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October 20, 2017
Natural language generation, a form of AI, can help business gain insights faster
By: Kaushal Mody

Over the past few years, we’ve seen headline after headline telling us that data is the new gold, or oil, or whatever precious commodity you care to name.

However, the fact is that data on its own has little value at all: It’s the insights that lie within data that are valuable to businesses. Companies that can most effectively and efficiently clean and consolidate their data, analyze and extract insights from it, and, crucially, act on those insights, are best placed to succeed.

The rise of enterprise data analytics and advanced analytics solutions have meant that companies are now able to extract insights more easily than ever, and at a much greater velocity. The challenges that were posed by big data have largely been solved thanks to data storage and processing at scale, as well as through open source platforms and advanced analytical tools.

However, the critical next step: Understanding what these insights mean for the business, and how best to translate them into meaningful action, has remained a challenge. It is in this space that Artificial Intelligence (AI), and in particular natural language generation, looks set to be transformational for businesses.

The power of natural language generation is its ability to reduce the time it takes for humans to understand what business data is telling them, and to act on these insights. Armed with better and faster insights, business leaders can make better decisions and thereby enhance customer experiences and drive growth.

Humanizing data

Natural language generation uses machine learning to mimic the ways human analysts learn from data and provide recommendations for action. As such, the technology turns raw data into human narratives; communicating meaning in the same way people do, and providing complete transparency into how analytical decisions are made.

With natural language generation integrated into their businesses, organizations can more fully automate decision-making, helping them make insight-driven changes to internal and external business processes and propositions to gain a competitive advantage.

Such is the power of natural language generation that Forrester named it one of the most important AI technologies for businesses to consider using to support human decision-making, while Gartner had already covered the technology in its 2016 Hype Cycle for Data Science.

It’s for the same reason that Accenture is now working closely with Narrative Science, a company that has developed an industry-leading advanced natural language generation platform. Significantly, we’re using this technology not only to improve and accelerate decision making at customers’ businesses, but also at our own; such is the potential we see for natural language generation.

Natural language generation in action

Whatever your industry, natural language generation has a role to play in accelerating business processes and driving efficiencies across the organization. By way of illustration, here are just three possible applications of the technology:

  • Sales: Natural language engines can deliver retail sales organizations' narrative-based reports into key information, such as product and level patterns in sales data. These reports can arm in-store sales teams on a regular basis (daily, or weekly) with reports on performance in comparison to local competitors, high-performing products and prominent trends in sales data; all of which can be used to optimize the sales process.

  • Procurement: Internal procurement performance reports are mostly made up of impenetrable charts and graphs, which lack any sort of narrative. What’s more, they often must be created manually, which requires a large data team. Natural language engines can automate the report making process, and present findings in a narrative format that provide tailored stories and targeted insights. Importantly, natural language platforms provide reports in real-time, relaying insights rapidly and consistently to procurement teams.

  • Finance & Accounting: Natural language generation can be used to turn corporate spend data into meaningful reports. For example, the platform can take finance data from across an organization to rank the local branches and/or business functions with the largest spend, breaking down budget trends and highlighting the areas where most money is spent. The report would provide an easy narrative explaining how processes can be changed internally, and external vendors rationalized to reduce costs.

Whatever your industry, it’s becoming increasingly important that you look at how you can use natural language generation to optimize data analytics and decision making. Businesses that fail to adopt this critical technology in time risk losing ground to competitors that can act faster and with more impact while exceeding customer expectations.

Read more about AI: The future of business.

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