Descriptive Analytics, Prescriptive Analytics, and Predictive Analytics
Nowadays, most business decisions are driven by emphasising data, but data is not the only goal here. Without valuable insights, figures alone are meaningless to lead further with actions.
To leverage business data, analytics solutions offer a convenient way. But the number of analytics solutions can be intimidating and may cover different analytics categories. On a regular basis, the volume of data in a business is interpreted, processed, and reconfigured. To handle this flood of data on an everyday basis, businesses are turning toward descriptive, predictive, and prescriptive analytics.
Let us have a more in-depth look into the type of analytics.
Under this commonly used form of data analysis, historical data is collected, organised, and presented in an easy-to-understand way. Unlike other analytics methods, descriptive analytics focuses on what has already happened in a business. This analytics is not used to draw predictions based on the insights found from data. Descriptive analytics is part of business intelligence and is used to extract insights from historical data. Standard applications are witnessed in data-warehousing and business intelligence tools
Simple mathematics and statistical tools like arithmetic, averages, and per cent changes are used in descriptive analytics. Whereas, complex calculations are required in predictive and prescriptive analytics. To make it easier for the audience to understand descriptive analytics, line graphs, pie charts, bar charts, and other visual tools are used.
Work process of Descriptive Analytics:
To discover historical data, data aggregation and data mining methods are used in descriptive analytics. The process of collecting and organising data to create chronological data sets is called Data Aggregation. The patterns, trends, and meaning are identified out of these data sets in the data mining phase. The last step is presenting the insights in such a simple way that the business audience quickly understands it.
Dan Vesset, the guest writer for IBM Business Analytics Blog, put descriptive analytics into five steps framework:
Deciding business metrics:
For evaluating business performance against the business goals, metrics are created. As per Vesset, descriptive analytics’s success relies heavily on essential performance indicator governance (KPI). He stated that ‘Without governance, there may not be a consensus regarding what the data means, thus guaranteeing analytics a marginal role in decision making’.
Data is sourced from different means, like reports, databases, the internet, or clients. Vesset says, ‘To measure accurately against KPIs, companies must catalogue and prepare the correct data sources to extract the needed data and calculate metrics based on the business’s current state.
Collecting and preparing data:
The data is then transformed and cleaned before taking it to further analysis stage. This time-consuming step is the most important one, as it ensures accuracy for the complete process.
To measure performance and find patterns in data, summary statistics, clustering, pattern tracking, and regression analysis are used.
Visual tools like bars, graphs, charts, etc. are used to present the data insights so that even a non-analytics person can understand it.
What can be found out with Descriptive Analytics?
In the day-to-day operations of a business, frequent use of descriptive analytics is required. Inventory, workflow, sales, and revenue can be considered the same as they provide a historical review of a company. Descriptive analytics reveals patterns and meaning of the historical data of a business.
According to DeZyre, an online learning platform, social analytics is an easy-to-understand example of descriptive analysis. For example, the comments that people post on social media sites, followers, likes, and posts are used for a better understanding of user attitude.
Merits and demerits of Descriptive Analytics:
The application of descriptive analytics does not require any comprehensive knowledge of predictive analytics, as it relies on historical data and simple calculations. This makes it easy to apply and quickly gain insights from the data.
The apparent limitation that these analytics possess is that it has a limited vision; it does not look beyond the data on the surface.
Examples of Descriptive Analytics:
Predicting and understanding the future of a business based on the data insights is known as Predictive Analytics. Predicting a business’ future based on the trends from historical data and customer insights can help the companies set realistic goals, manage performance, effective planning, and avoid risks. Businesses are trying to build three key solutions in analytics – forecasting, classification, and optimization. Predictive Modelling allows for achieving these solutions through statistical modelling and machine learning.
Forecasting is predicting or estimating future events or trends. By analyzing what happened in the past and keeping a keen eye on what is happening now, what is going to happen in the future is forecasted by analysts for businesses.
Some things are a bit easier to forecast in comparison to others. The rising time of the sun can be forecasted precisely, whereas another day’s lotto numbers cannot be accurate if forecasted. The predictability of an event will depend on the factors mentioned below:
For example: forecasting electricity demand can be an easy task, as it will be satisfying all the three conditions mentioned above. There is sufficient historical data on electricity demand, considering factors like holidays, weather conditions, and economic conditions. The forecasts can be accurate with skills and excellent model key variables and model electricity demand.
Types of Quantitative Forecasting Methods:
One of the simplest and easy to follow forecasting methods is the straight-line method. Historical data and trends are used by an analyst to predict future growth.
