Essential Strategy For Big Data | DataDrivenInvestor

November 22, 2022

2 min read

Machines complement humans. Data is multifaceted, and making sense of it demands a fresh approach with ancient roots

“Mega biblia, mega kakon” (Big books, big evil) — Callimachus.

When producing data was a significant problem, humongous work went into preserving, translating, and maintaining the meaning and significance.

Thanks to clerks dispersed across the globe and through the ages, we have learned fundamental principles and mundane shopping lists.

Today humans and machines generate data at breakneck speed. Data has become omnipresent; we store them in devices, on the cloud, and new data centers are sprouting up like mushrooms.

Costs associated with processing and storing data have fallen enormously over time, but our capacity to make sense of it has not grown proportionally.

Consulting businesses rethought their services in response, and new big data and data lake projects appeared on the digital agenda.

After ten years, we haven’t made any headway. There is less and less practical value emerging.

Why?

A sustainable and cooperative worldview has given way to scattered and specialized interests that make it difficult to respond to global challenges unitedly.

Technological advancements have not aided in saving time, managing massive volumes of data, increasing flexibility, improving the validity and consistency of research, and being liberated from manual and clerical labor.

The primary focus has centered on volume and breadth rather than significance and depth, with many businesses still investing years to mastering the newest technology, diverting their attention from observation and interpretation of facts (done in excel, in the best cases).

Big data is a transformational challenge

Machines supplement humans. No cutting-edge system can continuously generate value from data unless individuals contribute. Data is multifaceted, and making sense of it necessitates constant contributions from various experts.

An effective big data strategy must have two fundamental characteristics:

• It must facilitate, improve, and accelerate contemporaneous cooperation among users of the same data.
• It must incrementally and automatically exploit the discovery to generate causal suggestions for possible significance and meaning.

Since internal business boundaries no longer encompass all potential consumers of enterprise data, consultants should work on building a marketplace rather than pitching their platform.

Based on the principle of cooperation and incremental development, a big data strategy revolves around a few critical features constitutional for a marketplace:

• Accessible: A desktop program is not required. Internet access and a web browser democratize access for all users
• Concurrent: A domain team of internal and external professionals with relevant experience must simultaneously and interactively work on the same data
• Intelligent: A support system provides the most suitable description for each record automatically, allowing for time optimization and uniform outcomes
• Incremental: It enables the uploading of more data, from text to media, to support cutting-edge research and contextual causality
• Adaptable: A causal intelligence-based system fosters data analysis and processing components to change their behavior in response to various contexts
• Applicable: The tool is simple to use across domains
• Flexible: Not a platform, but a marketplace that, with just slight adjustments, enables users to add customizations and extensions on their own, meeting the precise needs of research instruments
• Playable: Real-time monitoring of the tasks completed (data set completion percentage, work completed by each researcher) through a supervision panel and a mechanism to help motivating and rewarding the effort

A day in the life

People collaborate globally to piece together a jigsaw of data stored in their systems. They add to a collection of normalized definitions, confirming data concerns and offering explanatory annotations for machines and humans.

Machines turn records and results into vectors and compare them in real-time to unsolved challenges, suggesting descriptions to better match.

Humans examine, check, and enhance definitions to validate results and comprehend their data lake.

Real-time rating lists of data issues, researchers, and objectives demonstrate the progress in allocating awards based on internally generated value and funding activities that create more value.