What are the 4 types of analytics?

When I talk to young analysts entering our world of data science, I often ask them what they think is data scientist’s most important skill.  Their answers have been quite varied.

My message to them is that their most important skill will be their ability to translate data into insights that are clear and meaningful to a non-quant. The Swedish statistician Hans Rosling is famous for this. It’s an often overlooked skill. The following TedTalk by Hans Rosling sheds some light:

On this theme, it would be worth unpacking some of the tools used to help individuals understand the role of analytics in helping develop valuable insights. One such tool is the 4-dimensional paradigm of analytics.

Simplistically, analytics can be divided into four key categories. I’ll explain these four in more detail below.

1. Descriptive: What is happening?

This is the most common of all forms. In business it provides the analyst a view of key metrics and measures within the business.

An examples of this could be a monthly profit and loss statement. Similarly, an analyst could have data on a large population of customers. Understanding demographic information on their customers (e.g. 30% of our customers are self-employed) would be categorised as “descriptive analytics”. Utilising effective visualisation tools enhances the message of descriptive analytics.

2. Diagnostic: Why is it happening?

This is the next step of complexity in data analytics is descriptive analytics. On assessment of the descriptive data, diagnostic analytical tools will empower an analyst to drill down and in so doing isolate the root-cause of a problem.

Well-designed business information (BI) dashboards incorporating reading of time-series data (i.e. data over multiple successive points in time) and featuring filters and drill down capability allow for such analysis.

3. Predictive: What is likely to happen?

Predictive analytics is all about forecasting. Whether it’s the likelihood of an event happening in future, forecasting a quantifiable amount or estimating a point in time at which something might happen – these are all done through predictive models.

Predictive models typically utilise a variety of variable data to make the prediction. The variability of the component data will have a relationship with what it is likely to predict (e.g. the older a person, the more susceptible they are to a heart-attack – we would say that age has a linear correlation with heart-attack risk). These data are then compiled together into a score or prediction.

In a world of great uncertainty, being able to predict allows one to make better decisions. Predictive models are some of the most important utilised across a number of fields.

Here are the Top Pitfalls to avoid in Predictive Analytics

4. Prescriptive: What do I need to do?

The next step up in terms of value and complexity is the prescriptive model.  The prescriptive model utilises an understanding of what has happened, why it has happened and a variety of “what-might-happen” analysis to help the user determine the best course of action to take. Prescriptive analysis is typically not just with one individual action, but is in fact a host of other actions.

A good example of this is a traffic application helping you choose the best route home and taking into account the distance of each route, the speed at which one can travel on each road and, crucially, the current traffic constraints.

Another example might be producing an exam time-table such that no students have clashing schedules.

Conclusion

While different forms of analytics may provide varying amounts of value to a business, they all have their place. To find out how data analytics could bring further value to your business, please drop us a mail to arrange for a chat.

Original. Reposted with permission.

Bio: Thomas Maydon is the Head of Credit Solutions at Principa. With over 13 years of experience in the Southern African, West African and Middle Eastern retail credit markets, Tom has primarily been involved in consulting, analytics, credit bureau and predictive modelling services.

Analysis of data is a vital part of running a successful business. When data is used effectively, it leads to better understanding of a business’s previous performance and better decision-making for its future activities. There are many ways that data can be utilized, at all levels of a company’s operations.

There are four types of data analysis that are in use across all industries. While we separate these into categories, they are all linked together and build upon each other. As you begin moving from the simplest type of analytics to more complex, the degree of difficulty and resources required increases. At the same time, the level of added insight and value also increases.

Four Types of Data Analysis

The four types of data analysis are:

• Descriptive Analysis
• Diagnostic Analysis
• Predictive Analysis
• Prescriptive Analysis

Below, we will introduce each type and give examples of how they are utilized in business.

Descriptive Analysis

The first type of data analysis is descriptive analysis. It is at the foundation of all data insight. It is the simplest and most common use of data in business today. Descriptive analysis answers the “what happened” by summarizing past data, usually in the form of dashboards.

The biggest use of descriptive analysis in business is to track Key Performance Indicators (KPIs). KPIs describe how a business is performing based on chosen benchmarks.

Business applications of descriptive analysis include:

• KPI dashboards
• Monthly revenue reports
• Sales leads overview

Diagnostic Analysis

After asking the main question of “what happened”, the next step is to dive deeper and ask why did it happen? This is where diagnostic analysis comes in.

Diagnostic analysis takes the insights found from descriptive analytics and drills down to find the causes of those outcomes. Organizations make use of this type of analytics as it creates more connections between data and identifies patterns of behavior.

A critical aspect of diagnostic analysis is creating detailed information. When new problems arise, it is possible you have already collected certain data pertaining to the issue. By already having the data at your disposal, it ends having to repeat work and makes all problems interconnected.

Business applications of diagnostic analysis include:

• A freight company investigating the cause of slow shipments in a certain region
• A SaaS company drilling down to determine which marketing activities increased trials

Predictive Analysis

Predictive analysis attempts to answer the question “what is likely to happen”. This type of analytics utilizes previous data to make predictions about future outcomes.

This type of analysis is another step up from the descriptive and diagnostic analyses. Predictive analysis uses the data we have summarized to make logical predictions of the outcomes of events. This analysis relies on statistical modeling, which requires added technology and manpower to forecast. It is also important to understand that forecasting is only an estimate; the accuracy of predictions relies on quality and detailed data.

While descriptive and diagnostic analysis are common practices in business, predictive analysis is where many organizations begin show signs of difficulty. Some companies do not have the manpower to implement predictive analysis in every place they desire. Others are not yet willing to invest in analysis teams across every department or not prepared to educate current teams.

Business applications of predictive analysis include:

• Risk Assessment
• Sales Forecasting
• Using customer segmentation to determine which leads have the best chance of converting
• Predictive analytics in customer success teams

Prescriptive Analysis

The final type of data analysis is the most sought after, but few organizations are truly equipped to perform it. Prescriptive analysis is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision.

Prescriptive analysis utilizes state of the art technology and data practices. It is a huge organizational commitment and companies must be sure that they are ready and willing to put forth the effort and resources.

Artificial Intelligence (AI) is a perfect example of prescriptive analytics. AI systems consume a large amount of data to continuously learn and use this information to make informed decisions. Well-designed AI systems are capable of communicating these decisions and even putting those decisions into action. Business processes can be performed and optimized daily without a human doing anything with artificial intelligence.

Currently, most of the big data-driven companies (Apple, Facebook, Netflix, etc.) are utilizing prescriptive analytics and AI to improve decision making. For other organizations, the jump to predictive and prescriptive analytics can be insurmountable. As technology continues to improve and more professionals are educated in data, we will see more companies entering the data-driven realm.

Conclusion

As we have shown, each of these types of data analysis are connected and rely on each other to a certain degree. They each serve a different purpose and provide varying insights. Moving from descriptive analysis towards predictive and prescriptive analysis requires much more technical ability, but also unlocks more insight for your organization.

What are the 5 data analytics?

What are the 4 steps of data analytics?

How many types of analytics are there?

What is the most common type of analytics?