DataOps and the path from raw to analytics-ready data

For the first time in human history, we have access to the second-by-second creation of vast quantities of information from nearly every activity of human life. It’s a tectonic shift that’s transforming human society. And among the myriad impacts is an important one for every business: the shift in data users’ expectations. In the same way that the advent of smartphones triggered expectations of access and convenience, the explosion in data volume is now creating expectations of availability, speed, and readiness. The scalability of the internet of things (IoT), AI in the data center, and software-embedded machine learning are together generating an ever-growing demand in the enterprise for immediate, trusted, analytics-ready data from every source possible.

It makes complete sense, since there’s a direct correlation between your business’s ability to deliver analytics-ready data and your potential to grow your business. But as every data manager knows, yesterday’s infrastructure wasn’t built to deliver on today’s demands. Traditional data pipelines using batch and extended cycles are not up to the task. Neither are the legacy processes and lack of coordination that grew out of the siloed way we’ve traditionally set up our organizations, where data scientists and analysts are separate from line-of-business teams.

As a result, enterprises everywhere are suffering from a data bottleneck. You know there’s tremendous value in raw data, waiting to be tapped. And you understand that in today’s data-driven era, success and growth depend on your ability to leverage it for outcomes. But the integration challenges presented by multi-cloud architecture put you in a difficult position. How can you manage the vast influx of data into a streamlined, trusted, available state, in enough time to act? How can you go from raw to ready for all users, in every business area, to uncover insights when they’re most impactful? And perhaps most importantly, how can you make sure that your competitors don’t figure it all out first?

The raw-to-ready data supply chain

There’s good news for everyone struggling with this issue.

First, the technology is finally here. Todays’ data integration solutions have the power to collect and interpret multiple data sets; eliminate information silos; democratize data access; and provide a consistent view of governed, real-time data to every user across the business. At the same time, the industry trend of consolidating data management and analytics functions into streamlined, end-to-end platforms is making it possible for businesses to advance the speed and the accuracy of data delivery. And that, in turn, is advancing the speed and accuracy of insights that can lead to new revenue creation.

And second, we’re seeing the emergence of DataOps, a powerful new discipline that brings together people, processes, and technologies to optimize data pipelines for meeting today’s considerable demands. Through a combination of agile development methodology, rapid responses to user feedback, and continuous data integration, DataOps makes the data supply chain faster, more efficient, more reliable, and more flexible. As a result, modern data and analytics initiatives become truly scalable, and businesses can take even greater advantage of the data revolution to pull ahead.

What is DataOps for analytics?

Like DevOps before it, which ignited a faster-leaner-more-agile revolution in app development, DataOps accelerates the entire ingestion-to-insight analytics value chain. Also like DevOps, DataOps is neither a product nor a platform; it’s a methodology that encompasses the adoption of modern technologies, the processes that bring the data from its raw to ready state, and the teams that work with and use data.

By using real-time integration technologies like change data capture and streaming data pipelines, DataOps disrupts how data is made available across the enterprise. Instead of relying on the stutter of batch orientation, it moves data in a real-time flow for shorter cycles. Additionally, DataOps introduces new processes for streamlining the interaction among data owners, database administrators, data engineers, and data consumers. In fact, DataOps ignites a collaboration mentality (and a big cultural change) among every role that touches data, ultimately permeating the entire organization.

What does DataOps look like from a data-user perspective?

In a subsequent post, I’ll delve more granularly into the technical and procedural components of DataOps for Analytics, looking at it from an operational perspective. For this post, where I want to highlight the business impact, I’ll start with a quick overview of what DataOps looks like from a data-user perspective.

• All data, trusted, in one simplified view: Every data-user in the enterprise has 24/7 access to the data (and combinations of data) they need, in an intuitive and centralized marketplace experience. Analysts of every skill level can load, access, prepare, and analyze data in minutes without ever having to contact IT.
• Ease of collaboration: It becomes faster and easier for data scientists and business analysts to connect and collaborate, and crowd-sourcing of key information. For example, the identification and surfacing of the most popular and reliable data sets becomes possible.
• Reliability and accuracy: Because the data is governed and continuously updated, with all users drawing from the same data catalogue, trust is high, teams are aligned, and insights are reliable.
• Automation: Users are freed to ask deeper questions sooner, thanks to the automation of key repeatable requests. And with AI-enabled technologies that suggest the best visualization options for a given data set, chart creation is faster and easier, too. Other AI technologies point users toward potential new insights to explore, prompting them to reach relevant and previously undiscovered insights.
• Ease of reuse: Data sets do not have to be generated again and again, for every application, but rather can be reused as needs arise and relevance expands – from planning and strategy to forecasting and identifying future opportunities in an existing client base.
• Increased data literacy: DataOps fosters the easiest kind of data literacy boost by automating, streamlining, and simplifying data delivery. Regardless of existing skill levels, every member of your team will find it much more intuitive to work with data that’s readily available and trusted. At the same time, DataOps buttresses the more active efforts of skills training by delivering reliable data in real time. Getting the right data to the right people at the right time keeps even the most advanced analysts moving forward in new directions.

 What are the business outcomes?

In every era, speed has given businesses a competitive advantage. In the data-driven era, where consumers expect real-time experiences and where business advantage can be measured in fractions of a second, speed has become more valuable than ever. One of the fundamental advantages of DataOps for Analytics is the speed of quality data delivery. The faster you can get data from raw to ready (ready for analysis, monetization, and productization), the faster you can reap all the benefits data promises to deliver.

But speed is just the beginning. By delivering governed, reliable, analytics-ready data from a vast array of sources to every user in the enterprise, the raw-to-ready data supply chain becomes an elegant lever for business transformation and growth. Here are four key areas where DataOps galvanizes transformation:

1. Customer intelligence: With an agile data supply chain, you can much more efficiently use analytics to improve customer experience and drive increased lifetime value. Discover deeper customer insights faster, and use them to customize interactions; increase conversion; and build long-term, one-to-one customer relationships by offering personalized experiences at scale.
2. Reimagined processes: Accelerating, streamlining, and automating your data pipelines enables teams across your organization to more quickly and effectively optimize every aspect of business for efficiency and productivity. This includes automating processes, reducing costs, optimizing the overall supply chain, freeing up scarce resources, improving field operations, and boosting performance.
3. Balanced risk and reward: Nimble data-delivery empowers analytics users to get timely insight into internal and external factors to make faster, smarter decisions around risk. Leaders can manage production; keep data current, consistent, and in the right hands; and stay compliant while preparing for the future.
4. New business opportunities: And finally, a raw-to-ready data supply chain gives you the power to develop new products, services, and revenue streams with insights gleaned from data and/or to monetize the data itself. This may be the most exciting opportunity we’re seeing with DataOps for Analytics today; it’s certainly the most transformative. For example, consider how storied American conglomerate GE has transformed a century-old business model (selling hardware) to create a digital platform for commodifying their data. And think about how tech behemoths like Amazon and Google have used their massive stores of data and agile analytics capabilities to attack and disrupt traditional markets like insurance, banking and retail.

The heart of digital transformation

If you’re launching or underway with strategic digital transformation programs for competitive viability and if you’re a CIO or CDO, data is the key. To thrive, your initiatives need an agile, integrated data and analytics ecosystem that provides a raw-to-ready data supply chain, accelerates time-to-insight, and enables a rapid test-and-learn cycle. That’s DataOps for Analytics, and it’s the dawn of a new era in the evolution of the data-driven organization.

Author: Mike Capone

Source: Qlik