February 12, 2023
The fourth part of our "Exploring Dark Data" series focuses on practical tips to the problem of having too much data and not enough insights. We provide actionable tips and advice to help you tackle this challenge and transform your data into a valuable asset.
Data surplus and shortage of insights have become a common challenge for enterprises in analytics. Data warehouses, data lakes, and other data repositories are increasingly filled with large amounts of data, leading to growth in volume, variety, and velocity of data. Despite tapping into new data sources such as social media and IoT, IT teams still face challenges in generating valuable insights. This is due to various reasons, such as manual coding for ETL processes, inadequate understanding of data usage, and thus leading to dark data.
According to Forrester, the average enterprise only analyzes 37% of its structured data and 22% of its semi-structured and unstructured data, leading to an accumulation of dark data. Dark data incurs costs to capture and manage, often requiring capacity upgrades, but it also holds potential latent insights that enterprises are missing out on. By identifying and effectively utilizing dark data, enterprises can unlock valuable insights, improve their decision-making, and increase the efficiency of their operations.
Enterprises today aim to increase data analysis and lower costs associated with unanalyzed dark data. Here are some starting points for achieving both.
We hope you are not surprised by this. The fact of the matter is that organizations simply do not analyze enough of their data.
The value of data points lies in their correlation with other data. Decision-makers can benefit from analyzing more than just revenue by country. For instance, they can review revenue by customer, sales rep within countries, product mix, and overall averages for each. Structured data is easily correlated for insights and is stored in data warehouses. On the other hand, unstructured and semi-structured data like customer-service interactions are often dark data. But by applying new semantic analysis and correlating the data with external social media trends, BI or data science teams can extract new insights and make better decisions about customer-service policies and upselling opportunities.
It is essential to re-evaluate data storage architectures to ensure that valuable resources are being used in the most efficient way possible. The choice of data storage system must balance the need to meet regulatory requirements with the need to minimize costs. By storing non-critical data in Hadoop or the cloud, enterprises can free up valuable storage space in premium data warehouses for more valuable data, allowing for better use of resources and cost savings.
Automation is crucial for reducing dark data in enterprises. Manual ETL processes can be time-consuming and prone to errors. Automating these processes with software leads to improved data processing accuracy and efficiency. It also reduces the effort required for ETL tasks and speeds up the delivery of analytics-ready data to the business.
Automation helps eliminate errors in data processing, as manual tasks can result in inaccuracies and inconsistencies in the data. High-quality data is essential for effective business decision-making, and automation ensures that the data delivered to the business meets this standard.
With automation, IT organizations can focus on more strategic initiatives. Instead of spending time on manual data processing, they can concentrate on data analysis, visualization, and insights discovery, leading to more innovative and effective use of data, ultimately driving business success.
Data usage tracking is a key aspect of dark data cost reduction in enterprises. By locating unused databases and tables in their data warehouses, companies can release valuable resources, enhance query performance, and delay costly hardware upgrades. They can then concentrate on data that is actually being utilized, making their analytics initiatives more economically sound.
Knowing how data is utilized within an organization is crucial for informed data management decisions. It helps prioritize efforts and ensures resources are directed toward the most valuable data. Tracking usage also enables identifying redundant or outdated data that can be safely discarded, freeing up storage and reducing the cost of data management.
We've come to the end of our four-part series exploring the world of Dark Data. With the four tips shared in this article, you have taken a step forward in managing your dark data. But don't stop there. To continue learning and refining your approach to Dark Data, look out for our upcoming eBook that covers managing Dark Data from strategy to execution at small scale, quick wins, and big scale initiatives.
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