What is Intelligent Search and How it Works
by Chris Afiesh, Director of Sales
What is Intelligent Search and How it Works
by Chris Afiesh, Director of Sales
Today any digital product, be it a fin-tech platform, a CRM portal, a social media platform, or an e-commerce site, is incomplete without search. Research shows that 68% of online experiences begin with a search engine. Search provides an easy way to access information and gain insights faster. The ubiquitous search box may seem like an everyday utility but it has evolved a long way to become a must-have capability of modern data analytics platforms.
A properly implemented search technology coupled with enterprise data can be of immense value to any organization. Search technology is offered by a few data analytics vendors in the market, however the maturity and richness of their features span across a few generations.
- The first generation of search technology used in analytics platforms is a keyword-based search, where users must query attributes or words that are pre-populated at the time of configuration.
- The second generation of search technology understands more scenarios and keywords, yet is still limited to the context of a report or dashboard.
- The latest generation of search is the intelligent search technology, which provides precise results by understanding natural, incomplete, and even ambiguous phrases.
What is Intelligent Search?
Intelligent search is the ability to understand search queries entered in simple language, identify the user’s intent behind the query, and provide contextual answers by combining the powers of AI, Natural Language Processing (NLP), and Machine Learning (ML) technologies.
Intelligent search enables users, even the non-technical business users, to ask their questions in simple human language, instead of learning technical syntax and SQL queries. For example, business users can ask an ambiguous question such as “how are drive thru sales doing” in the search box. An intelligent search-based analytics platform will interpret this question and respond with results supported by narrations and visualizations. This empowers users to become self-reliant, proactive, and data-driven in their decisions.
Traditional Search vs Intelligent Search: 5 Key Differences
Here are the top 5 differences between analytics platforms offering traditional search and those offering intelligent search.
Traditional search in legacy analytics platforms was designed for technical users like IT teams and analysts. Querying data in databases required a long learning curve. It involved using the programmatic interfaces of analytics tools, constructing complex SQL queries, and analyzing reams of reports to extract insights. These complexities put data analytics beyond the reach of business users.
Intelligent search removes these complexities by bringing data closer to business users. Intelligent search provides an intuitive user interface where anyone can ask questions or request data. Instead of keywords, users can use simple human language as they naturally speak or write. For example, “How much did the sales grow this year?” or “What are the top 5 outlets by sales?” This way, intelligent search opens the gateway to enterprise data and makes it accessible for everyone.
Scope of Search
Search in traditional analytics has always operated in the paradigm of “you only get what you ask for”. Keywords, reports, dashboards and search queries are predefined and therefore limit the scope of search. Users need to know what they are looking for when they browse through predefined dashboards or reports for answers. With traditional search, users have to first know and remember the right keywords, and then form the search query using those keywords. This takes significant time and limits their ability to ask the questions the way they think of them naturally.
Intelligent search offers users advanced capabilities like natural language search, search suggestions, ambiguity corrections, and context recognition. When users don’t know what they are looking for or can’t think of where to start searching, search suggestions offer ready-made search queries and most searched terms to guide users in exploring their data. Intelligent search leverages AI to understand the intent of a user’s question. Same words can have different meanings and contexts. For example, questions like “sales in California” can be asked for both the state and the city of California. Intelligent search makes users aware of such possible choices and helps them choose their required context for search.
Depth of Insights
Traditional search provides answers only to the asked question. So the onus of asking the right questions falls on the user. This makes it harder for users to uncover hidden insights. Further, the technical complexities in the analytics process discourage them from performing advanced analysis such as identifying root causes, monitoring anomalies, tracking trends, and so on. Such advanced analysis requires weeks to extract any useful insights.
Intelligent search makes advanced analytics easy by presenting advanced insights and related information along with the answer. Based on the original query, intelligent search looks for any anomalies, analogies, clusters, trends and predictions related to the query and presents them upfront. This helps users understand not only what happened, but why it happened and how to proceed ahead.
Time to Insights
Traditional search follows a lengthy and time-consuming process where a business user asks for a report and it is returned by the analysts or experts after a period of time. Any quest for information comprises an iterative process of questions and answers. So every time business users have a follow-up question or a related question, they have to initiate the process all over again. The time delay in extracting and receiving insights makes them obsolete and irrelevant for converting any opportunities.
Intelligent search reduces the time-to-insight by eliminating the dependency on analysts and empowering users with self-service analytics. When users have direct access to insights, they can get answers at the same time whenever any questions arise. Going beyond answers, intelligent search also presents answers to questions related to the original query. This saves time in asking iterative questions and provides users a faster way to understand the comprehensive picture. This way, they can fix issues, avoid disruptions, and convert opportunities in time.
Data Volume and Currency of Insights
As data volumes and complexity both grow rapidly in the modern digital world, traditional search falls short of fulfilling the insights requirements. It is not equipped to process vast amounts of data from different data sources and different formats. Traditional analytic tools often limit search to historical data. This limits users on gaining any visibility of current happenings in business.
