Mastering Analytics With DataOps And Data Fabrics

(SPONSORED) Organizations face ever-increasing volumes of data that typically reside in silos and data lakes, whether on premises or in the cloud. The goal of accruing all this information is to gain comprehensive business insights and apply those 360-degree views to achieve successful outcomes. The dilemma for C-suite and IT leaders is how to efficiently mine and analyze all those disparate data sources.

In this question-and-answer piece with Radhika Krishnan, Chief Product Officer at Hitachi Vantara, we explore the effectiveness of adopting DataOps and Data Fabrics for managing and analyzing the variety of data that businesses accrue. Specifically, we look at how DataOps can improve the processes for ingesting, storing, and analyzing diverse data sets. We also consider the role of data fabrics for enabling a flexible, composable and automated system to manage these vast information stores.

Taking the Pulse of DataOps and Data Fabrics

DataOps methodologies enable companies to analyze data more efficiently by creating cross-functional, agile teams, breaking down those silos and sharing information across an organization. Employing close collaborations between business and engineering teams, DataOps enables organizations to efficiently ingest data, integrate it, assess quality, establish lineage, and create profiles. Organizations also employ data fabrics to correlate and unify all their data, enabling them to work with that information wherever it resides.

Together, these two approaches offer a comprehensive approach for uncovering the untapped value of dark data. In this interview, we explore the advantages of both approaches:

Q: What are the key benefits to DataOps and what should companies have in place for successful adoptions?

Radhika Krishnan: Every organization can gain efficiencies from DataOps adoption. There are multiple angles to think through in regard to the benefits. Customers see very tangible improvement in terms of ROI and business agility. For example, a banking firm that’s looking to run applications for fraud or anomaly detection. A retail company that wants to improve their supply chain turnaround. Or a manufacturer ensuring the efficiency of the shop floor and their organizational equipment effectiveness (OEE). When applying DataOps, they started receiving returns early on in the process. They also found they were much more nimble in terms of responding to the market changes within their business.

The end result is that data becomes very integral to their business and DataOps insights are key to driving meaningful improvements. Fully automating data as it flows through the pipeline also becomes critical. When this is not in place, you end up with data islands and silos. Companies also require a way to orchestrate data -- being able to operationalize and assign meaning is key. That combination of automation and orchestration on one end with semantic meaning and enrichment on the other are foundational for success in this area.

Q: How important is data management and what should companies consider when building a comprehensive data fabric?

R.K.: Ingesting the data is not enough if you’re not also curating, managing quality, and ultimately publishing it for business purpose. As the data grows to massive scale, the construct of having a very flexible, automated fabric or platform becomes critical. In most industries where regulatory rules and compliance are dominant, you really don’t want inconsistency in the way you manage your data.

You need a data fabric that not only composable and flexible, but also safely open in nature. Then you can apply various pieces of functionality using a LEGO-block approach as the data flows through the fabric. So, characteristics like cloud agnosticism and being able to operate in the cloud or on premises are the critical elements of data fabrics.

Q: Finally, how does DataOps benefit team collaborations and improve data analytics results?

R.K.: Organizations can use DataOps to bring their data engineers, business end users and IT teams together to collaborate and provide faster access to their data wherever it resides. There’s not only greater agility, but they can also derive insights from that information much quicker.

Empowering the self-service aspect is important -- end users can get to the datasets themselves; they don’t have to describe it to someone else or retrieve it by programming complex code. With the right DataOps methodologies and data fabric in place, business end users can operate on their own to combine different data elements and use that synthesis to achieve their business goals.

Choose Analytics Mastery With Hitachi Vantara Lumada

Offering a unified platform with a single suite of integrated products, the Lumada DataOps Suite from Hitachi Vantara combines AI, automation, advanced analytics, and management capabilities to make sense of complex data. Organizations can ensure that analysts and business leaders have the right tools and functionality to quickly analyze diverse data sets and gain a competitive advantage.

To learn how you can accelerate and improve your data analytics initiatives, visit:

https://www.hitachivantara.com/intelligent-dataops