Data & Analytics – Everything You Need to Know

Data analysis requires a solid foundation before a business can get into the details. Analyzing data begins with determining why you need it. Along with finding a purpose, consider what metrics to track. Identify the sources of data when collecting them.

Data can be sourced and collected in various ways, such as through accessing or purchasing external data, utilizing internal data, and implementing novel gathering techniques.

It's another solid data strategy that requires you to plan to apply analytics to your data to root out business-critical insights that can improve decision-making, enhance operations, and generate value.

The next step in developing a solid data strategy is considering the ramifications of technology and infrastructure. This is done once you have decided on the following:

• How do you want to use data?
• What sort of data is ideal for you?
• How do you want to analyze that data?

It is critical to have people with specific skillset to make the most of data. Your organization can improve data-related competencies in one of two ways:

• By strengthening internal expertise
• Through talent or outsourcing data analysis

‘What’s happening in your business?’ Descriptive analytics focuses on describing the current state of a business or process. It can identify what has happened and when but does not offer insight into why it occurred.

'Why it’s happening in your business?’ Diagnostic analytics is a branch of analytics that uses data to derive insights and make predictions. It identifies root causes and interprets metrics that lead to actionable insights.

Explains ‘What’s likely to happen in the future based on previous trends and patterns?’ Predictive analytics is a way of forecasting or determining future outcomes by looking at past results. It can help us to discover patterns and trends in our data that we didn't realize were there, illuminating hidden opportunities and threats.

‘Helps distinguish the best course of action to bypass or eliminate future issues.’ Prescriptive analytics, also known as predictive analytics, is a data-driven technology that helps organizations make better-informed decisions in real-time.

Combining different intelligent technologies, such as AI, MI, etc., to perform certain tasks.’
Cognitive analytics is a highly advanced form of machine learning that uses artificial intelligence and high-performance algorithms to challenge the status quo of traditional data analysis. In other words, it is a valuable tool that facilitates the creation of better, more transparent, and more comprehensive views of all employee actions, allowing companies to distinguish the normal from the abnormal and thus leading to smarter decisions.

Companies collect customer data through various channels, including physical retail, e-commerce, location data, and social media. By employing data analytics to create comprehensive customer profiles, businesses better understand consumer behavior and provide a more personalized experience.

• Data analytics provides in-depth insights into customers' needs and preferences.
• Storing your data in one central location and allowing your customer service team access to that data can help ensure consistency in the quality of your service.

Data analytics can also assist organizations in driving business decisions. While predictive analytics can forecast what may happen in response to business changes, prescriptive analytics can suggest how such changes should be handled. For instance,

• Prices or product offers can be changed to see how they affect clients’ demand.
• A/B testing of these changed products or offers can then be done to validate hypotheses derived from such models.
• Organizations can use data analytics consulting to evaluate the performance of the adjustments and visualize the outcomes after gathering sales data on the revised products. This will assist decision-makers in determining whether or not to implement the changes across the company.

Data analytics can help organizations increase operational effectiveness and do a great job at predicting outcomes. For example,

• Data collection and analysis involving the supply chain can tell the source about production delays or bottlenecks and help predict potential future issues.
• An organization could replace this vendor if a demand forecast indicates that it won't be able to manage the volume needed for the holiday season. This would prevent production delays.

According to Gartner, businesses will be attacked by malware and experience interruptions to their business. Organizations can use data analytics to establish the root causes of previous data breaches by analyzing and visualizing pertinent data.

• For instance, the IT division can employ data analytics programs to analyze, process, and visualize audit logs to determine the point of origin and the course. IT can further use this information to find vulnerabilities and patch them.
• To stop upcoming threats, IT departments might also utilize statistical models. Attacks frequently entail strange access patterns, especially load-based assaults like a distributed denial-of-service (DDoS) attack. Organizations may build these models to run continually, with monitoring and alerting systems added on top to find and highlight anomalies so security experts can act immediately.