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Data Is The New Operating System: Build Your Cloud To Embrace It

Forbes Technology Council
POST WRITTEN BY
Derek Schoettle

According to one IDC prediction, by this time next year, the vast majority of developers (75%) will be building cognitive and AI functionality into one or more applications they build.

We hear a lot of talk about AI and see a tremendous amount of interest in it from companies across every industry. Whether it be learning from previous flight routes to improve airplane fuel efficiency or optimizing inventory levels to prepare for customer trend patterns, nearly all businesses today can be improved by infusing data intelligence into their operations.

The key to AI starts with a strong data foundation. True innovation in this space cannot be achieved until an organization has a strong grasp on all of its incoming data and makes every decision with data as its backbone.

Every company has the potential to do this, but what does that actually look like? From a technology perspective, this means that every employee should have access to the data that matters most to their jobs and should be empowered to share it with others across a business.

There are a couple of key components that support a complete data strategy: a strong data governance plan and a culture that prioritizes data. Below, I’ve outlined more details on how to construct these pillars to help your business become a data-driven organization.

Evolving Data Governance With The Cloud

This first step in building a data-centric company is implementing a cloud platform designed to deliver real-time data analysis at scale.

However, a solid cloud foundation is only as good as the employees who are able to tap into its data quickly and use it to shape decisions. This is where the next layer of a core data and cloud strategy comes in: data governance.

Data governance ensures that every stakeholder across a business has access to the information that is most relevant to their respective duties. When employees have quick access to data and can easily share it with others, more decisions will be supported by the most current data and guided by analytics.

Today, many data governance plans are created in response to emerging regulations like the EU's General Data Protection Regulations (GDPR). This reactive strategy typically focuses on avoiding noncompliance and concentrates on enforcing restrictions instead of achieving business goals. By flipping the focus of data governance from restricting the usage of data to enabling its safe access, sharing and reuse, organizations can begin to realize the impact and value of proactive data governance.

To begin forming a more proactive approach, organizations have to overcome one of the largest challenges with data governance: a vast amount of rules and potential regulations, which can impact every single piece of data around the various ways it can be shared. Determining who is able to see and work with certain pieces of information has historically required a time-consuming process of regulatory and compliance research and investigation.

However, we now have technology to do this for us. Cloud-powered tools such as machine learning can be put to work against this obstacle, enabling teams to rapidly view an analysis of the security and safety of each piece of data, as well as recommendations as to how it should and should not be shared across a business to stay within regulations and compliance guidelines.

Governance doesn’t just help data stay secure -- it also gives users the confidence to discover data anywhere within their organization, knowing that whatever they can access, they can use and share safely.

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Infusing Data Into Culture

Becoming a data-driven business starts with company culture, too. Despite having cloud and technology infrastructure in place, different departments can often still be reluctant to share data due to long-instilled instincts and hesitations. To overcome this ingrained reluctance, data owners must be assured that the data they share will be accessed, used and protected appropriately.

In addition to reinforcing the priority status of data and analytics across business teams through communication and C-suite strategies, implementing tools that increase data’s visibility and collaboration across teams is also key. This could include cloud-hosted, centralized data workspaces for teams such as notebooks. It could also mean driving the use of cloud tools that visualize data to make it easier for multiple teams to understand patterns in data instantly, regardless of their knowledge of programming languages or coding frameworks.

We can all agree that data is important, but the ultimate goal of becoming a data-centric organization lies in making it easy and simple for teams to tap into data on the cloud for machine learning, artificial intelligence and deep learning to fuel decisions and new innovation.

We’ve barely scraped the surface when it comes to the potential of data. To truly implement and build AI solutions, we empower our teams and simplify our processes.