The Importance of Big Data Automation

by December 15, 2017

Big data automation must be part of your survival kit in the digital age

For better or worse, big data has irrevocably altered the digital landscape. The explosion in variety, velocity, volume and value of information presents an abundance of previously unimaginable opportunities, but it also creates a number of challenges that need to be successfully navigated. Big data automation can mean the difference between harnessing business insights at speed or getting buried in your data.

This reshaped technical world poses the following question to organizations: do you risk presenting, stale, incorrect or erroneous data to your customers? Because, with 2.5 quintillion bytes of data now being created every day, finding a way to manage and harness such potential is a new experience for everyone. And if you don’t take advantage, your competitors will.

Vast Amounts of Unstructured Data

By now we are mostly well versed in the necessity for and the advantages of big data; it can be used to improve decision making, get a better understanding of our customers, enhance security, increase results or augment your pre-existing data warehouse capabilities.

This new wave of data consumption is now ubiquitous and the accessibility has given businesses the opportunity to streamline internal and external processes. The way information can be discovered, stored and distributed has been significantly enhanced, and will shape an organization’s understanding – enabling it to be far more competitive within the market.

Big data is giving everyone the opportunity to make faster and more informed decisions, recognize new revenue streams and deliver highly personalized customer experiences at massive scale. While this could be a game changer for your organization, there are nonetheless certain hurdles to overcome. Vast quantities of unstructured data now reside in data lakes; everything is here, but comprehending it is quite a different matter.

The Challenges

We can broadly group the challenges of big data into four categories, although the nuances may vary from company to company.

Acquisition

Often data is generated in too many places and stored in too many different silos. The challenge is to collect, transfer and store information from an array of locations efficiently. Many businesses struggle with this, often running into synchronization issues that in turn make it extremely difficult to guarantee data freshness.

Distribution

Not only is pulling the data into your systems an issue, but so is pushing it out. Indeed, the data warehouse is no longer the only place where information is stored; it is now held across a number of a destinations including data centers, the cloud and SaaS applications. This requires complex coordination and an array of technologies simply to get the data downstream and to dependent applications.

Scalability

While fighting these first two challenges, your data flows need to be continuous, from the source right through to the analytics. But guaranteeing continuous access to data simply isn’t possible when you only have limited control over end-to-end data flow performance. Therefore, the question arises: how do you continually monitor your data processes, when you’re managing petabytes and thousands of sources?

Skills

Clearly, designing and operating big data flows requires highly skilled staff, as does integrating applications and running processes. Even if you have the resources to hire the requisite army of employees, how do you deal with team turnover and training new personnel on the complexities of the tasks at hand?

Delivering on the Promise

The primary way companies are addressing these issues is to go down the route of investment in human capital. But clearly this is not scalable nor sustainable.

Instead a solution that coordinates the disparate elements that go into harnessing big data will provide the necessary platform for your organization to exploit and disrupt the market. Big data automation provides a better result – faster and more frequently than humans.

CA Automic Workload Automation simplifies, accelerates and automates the flow of big data, from staging to reporting, through the use of intelligent business automation. It incorporates a unified web interface, enabling non-technical users to visually design data processes without coding, easily monitor the execution of the workflows and scale operations to thousands of data flows. Moreover, every operation is audited, monitored and tracked to better meet compliance standards and corporate rules.

By eliminating complexity, automation opens up big data to business users and data scientists, providing them with integrated tools tailored to their role – empowering the workforce to manage their own workloads. Running across the entire data pipeline an automation tool also ensures visibility across all the processes involved, thus guaranteeing agility in the acquisition, processing and distribution of information.