Effective data analytics helps you or a business enterprise examine data to answer questions, identify trends, and find new insights. Business operations benefit the most from data analytics that facilitates data-driven decisions and improves market positioning despite stiff competition and internal environment hurdles. You will need formidable skills and adequate resources to apply data analytics for various business aspects such as; marketing, product management, human resource management, and global expansion.
The requisite techniques can be outsourced to utilize frameworks and software such as Power BI, Google Charts, Zoho Analytics, and Microsoft Excel. The type of insight derived from data analysis depends on your specific approach. This article seeks to inform you about the Descriptive, Diagnostic, Predictive, and Prescriptive data analytic techniques that will transform your decision-making insights.
- Descriptive Analytics
This category of analyzing information gives you a comprehensive idea of what has been happening over time and presently. The foundational approach relies on pulling trends from raw data and responding to the question, “What happened?” You should successfully identify dips and spikes concerning a product, activity, or overall trend by incorporating additional concepts of data visualization to communicate the results. Some of the most reliable visualization techniques include bar charts, maps, and graphs of various forms that can display different colours for each result.
Descriptive analytics do not respond to concerns of cause-and-effect, but you can engineer the raw data to show dates, volumes, and other values for a digestible snapshot. To understand the idea of descriptive data analysis, consider the information from Google Analytics about a website. The information can be achieved by applying either data aggregation or mining. Conducting data aggregation is when you gather information and present a summarized format. Data mining then follows when you explore to generate patterns and trends.
- Diagnostic Analytics
The diagnosis step facilitates answering the logical question, “Why the trend anomalies?” by comparing trends shown in the descriptive analysis. This analysis technique explains the reasons behind visible movements and correlations between variables from the raw data. You will successfully determine causal relationships by running diagnostic analytics because logical reasons depend on reading the outcomes from existing data. Furthermore, analysts get to the root of an issue by connecting the dots, as the past significantly affects the present and the future. After all, we should base decisions on critical thoughts to avoid fatal errors.
We all must satisfy the inquisitive nature of understanding why some trends developed, and diagnostic analyses serve the purpose well. You can get the root cause of anomalies in any trend by importing additional information to match events and trends in an activity. The diagnostic approach facilitates digging deeper and finding reasons to justify breaks from normal. For example, when the data analysis indicates a spike in sales, you can search for events that mentioned the product and generated increased popularity. Your decisions will be more credible and justifiable when descriptive and diagnostic analyses combine to provide a wholesome view of business operations.
- Predictive Analytics
This data analysis involves analyzing the trends alongside present information to predict the future. You can benefit tremendously from information snippets of the days to come because the advantage facilitates planning, organizing, and putting all hands on deck. The predictive analysis relies on historical data generated from a combination of descriptive and diagnostic analytics. The technical approach answers the question, “What will likely happen in future?” The trends often repeat even when the micro and macro environmental factors vary.
Future planning should utilize predictive models based on impactful variables to inform corrective measures. Predictive analytics can enable a classification algorithm of logistic regression that gives binary outcomes when independent variables are used. The depth of predictive analysis varies according to the independent and dependent variables because establishing future trends requires different evaluations of historical inputs. Your ability to incorporate machine learning promises more accurate evolutions of insights into the future. Machine learning enhances decision accuracy by reducing guesses, as predictions are objective and dependent on historical data.
- Prescriptive Analytics
The prescriptive analyses guide the users to determine the best course of action after combining all the data and variables. The prescriptions act as roadmaps for suggesting actions in tandem with past and present events to avoid possible mistakes. Making data-driven decisions requires actionable plans targeting consumers, producers, and external stakeholders. The decisions made using prescriptive analytics are justifiable because the evidence is derived from all three types of data analytics mentioned above. Machine-learning algorithms apply “if” and “else” statements to evaluate large volumes of data and recommend options that optimize results. You will achieve better results by applying prescriptive analytics than going blindly into the future.
Evaluating the different possible outcomes based on data entries enables decision-makers to take actions that optimize success strategically. Knowing the various options possible in future is the most complex level of all four data analytics because all patterns are used as input. Using predictive analytics requires computational, statistical, algorithmic, and machine learning techniques for the decisions to reflect comprehensive traits of the past into the future. Predictions do not always produce the desired results, but business decisions require facts to achieve results compatible with reality.
Verdict
Descriptive, diagnostic, predictive, and prescriptive data analytics generate the most benefits when applied in tandem because each technique depends on the other. Major business operations decisions should lead to better performance than in previous seasons by avoiding mistakes. Your understanding of factors causing trends in the raw data facilitates the evaluation of underlying issues during the diagnostic analysis. The predictive analysis suggests possible future trends, while prescriptive techniques identify the actions needed to achieve positive growth.
Strengthening data analytical skills involves different activities, and intuition can supplement objective data. You might not effectively apply data analytics if the raw data does not capture all aspects of factors affecting the issue. The various data analytic methods require skills to execute effectively and the raw data should provide all necessary information to facilitate the process. Using the data analytic methods significantly improves your understanding of any issue that has raw data because causes, trends, and future predictions are achievable.