Happy Nice Mall

The potential impact of the ongoing data explosion around the world continues to excite the imagination. A 2018 report estimates that every second of every day, every human being produces. 1.7 MB of data on average—and annual data generation more than twice since and is predicted to more than double again by 2025. A report by the McKinsey Global Institute estimates that ingenious use of big data can create additional data. 3 trillion dollars In economic activity, it enables a wide variety of applications such as driverless cars, personalized healthcare and traceable food supply chains.

But adding all this data to the system also creates confusion about how to find, use, manage and share it legally, securely and efficiently. Where did a particular dataset come from? Who owns what? Who is allowed to see certain things? Where does he reside? Can it be shared? Can it be sold? Can people see how it is used?

As data applications grow and become more pervasive, manufacturers, consumers, and data owners and guardians are realizing there is no playbook to follow. Consumers want to be connected to data they trust to make the best possible decisions. Manufacturers need tools to securely share their data with those who need it. But tech platforms are in short supply and there is no real source of common truth to connect the two sides.

How do we find the data? When should we move?

In a perfect world, data would flow freely like a utility that anyone could access. It can be packaged and sold like raw materials. It could easily be viewed by anyone authorized to see it, without any complications. Their origins and movements can be traced, eliminating concerns about nefarious uses anywhere along the line.

Of course, today’s world doesn’t work that way. The big data explosion has created a long list of problems and opportunities that make it difficult to share bits of information.

With data being created almost everywhere inside and outside of an organization, the first challenge is determining what is being collected and how it will be organized so that it can be found.

Lack of transparency and sovereignty over stored and processed data and infrastructure leads to trust issues. Moving data from multiple technology stacks to central locations is expensive and inefficient today. The absence of open metadata standards and widely accessible APIs can make it difficult to access and consume data. The existence of industry-specific data ontologies can make it difficult for people outside the industry to leverage new data sources. Multiple stakeholders and the difficulty of accessing existing data services can make sharing difficult without a governance model.

Europe continues to lead

Despite the problems, large-scale data sharing projects are underway. Backed by the European Union and a non-profit group, one is creating an interoperable data exchange. Gaia-X, where businesses can share data under the protection of strict European data privacy laws. The exchange was designed as a vessel for sharing data across industries and a repository for information on artificial intelligence (AI), analytics and IoT related data services.

Hewlett Packard Enterprise recently launched a solution framework to support the participation of companies, service providers and public institutions in Gaia-X. The data domains platform currently in development, based on open standards and cloud native, democratizes access to data, data analytics, and artificial intelligence by making it more accessible to domain experts and common users. It provides a place where field experts can more easily identify reliable datasets and securely perform analytics on operational data without the need to move costly data all the time to central locations.

By using this framework to integrate complex data sources into their IT environments, organizations will be able to provide data transparency at scale so that everyone, data scientist or not, knows what data they have, how to access and how to use it. in real time.

Data sharing initiatives are also at the top of the agenda of businesses. A key priority facing businesses is examining data used to train internal AI and machine learning models. Artificial intelligence and machine learning are widely used in businesses and industry to drive continuous improvements in everything from product development to hiring and production. And we’re just getting started. IDC is the global AI market It will grow from $328 billion in 2021 to $554 billion in 2025.

To unlock the true potential of AI, governments and businesses need to better understand the collective legacy of all the data that drives these models. How do AI models make their decisions? Do they have prejudices? Are they reliable? Were untrusted individuals able to access or modify the data for which a business trained its model? Connecting data producers to data consumers more transparently and more efficiently can help answer some of these questions.

Creating data maturity

Organizations won’t be able to figure out how to unlock all their data overnight. But they can prepare themselves to leverage technologies and management concepts that help build a data-sharing mindset. They can make sure they develop the maturity to consume or share data strategically and effectively rather than temporarily.

Data producers can prepare for wider data distribution by taking a series of steps. They need to understand where their data is and how they collect it. Next, they need to make sure that the people consuming the data have the ability to access the right datasets at the right time. This is the starting point.

Then comes the harder part. If a data producer has consumers who can be inside or outside the organization, they need to be connected to the data. This is both an organizational and a technological challenge. Many organizations seek governance over sharing data with other organizations. Democratizing data—at least among organizations—is a matter of institutional maturity. How do they handle this?

Companies that contribute to the auto industry actively share data with dealers, partners and subcontractors. It takes a lot of parts and a lot of coordination to assemble a car. Partners easily share information about everything from engines to tires to web-enabled repair channels. Automotive data fields can serve more than 10,000 vendors. But there may be more isolation in other industries. Some large companies may not want to share sensitive information, even within their own networks of business units.

Creating a data mindset

Companies on both sides of the consumer-manufacturer continuum can improve their data sharing mindset by asking themselves these strategic questions:

• If organizations are building AI and machine learning solutions, where do teams get their data? How do they connect to this data? And how do they track this history to ensure the reliability and source of data?
• If the data has value to others, what is the monetization path the team is taking today to expand that value, and how will it be managed?
• If a company already exchanges or monetizes data, can it empower a broader set of services across multiple platforms on-premises and in the cloud?
• For organizations that need to share data with vendors, how are these vendors coordinated to the same datasets and updates today?
• Do manufacturers want to copy their data or force people to bring models to them? Datasets may be too large to be replicated. Should a company host software developers on its data platform and move models in and out?
• How can employees in a data-consuming department influence the practices of upstream data producers within their organization?

take action

The data revolution is creating business opportunities, as well as many confusions about how to strategically search, collect, and manage this data, and how to derive insights from it. Data producers and data consumers are increasingly disconnected from each other. HPE is building a platform that supports both on-premises and public cloud using open source as a foundation and solutions such as the HPE Ezmeral Software Platform to provide the common ground both parties need for the data revolution to work for them.

Read the original article Enterprise.nx.

This content was produced by Hewlett Packard Enterprise. It was not written by the editorial staff of MIT Technology Review.