Are you looking to tap into data spread across your systems to improve decision making but don‘t know where to begin? This comprehensive guide on data discovery will help you navigate key concepts, top tools, use cases and best practices to extract maximum value.
What is Data Discovery and Why it Matters?
Data discovery refers to the process of exploring, visualizing and analyzing data from diverse sources across your technology landscape to uncover actionable insights. It empowers business teams to slice-and-dice data, spot trends and patterns for data-driven decision making without needing technical skills.
With crucial business data trapped in application silos and legacy systems, data discovery offers a unified 360-degree analytical view. It enhances visibility into key performance indicators around customer experience, supply chain, production quality, sales performance and more.
According to industry surveys, top benefits realized by organizations leveraging data discovery include:
- 83% faster time to insights
- 76% better data accessibility for users
- 72% improvement in data-driven decision making
- 65% rise in employee productivity
With ability to drive multi-million value creation through better resource planning, campaign targeting, personalized customer experiences and more, data discovery is a top digital transformation priority.
Key Capabilities Powering Modern Data Discovery
Leading data discovery platforms on the market today offer an extensive feature set that empowers you to tap into data easily without extensive technical skills. We will cover key capabilities you must evaluate:
Flexible Data Connectors
The platform must offer pre-built connectors and APIs to integrate 1000+ data sources from enterprise databases, cloud platforms like Google Analytics 360 to IoT data from edge devices, machine sensors in manufacturing plants. This enables creation of a unified view matching your unique environment.
Interactive Visual Exploration
Intuitive drag-and-drop capabilities are needed to build rich, interactive visualizations like treemaps, heat map, histograms on the fly without coding. This allows business users to uncover insights independently through visual data analysis.
Collaboration Tools
Ability to share live dashboards, analysis through screenshots, presentations and annotations enables seamless collaboration. This enhances productivity through better decision making across cross-functional teams.
Augmented Analytics
Look for auto machine learning functionality that can analyze datasets and provide smart recommendations. Some examples include forecasting algorithms predicting future demand and smart alerts flagging anomalies in supply chain.
Enterprise-grade Governance
To ensure widespread adoption, granular access control, data security capabilities, detailed audit trails are needed, especially when dealing with customer data. Integration with centralized identity systems is a plus.
Scalable Infrastructure
You need ability to cost-effectively scale on demand to support increasing data volumes, complexity of analysis and growing user base analyzing data concurrently. This requires state of the art cloud infrastructure.
Now that you understand must-have features, let us walk through steps constituting an effective data discovery program.
Steps in the Data Discovery Process
While rich functionality is table-stakes in data discovery software, you need an end-to-end process spanning people, technologies and governance to extract full value. Walking through the key phases:
Identify Relevant Data
First, you need to identify datasets scattered across your systems like web and advertising data from marketing systems, transaction data from billing systems that can offer actionable insights tied to business KPIs. Cast a wide net at this phase concentrating inclusion before analysis.
Ingest Data
In this phase, connect to chosen datasets via pre-built or custom connectors. Handle issues like missing data attributes, quality errors through transformations before loading into the data discovery system. You may need to iterate multiple cycles to get this right.
Interactively Explore and Visualize
This phase focuses on leveraging graphical capabilities to analyze trends in the form of charts, drill down into outliers visible in heat maps and develop hypotheses around what drives metrics like customer churn.
Contextualize Insights
Step back and interpret insights in context of organizational goals, operational constraints and practical feasibility. Distill findings into actionable recommendations like tweaking offer marketing spend or changing call center staffing.
Operationalization
Finally, focus on driving ongoing value by incorporating insights through embedding analytics into daily workflows across sales, marketing, supply chain via notifications, alerts powered by the data discovery tool.
With a structured process combining people, process and technologies, you can build a high-impact data discovery program. Now let us explore top software solutions dominating the category.
Comparison of Leading Data Discovery Platforms
Multiple technology vendors offer data discovery capabilities but with varying strengths and limitations. I compare key capabilities across top 7 tools:
Tableau
Tableau pioneered modern business intelligence and offers a full spectrum platform with industry-leading visual analysis capabilities.
