Data discovery involves three steps.
- Integrating, cleansing and preparing information for analysis.
- Visualizing it as charts, graphs, dashboards and maps.
- Organizing it into reports or dashboards to support business decisions.
Three categories of information discovery tools are presently available.
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Text-based search tools have a search engine-like interface to enable keyword searches of surveys, documents, presentations and product literature. They serve organizations that seek to incorporate unstructured information into their metrics.
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Visual discovery tools with direct querying, interactivity and manipulation capabilities support self-service analytics. Clicking on elements and dragging and dropping dimensions and measures into visualizations triggers SQL queries at the back end. Changing the visualization type refreshes information automatically.
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AI-based tools perform pattern identification with machine learning, so you don’t have to. But, the system might flag one-off occurrences as trends, and human context is indispensable in such cases. A tourist event in a city might drive up sales temporarily, which you can tell the system to ignore to avoid false positives.
Examples of AI-based information discovery tools include IBM Watson and BeyondCore. But it’s early days, and small and mid-sized businesses (SMBs) might find visual tools better suited to their needs.
Data virtualization and direct query methods make information discovery systems ideal for small organizations that can’t afford data warehouses. Mid and large-sized enterprises will likely prefer visual discovery tools, though software with artificial intelligence (AI) might be in the higher cost bracket.
What Is Smart Data Discovery?
BI systems were always user-focused — earlier, end users needed experts to get the desired information. With the launch of enterprise resource planning systems (ERPs) and data warehouse tools, organizational information became more accessible to BI vendors. It encouraged them to innovate and develop software that could query a single consolidated repository.
This method was faster than pulling information from distributed systems and networks and sped up downstream processes like statistical modeling, enterprise reporting and big data analytics.
But the gap between independent insight and end users persisted. The process wasn’t intuitive — you needed to know which metrics you wanted and how to source them. Information discovery required modeling and statistical analysis skills. Additionally, it could introduce human bias into the results.
Smart discovery tools autonomously perform data prep, modeling visualization and advanced statistical analysis. Plus, computers and software programs are better at asking questions than humans. Using this premise, vendors ventured into the smart data discovery domain, removing the hypothesis phase and, consequently, human bias from the analytics process.
Smart discovery or autonomous analytics isn’t about “which metrics do I want?”. Instead, it adopts a business approach, asking questions like “why did our campaign fail?” or “why did sales fall in the north region?”
Thanks to AI-driven smart data discovery, you can connect to the preferred source with point-and-click actions, give natural-language text instructions to view the desired results and watch the software work its magic.
No matter the technical skills, smart information discovery helps you better understand key business trends by using advanced analytics without needing complicated formulas.
BI platforms that integrate information discovery with reporting and dashboarding capabilities are worth considering when looking to buy software solutions.
Primary Benefits
Data discovery is a BI pipeline component with the same benefits. It helps you visualize datasets, trends and outliers, manage risk and compliance, and get a high-level overview of critical metrics and how they impact your business.
Gain Actionable Insight
Modern data discovery software shows the desired information in everyday language on a user-friendly interface while deriving KPIs, business trends and patterns behind the scenes. It empowers users unfamiliar with your system, like clients, to view the metrics independently.
Manually exploring information and gleaning the required metrics is a thing of the past. Connect to the right sources and ask the right business questions, relying on the system to do the rest.
Save Time and Money
Customer preferences and market trends change every day. Data discovery systems give you the latest results by continually analyzing incoming data while combining it with existing information. They automatically find and explain insight from voluminous datasets of varying complexity, presenting the outcomes with automated recommendations at the click of a button.
Embeddability drives productivity by bringing visualizations and data discovery platforms where you work. It saves time switching between applications, telling you what you need to know at the exact moment in your regular business workflows.
Incorporate Big Data
Websites and social media platforms significantly contribute to business insight, but semantic layers in traditional BI systems are often hard-coded and inflexible. Tweaking them every time to incorporate new information types isn’t practical in a dynamic data environment.
Data discovery tools address this shortcoming by connecting directly to operational databases, removing the need to go through the semantic layer. They perform data preparation on the fly, assigning terminology and mapping it to the relevant datasets as they go.
It saves your IT team the task of standardizing business terms across your organization. Additionally, you have greater flexibility to perform sophisticated queries and create models instead of being restricted to the models coded into the semantic layer.
Classify Data Automatically
Associative engines in discovery tools help you uncover correlations, trends and outliers. Their primary role is metadata scanning and indexing in BI tools, extract, load and transform (ETL) tools, and third-party metadata catalogs.
Identifying outliers and trends with Smart Data Discovery. Source
They identify and profile information sets, providing their lineage from high-level system views to granular column-level perspectives. These tools are intelligent enough to scan static and dynamic code in SQL scripts and stored procedures.
Advanced dependency tracking helps you understand each transformation and why it happened. AI-driven domain discovery, information commonality, business term associations and recommendation technologies automatically curate extracted metrics.
Using BI vs. Data Discovery
Information discovery and BI have a common purpose — maximizing information use for your business by consolidating insight siloed in disparate sources. There’s a fundamental difference, however. BI is a consistent, continuous process of information flow for everyday operations and decision-making, while data discovery is the on-demand analysis of business-critical information and trends.
Your BI tool will provide the following functionality.
- Identify short and long-term trends.
- Generate live information for routine operations.
- Provide enterprise reports.
- Share insight via collaborative interfaces.
Use a data discovery tool to address these needs.
- Enable tactical analysis and planning.
- Get answers to specific business queries.
- Perform in-depth analysis.
- Incorporate unstructured information into reporting and analytics.
In summary, BI provides comprehensive information, while data discovery enables specific analysis with deep-dive exploration and investigation.
