Data Analytics

Analytical Data

Use the Actian Data Platform features for an in depth look at your data analytics

Why is Analytical Data Important?

Without analytical data, decision-making would be less confident. Business changes would be harder to justify without data to back up assumptions. Job security could suffer as a business might not tolerate multiple bad decisions from the same person or team. Every business needs regular course corrections to adapt to changing markets and customer preferences. These changes are more likely to be ineffective or lead to negative consequences without data for verification. Decision-making effectiveness is measured using metrics or key performance indicators (KPIs). KPIs demonstrate progress against business goals such as increasing revenue and profitability. A business without any measurable improvements would eventually see lower growth and a lower position in the market.

Sources of Analytical Data

Data warehouses, data marts and Machine Learning (ML) models need copious amounts of data to provide accurate insights and predictions. Below are some common sources of data:

  • Transactional systems such as a POS system enable real-time analytics about customer purchases.
  • Visitor logs from a website inform sales and marketing systems about potential customers’ interests. In real-time, this form of data can drive recommendation engines.
  • Service desk ticket data is vital for good account management and problem management to uncover recurring issues and their root causes.
  • Twitter feeds are great for performing sentiment analysis. These can be coupled with survey data to calculate net promotor scores (NPS) to gauge customer satisfaction.
  • Geographic data streams from cell phones can trigger beaconing and geofencing. This data is essential to perform location-based data analysis.
  • Machine learning models use data in interaction logs to build more personalized digital advertising suggestions.

Storing Analytical Data

Before data warehousing emerged, data was stored in file systems and analyzed using statistical libraries such as X11 and Box Jenkins. In the early days of data warehousing, analytical data was almost exclusively stored in relational databases and analyzed using SQL queries. Today, a columnar database with vectorized data processing has become the de facto standard to accelerate analytics.

Advanced analytics can perform sentiment analysis on unstructured data, such as in transcribed video and audio files. This technique can reveal clusters of correlations from raw data gathered in a data platform. Before the availability of modern data platforms, it was uneconomical to analyze or mine unstructured data for insights.

Analytical Data Use Cases by Industry

Different businesses use data to understand their industry-specific consumers and market dynamics. The following are examples of vertical-specific examples:


Patient care is at the center of the healthcare world. Diagnostic and clinical trial data drives research into new drugs, vaccines, and cures. Blood test data generates metrics that are compared to historical data to assess wellness.


Internet of Things (IoT) has revolutionized the logistics industry. Sensors are used to ensure the freshness of perishable foods from the field to the supermarket. Sensors accompany the produce during transportation to ensure sufficient cooling is maintained. Trucks are fitted with digital fuel gauges to keep refrigerants flowing as the goods travel to their destination. Data analytics allow shippers to catch threats to consumer health before goods are purchased to avoid costly recalls.


Service availability is one of the primary metrics that telco providers care about because outages trigger customer churn. Quality of service (QoS) systems, such as those provided by Vector Analytics Database customer Expandium, enable network providers to respond proactively to outages before too many customers are impacted. Billions of call records are ingested daily into this columnar database.


Actian customer Kiabi analyzes buying data from POS systems in real-time to track the fast-changing regional tastes of French fashion purchasers so they can optimize replenishment across their stores.

The US convenience store operator Sheetz analyzes purchases across its stores to optimize cross-product promotions thanks to basket analysis data.

The Actian Data Platform

Actian started as a provider of transactional databases and broadened its product portfolio with columnar database technology for fast and low administration analytical data processing. Today, the Actian Data Platform provides a cross-cloud solution encompassing analytical, transactional and edge databases with powerful data integration technology. This portable data management technology combination allows organizations to perform analytical data processing anywhere their data resides.

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