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Tag: natural language generation

  • Daily Affirmation

    Daily Affirmation

  • Natural Language Query (NLQ) is Key for Self-Service BI

    Natural Language Query (NLQ) is Key for Self-Service BI

    Modern business intelligence (BI) and analytics software solutions offer more ways to explore data and find insights than ever before. However, ensuring everyone in the enterprise can use analytics independently, when they need it, remains a challenge.

    For analytics to be adopted across the full enterprise, your tools need to be tailored for all users.

    While many BI solutions today provide streamlined experiences, some data literacy and knowledge is required to fully understand them. Dashboards, for instance, are considered self-service BI tools, but they are often still too technical for the average person to use effectively.

    Today, only two out of five (40%) organizations report their people can analyze data without getting help from IT, according to Ventana Research. To better enable business users to interrogate their data independently, certain BI vendors now provide a natural language query (NLQ) capability that sits alongside their dashboards and reporting. Rather than having to search for answers in a traditional dashboard or chart, NLQ allows analytics users to ask questions of their data directly.

    An Overview of NLQ

    Natural language query is a self-service BI tool that provides the ability for an analytics user to ask a question using non-technical language and get an automatically generated answer instantly. Using data modeling and machine learning technologies to parse questions for key terms, NLQ scans related databases and generates a tailored report or chart that provides relevant insight.

    The integration and implementation of natural language query capability, as well as the complexity of the questions and the types of data supported, varies between vendors. The most common and traditional approach is search-based NLQ, which places a free text search bar somewhere within the user interface (typically on a dashboard) that allows BI users to begin typing a question and get a list of reports and charts generated quickly as potential answers.

    A smaller number of vendors also include support for voice commands in their NLQ solution.

    As NLQ increasingly becomes a part of BI ecosystems, however, some vendors have evolved their natural language offering to account for additional barriers: A user still has to know what question to ask, and how they can use it to avoid inaccurate or irrelevant results to their question.

    The new approach is called guided natural language query, or guided NLQ, which takes the concept of NLQ further by programming the solution itself to guide the user as they type a question, helping them structure it using pre-defined sequences and suggested prompts.

    This eliminates the burden of knowledge or any potential problems in accurately interpreting the semantics (language) of a user’s question, as the system itself assists them in building it, from start to finish. They don’t need prior technical knowledge or need to go to IT to get started finding answers from data.

    When employed correctly, guided NLQ can help make the use of analytics more pervasive and open up analytics for every type of business person in the enterprise—not just experts.

    Self-Service That Suits Every Enterprise User

    The aim of NLQ is to help workers not accustomed to traditional, structured BI tools find insights and information they need to inform and make business decisions, while also being a helpful tool for advanced users throughout the enterprise.

    For non-technical analytics users, NLQ is an easy way to get detailed answers from their data faster than exploring a dashboard or chart and use it as a springboard for their ad-hoc reporting.

    Similarly, advanced users can leverage NLQ, whether guided or traditional, to quickly generate a report or chart and further build upon it.

    Generated insights using NLQ can be added into existing analytic content, such as dashboards, reports and data stories, to enhance them with additional detail, relatively quickly and easily.

    Finally, NLQ can be embedded into the interfaces of your users’ workplace applications, to make analytics far more ubiquitous in their day-to-day workflows. They can ask a question, get an answer, and start exploring data much faster and more easily than via dashboards alone.

    Ultimately, having an awareness of your users’ varying self-service BI needs, and how the new wave of tools such as Guided NLQ can help to further open up analytics to more people, will help you ensure your analytics tools are adopted and usable by everyone throughout the enterprise.

  • How IT Can Use AI To Prepare For Uncertainty

    How IT Can Use AI To Prepare For Uncertainty

    The past year has been unpredictable for businesses – from migrating to remote work to planning for a return to offices to keeping pace with shifting government guidelines. With so much in flux, it was and is a good reminder that organizations must be prepared for the unexpected and have contingency plans in place.

    From an IT standpoint, this means ensuring that employees are well equipped to work efficiently and safely, no matter what work may look like in the year ahead. Fortunately, artificial intelligence (AI) technologies are evolving quickly, enabling enterprises to better manage an ever-changing hybrid workforce.

    The following are three AI-enabled strategies that organizations should implement to help prepare for times of uncertainty:

    Invest in Self-Driving Networks

    When companies quickly and unexpectedly moved their employees to remote work last year, networks experienced increased pressure on multiple fronts: usage, quality and issue remediation. These factors compounded on top of the fact that networks have continued to become more complex in the past few years as workloads become more distributed across public and private clouds. Even under the best circumstances, building and maintaining a traditional network requires a team of dedicated experts and can be time consuming. Due to their complexity and the increasing amount of data, networks are becoming more difficult to manage via command-line interface (CLI) and are required to adapt to changing workloads and employee connectivity – this is where AI comes into play.

    A self-driving network eases complexity by using AI and automation to run the network’s end-to-end user experience with minimal human intervention. This includes monitoring for issues such as bad cables and capacity hotspots within the network and solving them before they impact the end user. By ensuring networks are running smoothly on the foundational level, self-driving networks allow IT teams to spend their valuable time tackling more complex problems and driving innovation.

    Empower IT With AI Assistants

    Another way to free up the IT team’s time is to make a new hire – not a traditional employee, but rather an AI assistant. More specifically, an AI assistant that can answer questions and manage networks on par with a network domain expert – one that learns and improves as you work with it. As AI truly delivers on completing functions on par with humans, this is where AI and machine learning are disrupting industries.