Modern analytics tools with intelligent search have the capability to query data from multiple data sources at faster speeds. In addition to historical data, intelligent search can also provide insights from the latest data. This enables users to not only access real-time data but also gain real-time insights as they happen in business.
How Does Intelligent Search Work for Today’s Modern Analytics Platform
With intelligent search, modern analytics platforms parse and process natural language queries in the following ways:
- As users type their question in the intelligent search box, they are shown search prompts which guide in constructing their question. Along with search prompts, related search suggestions and past searches are also shown to help users reach their questions faster.
- Once the user enters the question, the intelligent search technology interprets using a combination of natural language processing, language dictionaries, and other technologies. The question is also checked for ambiguity correction, context normalization, and phrase completion.
- Then it is automatically transformed into a logical SQL statement. This SQL is sent to the connected data store for fetching the appropriate results.
- Modern analytics platforms like MachEye go above and beyond search results to run advanced machine learning models that enhance the results with relevant and related insights, such as anomalies, trends, or root-causes analysis.
- This entire process of extracting contextual answers and advanced insights takes merely seconds with intelligent search, as compared to traditional analytics which may take days or even weeks to perform deep analysis and extract relevant results.
Benefits of Integrating Intelligent Search in Your Data Stack
Collecting and processing enterprise data is only as valuable as the action it inspires. Organizations spend millions of dollars developing robust data pipelines, yet often lack the ability to get the right data, to the right person, at the right time. This ability is provided by intelligent search, thus making it an essential part of every organization’s data stack.
1. Empower business users to become confident decision makers
Intelligent search enables users to ask business questions just the way they use internet search or ask a colleague. With intuitive search interfaces, users can get started with data analytics from day one, without going through extensive technical training. By removing dependency on experts and IT teams, business users can get quick answers to their business questions in real time. This improves their productivity, increases their confidence, and makes them better decision makers.
2. Gain more insights in a single search
By using intelligent search, users can go beyond the “what” to find out “why” business changes happened. This helps them understand the root cause and identify key influencing factors. For example, a simple query like “sales in 2022” entered in MachEye’s intelligent search box not only returns the sales figures for the year, but also provides the growth comparison to previous years, shows a breakup for sales by region, product, or store, and highlights anomalies if a particular region or product fared exceedingly well or had significant drops in sales along with influencing factors.
3. Spread data literacy and improve data democratization across the organization
Intelligent search technology helps organizations enable a data-driven culture where every user feels empowered to conduct powerful analysis tailored to their role and interests. By opening up access to data, intelligent search democratizes the benefits of data and insights to everyone in an organization.
4. Improve returns on analytics investment
Providing a self-service search-based platform reduces the number of trivial requests for IT, allowing them to dedicate more resources to address key business priorities. Intelligent search enables more and more decision makers to become data-driven and encourages them to use self-service analytics for decision-making. This improves productivity, increases operational efficiencies, reduces costs, and ensures quicker time-to-market. This way, organizations are able to increase their ROI on analytics platforms and plan better for the future.
Why is Intelligent Search Essential for the Future of Modern Data Analytics?
What does a marketing analyst, a supply chain manager, and a chief executive officer all have in common?
Answer: Success in their roles largely depends on quick access to accurate data and insights.
The explosion of data in the digital world has made it increasingly challenging for users across an organization to explore data and make informed decisions on the fly. It is commonly believed that AI is vital for companies to survive and thrive, but how do organizations enable widespread usage of AI and ML? Historically these sophisticated techniques have resided only in groups that have data science expertise, and certainly could not be utilized by non-technical business users. Innovative analytics providers such as MachEye have shattered this bottleneck by offering a search-based analytics platform, where users can consume actionable insights without data science training.
Gartner, in their 2022 trends, recommended that leaders should prioritize data literacy and put in place strategies to address the scarcity of data and analytics talent. Organizations that provide self-service analytics capabilities will have a significant advantage over their competitors. Intelligent search is the biggest enabler of self-service analytics, data democratization, and data literacy. A simple search box that is also intelligent, contextual, seamless, and intuitive to use can help unlock data and insights for all decision makers at all levels within an organization.
Easily Embed Intelligent Search with MachEye
MachEye has transformed the data analytics paradigm by bringing the convenience of everyday internet search to enterprise data through its modern analytics platform. MachEye’s Intelligent Search is an AI-powered search technology that understands natural language, offers contextual search suggestions and ambiguity corrections, and interprets search queries to deliver instant results in the form of text summaries and interactive visualizations.
With its robust APIs and SDKs, MachEye's AI-powered enterprise search can be fully embedded in existing business applications. By residing within the applications, MachEye ensures insights are readily available where users make decisions and take actions. Organizations can address the needs of their employees and customers better by embedding the required analytics capabilities. For example, a RevOps company offering revenue intelligence to sales teams empowered its users by embedding MachEye’s intelligent search box to ask ad hoc questions about deals, pipelines, and wins in simple natural language. Data is democratized, insights become actionable, and time-to-insights is reduced significantly with MachEye.Start Your Free Trial