Digital Information – what to do with it?

Most of us have already access to a staggering amount of digital information, due largely to pervasive technology. More knowledge than ever is at our fingertips. Humans and machines will only continue to accelerate the creation and storage of this growing amount of data and media to be searched, harvested, and rendered.

Google has proven that great search algorithms can provide useful results running simultaneously on a platform of thousands of central processing units. While it does have great software and thousands of computers, Google does not own the data on the Web, nor does Google use large private data systems to provide any complex combined analysis of information from the Web at large. We should be thankful for that!

In the future, we will all have network access to easy-to-use, massively powerful decision-making and planning tools running on common utility supercomputer grids, distributed across the Internet.

There are five component-technology clusters supporting the change from sparsely sourced to deeply informed decision making and planning: 

Data mining is the use of statistical and visualization techniques to uncover trends and relationships within massive databases. Data mining is common with financial data, medical data, census data, and across the web as a whole. The trends sought after are often non-obvious and require substantial data manipulation, either through a directed search to test a particular hypothesis or through less bounded exploration to find unexpected results.

Numerical modeling is a specifically mathematic analysis applied to utilizing and harvesting floods of numeric data. One example would be harvesting and managing real-time information from thousands of sensors embedded in things like RFID chips on items in a warehouse or on a factory assembly line.

Knowledge visualization and simulation are specific graphical and tactile userinterface techniques for interacting with massive and complex knowledge. The greatest benefit of visualization is its ability to communicate concepts difficult to adequately describe or grasp in words: things that are too large (a galaxy), too small (an electron), too slow (an eon), too quick (a nanosecond), too complex (an engine), an ecosystem (a weather system), or too abstract (an equation, a heuristic, a process, a trend, or an analytic model). Knowledge visualization and computer simulations give us the ability to operate and manage otherwise unimaginable tasks. Microsoft’s Excel and Power BI programs are of big help. Our webinars are attracting hundreds of participants.

Pattern processing is the mathematical analysis of two-dimensional images like sensor patterns, photographs, satellite pictures, facial expressions, video images, and voiceprints. As we continue to learn how to better process patterns, our computers will be able to interpret meaningful information from an otherwise opaque environment. Pattern processing is an integral part of voice recognition and biometric authentication techniques for confirming our identities based on retinal patterns, genetics, voice, and fingerprints.

What’s the IMPACT ON WORK?

Compared to our still somewhat limited capabilities today, working people will have to be upskilled to be able to use the powerful capabilities to access, manage, manipulate, and visualize abstract processes and vast datasets.

Subsequently, each decision and plan we make will be based on a much deeper understanding of relevant data. Mathematics will become a necessary resource for sorting this information by redefining our workflow processes.

Important is that machines will not replace humans but they will be necessary mediators between data overflow and human analysts. The employee of the future will have to have sharp analytic capabilities, able to make sense of the filtered data.

We will have to hire data scientists or train our own people. Your feedback would be appreciated; contact me at [email protected]