It should be a non-controversial position that access to high-quality data can be the difference between innovation and success or falling behind competitors able to make more effective use of the digital and data economy.
But for the enterprise gearing up its analytics, perhaps setting up centres of excellence for Alteryx users, spinning up analytics workloads on Spark, or looking to integrate artificial intelligence (AI) or Internet of Things (IoT) initiatives, the lines between success and failure are more blurred. How you manage your data pipelines and your DataOps function (that’s data operations for the newcomers) is the difference between a new world full of promise or frustrating swamp of un-actionable data.
The very term ‘DataOps’ can be enough to turn some users away, but we’re not talking jargon and we’re not trying to be too technical. The language of the Data stack is different because it is a different set of methodologies and technologies, born from scientists, engineers, and analysts. To get involved means tackling that learning curve and discovering new processes, roles, technologies, and terminology!
Quick side-bar: What is DataOps? DataOps blends people, process and technologies to enable data teams to deploy and better manage their data pipelines and the data analytics lifecycle. They blend data expertise with core IT disciplines
Mastering these new elements is only truly possible with a good DataOps team with the right practices and solutions at their fingertips. Here’s a little primer as to how enterprises’ new best friends in the DataOps team provide benefits across the big four Vs of data: Volume, velocity, variety and veracity. And what’s more – what this means for your business, too.
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Let’s go out on a limb. You’re reading this because you’ve got data in your organisation and you’re trying hard to make it work as part of your general business intelligence process. You’ve probably started to get excited – or worried – about just how much data you have right now. That’s the volume part. With the collection of data now coming from more data points than ever before, the sheer volume is what makes data “big”.
DataOps are the team to help shape this flow and ensure that those analysts and decision-makers who need to access the data can do so. The volume of data creates a few challenges the DataOps team should be able to manage with agility, given the right tools for the job.
DataOps allows massive amounts of data to be translated into a comprehensive view of your customers – and rather than large amounts of historical data becoming unmanageable, more insights become available. The result of this is having more insights available to you to make informed business decisions when acquiring, retaining, growing and managing those customer relations.
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Whereas volume is the result of having data come in from lots of different sources, velocity is the measure of how fast this data is collected: the frequency of incoming data that needs to be processed. Just think about the speed at which payment data is being sent between ISPs and consumers every second of every day, and you will appreciate the velocity of data to an enterprise.
DataOps streamlines this collection and accelerates the processing of data to become smooth and manageable via defined data pipelines. With efficient data operations, higher velocity will result in more flexibility for you to find answers to your questions via systems such as queries, reports, dashboard and interfaces. The more rapidly you can ingest data and analyse it the higher number of timely correct decisions can be made to achieve business goals.
At the beginning of the data cycle, data was limited in format but this is no longer the case. In our current digital age, there is an endless variation to data, so much so that it is possibly even more impressive than the volume of data. Previously most data was either a string or number, but now we have more advanced types. Just think about how many different media formats you have on your phone: video, images, text, audio, this all contributes to the variety of data that needs to be processed. This diversity is not only a result of devices or sources of data generation but both structured and unstructured data. With DataOps, the variety of data becomes a benefit rather than a negative. The automated nature of DataOps allows this to happen by programmatically processing and understanding the incoming data as well as creating automatic alerts and troubleshooting processes.
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Unstructured data is a fundamental of Big Data. To understand it, compare it to structured data which has a well-defined set of rules. While money will always be in numbers, names are expressed as text. With unstructured data, there is a distinct lack of rules. A picture, a voice recording, a Facebook post. They all represent ideas and thoughts based on human understanding and one of the goals of DataOps is to process and translate this unstructured data in a way that the business can understand and act on.
This requires orchestration which is the heart and soul of DataOps. Good orchestration coordinates for all types of data and all areas of a data development project: code, data, technologies, and infrastructure. This is responsible for moving the different types of data through the pipeline and instantiating the data tools that operate on that data.
Veracity is, in layman terms, the amount you trust your data. If the answer is ‘not a lot’, then your data isn’t going to be the most useful for informing future decisions. However, if you can ensure your data is trustworthy, all insights and data outputs become more valuable.
There can be an inherent uncertainty within data. In amassing a lot of it, it can get messy. DataOps is the way to ensure that any areas are corrected.
When applied to veracity, DataOps ensures that data is consolidated, cleansed of any impurities such as duplicate or false values, consistent and current. This again will contribute to you making the right decisions.
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Don’t let your data fear control you
DataOps is the vehicle to ensure that big data does not remain a daunting and unscalable beast. The goal of DataOps is to bring structure and automation to the development of data applications and pipelines. While under the control of strong DataOps, teams can evolve from untamed data silos, backlogs and endless quality control issues to an adaptable, automated and quicker data supply chain that adapts and improves to deliver value to the business. Optimising each of the four Vs is the gateway to this happening, and when put into practice, your data will work for you.