Data is a powerful tool that enables organizations at a staggering scale. When implied correctly, it holds the potential to drive decision-making, impact strategy formulation, and improve organizational performance. Analytics is valuable in areas rich with recorded information and relies on the simultaneous application of statistics, computer programming, and operation research to qualify performance. Concisely, it is critical to design and built a data warehouse or Business Intelligence (BI) architecture that supplies a flexible, multi-faceted analytical ecosystem, perfected for efficient ingestion and analysis of large and diverse data sets.
According to The Global State of Enterprise Analytics report by business intelligence company MicroStrategy, 56 per cent of respondents said data analytics led to “faster, more effective decision-making” at their companies. Data analytics is the practice of examining data in order to answer questions, find patterns and extract valuable insights. It aims to dig into actionable insights to deliver smarter decisions and better business outcomes. Since analytics can require extensive computation (because of big data), the algorithms and software used in analytics harness the most current methods in computer science. You can use tools, frameworks, and software to analyse data, such as Microsoft Excel and Power BI, Google Charts, Data Wrapper, Infogram, Zoho Analytics and many more. These tools enable us to examine data from different angles and create visualizations that illuminate the story you’re trying to tell.
There are 4 different types of analytics described in terms of their nature, complexity and use:
Descriptive (business intelligence and data mining)
Prescriptive (optimization and simulation)
Addresses the question, “What happened?”
Descriptive analytics looks at data and studies past events for insights as to how to approach future events. It looks at past performance and patterns to understand the cause of success or failure in performance by mining historical data. Major management reporting such as sales, marketing, operations, and finance uses this type of analysis.
The descriptive model quantifies relationships in data in a way that is often used to classify customers or prospects into groups. It identifies many different relationships between customers and the product. Descriptive analytics juggles raw data from multiple data sources to give valuable insights into the past. The findings simply signal that something is wrong or right, without explaining why.
Now, the next logical question is, “Why did this happen?”
At this stage, historical data is evaluated against other data to answer why something happened. Taking the analysis further, this analysis compares coexisting trends or movements, uncovers correlations between variables, and determines causal relationships where possible. Diagnostic analytics gives in-depth insights into a particular problem and helps in getting at the root of an organizational issue. It is key for a company to have detailed information at their disposal, otherwise, data collection may turn out to be individual for every issue and time-consuming.
It answers the question, “What might happen in the future?”
This type of analytics is used to make predictions about future trends or events. It uses the findings of descriptive and diagnostic analytics to detect clusters and exceptions and to predict future trends, which makes it a valuable tool for forecasting. Predictive analytics belongs is a type of advanced analytics with many advantages like sophisticated analysis based on machine or deep learning and proactive approach that predictions enable. However, data consultants state it clearly: forecasting is just an estimate, the accuracy of which highly depends on the data quality and stability of the situation, so it requires careful treatment and continuous optimization.
“What should we do next?”
It considers all possible factors in a scenario and suggests actionable takeaways. This type of analytics can be especially useful when making data-driven decisions. Prescriptive analytics is used to prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. It uses advanced tools and technologies, like machine learning, business rules and algorithms, that make it sophisticated to implement and manage. Besides, this ultramodern type of data analytics requires not only historical internal data but also external information due to the nature of the algorithms it’s based on. That is why, before deciding to adopt prescriptive analytics, we strongly recommend weighing the required efforts against an expected added value.
How to use Data Analytics in Decision Making
Different types of data analysis should be used in tandem to create a full picture of the story data tells and make informed decisions. Initially, to figure out the respective company’s current situation, use descriptive analytics, followed by diagnostic analysis to understand how they got there. Predictive analytics helps in figuring out the trajectory of a situation—will current trends continue?
Finally, prescriptive analytics helps consider all aspects of current and future scenarios and plan actionable strategies. Based on the problem one is trying to solve and the projected goals, one may opt to use two or three of these analytics types—or use them all in sequential order to gain the deepest understanding of the story data tells.
According to the BARC’s BI Trend Monitor 2019 survey, C-suite advanced analytics is named among the most important business intelligence trends. Access to data is more important than ever. If businesses formulate strategies and make decisions without considering the data, there’s a major possibility to miss opportunities or red flags that it communicates.
Algorithms and machine learning are other fields that can be used to gather, sort, and analyse data at a higher volume and faster pace than humans can. Writing algorithms is an advanced data analytics skill and helps one experience the benefits of data-driven decision-making.