Click to learn more about author Nahla Davies.
In an increasingly tech-reliant world, data informs and powers much of our day-to-day lives. Data can be used to enhance AI capabilities, create personalized experiences, or be applied in medical research to help save lives. However, the biggest question remains: What is the best method to store, organize, and use the vast amounts of data at our disposal?
Enter unstructured data management. Organizations are increasingly looking to unstructured data for analytic, regulatory, and decision-making processes. From business intelligence to marketing campaigns, it’s not uncommon for unstructured data analysis to drive human decision-making. So, let’s take a look at unstructured data management to answer the question “Is unstructured data management the future of data analytics?”
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What Exactly Is Unstructured Data?
Unstructured data refers to information gathered that is not organized in a structured database format. Some datasets aren’t deemed important until their significance is realized in the future, hence the need to retain the information they contain through the storage of unstructured data.
Unstructured data has been predicted to increase by 175 billion zettabytes by 2025, so the methods by which we store such data is more important than ever. While an increasing reliance on data-driven processes provides many business benefits, it also poses a fair number of challenges as well.
Software developers and other professionals with coding skills are in high demand because of the personalized software applications that are needed for Data Management. According to one survey, approximately 90% of graduates of coding development courses found a career within their fields less than six months after graduating, a percentage that is much higher than many other college degrees can boast. It’s clear that Data Science and its many applications is a burgeoning industry.
For example, it’s doubtful that Mark Zuckerberg realized the power of his website Facebook when it was originally created as an online social platform in 2004. However, the site later proved to be a treasure trove of information on its users. This data would later become the driving engine behind many high-performing marketing and ad campaigns – Facebook’s primary source of revenue.
Websites, software, IoT devices, and more continually track information and behaviors about their users that may be pivotal to new developments in the future. Examples of unstructured data include analytics from machine learning algorithms; sensor data, ticker data, or other functional data from IoT devices; and rich media, such as geospatial data, weather data, or surveillance data.
How Can Data Management Handle Unstructured Data?
In a world steeped in data, there is a need for Data Management solutions that empower organizations to access the data they need when they need it. As much as 90% of the world’s data is unstructured, waiting to be utilized. With endless cloud storage options now available, the problem of too much data has seemingly been solved – but not quite. While the ease with which data can be stored is a huge improvement, these massive amounts of data are useless without proper organization and analysis.
Furthermore, storing and managing data can be costly, with expenses piling up as more data is generated. When you add the fact that most organizations keep duplicates of their data for safety purposes, the problem of too much data becomes even larger.
New Data Management solutions are needed for organizations that generate a lot of data, and many organizations require software that is custom-built for their needs. As many companies don’t have a Data Management expert and software designer on their payroll, freelance developers and coders have stepped up to fill in the gap.
Organizations commonly employ freelancers and consultants to build Data Management solutions that fit with their operational needs. Companies can expect to pay an experienced freelance developer at least $60 per hour in the United States, although that rate varies depending on experience.
In many scenarios, organizations are charged every time they view the data they own, on top of the expenses incurred for storing and managing it. To solve this problem, many companies rely on a separate storage plan for the data they own that is not currently needed, known as “cold” data. Cold data is rarely backed up to the same standards as hot data, which is why the two are managed differently.
For example, an effective Data Management system can help pharmaceutical companies that are studying heart disease by isolating research data that is directly relevant to the medicine or treatment they are studying, while filtering out the data that is not. The other data – the “cold” data – can then be stored separately, where it can wait to be retrieved in the future should the research become needed later on.
How to Manage and Use Unstructured Data
Data is a valuable corporate asset, and it’s important for organizations to be strategic about how they manage and store their unstructured data. In fact, many organizations may find in the future that they are sitting on a veritable gold mine of data.
Companies require historical data to create new products and services, or to perfect the ones they already have. This is clearly seen in critical industries like manufacturing, a sector that is vital to our economic growth and everyday life.
Another example of a data-driven industry can be found in the marketing and advertising world. Through data analytics, marketing campaigns can be enhanced by highly targeting qualified prospects based on their online behavior.
In the past, archiving practices for data were laborious, time-consuming, menial, and full of mistakes. Like the Dewey Decimal System of libraries, data in the past were commonly stored through asset tags, and these were cumbersome to locate. Now, with an enhanced ability to store and organize data, information can be pulled quickly through the cloud.
Tools and practices must evolve to help IT departments better leverage the data at their disposal. The responsibility of Data Management systems is even greater when one considers the increasing cybersecurity risks involved in large amounts of centrally managed data. For this reason, companies must pay increasing attention to compliance with industry regulations surrounding data collection and storage practices.
The Future of Data Management
It’s clear that data management will be an important focus for the future, and we can expect that a variety of startups will be created hoping to solve the data problem. Data analytics companies such as Apache Spark, Databricks, and SnowFlake enjoy generous investor funding because investors know this industry will be highly valuable in the future.
Organizations should remain focused on the amount of data they generate about their products, services, and customers and consider the best ways to manage and store these datasets. With data driving countless decisions and processes around us, it’s important to stay on top of these valuable resources.