Uncovering insights is hard because we depend on data to answer questions. The problem is that most companies are not collecting “the right” (well-selected) data, they don’t have the right processes in place for analyzing that data, and the insights that come from this analysis aren’t being applied back into the business. Even reports generated by humans are most of the time a one-way street and outdated once presented. As a result, they’re holding back from making critical decisions on how to improve their business. Here’s how to get actionable data with AI-based tools for natural questions.
The solution to these struggles is the strategic adoption of smart systems that enable natural human-machine interaction. This includes Natural Language Processing (NLP), Natural Language Question Answering (NLQA), and semantic content processing. AI data analysis and enterprise search provide real answers to natural questions. These factors remain crucial for acceptance and efficiency and will continue to be optimized in 2021.
The Challenge of Making Data Actionable
Enterprise data is a hot commodity, but it can also be a hot mess. With endless streams of information from multiple sources, how do you make your data actionable? As businesses continue to expand and data analytics becomes more important than ever, the importance of managing all this data will only grow. With a flood of data at their fingertips, enterprises are drowning in data and struggling to make it actionable. They are looking for ways to make sense of all the information and leverage it so they can take on their competitors and grow their business.
Essentially, this isn’t a problem of accessing data. It’s a strategy issue. If you only get insights from your data that is stuff you already know, or if it isn’t really that important, then it can seem like a big waste of time. Not to worry. There are answers. Take a look at this list of 7 ways Enterprise organizations are turning their massive repositories of data into actionable insights:
Seven Ways to Get Great Insight from Big Data:
- Ask questions aimed at improving your business, business case, revenue generation, and bottom line.
- Explain why something happened with adequate specificity and diagnostics.
- Categorize and cluster your visitors, prospects, and customers into meaningful segments.
- Break up any data silos, cut out irrelevant information, and find the important hidden and buried data.
- Reveal new patterns with novel insights instead of the same old results.
- Get the best brains to interpret your data and its relationships. Use intelligent tools and your best people to figure out what your generated wholistic views tell you.
- Predict what will happen and identify the best course of action to make your business ready for the future.
As tempting as it may be, you can’t just take big data, shrink it, and then hope it will magically be great. Your organization needs to pull useful observations from your data and prescribe the right remedies as well as the right path forward.
Now that you have a solid grasp on the business problem, let’s get down to brass tacks and discuss the mechanical components of the AI solutions that form the solutions. What follows are descriptions of how your data is sorted, categorized, labeled, and how your questions about it are answered. The details explain everything you need to know to be dangerous when you are looking at adding to your list of helpful internal efficiency software.
Natural Language Search is Changing the Future of Business Intelligence
Natural Language Processing allows computers to understand the meaning of natural language (like sentences in a text, voice in audio and video) and natural language queries. The first stage of the evolution of AI was to develop Natural Language Processing (NLP) systems that could understand text and extract meaning from it. The second stage is to develop systems that can understand the text and make sense of it based on context. In the third stage, NLP or Natural Language Processing are used to provide the answers users seek when they conduct searches.
NLP helps computers understand text and extract meaning from unstructured data. Natural language question answering (NLQA) and semantic content processing are also important elements of the AI that run the most essential tools of your internal SaaS, Knowledge Management, and Enterprise Search ecosystem. While Natural Language Processing is the process of parsing text, voice, and natural language search queries, Semantic Search, on the other hand, is the process of understanding those queries by extracting user intent information, or in other words, it knows what you’re looking for.
Today, Natural Language Search is the only method of searching the internet that can deliver relevant results. With more than 5.6 billion searches every day, search engines have to be AI-driven. Employees now turn to AI-based tools for instant answers, personalized answers, and answers that help them find the data they need. So how do you get the top ranking for your answer, the one your searchers actually want? The answer is NLP and machine learning, the study of language, and how humans and computers understand it.
Exciting Applications of X Analytics
While tools like Natural Language Processing help computers understand text, sentences, and meaning, it’s also critical to gain insights from video and image related material as well. To complete your awareness of the tools your business needs to benefit from data, you also need to know about X Analytics. X Analytics involves analyzing video and image content to tackle business challenges. In terms of the big picture, X Analytics integrates the world of human perception into the process of information and knowledge acquisition. But what are the AI-based tools needed to analyze video and image data? The short answer is – a lot of them. The video and image analytics market is expected to reach $24.34 billion by 2027 according to Allied Market Research.
Using video image content is not just about how many people are viewing your videos and images; it’s also about how people feel when they watch them, what they read on your images, and what they’re doing when they read them. Video analytics will help you understand how your video and image content is performing as internal tutorials, customer-facing how-to’s, and many other use cases. To accomplish this, your video analytics strategy should include the tracking of engagement, views, and clicks on your videos. X Analytics and AI-based tools allow you to see how many people engage with, stop watching, or skip over your videos. This helps uncover image topics that matter to your audience and business goals. Video analytics is all about understanding your customer and their world. It’s about understanding the context in which they perceive your brand and then using that business intelligence to make decisions that drive progress.
The ability to analyze data holistically is one of the biggest advancements we’ve seen in X Analytics. There are many fascinating use cases for holistic insights from video analytics that differ across multiple industries. X Analytics can be used for optimizing the supply chain or providing support for medical diagnoses. Vibration and audio data analysis can be used to boost the efficiency of predictive maintenance. X Analytics can leverage behavioral intelligence gathering to combat fraud in the financial services industry. These examples merely scratch the surface of potential applications. To better visualize the data for these analytic situations, heatmaps and scroll maps are used to measure video engagement and engagement rates.
AI-based Tools
To put it plainly, all of these factors of gaining insight through strategy, and choosing tools like NPL, and X Analytics are working to digitalize the human experience and transform knowledge management systems into real, effective partners in the workplace. Having the right business intelligence and search tools in place is critical for making informed decisions. The patterns your AI-driven tools reveal are immediate insights that can improve customer interactions, engagement, and loyalty.
