computer science

Guided analytics

Guided analytics is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Guided analytics rather than just read about it. In short: Guided analytics is a sub-field at the interface of visual analytics and predictive analytics focused on the development of interactive visual interfaces for business intelligence applications. Such interactive applications serve the analyst to take important decisions by easily extracting information from the data.

Key takeaways

Guided analytics belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Guided analytics to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Guided analytics from memory before moving on to harder problems.

Reference excerpt

Guided analytics is a sub-field at the interface of visual analytics and predictive analytics focused on the development of interactive visual interfaces for business intelligence applications. Such interactive applications serve the analyst to take important decisions by easily extracting information from the data.

Overview Guided analytics applications lie in the intersection between business intelligence and predictive analytics. A great number of business analysts rely on business intelligence tools to flexibly extract specific information from data. It is often required to automatically run an analysis on the raw data before information can be extracted. However, it is not always possible to automate the entire process from any kind of raw data access to the extraction of useful information. Furthermore, the expertise of data scientists will be necessary each time new data or questions come into the picture. This is especially true when predictive analytics (machine learning) is applied. To create an application that is flexible to different data problems and usable by the domain experts without continuous help by a data scientist, it is required to insert a number of interaction points in the analysis process. The interactions will determine the sequence of steps in the analysis. In this way, the application guides the user with no need of customization by the data science expert. Guided analytics is about building such interactive applications. By mixing and matching automation and interaction, guided analytics applications empower business analysts to independently extract insights and future outcomes from the data.

History The term “guided analytics” was coined for the first time in an online magazine by a TIBCO expert in 2004. Back then, predictive analytics in business intelligence was fairly new. Guided analytics applications were focused entirely on interactive visualization to ease the access to trustworthy KPI metrics through a database. This was seen particularly useful in the pharmaceutical industry. In 2007, a paper published by IEEE Computer Society Press presented guided analytics as one of the future trends in business intelligence. In 2012, one of the main visual analytics experts, Ben Shneiderman, is co-authored in a paper mentioning guided analytics among the “interactive dynamics for visual analysis”. A good quote from J. Heer and B. Schneirderman paper is “visual-analysis systems can incorporate guided analytics to lead analysts through workflows for common tasks”. In 2016, vendors like Qlik and Tableau proposed guided analytics for exploratory data analysis tasks. Around 2018, guided analytics was mentioned by business intelligence vendors when describing applications for advanced analytics use cases, rather than only for interactive dashboards. In fact, guided analytics can also be used in each phase of the CRISP-DM data science cycle. In 2018 and 2019, KNIME has released a number of analytical blueprints for guided analytics workflows with a special focus on automated machine learning. KNIME proposed guided analytics as a key mechanism to abstract data science for other users.

References

Worked examples

Example 1 — a first encounter with Guided analytics

Start with the simplest possible case. Write down what Guided analytics claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Guided analytics before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Guided analytics ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Guided analytics

In research: Guided analytics appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Guided analytics in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Guided analytics is common in secondary-school and first-year university syllabi. It links to neighbouring topics Big data, Business intelligence terms, Computational science, so understanding it makes those chapters shorter.
In everyday life: Look for Guided analytics outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Guided analytics in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Guided analytics means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Guided analytics out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Guided analytics in simple terms?

Why does Guided analytics matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Guided analytics?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Guided analytics.