By: Nicholas Hartman, Director at CKM Advisors

*This is a re-print of a post from ckmadvisors.com. The original post is available here.*

Whenever I introduce the data analytics we do here at CKM, an ever increasing percentage of people will respond along the lines of “So like Hadoop / NoSQL / [insert generic ‘big data’ term]?” Many are surprised when I respond saying not always.

The hype around so called ‘big data’ seems to have convinced many that unless the data and analytics are ‘big’ it won’t have a big impact. In reality, for many organizations there’s still tons of value to be generated from smarter use of ‘small’ and ‘medium’ data. The missing gap is often data science skill, not big data technologies.

For example, a process may have multiple enterprise systems that store important transactional data in separate silos. A tremendous amount of value can be derived from having a data science team integrate this process data across silos and identify problems/root causes that span these silos. The quantity of data in these cases may be less than 1 TB, sometimes much less. However, a good data science team could still use that information to completely transform a company’s operations. In many cases that team may need nothing more than a simple server and a few open source tools.

At the end of the day, we’re less concerned if the data is small / medium / big or ginormous than we are with the problem we’re trying to solve. With that foundation, we’ll then tap into any of our suite of available tools to best implement the algorithms developed by our data science teams.

Hadoop, NoSQL and other technologies are fantastic, we just don’t need them to solve every data analytics challenge we face. In some cases, these technologies would actually make it more difficult to solve the problem. If we have a 500 GB dataset of relational datasets then a well tuned MySQL / MS-SQL server coupled with a single linux box for running analytics code may be all we need. If we want to conduct lanaguge analysis on 500 GB of free-text then yes we might farm that out to a Hadoop cluster.

Next time I'll discuss our response to another broad question we get all the time: “Should we invest in big data?”

© 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