AI will help answer the big question about data

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AI isn’t the only answer to the ROI question about data initiatives, but it’s an answer that comes with business cases. See why it's an important trend this year and discover our seven other AI predictions you can't afford to ignore in 2018.

Many companies haven’t seen the payoff from their big data investments. There was a disconnect. Business and tech executives thought they could do a lot more with their data, but the learning curve was steep, tools were immature, and they faced considerable organizational challenges.

Now, some are rethinking their data strategy as the landscape matures and AI itself becomes more real and practical. They’re starting to ask the right questions, like: How can we make our processes more efficient? and What do we need to do to automate data extraction?

At the same time, organizations are now able to take advantage of new tools and technical advancements, including:

  • Easier methods for mining less-structured data, including natural language processing for text indexing and classification
  • Enterprise application suites that incorporate more AI
  • Emerging data lake-as-a-service platforms
  • Public clouds that can take advantage of different kinds of data
  • Automated machine learning and data management

View the full predictions report

Feeding the AI beast

Despite these advances, many organizations still face a challenge. Many kinds of AI, such as supervised machine learning and deep learning, need an enormous amount of data that is standardized, labeled, and “cleansed” of bias and anomalies. Otherwise, following the ancient rule— garbage in, garbage out—incomplete or biased data sets will lead to flawed results. The data must also be specific enough to be useful, yet protect individuals’ privacy.

Consider a typical bank. Its various divisions (such as retail, credit card, and brokerage) have their own sets of client data. In each division, the different departments (such as marketing, account creation, and customer service) also have their own data in their own formats. An AI system could, for example, identify the bank’s most profitable clients and offer suggestions on how to find and win more clients like them. But to do that, the system needs access to the various divisions’ and departments’ data in standardized, bias-free form.

The right approach to data

It’s rarely a good idea to start with a decision to clean up data. It’s almost always better to start with a business case and then evaluate options for how to achieve success in that specific case.

A healthcare provider, for example, might aim to improve patient outcomes. Before beginning to develop the system, the provider would quantify the benefits that AI can bring. The provider would next look at what data was needed—electronic medical records, relevant journal articles, and clinical trials data, among others—and the costs of acquiring and cleansing this data.

Only if the benefits—including measures of indirect benefits and how future applications can use this data—exceed the costs should this provider move forward.

That’s how many organizations will ultimately reform data architecture and governance: with AI and other technologies offering value propositions that require it.

Implications

Success will lead to success

Those enterprises that have already addressed data governance for one application will have a head start on the next initiative. They’ll be on their way to developing best practices for effectively leveraging their data resources and working across organizational boundaries.

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Third-party data providers will prosper

There’s no substitute for organizations getting their internal data ready to support AI and other innovations, but there is a supplement: Vendors are increasingly taking public sources of data, organizing it into data lakes, and preparing it for AI to use.

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More synthetics are coming

As data becomes more valuable, advances in synthetic data and other “lean” and “augmented” data learning techniques will accelerate. We may not need, for example, a whole fleet of autonomous cars on the road to generate data about how they’ll act. A few cars, plus sophisticated mathematics, will be sufficient.

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Anand Rao

Anand Rao

Global & US Artificial Intelligence and US Data & Analytics Leader, PwC US

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Chris Curran

Chris Curran

Chief Technologist, New Ventures, PwC US

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Michael Shehab

PwC Labs & Tax Technology Leader, PwC US

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