Organizational data has become the key to business survival. Each day it becomes more and more critical. As a result, data experts have developed a separate field of “data mining”.
If data is extracted, analyzed, and used effectively, it has the power to answer many of the toughest organizational questions as well as help organizations realize their most ambitious goals.
Professionals in the DevOps field now see a significant opportunity to impact the data mining process. By integrating fresh capabilities and strategies into the platforms and applications they create for their organizations, they can help transform the way organizations utilize data.
At the same time, recent advancements in data science, such as the use of parallel processing platforms where massive datasets are interrogated in near real-time, are making results more all encompassing.
With Parallel Processing, Time-to-Insight Is Shrinking
The inclusion of parallel processing in handling data improves analytics along two important paths:
When organizations can process billions of data in milliseconds, then the value of data changes tremendously. This is well illustrated in most industries, such as the financial services sector, where a large difference is observed between queries that are answered in seconds or minutes and those that take a fraction a second. Because time is money, those small differences in time count significantly towards the bottom line.
The conventional pattern in analytics is to ask a question and get an answer. However, curiosity is created when a user asks one question and gets an answer for it but then has three or more questions based on that answer. The trouble is identifying where it is worthwhile to allocate time for answering all of these questions. In most organizations, this is a pain point that has been largely ignored.
If the time/value equation never improves, and with the lack of speed and agility, decision-makers are likely to end up in a rut where they ask the same questions repeatedly.
There’s a huge opportunity to ask new questions and dig even deeper into data and develop new ways of looking at problems in entirety only if queries take just milliseconds to answer. DevOps can leverage the power of parallel processing to advance their organizations and substantially raise the value of data.
Below are additional ways in which DevOps can significantly increase the value of organizational data.
Multiple Processors to Scale Processes
Adopting a central data repository is likely to facilitate analytics throughout an organization, especially in the case of enterprises that heavily consume business intelligence (BI). Usually, a centralized location is known to be advantageous, but it also compels DevOps to deliver satisfactory performance.
With the introduction of parallel processing in this approach, more is possible even in mixed CPU/GPU environments. This is because the parallel code allows a processor to process or compute multiple data items simultaneously. This beneficial functionality is needed to achieve optimal performance on GPUs, which contain thousands of execution units.
Optimization of hybrid execution also works perfectly well with CPUs, which, unsurprisingly, have increasingly wide execution units that can process multiple data items at once. Furthermore, parallelized computation also has the effect of improving query performance in CPU-only systems.
With accelerated analytics, it’s possible to have a single query to be span in more than one physical host if data is too large to be computed in a single machine. In fact, accelerated analytic platforms can scale up to tens and sometimes even hundreds of billions of rows on a single node or across multiple nodes. Because loading can be done concurrently, load times improve directly with the number of nodes.
DevOps Can Connect Data Silos for Deeper Understanding
The generation of new insights does not happen when questions are asked only in one silo or another. Realistically, new insights are generated at the place where silos intersect and relate to one another. Today, decision-makers don’t have to stress about looking under one rock to find another five rocks; instead, they can consider their organization’s data stores as one interconnected resource.
The above does not entirely cover the advantages of connected silos. With accelerated analytics, internal datasets and external sources can easily be combined.
Advanced Analytics for Tightening Datacenter Security
That cybersecurity is essential is not any big revelation to developers today. The big challenge, however, is how to stay on top of the cybersecurity challenge when, for instance, log files can grow to billions of rows of data in just 24 hours.
If one can pull security data into an accelerated analytics platform, then they can add instant, useful data insights to the security of a data center. Innovative dashboards that can pull and show such capabilities make it possible for data security experts to integrate security datasets and log files in a near-real-time manner. Furthermore, intrusion forensics have adopted spatiotemporal analysis, which are becoming increasingly valuable. Spatiotemporal analysis refers to the ability to bring together and analyze organizational data, both for location and time.
If security professionals can prove the region of the world where an attack is originating from, then it becomes much easier to minimize it, or stop it, altogether.
Time for a Second Look at Data
Despite the existence and rise of big data, organizations are still ignorant or reluctant to review their repositories to find solutions to their hardest questions. They believe or assume that their platforms are too slow, unwieldy, or tough to leverage.
To answer their hardest questions, organizations only need to build more confidence in the ability of their data to provide solutions to these difficult questions. Most teams have resorted to only asking the questions they are sure data will answer, instead of going a step further to ask the difficult questions and investigate in their data repositories.
Today, the questions that were once considered impossible are no longer considered so if the right processes and technologies are put in place. The data an organization has in its repository is enough to provide unimaginable solutions. All that’s left to do is ask the right questions.
In this way, DevOps experts are in a prime position to impact the value of organizational data and take their organizations to the highest levels of success.