What is a Descriptive Analytics?
Descriptive analytics is the evaluation of historical data to understand developments in a firm better. Descriptive analytics is the use of historical data to make comparisons with other reporting periods for the same company (e.g, quarterly or annual) or with others in the same industry. The most typically reported financial metrics are the results of descriptive analytics, such as year over year (YOY) pricing changes, month-over-month sales increase, user count, or total revenue per subscriber. All of these measures explain what happened in a business over a specific period. Descriptive analytics examines a wide range of raw data and concludes that managers, investors, and other stakeholders may find valuable and intelligible. This information provides an accurate view of historical performance and how it differs from other comparable eras. It can also be used to compare performance against others in the same industry. These performance measures can be used to identify areas of strength and weakness, informing management initiatives. Descriptive analytics is one of the most basic types of business intelligence that businesses use. It can frequently be industry-specific (for example, seasonal variations in shipping completion times), although there are widely acknowledged standards that apply throughout the finance industry. Companies can employ descriptive analytics to acquire useful insights about their performance. Because it is an industry-specific tool, one company can use it to compare its performance and market position to that of its competitors by analyzing prior performance, such as revenue and sales growth. It is also essential to identify current financial trends, as well as individual ambitions inside the organization.
Fast Fact
According to a recent study, 91.9% of firms saw meaningful value from data and analytics efforts in 2023. Furthermore, three out of every five firms use data analytics to drive business innovation.
What are the various steps required in conducting descriptive analytics?
Descriptive analytics entails numerous critical steps. The initial step is to collect relevant data from a variety of sources, including sales records, customer databases, and operations logs. This data can arrive in many formats and from multiple systems, necessitating careful integration. The next step is data cleaning, which includes removing duplicates, fixing errors, and dealing with missing numbers to ensure the dataset's accuracy and reliability. Data aggregation is the process of combining and summarizing data to offer a unified view. This process frequently includes organizing data into structured representations, such as databases or spreadsheets, to aid with analysis.The next phase is data analysis, which involves using statistical methods and computational tools to understand the data. This includes computing descriptive statistics such as mean, median, mode, and standard deviation to summarise the data's central tendencies and distribution. Visualization technologies are then used to generate graphs, charts, and dashboards that make complex data easier to interpret. The third stage is to interpret the results, which involves translating the findings into actionable insights. This entails finding trends, patterns, and anomalies that can help guide decision-making. Throughout the process, ongoing feedback and iteration are required to develop the analysis and guarantee that it satisfies corporate objectives.
How can companies take advantage of Descriptive Analytics?
Companies can benefit from descriptive analytics by employing them to improve decision-making processes. Businesses can improve their understanding of performance metrics, consumer behavior, and market trends by transforming raw data into valuable insights. This improved understanding enables businesses to make better decisions about product development, marketing tactics, and operational improvements. Businesses can manage inventory levels and personnel requirements by, for instance, examining sales data over time to uncover seasonality patterns. Furthermore, understanding client demographics and purchasing behaviors enables tailored marketing strategies that are more likely to convert leads into sales, resulting in increased income.
Furthermore, descriptive analytics helps to uncover areas of operational inefficiencies and potential cost reductions. Companies can identify bottlenecks and wasteful areas by evaluating data on supply chain operations, production processes, and staff performance. This results in more efficient resource allocation and streamlined operations. In addition, continuous monitoring of key performance indicators (KPIs) using descriptive analytics allows firms to recognize and respond to issues before they escalate. This proactive strategy not only improves overall operational efficiency but also increases customer satisfaction by guaranteeing that high-quality products and services are delivered consistently.
Which components are included in descriptive analytics?
Descriptive analytics consists of many important components that summarize and interpret historical data in order to find patterns and trends. These components include data aggregation, which collects and combines data from multiple sources; data mining, which investigates large datasets to uncover undetected trends and relationships; and data visualization, which utilizes graphical representations such as charts, graphs, and dashboards to render complex data easier to understand. Another important component is statistical analysis, which uses mathematical tools to examine and summarize data, revealing underlying patterns and relationships. Furthermore, descriptive analytics frequently employs key performance indicators (KPIs) and metrics to assess and track performance over time. Data cleansing is also an important step in ensuring that the data used is reliable and consistent by detecting and correcting problems. Furthermore, reporting systems play an important role in helping firms comprehend their current situation by giving extensive summaries and reports. These components work together to provide firms with significant insights into historical performance, which guide decision-making and strategic planning.
What value does Descriptive Analytics, along with primary research, bring to the table?
Descriptive analytics, when supplemented with primary research, provides a full insight into both historical performance and current market conditions. Descriptive analytics analyzes historical data to detect trends, patterns, and anomalies. This retrospective study helps businesses understand what happened over a certain period, allowing them to make informed decisions based on factual facts. Businesses may discover areas of success and potential development, optimize their operations, and allocate resources more effectively by aggregating and visualizing data. Primary research, on the other hand, entails gathering fresh information directly from sources like surveys, interviews, and observations. This strategy delivers real-time information about client behavior, preferences, and new trends. When paired with descriptive analytics data, primary research provides a more complete and nuanced view of the present market scenario. This synergy enables organizations to test previous data results against current market attitudes, modify their plans to change client wants, and maintain a competitive advantage. Together, these approaches form a solid foundation for strategic planning and decision-making, improving the accuracy and relevance of business insights.
How can Descriptive Analytics with secondary market research correlate?
The synergy between descriptive analytics and secondary market research produces a strong foundation for comprehending market dynamics and guiding strategic choices. Descriptive analytics uses historical data from internal sources, including sales records, customer databases, and operational indicators, to uncover trends, patterns, and anomalies in a business. This internal study assists businesses in understanding their past performance, consumer behavior, and operational efficiency by offering a complete perspective of historical trends as well as insights into their operations. Secondary market research is reviewing data gathered from external sources such as industry studies, market surveys, and competition analysis to gain a broader perspective on market conditions and industry trends. Businesses that combine descriptive analytics with secondary market research can correlate their internal performance with external market dynamics. This connection allows businesses to compare their performance to industry norms, find market opportunities, and forecast external influences that may affect their operations. Combining these two methodologies results in a more comprehensive study allowing for strategic decision-making that is in line with both internal capabilities and external market realities.
Author's Detail:
Manoj Phagare /
LinkedIn
Manoj Phagare is a dynamic and results-driven research analyst with a passion for transforming raw data into actionable insights. Armed with a solid foundation in market research and data analysis and working in various domains including chemical & materials and paints & coatings. He thrive on the challenge of uncovering patterns, trends, and opportunities that drive strategic decision-making.His analytical mindset, coupled with effective communication skills, allows him to bridge the gap between data analysis and practical business applications.
In his current role, Manoj is a key player in market research and competitive analysis. He have a proven track record of synthesizing disparate data sources, employing statistical models, and delivering comprehensive insights. He have played a pivotal role in shaping evidence-based strategies that fueled the success of key business initiatives and Collaborating with cross-functional teams.Manoj remains an invaluable asset in the dynamic landscape of market research.