Key Strengths
- Very intuitive drag-and-drop interface enabling responsive analysis
- Broad range of complex chart types beyond basics
- Built for high performance – handles billions of rows
- Fully customizable dashboards catering to diverse needs
- Centralized security, user management for large deployments
Limitations
- Steep licensing costs make scaling expensive
- More focused on historical analysis vs predictive modeling
Microsoft Power BI
An integral part of Microsoft‘s data analytics suite, Power BI brings cost effective capabilities seamlessly integrated with their ecosystem.
Key Strengths
- Smooth data discovery experience for Excel users with import capability
- Advanced AutoML functionality requiring less technical skills
- Interactive reports using natural language queries
- Lower TCO compared to alternatives through bundled licensing
Limitations
- Custom visualizations require coding and less drag-and-drop flexibility
- Functions best only when fully embedded in Microsoft stack
Qlik
Positioning itself as full life cycle analytics company across data integration, warehousing and discovery – Qlik balances features with flexibility across diverse environments.
Key Strengths
- Associative engine analyzes relationships not visible in standard SQL
- Cloud-optimized SaaS deployment as well as hybrid models
- Can embed analytics within operational applications through APIs
- Predictive modeling capabilities better than traditional BI tools
Limitations
- Visualizations less interactive compared to rivals
- Requires involvement from data engineers to data scientists to maximize value
TIBCO Spotfire
One of earliest enterprise-grade BI platforms, TIBCO Spotfire uniquely offers advanced data connectivity and predictive capabilities targeted primarily at engineering teams.
Key Strengths
- Connectors tailored to industrial data sources like sensors, smart devices
- Advanced integration with statistical, predictive models
- Provides industry-leading scalability and performance
Limitations
- Steep learning curve for casual business users
- Limited adoption beyond manufacturing, transport verticals
Databricks
A pioneer in big data processing, Databricks provides a unified data discovery platform optimized for extremely large datasets leveraging capabilities like Spark, MLflow and Koalas.
Key Strengths
- Integrated environment for ETL, exploration for data lakes
- AutoML, MLOps capabilities via MLflow unmatched by rivals
- Cost effective compared to other cloud platforms
Limitations
- Needs skilled data engineers to maximize value
- BI-style dashboards are less customizable
Apache Spark
Spark has revolutionized large-scale data processing and offers compelling data discovery capabilities via its vibrant open source ecosystem.
Key Strengths
- Leading framework for querying massive datasets
- Choose from multitude of visualization tools like Ploty, Matplotlib
- Leverage managed services like AWS EMR to run huge workloads in cost-effective manner
Limitations
- Being open source requires heavy technology integration
- May need complementary capabilities e.g ML, streaming
Sisense
Sisense focuses on ease of use aspects to offer a BI platform tuned for business users working with complex large datasets.
Key Strengths
- Code-free ETL for data transformation tasks
- Class-leading natural language interface powered by AI
- Cloud native performance to offer speed-of-thought analytics
Limitations
- Advanced analytics capabilities lag some competitors
- Rapid innovation causes feature gaps
This high level comparison shows that while needs vary by data infrastructure maturity, preferred deployment models there are multiple technologies suiting diverse analytics needs. Beyond tools, success requires focus on people and processes.
Best Practices for Maximizing Value
To scale adoption and maximize value from investments in data discovery, we share tips based on extensive practitioner experience:
Secure Executive Buy-In
Tie your data discovery initiative to strategic business priorities to secure sponsorship from CXOs acting as program ambassadors. Align key performance metrics to initiative‘s goals.
Equip Users Through Training
Make onboarding effective by explaining statistical concepts appropriate for analysis vs just tool usage mechanics to prevent erroneous interpretation.
Promote Culture of Data-Driven Decisions
Encourage data-led decision making through internal comms from leadership explaining benefits. Incentivize usage by tying data insights to performance management for roles like campaign managers.
Standardize Through Central Governance
Manage standards for usage, metadata via centers of excellence to drive consistency vs fragmented efforts across business units.
Scale Gradually
Start with well-defined pilot projects, limited tool rollouts that offer quick resource optimization wins before pursuing wider scoped objectives. Quick wins build credibility, momentum internally.
The guide summarizes everything you need – from providing clarity on data discovery, detailing key capabilities modern tools offer, sharing tips on process optimization and things to watch out for during implementation.
The data discovery category offers you unprecedented opportunity to enhance visibility into factors impacting core business KPIs through easy data exploration. Evaluate offerings matching analytical and deployment needs to unlock data-led growth.
