Data is undoubtedly the most valuable resource that organizations have in their arsenal. By deploying solutions to analyze it and derive actionable insights, organizations are propelling themselves to unimaginable heights of success.
Technology is always growing and changing, and today, we have new architectures and techniques to collect, process, manage, and analyze the massive quantities of structured, semi-structured, and unstructured data that companies collect. This is referred to as big data.
First off, let’s define these terms.
- Big Data: Refers to the massive and complex data sets that are collected and transmitted to and from sources within an organization.
- Structured Data: Adheres to a pre-defined data model making it easy to search.
- Semi-Structured Data: Contains internal tags or markers which separate data elements but do not adhere to the tabular nature of structured data.
- Unstructured Data: Essentially, everything else. This can take on a variety of formats including text, image, sound, and video and may be generated by humans or machines. Unstructured data is difficult to search, process, and interpret.
To do this effectively, companies must understand the current and emerging big data and analytics (BDA) trends. They must spearhead the adoption of these trends to streamline operations, attract and retain more customers, and increase productivity and profitability.
These are the big data trends to plan for in 2022:
More Data Requires Advanced Processing
Organizations collect massive amounts of data from a variety of internal and external sources such as databases, cloud systems, smart devices, voice assistants, video streaming, and IoT. Much of this data is coming in unstructured or semi-structured, and according to IDC, 90% of unstructured data goes unprocessed. This represents a major opportunity for organizations.
With the amount of data generated expected to double every two years for the next decade, organizations will be forced to reexamine and redefine their data processing needs. Traditional data warehouses will still be used for structured data, but processing and analyzing the wealth of semi- and unstructured data requires something else. Technology is moving towards solutions that can collect, process, and store their own data before sending insights to centralized servers. This is called edge computing, and it will considerably reduce the cost of data computing and processing.
Growth of Data Lakes and Innovations in Cloud and Hybrid Platforms to Power Big Data Storage
Right now, organizations have turned to cloud and hybrid cloud servers to store and process the massive volumes, velocity, and variety of big data. Before, enterprises had to build, secure, and manage their own storage infrastructure.
Cloud computing made it easier for organizations to get unlimited storage without maintaining expensive and complex data centers. Data lakes are the new data architecture approach since they store structured and unstructured data in their native format. Businesses can then access and transform data according to requisite needs, instead of processing all data before storage, including data that won’t be used immediately.
Dramatic Increase in the Adoption of Advanced AI, ML, and Analytics Approaches
Traditional analytics approaches are inadequate for big data, especially for real time data analysis at scale. Through advanced analytics automation solutions that leverage machine learning and artificial intelligence, companies can now process petabytes of data within seconds and generate actionable insights.
AI and ML tools can be used to spot anomalies, trends, or patterns and make predictions based on past behavior and other variables. Reporting tools are now being replaced by these predictive systems, which offer greater visibility into business operations, customer needs, and customer behavior.
These innovations also extend to data visualization tools, which allow non-technical users to spot trends and patterns, make data driven decisions and improve outcomes.
Enter the Age of DataOps and Data Stewardship
We will be witness to the continuous evolution of the various aspects of data storage, processing, and analytics for years yet. DataOps is an emerging field that is concerned with the development of agile and iterative approaches to the management of the data lifecycle through an organization. DataOps allows organizations to plan from a broad perspective, rather than working with data as though its processes are separate from one another, from generation to deletion or archiving.
Similarly, organizations must plan for data stewardship and address issues related to privacy, security, and data governance. New, more stringent regulations make companies liable for breaches/loss of personal information in their charge, and so they must work harder to ensure security and privacy. They are leveraging new tools to ensure that data remains secure, whether at rest or in motion.
Next Steps: Put Your Organization at the Center of the Data Analytics Revolution
These big data and analytics trends are definitely worth looking forward to in 2022 and even beyond. As an organization, it isn’t enough to just know about these changes; you must be proactive in staying ahead of the curve, or else you risk becoming irrelevant to your customers.
While exciting in its potential and prospects, big data and analytics management is daunting for most businesses. Do you feel overwhelmed by the implications of these changes in your field? You are not alone, and we are here to help.
Reach out to us today, and let’s think about what the future looks like for your business.