Data are numbers and a record of transactions performed until analyzed to extract information from that raw data. To derive the greatest value from data analytics, organizations need to identify their true "North Star." It is a process that articulates the company's goals and assists businesses and other entities in determining the required direction and information necessary to achieve data discovery success. Once that is done, IT professionals can use the applicable data to enhance programs and products and guide future actions.
The importance of quality data
Data discovery involves detecting patterns and trends using advanced analytics or by visually navigating through the raw numbers. It's a task humans or, in some cases, artificial intelligence perform using algorithms programmed to mine data. In the data discovery process, it's important to ask two key questions: What information needs to be gleaned from this data? Which programs or algorithms will it help shape as the company strives to reach its ultimate objective or endpoint?
Data discovery only works at its optimum level when the raw information for developing a software program is fully vetted and cleaned before it is loaded. Creating available, easily understood, good data is essential for organizational productivity. It also frees up employees in the IT and engineering departments to work on new projects or more complex issues.
The following three steps are key to optimal data discovery:
- Scan the data. Develop general classification and sorting guidelines (e.g., sort by male or female).
- Label the data. Make it easier for end users at a variety of skill levels to derive the needed information and identify data subsets where necessary.
- Add more visibility. Provide pathways to guide and help those analyzing the data find what they need to know more quickly. Lead those analyzing the data toward the most important information to monitor.
When data is classified, labeled and visible, it's easier for IT professionals to craft algorithms. Businesses that do not classify or label the raw data correctly will fall behind and be less productive than those utilizing data discovery to its fullest potential.
Unstructured and structured data
There are two types of data used in the discovery process. Unstructured data refers to reams of raw information where there is no established framework on how to use it. Patterns lack identification and the data needs classification and labeling. This data may appear in varying formats, such as videos, emails or text. Using the previously mentioned tools and technologies to build a data platform will structure that information, enabling proper classification, labeling and expedited analysis.
Structured data is just that -- data conforming to a well-defined structure, following a consistent order, so it can be accessed by someone at a computer terminal or by a computer program, such as an algorithm. A SQL is often employed to manage a database that holds the structured data.
The amount of money an organization can invest in the data discovery process makes a difference. It's no coincidence companies like Amazon, Google and Microsoft often have a leg up on smaller competitors after choosing to spend heavily on the front end before going to market with new products or services. At that level, "gut decisions" are not the norm. Rather, directives on how to move forward are based on advanced data analytics.
It's one reason smaller companies often merge, combining resources to stay competitive with the major players in their business sectors, unless they have a niche product. The ability to store data and process transactions in the cloud, rather than maintaining an expensive, on-premises server farm, helped level the playing field somewhat over the past decade. The caveat is there are cloud provider rental fees and limited vendor options to consider with cloud-based solutions.
Walk first, then run
In an IT-driven world, it's crucial to understand how important the data discovery function is before launching a new product or application. Utilizing data discovery to its maximum potential can uncover new opportunities for a business by spotting successful trends and patterns in the data that can be replicated elsewhere, perhaps for another product or service.
Discovery may also detect security issues and help develop data protection routines. That's why it's best to follow this three-point data discovery system:
- Identify needs.
- Combine and analyze relevant data sources.
- Record findings to help create routine functions.
Numbers don't lie
The adoption of cloud-based data and increased demand for data privacy and security have led to a global data discovery market size forecast of $14.4 billion by 2025, growing annually by almost 16 percent since 2020.
By one estimate, in 2021, the global data discovery market was worth $8.45 billion. The increasing need to mine both structured and unstructured data acquired by a wide variety of digital resources and to turn that information into tools even nontechnical staff can use to find patterns and outliers, creates a market for programs that can capture and interpret that data.
There is one caveat: Security risks, especially when third parties store an extensive data collection in the cloud, may hamper growth in the data discovery market. It also may create an opportunity for security tools that can shield sensitive information from bad actors. The data discovery market includes software and service providers, as well as businesses large and small, in a wide range of industry sectors that benefit from enhanced data analysis.
Complexity of data analytics
The data world is complex. Employing basic data discovery steps that benefit multiple levels of an organization make that world a little less mysterious. Data discovery can also scan key metrics, look for internal security violations or address IT compliance mandates. The right analytics team and adequate data storage capacity must be in place as well. Use data discovery to its full advantage by ensuring it answers the right questions. Recognizing the trends from collected, collated and distributed data is how an organization can plan for its future.
About the author
Koushik Nandiraju is an award-winning data engineer with extensive experience preparing data while developing, constructing, testing and maintaining complete data architecture. He holds a Master of Science in Applied Computer Science from Frostburg State University as well as multiple technology certifications. He has spent 10 years working in the pharmaceutical, automotive and laboratory industries.