The Future of Analytics: The Cloud, Big Data and AI

future of analytics

Cybersecurity experts are in higher demand than ever — and is it any wonder? The vast connectivity of technology these days comes with undeniable opportunities, but also a host of security and privacy concerns that increasingly attract the attention of governments.

Now that our computers are powerful enough and can be networked well enough, we can take the lid off much of this data that industry has always relied on but didn’t always have the means to access or study meaningfully. Now, analytics is a booming industry all its own. It’s made up of connected tools and equipment that gather and organize data, smart algorithms that plot customer demand to help make production decisions and autonomous factories that function on their own and oversee their own maintenance.

With the right strategy and protections in place, analytics can help make everything about doing business smoother, more accurate and less wasteful. It adds up to a powerful, technology-driven competitive advantage.

What Does a Competitive Advantage Look Like in 2018?

More and more, having a competitive advantage over your rivals in 2018 means making investments in data services and analytics. Data services gather “evidence” from every part of an organization or supply chain, and analytics appraises the evidence to inform better decision-making. But decisions about what? Business-focused software, powered by big data, uncovers opportunities in areas like:

  • The current performance of existing equipment and infrastructure
  • The cost-benefit analysis of upgrading to “smarter” and more connected equipment
  • Realizing operational and administrative inefficiencies
  • Streamlining invoicing and payments
  • Helping multiple vendors in complex supply chains exchange information more quickly

Companies like Amazon, Google, and Microsoft, not to mention hundreds of small software developers, have put data storage solutions, including remote cloud storage, within reach of a huge variety of companies. But having a place to put all the data you’re collecting is just the first step.

How Do the Cloud, Big Data and AI Make Analytics Smarter?

If a company has made the decision to store their data in custom-built servers in-house, partner with an outside service for cloud storage or some combination of the two, it’s hopefully because they have a problem they want to solve or a question they want the answer to.

Big data and data analytics provide a “brain” so that companies and organizations can make evidence-based decisions. But cloud services and connected devices (the “Industrial Internet of Things”) are the central nervous system and the sensory appendages. The phrase “the cloud” simply refers to internet connectivity. What we’re really interested in is what kind of device is doing the gathering and what’s being “seen.” Here are some examples:

  • Cloud technology helps freight services make more accurate delivery estimates by keeping vehicles on the ground in touch with dispatcher and route planning tools behind the scenes.
  • The industrial internet of things relies on cloud connectivity to provide insight into energy use and equipment health. Temperature and vibration sensors can determine whether equipment is performing ideally.
  • Other devices can shut down conveyors, generators, pumps, lights, and even climate control when they’re not needed for the facility to function.

McKinsey reports that about 85 percent of surveyed executives from a variety of fields indicated a general dissatisfaction with how well their companies actually use data to make decisions. But they might be leaving money on the table. And when artificial intelligence enters the mix, the cost-benefit analysis looks even more impressive.

The internet of things gathers actionable data, and then big data organizes it and sometimes sells it in the form of management software, analytics dashboards, outsourced data specialists and more. Artificial intelligence is a somewhat misunderstood part of this new “data industry,” the newly emerging exchange of investment dollars and human capital.

Here’s a reductive way to look at how artificial intelligence helps us make analytics smarter:

  • Managers and executives once had to juggle multiple variables across multiple locations to arrive at production decisions. These days, artificial intelligence can make more timely decisions with respect to balancing supply and demand and choosing which vendors are most appropriate given other outside variables, such as weather events and local politics.
  • Artificial intelligence can help operations managers identify areas in factories where energy or effort is being wasted, either in terms of wasted labor or in the unnecessary waste of electricity or other sources of energy.
  • Marketing teams have AI-driven mapping software at their fingertips that can help them pull useful demographic data from a variety of sources. This data can inform expansion decisions and, when it comes to healthcare providers, uncover unserved or underserved communities.

You’re going to hear “machine learning” used interchangeably with “artificial intelligence,” and that’s mostly okay. AI is a broad concept that refers to programs that can mimic human decision-making thanks to rigorously designed computer code. Machine learning is a step further than that: It’s the process whereby a program gets smarter by learning rather than having every eventuality programmed into it by a human.

It’s a lot of opportunity for cybersecurity experts or those pursuing a future in the field. It’s a lot of new technologies that require experts who know not only how the code works but also how to make it relevant and relatable to decision-makers in both the public and private sectors.

And for business owners: Even if artificial intelligence sounds out of reach, you’d probably be surprised by how many business solutions available today already employ AI of some kind. It’s just waiting for the right problem to solve.

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Nathan P. Sykes

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