What are data insights?

Collecting data is crucial, but its true power is in the insights you can extract from it. To unlock this power, companies implement advanced data management solutions that enable them to derive actionable insights.

Insights can help transform complex data into clear, strategic information that businesses can use to make well-informed decisions about their operations. For example, understanding customer behaviors, preferences, and needs through data insights can enhance the customer experience. Insights into operational processes can boost efficiency and reduce downtime in supply chains.

What’s the difference between data analytics and data insights?

Although data and insights are often thought to be synonymous, there is a crucial difference. Data consists of unanalyzed information, statistics, and facts stored digitally. In contrast, insights emerge from the analysis of this data—revealing patterns, correlations, and trends that help companies streamline operations.

To make the raw data more helpful, you analyze it to produce insights. Insights are the patterns, correlations, and trends found after data analysis.

The difference between data analytics and data insights is simple: data analytics collects, organizes, and analyzes large amounts of raw data to uncover patterns. On the other hand, data insights highlight the relevance and meaning of the analytic results and identify meaningful conclusions.

Why do we need data insights?

Companies recognize the critical importance of data-driven initiatives and are prioritizing these efforts due to their substantial benefits:

• Improved decision-making: Data insights reveal patterns and trends that surpass what intuition alone can provide, enhancing understanding of customers, operations, and markets.
• Target market insights: By analyzing data, companies can effectively tailor their offerings and marketing strategies to meet customer needs.
• Operational efficiency and risk minimization: Insights pinpoint areas for cost reduction and proactive risk mitigation.
• Predictive analysis: Advanced analytics interpret insights to help businesses anticipate and prepare for future market shifts and opportunities.

How do you get insights from data?

Extracting insights from data involves several strategic steps:

Define objectives

Defining your objectives is the ideal starting point to ensure that data insights are tightly aligned with strategic business goals and priorities. Clear objectives maximize the value and impact of insights and data.

Business objectives also provide the purpose and directed outcome that insights aim to inform. Well-defined objectives ensure your analytics team focuses data collection and analysis on only the most essential and relevant areas to answer meaningful questions.

Collect data

The second step is gathering, measuring, and analyzing data from various sources to gain valuable insights. There are different types of data:

• First-party data collected directly from customers
• party data from partners
• Third-party data from external sources

Popular data collection methods include surveys, interviews, online tracking, social media monitoring, focus groups, automated tools, etc. Proper data collection should gather relevant insights that can improve your business processes and customer experience. More importantly, you can safeguard sensitive data with data protection solutions to enhance data integrity.

Manage data

After collecting your data, you must combine it from multiple sources to create a unified view to facilitate analysis and reporting. This step, called data integration, involves data replication, extraction, transformation, and loading.

Data integration and management aim to bring inconsistent, incompatible data from different sources to form a semantic and structural consistency.

Analyze data

After collecting and organizing data, you must analyze it to gain insights and discover patterns. Most data analytics software and tools provide techniques for interactively exploring integrated data through visualizations, queries, simulations, and other methods.

Understand and take action

Once an analysis of integrated data sources is complete, document by summarizing the methodologies, insights, and limitations. Communicate the results to stakeholders through reports, presentations, or dashboards tailored to different audiences. Continuously monitor changes by tracking metrics to conduct insight assessments on how well recommendations address opportunities.

What are real-world examples of insights derived from data?

Data insights are crucial across various sectors, significantly impacting decision-making and operational efficiencies. They provide strategic advantages that can be seen in numerous practical applications across industries:

• Retail: By analyzing shopping patterns and purchase histories, retail businesses can gain valuable insights into consumer behavior. Utilizing sales data to identify best-selling items enables retailers to optimize their product assortment and adjust shelf placement for maximum visibility and sales.
• Healthcare: In the healthcare sector, analytics of electronic health records (EHR) can reveal patterns in treatment and patient outcomes. These insights support the development of evidence-based clinical guidelines and facilitate personalized care plans, enhancing patient health management.
• Manufacturing: For manufacturing industries, analyzing equipment sensor data and operational logs helps in the early detection of maintenance issues. This predictive insight allows for proactive scheduling of repairs, minimizing equipment downtime, and improving overall operational efficiency.

Cohesity and data insights

A company’s best use of data is deriving insights better to understand operations, customers, markets, and competition. Cohesity enhances organizational understanding by providing cutting-edge data insight solutions to help organizations harness the full potential of their data.

Our data security solutions safeguard valuable information against modern cyber threats, ensuring data remains secure and actionable. With the rise of cybercrime and data breaches, data backup and recovery solutions guarantee that your data is protected and quickly recoverable, maintaining continuous access to critical insights.

Additionally, Cohesity supports business continuity by enabling offsite archiving and utilizing machine learning to categorize data efficiently, further enhancing insight generation.

For further exploration, join our expert-led live demos or test our SaaS solutions with a 30-day free trial.