The growth of the digital economy has resulted in torrents of data. This problem will only continue because data is the language of technology. As companies continue to increase their reliance on technology, the data they create and their need to analyze it, will also increase.

The growth of data has given rise to a class of problems that we call, for lack of a better term, big data analytics. The common requirements for solving this class of problems, loosely, are:

- Tell me what’s in my data
- What are some outcomes that I can track? (Machine failure, network slowdown, etc.)
- What indicators are related to these outcomes?
- How can I respond to these indicators and influence these outcomes?

The broad approach to these kinds of problems is search or query based analytics. The approach is rooted in traditional statistics, where a central tenant of the scientific method is hypothesis testing. If we do not know what’s in the data, we present a hypothesis and then use queries, or questions, to piece a solution together.

A result of this lineage is modern business intelligence, an ad hoc analysis designed to answer a single business question. The answer to this question is typically a statistical model, analytic report, or other type of data summary delivered on demand to the business user.

SAS, the reigning giant of statistical modeling software, defines big data analytics as “(T)he process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.”

But the number of possible queries in a data set is very large.http://www.numberempire.com/combinatorialcalculator.php

Analysts and data scientists continue to discover new ways to store more data and make our queries run faster, but the additional complexity of more data very quickly outpaces our ability to create more and better queries.

Gartner stated at a recent conference,

“Data is inherently dumb. It doesn’t do anything unless you know how to use it, how to act on it, because algorithms is where the real value lies. Algorithms define action”

The No Query approach requires that the algorithm computes the queries and ranks them based on relevance (like Google’s page rank algorithm).

Would love to hear from you on what kind of tools you use and how you query your data. What are your challenges in querying your data?

The original blog can be seen here.

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

**Technical**

- Free Books and Resources for DSC Members
- Learn Machine Learning Coding Basics in a weekend
- New Machine Learning Cheat Sheet | Old one
- Advanced Machine Learning with Basic Excel
- 12 Algorithms Every Data Scientist Should Know
- Hitchhiker's Guide to Data Science, Machine Learning, R, Python
- Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Pyth...
- How to Automatically Determine the Number of Clusters in your Data
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- Fast Combinatorial Feature Selection with New Definition of Predict...
- 10 types of regressions. Which one to use?
- 40 Techniques Used by Data Scientists
- 15 Deep Learning Tutorials
- R: a survival guide to data science with R

**Non Technical**

- Advanced Analytic Platforms - Incumbents Fall - Challengers Rise
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- How to Become a Data Scientist - On your own
- 16 analytic disciplines compared to data science
- Six categories of Data Scientists
- 21 data science systems used by Amazon to operate its business
- 24 Uses of Statistical Modeling
- 33 unusual problems that can be solved with data science
- 22 Differences Between Junior and Senior Data Scientists
- Why You Should be a Data Science Generalist - and How to Become One
- Becoming a Billionaire Data Scientist vs Struggling to Get a $100k Job
- Why do people with no experience want to become data scientists?

**Articles from top bloggers**

- Kirk Borne | Stephanie Glen | Vincent Granville
- Ajit Jaokar | Ronald van Loon | Bernard Marr
- Steve Miller | Bill Schmarzo | Bill Vorhies

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives**: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central