AnalyticOps – Making Data Digestible Across All Organizational Levels

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It’s a bit of a Catch-22 to improve organizational operations through analytics. As business data is gathered and processed, analytics experts can glean insights and develop new strategies that can help improve workflow efficiency or generate better outcomes, such as higher revenue and faster times to market. The problem arises when the analytics system itself isn’t agile or automated. The organization can experience the opposite effect where the limits of the analytics process can slow down growth.

And as an organization implements new changes—be it staff additions, operational tool replacements, or software updates—assessing the effectiveness of those changes must wait until analytics gains enough information to produce comprehensive reporting. Only then can the next best steps be mapped, and decision-making can be optimally informed.

This is where AnalyticOps comes into play.

What is AnalyticOps?

At its core, AnalyticOps is a framework that helps develop automated analytics processes across a whole organization. AnalyticOps covers all analytics activity within a company’s network and looks for ways to streamline the data processing output. Hence, reporting is more readily available and digestible for those who aren’t already data scientists.

Alongside automation of analytics as a whole, AnalyticOps also focuses on integrating the resulting insights into new operational methodologies and frameworks. AnalyticOps aims to create a continuous, self-reinforcing cycle of bringing increased value to an organization with as little ongoing effort and oversight as necessary.

Challenges associated with AnalyticOps

Organizations must constantly be looking to improve in areas of operational development and be able to swiftly implement changes derived from analytics. Without that speed of ROI, organizations will often get frustrated by the lack of results. They will be inclined to think analytics is simply a fun “thought experiment” that produces no concrete improvement. A sense of irrelevance is deadly for analytics and often turns budgetary investments to other, more short-term efforts.

Another big challenge is the need for consistent governance, as analytics models can be developed in isolation from the rest of the organization and not receive the necessary support. Suppose an organization has an analytics approach that works well enough for the leadership. In that case, they may not see the need to “fix what isn’t broken” by looking for ways to adapt and continuously improve current processes. While companies are often eager to establish a DataOps or DevOps culture and constantly strengthen it, AnalyticOps can be overlooked because it isn’t seen as an area separate from other data governance and data management efforts already in motion.

Main benefits of AnalyticOps

AnalyticOps can help establish automated and repeatable data pipelines that can constantly be refined according to an organization’s needs. The analytics process can be far more flexible, with predefined elements that can be swapped in and out to deliver reporting that helps positively impact desired outcomes.

With AnalyticOps in place, organizations can become more proactive in managing their analytics throughout the production cycle. AnalyticOps can help establish greater data visibility across an organization, helping teams collaborate better and creating self-improvement loops as data is fed back into established models.

Collaborative decision-making is also improved via AnalyticOps, as cross-functional teams can ensure their processes are grounded on and driven by the data rather than guesswork. Changes can be more readily tracked, and improvements can be scaled across a network or swiftly revoked if results trend negatively.

As AnalyticOps strategies mature, an organization will often see greater sales acceleration, improved customer experiences, and higher cost savings throughout growth initiatives. All of this contributes to added competitive advantages and market agility. And as organizations continue to expand their use of IoT and other data-intensive systems, the value of data gathered from smart and connected devices, sensors, applications, and networks can be more fully realized.

So while it might seem like a niche development for the Ops side of the business, AnalyticOps is quickly becoming a crucial element of organizational growth that deserves as much investment as the more familiar DataOps, DevOps, and DevSecOps fields.

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