Big Data Automation: The Next Frontier for Innovation
Automation enables you to manage big data and innovate at the pace of business.
Big data is big business. The data collected across your organization’s business units, applications, and external sources is growing exponentially. It’s pouring out everywhere: from digital customer transactions and Internet of Things (IoT) sensors, to social interactions and support services. And it won’t stop: more and more applications, for example, are hosted as a service in the cloud and within big data instances like Hadoop.
Reliable, timely big data is crucial for faster, more informed decision making. It enables healthcare scientists to examine vast volumes of genome sequencing data to crack some of society’s most difficult diseases. It allows retailers to predict which clothing range will fly off the shelf next season. It means phone companies can examine millions of call and data logs to offer customers ‘in the moment’ offers. And it enables police forces to follow patterns of citizen behavior to plot the next crime hotspot.
But here’s the problem: Big data is a complex beast. You need to efficiently capture and store the data as it emerges in any volume, velocity or variety. You have to distribute it to hundreds of downstream applications—sometimes in real-time. You need to be certain the data flows are continuous and scalable, from the source to the analytics. And you need the skills and resources to design and operate the big data flows.
These are just some of the challenges organizations face as they struggle to get the value they need from data.
- Different formats of data: Multiple data sources need to be brought together for meaningful analysis. Unfortunately, such data can be in different formats (relational database, simple file structures, images) and may need to be transformed into a normalized format.
- IoT compounds complexity: Ensuring that IoT data is dealt with effectively will require accurate and effective data discovery along with extraction, transform, and load actions, as well as automated data movement.
- Human error: Errors will occur during manual activities on large estates of disparate and dispersed data. Time will be wasted and the data won’t be trusted. Managing the volume of data produced by the IoT requires unsustainable manual intervention.
- Big data accessibility: By making big data accessible throughout an organization, individuals can concentrate on being ‘data savvy’ rather than ‘data intelligent’—and the complexities of the underlying data sources and structures can be hidden from the users.
Automate to Innovate
Big data automation is growing as a need for almost every organization, with the IoT driving the stream velocity of data. While users require fast availability of data for analysis, the true value of data can only be extracted and managed via intelligent and advanced data automation.
Democratization of big data accessibility will drive an organization’s decision-making capabilities and help streamline internal and external processes. It will enable intellectual property to be more accurately discovered, stored and distributed. It will build an organization’s knowledge, enabling it to be far more competitive within its markets. This means that an organization can be more responsive, more flexible in its sector and better at predicting future states in its markets.
Power Company Reduces Processing Time by 70% Using Automation
Take this example of the value of big data automation, cited in a 2016 report by QuoCirca. A large power company was implementing smart meters among more than 10 million customers and wanted to use the meters for other value add services. The company needed a means of processing the massive number of meter readings and identifying exceptions, so as to make the smart meter estate work to its advantage.
Using CA Automic Workload Automation, meter readings, aggregation and analysis were all automated. Only exceptions are now raised for human intervention, and the help desk and IT department have become more proactively focused on innovating new services that add value to the bottom line. Overall, the power provider is able to take 86 million meter readings per day and has reduced processing times by 70%.
As big data extends its reach, organizations must ensure that any system chosen can not only deal with today’s data variety, volume and velocity, but that it also enables greater veracity and value of the data to be managed. They also need to ensure that the system will be able to embrace and deal with new data types as they come through.