    Today’s AI assistants, through conversational interfaces, are delivering more than simple “yes” and “no” answers to IT questions – they are able to answer complex open-ended questions such as, “Why is Bob having a poor Zoom experience?” They’re even learning to solve more complex problems, helping IT teams minimize the number of support tickets issued by frustrated remote workers. As businesses recognize the value AI assistants bring to their team, they now see them as members of the IT team that help them simplify the ever-growing complexity of IT networks.

    Educate Employees on the Benefits of AI

    In 2021, we expect the adoption of AI in networking to accelerate. Ensuring the right AI technologies are property executed starts with trust. Therefore, getting teams to perceive AI assistants as more of a teammate and less as a job-threatening technology is an important step toward successful AI implementation. The end-user experience is more important than ever, and AI is becoming more crucial for enterprises to manage this experience.

    To many employees, the concept of AI can be scary. A common misunderstanding is that AI will take over humans’ jobs. However, people are starting to understand the importance of AI by seeing it solve real problems. As companies implement AI technologies into their solutions, they should first prioritize open conversations about the business benefits of AI. By ensuring everyone is on the same page, teams can work toward building a better process together. After all, the goal is for AI to ultimately make their jobs easier.

    The last year has been difficult to navigate, but it serves as a reminder of the importance of planning for the unexpected – this is necessary to continue running a business efficiently and supporting employees remotely. Though there are sure to be more uncertain times ahead, we have the tools to prepare the best we can today.

    By taking steps toward building a self-driving network, investing in AI assistants and educating employees on AI, organizations can have peace of mind that they’re on the path to building themselves, their employees and their stakeholders a stable future that’s prepared for whatever uncertainties come their way.

  • Why You Need to Embrace Automation

    Just in case you hadn’t noticed: The robots are coming.

    Well, you probably had noticed. It’s hard to miss. Newspaper articles and blogs everywhere herald their arrival. The stories usually follow the same general formula: A catalogue of amazing things that robots (really, AI systems) can do. Debate about forced unemployment vs. people freed up to do more interesting work. And then some comments about whether it’s for the good or bad. You can generally infer the general tone from the choice of image accompanying the story: a cute Japanese robot with a plastic smile or a more sinister face with the Terminator’s jawline.

    Of course, workplace automation has been around for almost as long as we have had workplaces. Each new wave of technology disrupts the employment landscape in its own particular way. However, this time it’s serious: It’s not just manual labor that’s under threat, but increasingly, we’re told, white-collar workers are being supplanted by machines. A recent McKinsey report has generated a wave of headlines: “Even the CEO’s job is susceptible to automation,” wrote Patrick Nelson at Network World; and Ana Swanson at the Washington Post reported, “How robots will even affect the jobs of people we thought were immune.”

    Not surprisingly, a closer read of the McKinsey material gives a more nuanced appraisal than a headline can offer. Few of us will actually be replaced by a robot, but many workers will lose some part of their current jobs to automation. The McKinsey report states that up to 45 percent of the activities that employees perform today could be automated, but very few occupations will be automated entirely in the near or medium term. The things that require creativity and emotion remain uniquely in the domain of humans, and they are likely to remain there for quite some time.

    It’s against this background that we see a steady increase in what is often referred to as robot journalism: machines writing news stories. So far, these applications rely on scenarios where the general ‘shape’ of a story remains pretty consistent because of the regularity of the data underlying those stories. For example, a report on stock performance is always going to mention the current stock price, what the stock price was at some particular point in the past, the price highs and lows, and perhaps something on volume or analyst sentiment. These reports often are found in the back pages of a newspaper, and are the stories that few journalists actually want to write, since they allow almost no space for creativity and emotion.

    The reality is that today’s news-writing software is still a long way from replacing human journalists. Which is not to say that what a robot journalist produces isn’t useful; it surely is. Last month I spoke about robot journalism at the news:rewired conference. It was fascinating to see how most of the major news media are exploring how to get robots on staff. So while we’re going to see more robot journalists, it doesn’t necessarily mean fewer human journalists. A recent study by researchers at Oxford University and Deloitte explored how susceptible various jobs were to automation—journalists ranked 285th out of 366 jobs, with an ‘automation risk’ of just 8 percent.

    But if you really want to see where the action is regarding automated writing, just look at it from a different angle. Rather than Natural Language Generation displacing people from their jobs, it’s offering a solution to the problem of shortage: A shortage of workers or expertise. And that story isn’t about news; it’s the Big Data story. You can take issue with the astounding numbers that are thrown around, but even if they’re out by a factor of 10, they’re still significant: by 2020, about 1.7MB of new information will be created every second for each person on earth. But less than 0.5 percent of all data is ever analyzed.

    In 2011, McKinsey noted, “The United States alone faced a shortage of 140,000 to 190,000 people with analytical expertise, and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.” And subsequent reporting points to massive year-on-year leaps in demand.

    Regardless of how few or how many people are displaced by robots, they are not all going to become data scientists. The only way this volume of data will be analyzed and explained is if we have machines doing it for us. And that’s the real calling and promise of Natural Language Generation (NLG). Interactive visualizations and dashboards don’t cut it; the meaning in all that data will be lost unless we call in the robots. But you need more than robot journalists, tirelessly churning out one formulaic story after another. You need articulate robots that use sophisticated NLG to construct fluent narratives from idiosyncratic data sets. So, accept the data reality, embrace automation, and find an NLG robot to explain the insights buried in your data.

    About the Author:

    2,41 CWDr. Robert Dale, Chief Technology Officer and Chief Strategy Scientist at Arria NLG, is recognized as one of the world’s foremost experts in Natural Language Generation (NLG) research and Development, having authored or edited seven books and 160 papers on computational linguistics. He was a Professor in the Department of Computing, Director of the Centre for Language Technology at Macquarie University.  He co-authored the seminal textbook “Building Natural Language Generation Systems” with Arria NLG Chief Scientist and co-founder Professor Ehud Reiter.