Moving averages focuses on the primary pattern of the data and estimate future values accordingly.
Simple Linear Regression:
For analyzing the relationship between variables for predicting the future, regression analysis is used.
Multiple Linear Regression:
Analysing and forecasting two or more independent variables for predictions, analysts use multiple linear regression.
Predictive modeling is a crucial part of the data science umbrella. Based on the historical data, the final submissions are generated using predictive modeling. This system uses statistics to forecast in-depth insights and outcomes from a data set that further helps the businesses to make decisions. Both data science and predictive modelling use statistics as common background.
How data science creates a Predictive Model:
Creating a Predictive Model:
Models can be created to run algorithms on the data set with the help of various tools and software solutions.
To evaluate the performance of the model, the model is tested on historical data.
Visualization tools are used for better understanding and for validating the model.
As per the appropriate solution to the problem, the best fit of the model is evaluated.
Classification can be performed on both unstructured and structured data. When data is categorized into a given number of classes is called classification. The main focus of classification is to identify the categories the data may fall under.
Data can be classified into the following categories:
Input data is categorized into a specific category.
Class labels/categories for the new data are predicted based on the input values given for training.
When a phenomenon is being observed from an individual measurable property, it is called a feature.
Classifying data in two possible outcomes is a binary classification.
Multi-class classification is classifying in more than two classes; each sample is assigned to only one target label.
Each sample under multi-label classification is assigned to a set of target labels.
How data science builds a classification model:
Optimisation is defined as maximising or minimising a real function to compute the value of the function by systematically choosing the inputs. When optimising, an analyst is always looking for the best solution.
Almost all data science and machine learning algorithms are viewed as solutions to optimisation problems. Optimising models through data science will help in:
Work process of Predictive Analytics:
Predictive analytics is based on probability. Data mining, statistical modelling, and machine learning algorithms are followed under predictive analytics to anticipate possible future outcomes. Mathematical relationships between variables, classification, regression, and clustering techniques are used to predict the outcomes.
What can be found out with Predictive Analytics?
Besides informing the business about the future, predictive analytics also helps the executives and managers take a data-driven approach while preparing strategy and making decisions. Additionally, to understand customer behaviour and purchasing patterns, predictive analytics can be used to identify the sales trend of a business. Other than these, predictive analytics can also forecast a few key business needs such asinventory demands, supply chain, and operations.
Merits and demerits of predictive Analytics:
Predictive analytics can be used as an essential tool to anticipate the future for better business strategies, but we cannot rely on it entirely. Being based on probability, predictive analytics is never completely accurate. The analytics can help the business to improve the following:
Examples of Predictive Analytics:
Descriptive analytics tells us what has happened, predictive analytics tells us what can happen, and prescriptive analytics tells us what should be done and drive actionable insights for the solution. The final and most superior stage of data analytics helps executives, managers, operational employees to make the best decisions based on the available data and calls business to action.
Work process of Prescriptive Analytics:
Prescriptive analytics works based on the insights fetched from descriptive and predictive analytics and further recommended the best possible actions for the business. This is the most intricate stage of the data analytics process, as it requires more specialised knowledge of business analytics to perform day to day operations.
Halo Business Intelligence states that to make predictions and recommendations, rules, statistics, and machine learning algorithms are to be applied to both internal and external data. Internal data is from within the business, and external data is the data derived from social media. Machine learning capabilities are far from the capabilities a human possesses.
There is a misconception that machine learning and prescriptive analytics are the same. Machine learning is a part of artificial intelligence. It is used to understand large sets of data without defined directions. Whereas, prescriptive analytics uses statistical tools to go through historical data and use the derived insights accordingly.
What can be found out with prescriptive Analytics?
The job of prescriptive analytics is to foresee what, when, and why something may happen. Recommendations are made after considering each possible repercussion of every decision option. A business tends to take the most advantage of the recommendations made for alleviating future opportunities of the company. Before any decisions are made, prescriptive analytics make it possible to consider every possible outcome as it anticipates multiple futures.
Insights can have a significant impact on business strategies and decisions if prescriptive analytics is performed effectively. Companies will look forward to improving their production, customer experience, and business growth based on these insights.
Merits and demerits of prescriptive Analytics:
As discussed above, if prescriptive analytics is performed effectively, the invaluable insights will make the best decisions and strategies for business. For performing prescriptive analytics, large data is required to produce useful insights, which is, however, not always possible. Also, this analytics relies on machine learning, which cannot always be held accountable for the external variable. Additionally, the use of machine learning reduces the possibility of errors.
