Data security experts predict that by 2025, the world will store 200 zettabytes of data. This estimate could increase significantly based on the data explosion driven by changes in user behavior during the COVID-19 crisis and businesses’ rush to jump-start digital transformation initiatives.
Today’s enterprises rely on this data to derive actionable insights that enable smarter, more targeted business decisions. But there are a few factors, including the high volume, that make it difficult to extract reliable, usable information from the data.
Common Data Analytics Challenges
Vast Quantities of Data
As mentioned above, users are generating historically high volumes of data each day. For organizations that need to use this data for business processes, a traditional approach to data management and analysis is inefficient at best.
Disparate Data Sources
Your organization is constantly generating and consuming data—whether it’s from an email opt-in form or a wind turbine speed sensor. Although much of the data you collect is valuable, some of it isn’t, and all of it needs to be analyzed to determine which camp it belongs in.
Unstructured Data
Unstructured data is the most difficult data to extract information from, but the insights it provides can be extremely valuable if you have the right analytics capabilities.
Data Automation Is Essential for Today’s Complex Data
The volume and complexity of today’s data make manual, human-powered approaches to data analysis impossible. The human brain simply isn’t capable of recognizing patterns, trends, and anomalies in data as thoroughly and efficiently as technology can.
Data automation takes traditional analytics capabilities and integrates computer systems, analytics software, and artificial intelligence to analyze huge datasets from disparate sources with minimal human intervention.
The insights made available through data automation enable several key businesses improvements, including:
- Driving business growth
- Identifying critical business insights and trends
- Reducing costs by identifying unknown bottlenecks, inadvertent errors, and constraints
- Improving decision-making
- Increasing productivity
Data Automation Improves Insights and Helps You Understand Data in a New Way
Traditional data analytics tools are limited in the depth and breadth of the information they can convey. Data automation allows users to explore their data at every level, from every angle, in near-real time.
Here are four ways data automation can be used to add maximum value to your data analytics:
1. Identify interconnections.
Data automation solutions that use multidimensional visualization instead of the traditional two-dimensional make it easier to understand interconnections between complex data. For example, adding a third plane to the visualization allows the user to compare the impact of two variables on the target data.
2. Accelerate analytics.
Data automation speeds up the analysis process by taking time-consuming, complex tasks out of the hands of humans and letting technology do the heavy lifting. This not only speeds up the data analysis but also improves the results because there is less chance of human error.
3. Identify key insights, outliers, and anomalies.
Traditional data analysis tools take a flat view of the data, which makes it difficult to see the complete picture. Data automation tools can view and display data in a variety of visualizations to provide a multidimensional view that quickly exposes data relationships, patterns, and unexpected results.
4. Predict the future.
Data automation can pull in data from a variety of sources and find insights and patterns from all forms of data. Leveraging predictive modeling, this data can be used to proactively plan for future events.
Data Automation in Action
In the wild, data automation can be applied to a wide variety of use cases. For example, an aircraft fleet maintenance team can analyze data from maintenance and repair records, sensors, and flight logs to determine which components are most likely to break down and when.
The system can then be automated to trigger routine maintenance to prevent the at-risk components from failing, as well as syncing with the inventory system to ensure the failure-prone parts are always available.
COVID-19 isn’t the only driver behind the current data explosion, but because of many changes brought about by the pandemic, the trend is unlikely to slow down anytime soon.
Enterprises depend on data for most business processes and initiatives, so it is crucial to find ways to extract the most value from the flood of data in a way that is both fast and cost-effective.
Data automation helps businesses streamline data analysis processes and generate robust analytics that provide a much more complete and in-depth view of the information hiding in today’s complex datasets.
