What is Descriptive Analytics?
Descriptive analytics involves interpreting past data to gain insights into the evolving dynamics within a business. It uses historical data to make comparisons across different reporting periods for the same company, such as quarterly or annually, or among similar entities within the industry.
The prevalent financial metrics commonly reported, like year-over-year (YOY) pricing alterations, month-over-month sales expansion, user counts, or revenue per subscriber, are outcomes of descriptive analytics. These metrics collectively describe the occurrences within a business over a specific timeframe.
How Descriptive Analytics Works
Descriptive analytics analyse a set of raw data to derive meaningful conclusions that managers, investors, and stakeholders can grasp and use. This data offers a picture of past performance and enables comparisons with similar periods, aiding in understanding variations. Moreover, it facilitates benchmarking against industry peers, identifying both strengths and weaknesses to guide management strategies.
Consider a sales report indicating $1 million in sales — while seemingly impressive, this figure lacks context. A 20% month-over-month decline raises concern, whereas a 40% year-over-year increase suggests successful sales or marketing strategies. However, a broader perspective including targeted growth is key for a comprehensive assessment of the company’s sales performance.
Descriptive analytics stands as a foundational aspect of business intelligence, often tailored to specific industries (such as seasonal fluctuations in shipment completion times). Nevertheless, there are universally accepted metrics prevalent in the financial sector.
What information does descriptive analytics provide?
Descriptive analytics serves as an important tool for companies seeking insights into their performance. Tailored to specific industries, this tool enables businesses to evaluate their position and performance in the market by analysing historical data, such as revenue and sales growth, in comparison to competitors. Additionally, it aids in identifying prevailing financial trends and setting individual goals within the organisation.
How descriptive analytics is used?
Descriptive analytics enables companies to gauge their performance and determine areas of inefficiency. Corporate management gains the ability to recognise improvement opportunities and inspire diverse teams to implement changes for sustained success.
Data aggregation and data mining serve as the primary methods for collecting data in descriptive analytics. The process begins with gathering data and then organising it into comprehensible information. This information equips management with meaningful insights into the company’s position.
Consider the return on invested capital (ROIC) as an example of descriptive analytics. It analyses three key data points: net income, dividends, and total capital to generate a straightforward percentage. This percentage facilitates comparisons between a company’s performance and that of others, offering a clear understanding of its place in the market.
How can companies use descriptive analytics?
Descriptive analytics constitutes an analytical approach to answer the query, “What transpired?” It analyses historical data to understand previous changes, allowing companies to compare data from various reporting periods or similar entities. Through the application of descriptive analytics, businesses can effectively determine operational inefficiencies and adjustments for the future.
Steps in Descriptive Analytics
To successfully integrate descriptive analytics into their business strategy, companies can follow a series of steps. The following list outlines these steps and provides a description of each:
- Metrics Identification. Prior to commencing the analytics process, it is important to determine the specific metrics the company aims to analyse. These metrics may include quarterly revenue, annual operating profit, or other key performance indicators.
- Data Identification and Gathering. This step involves locating all necessary data from both internal and external sources, including several databases. Thoroughly identifying and collecting this data is fundamental for generating accurate results.
- Data Compilation. Once all the required data is identified and gathered, the next phase involves preparing and consolidating it. Ensuring data accuracy and formatting it into a unified structure are integral components of this step.
- Data Analysis. Employing diverse tools and methodologies for analysing datasets and figures is a critical aspect of this phase. This step involves applying different analytical techniques to derive meaningful insights from the compiled data.
Upon completing the steps, it is imperative to present the analysed data to relevant stakeholders effectively. Using appropriate visual aids, such as charts, graphics, videos, and other tools, facilitates conveying insights to analysts, investors, management, and other involved parties. These provide a comprehensive understanding of the company’s trajectory and performance trends.
Advantages of Descriptive Analytics
Descriptive analytics, when integrated into the corporate workflow, offers an advantage by simplifying information dissemination and facilitating comprehension of intricate concepts for all key stakeholders. This is achieved primarily through easily interpretable visuals like charts and graphs. It’s commonplace to find comparative analyses displaying the company’s previous status alongside its current position.
This approach enables major stakeholders to assess the company’s performance relative to its industry competitors. Given that key variables (such as production costs, revenue streams, and product offerings) tend to align, it allows for a direct comparison. This empowers companies to identify potential areas for improvement within their business plans and models.
Disadvantages of Descriptive Analytics
While descriptive analytics aids in understanding past occurrences, it lacks the capacity to provide insights into future expectations. Companies cannot rely on it to anticipate the impact of market dynamics, shifts in supply and demand, economic fluctuations, and other variables on their future trajectory.
Stakeholders may encounter difficulties interpreting the nuances, particularly when explicit or implicit biases influence the analysis. For instance, stakeholders might selectively focus on favourable metrics while disregarding others. This selective approach may create an illusion of company profitability and mask areas that need change.
Other Forms of Analytics
Descriptive analytics presents information in a readily understandable manner, maintaining a perpetual necessity for this form of analysis. However, the focus may increasingly shift towards newer fields of analytics, such as predictive, prescriptive, and diagnostic.
These analytics build upon descriptive analytics by incorporating varied data sources to forecast probable outcomes in the short run. Going beyond mere information provision, these forward-looking analytics actively aid decision-making processes. Furthermore, they recommend courses of action aimed at optimising positive results while mitigating negative ones.
Predictive Analytics
Predictive analytics, as its name suggests, aims to forecast future performance by leveraging statistical analysis and modelling techniques. By analysing current and historical data, it assesses the likelihood of similar outcomes occurring in the future.
Businesses that use predictive analytics stand to gain advantages by detecting and rectifying inefficiencies. Moreover, they can optimise the use of resources like supplies, labour, and equipment, uncovering improved and more efficient operational strategies.
Prescriptive Analytics
Prescriptive analytics helps companies to use technology in analysing data to strategise and achieve targeted outcomes. By considering specific scenarios, available resources, and both historical and present performance, it generates actionable insights for future decision-making.
Stakeholders that use prescriptive analysis are poised to make important decisions across different timeframes. These decisions may involve determining the need for increased investment in research and development (R&D), assessing the viability of continuing a particular product offering, or evaluating the potential to enter a new market.
Diagnostic Analytics
Diagnostic analytics uses data to explore the connections between variables and uncover the underlying reasons for specific trends. It’s a method to determine the causes behind what happened. This analysis can be conducted either manually or with the aid of computer software.
In contrast to other analytics forms, diagnostic analytics doesn’t focus on comprehending a company’s past performance or forecasting its future. Its primary aim is to assist key stakeholders in identifying the fundamental reasons behind an event and implementing necessary changes for the future.
Summary
Descriptive analytics serves as a great starting point for companies to delve into their performance metrics. This method stands out as one of the most accessible forms of data analysis.
By offering a direct comparison of metrics, like quarter-over-quarter revenue, it provides management, investors, and analysts with a clear insight into the company’s performance vis-à-vis comparable benchmarks. Using historical data aids key stakeholders in comprehending past events, thereby helping them to make more informed decisions for the future.
DISCLAIMER: This article is for informational purposes only and is not meant as official business advice.