Data Analytics
Understanding data creates a solid basis on which to make well- informed decisions. When querying data, you need to have a precise goal in order for your analysis to be effective and to the point. Here at ITReview, we normally say “data speaks for itself, you just need to ask the right questions and create the right conditions to allow it to speak”. So, what’s your target market? What’s the purpose of this data analysis?
Depending on your market and goals, the data collected during the processing phase is analysed and managed by showing trends. From this data, you get a series of KPIs (Key Performance Indicators) able to guide managers’ choices, providing awareness about the information and real parameters to understand the value of the information.
Understanding data is the first step in planning a strategy
Different types of analysis are available depending on whether you want to look at past phenomena or new trends. However, the approach to all of them remains the same: it’s the data that’s in charge – it is consulted and then interpreted in order to get a relevant answer and make a well-informed decision.
Descriptive Analytics
Data can answer the question “What happened?”
To take a closer look at facts that you already know or discover links between data that you weren’t aware of. Descriptive analytics manages data coming from multiple sources such as surveys, reports or past events, such as sentiment analysis for a social media marketing campaign and studying its performance in terms of reactions and comments.
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Diagnostic Analytics
Data can answer the question “Why did this happen?”
The loss of a customer or exponential sales growth are phenomena that a company needs to understand. Through drill-down and correlations, diagnostic analytics uses different sets of correlated data to identify the relationship between variables and cause-and-effect relationships.
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Predictive Analytics
Data can answer the question “What could happen?”
In a volatile and fast-moving market, understanding changing requirements and phenomena could prove to be a key strategic tool to beat the competition.
Through our algorithms, developed using R or Python calculation engines and therefore Machine Learning, the information coming from descriptive and diagnostic analysis are loaded into the system to create a predictive model able to identify possible outcomes based on the analysis of trends and past events.
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Prescriptive Analytics
Data can answer the question “How should I react to potential future events?”
Data can help you to make decisions and find solutions, e.g. in order to avoid any production waste or to find new market niches. If you have a precise goal, prescriptive analytics interrogates data in the most complete way possible, analysing trends and learning from the past to highlight and suggest operational strategies and/or practical actions to be implemented.
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