Examples of Prescriptive Analytics:
John Dryden, back in the 17th century wrote, “He who would search for pearls must dive below.” Although the reference of the quote is not towards data science, it does perfectly describe its essence. There are another two types of analytics, apart from predictive, prescriptive, and descriptive. These are:
Another form of data analytics that helps in solving critical challenges is diagnostic analytics. Diagnostic analytics tend to find solutions for the problems by finding out why something happened and what the root cause behind it is. When a business with business intelligence dashboards wants to drill down inside the data, diagnostic analytics plays its part in data to find the reasons that affect the business. Assimilating diagnostic analytics with descriptive analytics helps the business in finding data patterns to build précised data-based decision models.
Companies can identify glitches and abnormalities using diagnostic analytics, for example, a sudden spike in the sales or increased website traffic, etc. To help the company understand the glitches, a data analyst will be required to single out the right data set; this may also involve gathering information from external sources at times. Using different data analytics types like regression analysis, filtering, probability theory, etc., the analysts find the answers.
Let us have a look at other examples: with diagnostic analytics, an insurance company will look for insights into age group and target area, whereas a hotel chain would look for the demand for VIP suites in a region. Meanwhile, an online retail store will use diagnostic analytics to determine the highest sales region for a particular product.
The most advanced form of data analytics is cognitive analytics. Cognitive analytics combines numerous intelligent technologies like deep learning models, machine learning algorithms, artificial intelligence, and others to process the data and derive results accordingly. To make cognitive applications smarter and effective over time, this analytics is inspired by mirror human thinking. Text and video analysis, image analysis, self-learning thermostats, web ready fridges, machine sensors, implanted medical devices and smart home sensors, natural language processing, etc can be currently considered as early examples of cognitive analytics.
Which analysis do your business needs?
Before choosing the right analytics your business needs, answer the questions given below:
The answers to these questions will help you evaluate the best-suited analytics strategy for your business. From the simplest to the most advanced, the strategy should allow incrementally implementing the analytics type. The next step is to design, implement, and launch the data analytics solution with the optimal technology stack.
If you are willing to hire trained highly qualified data analytics specialists, instead of going on with the in-house team for these tasks, it may turn a bit lengthy and costly. It is advisable to turn to an experienced data analytics provider with a background in your industry, to maximise the return of interest from implementing data analytics in your business. The right analytic strategy will figure out everything for your business, from the analysis for current data to selecting the right blend of data algorithms to bring the best solutions for the business.
Let us have a look at different surveys from 2016-2019 to identify if there is a popular type of data analytics.
More than 2,000 executives were asked for the 2016 Global Data and Analytics Survey to choose a category that best described their company’s decision-making process. What type of analytics they relied on most was further questioned to C-suite. The answers were: predictive analytics (36%) led in the “Highly data-driven” category; diagnostic analytics topped the list (34%) in the “Somewhat data-driven” category; descriptive analytics dominated (58%) in the “Rarely data-driven decision-making” category.
For BARC’s BI Trend Monitor 2017 survey, opinion on the growing importance of advanced analytics was shared by 2800 executives. For predictive and prescriptive analytics types, advanced analytics term was used here.
Advanced analytics was for the first time considered “critical” or “very important” by a majority of respondents, according to the 2018 Advanced and Predictive Analytics Market Research.
C-suite named advanced analytics, within the BARC’s BI Trend Monitor 2019 survey, among the most important business intelligence trends.
future in analytics with SRM and LEARNXT:
To discover insights that can help companies make decisions, deliver better products; create efficient business strategies, businesses are moving towards using data. The potential utility of descriptive, predictive, and prescriptive in the broad field of data analytics is crystal clear and enormous.
LEARNXT in collaboration with SRM provides students with various data science courses. Students willing to learn data science can enrol them in various MBA, MSc, PG Diploma, and certificate data science courses offered by this collaboration. LEARNXT is a digital learning platform that aims at empowering people to achieve their goals through education. You will get hands-on experience and insights through case studies, presentations, assignments, and assessments into data science’s practical and theoretical aspects.
Data analytics seems to change near the future of the IT industry. As per Deloitte’s Global Perspectives for Private Companies Report, many Australian private companies plan to invest in business intelligence and data analytics in the future. The Analytics Impact Index said that 60 per cent more profits can be seen by the business leaders in analytics, there will be a market increase in the coming years because of the adoption of business analytics.
When implementing more advanced analytics, creating a staff of analytics, experts can be costly for companies. Additionally, some businesses may require outsourcing the work to an analytics team when bringing analytics into their business strategy.
Stay tuned to LEARNXT to know more about descriptive analytics, prescriptive analytics, and predictive analytics.
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