthen their security procedures and resolve hacker issues. With proper utilization of data, the organizations will be able to safeguard their digital assets against data theft.
How to Deal with Dark Data?
There are steps designed to deal with dark data.
Set your goals for data sorting – To deal with a large quantum of unstructured data, first you need to think about what kinds of problems need to be fixed in your business operations or contact center environment. Without proper goal setting, it is hard to deal with so much data to sort through. So, start analyzing with those goals in mind.
Data Discovery – Then the useful chunk of data needs to be identified. Data discovery analyzes unstructured data to provide complete visibility of an organization’s data landscape. This process helps identify useful data using analytics tools, pattern algorithms, or queries.
Classification of data – The next step in dark data management is classifying enterprise data using a categorization engine. This process helps organizations determine the value, relevance, security, and risk of data, and identifies where it can be useful for business purposes.
Data management – With properly defined Policy-based data management procedure organizations can take a decision on classified data, whether to follow data cleansing or archive to lowest value storage platform.
Following these data management steps, organizations can categorize data as hot – critical data and cold data. Organizations can move cold data from high-cost storage platforms to low-cost storage platforms. Data management challenges organizations as they need many workers to segregate, analyze, create, manage, and dispose of data. There are several managed IT services to help you manage dark data.
The Role of Advanced Tools
Manual Assessment of dark data in any organization is a tedious task. Once you’ve found the dark data that you want to analyze and utilize, make sure that your team has the appropriate tools that they need to transform the retrieved data into actionable insights. Therefore, you should go for the right dark data analytics tools and methodology designed to shed light on it. There are multiple things like RPA tools, artificial intelligence, and machine learning that are helpful in data analysis. Proper analysis of dark data can offer tremendous opportunities for an organization in economics, compliance, and productivity.
To ensure organizations use sorted data effectively, they need to invest in the right tools. Video and sound analytics, computer vision, machine learning, and advanced pattern recognition are tools and techniques that you need to choose depending on your data type that may help illuminate dark data. Adaption of right kind of tool will provide the ability to discover, analyze, and visualize data from multiple platforms and locations via a single interface. This process increases data visibility and reduces the tendency to store the same data multiple times.
Advanced data visualization technologies can connect all available data sources and present them on a single dashboard. So, the users can have real-time visibility to the compiled data. Therefore, users can have a better understand of the available data and use their dark data to uncover the information they need for business insights.
A number of enterprises are working to create better artificial intelligence (AI) tools that can provide organizations with even more data than they currently have. It is crucial to have efficient data collection and analysis strategies in place. Betterment of dark data analysis may be the key to business efficiency, improved customer relationships, and higher profits.
Wrapping Up
Dark data has grabbed the attention of organizations in recent times. Until now, data analytics was restricted mainly to structured data to come to any conclusion. However, with the advancement of technologies, the situation is changing. With the recognition of dark data’s potentiality, the organizations have become concerned about dark data management and analytics for better business insights. In fact, businesses are gearing up to utilize the dark data and take its take advantage to drive innovation and enhance competitiveness in the future data-driven decade. Managed IT services are a way to gain access to advanced technology that can help in dark data management and utilization.
