Data analytics is analyzing raw historical data to make informed decisions. The term encompasses the technologies, processes, and methodologies to gain insights from historical data collected over time, converting it into information, insights, and, ultimately, impactful business knowledge.
This capability is critical in a data-driven world where organizations harness the power of data analytics to effectively conclude financial performance, business operations, efficiency, competitive advantages, ideation, and innovation, among other critical use cases. In a business context, data analytics is interchangeably referred to as business analytics and relies on statistical modeling algorithms and technologies to draw insights from vast data pools.
Figure 1: Types of data analytics and what they answer.
In this blog, we will discuss the four types of data analytics along with industry-proven best practices and considerations for your data analytics projects:
Descriptive Analytics – What, When, Where, and How Many?
Descriptive analytics answers the basic questions about the data: what it is, when and where it happened, and in what quantity. Fundamentally, these questions are answered by a classification model that assigns different descriptive categories to data, which are then presented as a report or a business intelligence dashboard. Based on the available information on parameters and metrics associated with data, a business analytics technology can generate two kinds of descriptive analytics reports: Canned Reports and Ad-Hoc Reports. Canned Reports assume existing knowledge or a predefined template around a set of metrics. The data is processed and classified based on predefined criteria. Ad-Hoc reports, on the other hand, are typically generated to answer specific business questions and require custom criteria for metrics used to classify data and create reports. The reports can contain data visualization capabilities to
provide an intuitive overview of the concerned business questions.
For instance, an eCommerce store analyzes seasonal trends for the sales of specific products and uses the analysis to guide its marketing strategy accordingly. For the holiday season, recreational products and entertainment electronics see the highest sales spikes. According to the descriptive analytics report, the business can focus marketing efforts for these products extensively before the holiday season instead of doing it all year round.
Diagnostic Analytics – Why did this happen?
Diagnostic Analytics refers to the practice of discovering the underlying causes: why did it happen, which contributing factors are responsible and how certain metrics and parameters can help address this question. Techniques such as data mining, data drilling, and correlation analysis are widely used for diagnostic analytics. This business analytics process is conducted across three stages: identification of trends and anomalies, data discovery, and finally, identifying causal relationships.
In the first stage, statistical models and algorithms are used to identify data points and trends to quantify the departure from norms based on existing knowledge. If the variation exceeds the natural or expected margin of error, the next stage of diagnosis is performed in the shape of data discovery. At this stage, additional data is extracted or acquired externally; metrics performance is analyzed in isolation, and possible patterns are discovered. Different events and parameters are correlated in the final stage to identify potentially causal relationships.
For instance, consider the business analytics use case of the pre-packaged meal service provider
HelloFresh to examine marketing demand. The company collects customer data across several fronts: food ingredients, customer demographics, and sale frequency. Since the product is a perishable food item, the company needs to optimize ingredient supply and product sales to avoid food waste and ensure fresh meal delivery to every customer. Diagnostic analyses around the culinary trends, demographic variations, and regional taste preferences helped the company identify an essential pairing: females in northeastern U.S. states are likely to order fish due to close proximity to the Atlantic Ocean, and a fish order over the weekend leads to variations of seafood orders early in the following week.
Figure 2: Levels of Analytics ranging mapping value vs difficulty
Predictive Analytics – What might happen in the future?
Predictive analytics identifies likely future trends, correlations, and causation based on historical evidence. Predictive models make generalizations about past data and predict behaviors or metrics performance for future data sets. These generalizations are effectively the structure of relationships, causation, and correlations between parameters that define the data set, and provide a measure of how well new information can fit into these given relationships. These predictions can be both short-term as well as long-term predictions; the accuracy and precision of predictive models depend on several factors, including the volume and quality (relevance) of data used to train the models; the complexity of the underlying predictive models and algorithms; and the computational resources required to train predictive models.
Use cases of predictive analytics can range from predicting sales of specific products for an upcoming holiday season, forecasting future cash flow needs of the organization, or predicting potential downtime on a manufacturing line. Depending on the complexity of the business problem and the availability of quality data, advanced Artificial Intelligence capabilities can be used to predict what might happen in the future accurately.
Prescriptive Analytics – What is the best action?
Prescriptive analytics provides actionable recommendations based on future scenarios. Predictive analytics takes the concept of data-driven decision-making based on applicable constraints in the future.
In this context, the business problem becomes an optimization problem that aims to find an optimal tradeoff between business goals and constraints. Unlike the traditional approach of testing future scenarios with techniques such as A/B testing a rules-based recommendation system, modern prescriptive analytics solve complex mathematical problems. The complexity depends on the number of parameter variables and constraints involved in solving an optimal tradeoff. To realistically emulate future circumstances, large volumes of data are collected across all parameters, and the optimization problem is solved using advanced AI capabilities running on resource-intensive machines. Depending on the quality of information and allowed margin of error, prescriptive analytics provides insights into the best course of action for the future.
Setting up a Data Platform: Best Practices for Data Analytics
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Technology Architecture: make data simple and accessible: In this context, a data lake architecture serves particularly well to process data streams in real-time and gain actionable insights faster than the traditional data warehousing system. Data Lake architecture makes data of all formats – structured, unstructured, and semi-structured – available for data analytics processing on-demand.
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Build a foundation of trust. Establish a data quality and management mechanism that helps process sensitive information to yield accurate insights in compliance with stringent regulatory requirements. Set up the policies and tools to discover, cleanse, protect and integrate critical data assets across the data analytics life cycle.
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Scalable system for on-demand insights. Complex data analytics problems require the processing of data workloads varying in volume. To gain actionable insights faster than your competition, the infrastructure resources should be scalable to meet the computing needs of your data analytics tools.
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Protect Data at all Costs. Establish data security and privacy capabilities beyond meeting governance and compliance obligations. Instead, focus your data security efforts on mitigating risks from cyber-attacks that are now increasingly sophisticated.
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Data Analytics as an end-to-end AI Pipeline. To maximize the value potential of your data analytics investments, follow a cycle of continuous improvement of an end-to-end AI pipeline. This data analytics life cycle begins with data preparation, followed by a data analytics model development and deployment. Most data analytics tools offer a variety of AI models and algorithms to fit specific data analytics problems. Much of the effort goes into creating a data pipeline and architecture to ensure that data is well managed and available for analysis to solve real-time business analytics problems.
Depending on the business use case and complexity of the analytics problem, your organization may use multiple types of data analytics: descriptive, diagnostic, predictive, and prescriptive. The idea of solving the analytics problem is to drive decisions and actions based on insights and patterns within data assets, which cannot be solved using traditional means of data analysis. Automated analytics processing with a data lakehouse solution can be a game changer in this context: automate data ingestion and processing, integrating multiple data sources into a unified platform and self-service analytics experience for users without the immediate involvement of IT or analytics experts.
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