In today’s digital age, businesses need data analytics to gain crucial market and customer insights. A robust solution should integrate analytics and data management capabilities for quick, easy access to information when needed.
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How can data analytics improve business decisions?
Deriving key performance indicators (KPIs) from scattered data can be challenging. Often, obtaining insights takes too long due to a lack of analytics tools. While data is available, there’s no quick access method. This leads to manual data gathering before analysis can begin.
Multiple data sources, such as sales applications and financial extracts, must be merged. This time-consuming process isn’t repeatable, creating data consistency issues when spreadsheets are shared and updated.
What is self-service data preparation?
Self-service data preparation automates these processes, making them repeatable and reducing time to results. An autonomous solution allows analysts to create secure, sharable data repositories quickly. This solves consistency and security issues, as updates are visible to all users.
From a governance standpoint, a centralised team can track all data, transformations, metrics, and analyses across business functions.
What are the best types of data analytics?
Data analytics has evolved from manual spreadsheet analysis to advanced electronic tools. However, this progress has brought challenges like technological silos and complex solutions requiring specialist knowledge.
Modern data sources have also strained conventional tools designed for structured data, as they struggle with unstructured information from emails, videos, and images.
The best type of data analytics depends on a company’s development stage. Four main types are:
- Predictive analytics: Identifies trends and correlations, useful for targeted advertising campaigns.
- Prescriptive analytics: Combines AI and big data to predict outcomes and suggest actions.
- Diagnostic analytics: Examines past data to understand causes of events.
- Descriptive analytics: Summarises raw data into easily understandable insights.
Businesses increasingly adopt sophisticated analytics with machine learning to make better decisions and uncover market trends. Those not using proactive, future-casting analytics may find their performance lacking as they miss hidden patterns and insights.
Discover and alerts notify of potential issues before they occur, such as low staff hours leading to fewer closed deals. Diagnostic analytics can also help find the best candidate for a new role.
Using data analytics in a fast-paced market
Data analytics helps drive better decisions by providing insights on:
- Customer profiles and targeting
- Market and competitor analysis
- Past events and current situation
- Future business outlook
Choosing a data analytics solution
Consider these factors:
- A single platform integrating analytics and data management
- Cloud-based with access to on-premises and hybrid environments
- End-to-end analytics support
- Ability to leverage all data types and sources
- Improved productivity and data integration
- Single source of truth
Accelerate data insights
For reliable analytics, consolidate data into a single source for consistency and accuracy.
Accelerate data insights
Choose a solution with augmented analytics to simplify and automate tasks. It should collect and merge data from various sources, suggesting new datasets for analysis.
Self-service analytics — free IT
Democratise analytics with a self-service solution. Users should be able to load, import, and analyse data without IT help. Look for point-and-click functionality and guided navigation.
Best practice solutions offer data discovery, collaboration, and governance features.
Visualise data
Opt for a solution that automatically creates visual presentations, revealing patterns and trends easily missed in raw data.
Mobile analytics
Select a mobile solution with voice-enabled access and real-time alerts. Advanced features like creating mobile apps without coding are beneficial.
Data analytics welcomes automation and autonomy
Automate analytics processes in the cloud to save time and reduce errors. A modern solution with AI, ML, and an autonomous data warehouse can transform your data analysis, making it faster and more accurate.
Oracle offers an integrated platform combining analytics and autonomous database for fast, actionable insights.
