With increased interest in big data, more and more organizations are adopting multicloud frameworks, which allows them to create, store and manage enormous volumes of data across different cloud platforms. Despite the prevalance of this trend, the hard truth is that businesses often have little or no visibility into much of the data that resides in these clouds.
With the construction of multicloud frameworks and the spread of data across various containers and components, it becomes increasingly difficult for organizations to identify and access all data. Unsurprisingly, dark data is a barrier as it crops up significant security and compliance risks. Unfortunately, data systems cannot be identified easily. It’s difficult to gauge what data is held in cloud components and how this data is protected.
Due to this complexity, organizations must go beyond basic inventory tools that provide limited insights into cloud data. It’s essential to adapt to a more sophisticated approach that can provide deep insights into an organization’s asset footprint and help reduce risks, too.
Risky Business
What lies beneath the dark data problem is a simple fact: Multicloud environments expand data storage and security concerns exponentially. Organizations have traditionally relied on native tools to manage cloud inventory. Still, these valuable tools are not able to detect dark data as their main focus is to deliver native asset discovery. Furthermore, these content security policy tools are unable to provide multicloud visibility.
This issue crops up when organizations move data to their preferred cloud service providers, being negligent about the technical changes to the underlying data systems. The process is quick and easy because it does not require changes in the underlying infrastructure or tables, schemas and other elements, but security and data compliance risks aggravate in the end.
This is a grave problem as the data undergoes stupendous growth and multicloud environments see a steep rise. It becomes difficult to gain visibility into an organization’s data footprint to comply with security, privacy and data compliance laws, regulations and requirements.
Issues with dark data
In order to be useful, dark data needs to be brought to the fore with the help of dark data analytics. If one is not actively analyzing or using it, it becomes useless clutter and will take up a great chunk of the space. With space for data growing, storage costs and security risks, too, see a spike.
Dark data may contain some sensitive information that is more prone to data breaches than the data that is being more closely monitored. One may not even come to know about a data breach at all. It may also be possible that some dark data does not have the same untapped potential as others. Nevertheless, organizations still need to protect, manage, and organize that information. To do this, they can put in place a process that gets rid of old unneeded data.
Types of dark data
Specific examples of dark data are far too many and largely depends on company to company and industry to industry. But below mentioned are some that could fall into this category if they are outdated, unstructured, or unutilized:
- Raw Survey data
- Email correspondences
- Financial statements
- Log files
- Geolocation data
- Previous employee data
- Old documents
- Presentations and notes
Benefits of assessing dark data
According to a report by the International Data Corporation, organizations that can collect and interpret all of the relevant data and deliver actionable information can achieve productivity gains of up to USD 430 billion by 2020. They can also take hidden information and turn it into powerful insights, leading to new opportunities, reduced risk, and increased return-on-investment (ROI).
On the other hand, if one does not know the best way to apply dark data, it can actually cost businesses. With more and more organizations taking advantage of their previously untapped data, those who are not may encounter lost revenue opportunities, lower efficiency, quality issues, and diminished productivity. Whether you want to maximize gains or minimize losses, this may be the perfect time to shed more light into the corners of your data and see what there is to be seen.
Make dark data bright
While situations vary from one organization to the other, based on data architecture, below are some actionable tips that can help turn dark data into rich insights and opportunities:
- Add more team members
Dark data will be of no use if one is not able to translate it. This is why it is essential to have a solid analytics team that has sufficient organizational, business, and technical knowledge.
- Ask questions
Discover what information can help you make better business decisions beforehand and allow that to make you analysis and decisions.
- Keep goals in mind
Analytics is business-driven, so examine what value your efforts must deliver. If some employees do not want to deal with dark data, it helps to align your efforts back to at least one of the company’s goals or objectives.
- Audit the database
Data sources and data collection tools must be reviewed and strategy must be built accordingly. It’s essential to slow the build-up of new dark data by quickly identifying what is valuable and what isn’t.
- Invest in the right tools
Video and sound analytics, computer vision, machine learning, and advanced pattern recognition are tools and techniques that may help illuminate dark data.
So, it is all about addressing the dark data challenges most seamlessly and holistically. But, there can be someone in your reach or partner company with less or zero visibility towards dark data. In that case –
Connecting all that data lets users visualize all their data sources in a single dashboard. It can help users identify patterns and extract valuable information from their data.
Instead of searching for a needle in a haystack, use charts and graphs to highlight essential information on your dashboard.
You can always refer to experts driven whitepapers to harness the power of data.
