It’s not just that the volume of data is growing. It’s also the type of data that’s posing so many challenges to so many companies.
They’re finding it almost impossible to co-opt their legacy database technology for use with cutting-edge data sources like Artificial Intelligence (AI) and the Internet of Things (IoT). Fortunately, database administration systems specialist (DBMS) Oracle has introduced its newest DBMS, Oracle 19c, to help every company make sense of both the vast quantity of the data it collects every day, and the depth and breadth of the complexity of that information.
Building the Future on the Past
Many companies continue to rely on their legacy hard- and software, most of which are iterations of previous versions. Their foundational structure is soundly grounded in proven programming.
However, it’s their age that makes these legacy systems so challenging for companies intending to grow in the future. Emerging technologies have evolved well beyond the foundations of those old-style programs, and continuing to apply patches and reprogramming won’t bring them current with the capacities of today’s newest computing strategies.
Natural Language Processing (NLP)
‘Natural language’ is human language, in all its forms. Whether it’s written, spoken, or even sung, human language is replete with both obvious and subliminal communications. For decades, computer scientists have been working to deconstruct human language articulation and reconstruct it into a digitally viable technology. Today’s NLP is the result of all that work; technology is becoming increasingly adept at capturing and translating human language into a digital format.
However, succeeding on the one project only reveals more projects:
- There are more than seven billion people on the planet, and each one generates trillions of words throughout their lifetime. That’s a lot of information to process.
- There are over 6,500 spoken languages on the planet, and each one has its own structure. So far, technology has not been able to capture, translate, and make use of all those differing dialects.
- Every NLP translation is composed of ‘unstructured’ data that doesn’t fit neatly into the rows and columns of traditional database architecture. Traditional databases and the programming that accesses those aren’t designed to manage all that unstructured information.
The rise of artificial intelligence has also facilitated access to NLP programming, which will require equally advanced technology to master.
For years, companies have invested in data centers (and, more recently, cloud services) to store and manage their growing data lakes. Devices deployed across each enterprise – from the CEO’s desk to the most remote worker’s smartphone – feed those data repositories, where analytics programming works overtime to generate relevant insights based on that aggregated information.
Tomorrow’s devices, however, are feeding back processed and actionable insights from the farthest network edges. Processing data at its (remote) source and not in the centralized data center offers significant benefits:
- It reduces latency. With IoT devices embedded in everyday operations, waiting for processing and direction from a remote server could be disastrous (think autonomous vehicles waiting for inputs about traffic patterns). Most future devices will generate their own determinations at the edge, without waiting for authorization from the central database manager.
- It embraces local bandwidth. Many operations are performed in locations that don’t have reliable network connectivity. Devices deployed in these settings need to manage their own computing.
- It eases congestion. Excess amounts of data are overwhelming legacy servers that weren’t designed to hold either the volume or the complexity of that information. Moving processing to remote devices will allow centralized repositories to function better for more localized needs.
Data began gaining significance when users realized it could help them understand past events. How things happened, who was responsible for them, and what was done about them were all sources of information upon which the C-Suite could base future decisions. ‘Backward-looking’ data was also able to help corporate leadership predict the future by establishing reliable timelines. Predictive analytics told manufacturers, for example, where their machines were in their expected life cycles, so repairs or replacements could be scheduled.
Tomorrow’s prescriptive analytics provides management with recommendations about optimal future courses of action. The programming analyzes every possible potential outcome, using all known variables to arrive at the best possible solution for the specific situation. It uses a variety of new technology, too, including AI, data modeling, machine learning, neural networking, graph analysis, etc.
To capture the value of tomorrow’s computing capacities, you’ll need a database management system that controls and optimizes your data. Oracle’s 19c database provides the tools you want; Datavail, an Oracle Platinum Partner, offers the Oracle expertise you’ll need. Contact us to learn more.
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