By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data Analytics instagram stories
    Data Analytics Helps Marketers Make the Most of Instagram Stories
    15 Min Read
    analyst,women,looking,at,kpi,data,on,computer,screen
    What to Know Before Recruiting an Analyst to Handle Company Data
    6 Min Read
    AI analytics
    AI-Based Analytics Are Changing the Future of Credit Cards
    6 Min Read
    data overload showing data analytics
    How Does Next-Gen SIEM Prevent Data Overload For Security Analysts?
    8 Min Read
    hire a marketing agency with a background in data analytics
    5 Reasons to Hire a Marketing Agency that Knows Data Analytics
    7 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Data Preparation: Is the Dream of Reversing the 80/20 Rule Dead?
Share
Notification Show More
Aa
SmartData CollectiveSmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Data Preparation: Is the Dream of Reversing the 80/20 Rule Dead?
AnalyticsData Management

Data Preparation: Is the Dream of Reversing the 80/20 Rule Dead?

BillFranks
Last updated: 2016/10/14 at 2:44 PM
BillFranks
7 Min Read
SHARE

I recently had someone ask me, “For years we’ve talked about changing analytics from 80% data prep and 20% analytics to 20% data prep and 80% analytics, yet we still seem stuck with 80% data prep. Why is that?” It is a very good question about a very real issue that causes many people frustration.

Contents
Breaking New GroundRevisiting A Well-Worn PathThe Big Challenge of Big DataKeep The Right Perspective

I believe that there is actually a good answer to it and that the perceived lack of progress is not as bad as it first appears. To explain, we need to differentiate between a new data source and/or a new business problem and existing ones we have addressed before.

I recently had someone ask me, “For years we’ve talked about changing analytics from 80% data prep and 20% analytics to 20% data prep and 80% analytics, yet we still seem stuck with 80% data prep. Why is that?” It is a very good question about a very real issue that causes many people frustration.

More Read

data Analytics instagram stories

Data Analytics Helps Marketers Make the Most of Instagram Stories

How Hospital Security Breaches Devastate Local Communities
What to Know Before Recruiting an Analyst to Handle Company Data
Data Ethics: Safeguarding Privacy and Ensuring Responsible Data Practices
AI-Based Analytics Are Changing the Future of Credit Cards

I believe that there is actually a good answer to it and that the perceived lack of progress is not as bad as it first appears. To explain, we need to differentiate between a new data source and/or a new business problem and existing ones we have addressed before.

Breaking New Ground

Whenever a new data source is first acquired and analyzed, there is a lot of initial work required to understand, cleanse, and assess the data. Without that initial work, it isn’t possible to perform effective analysis. Much of the work will be a one-time effort, but it can be substantial. For example, determining how to identify and handle inaccurate sensor readings or incorrectly recorded prices.

From the earliest days of my career, some of the most challenging work has been working with new data. For the first couple of analytics on a new data source, the ratio of data prep and other grunt work to analytics is certainly much closer to 80% prep/20% analysis than to 20%/80%. However, as time passes and more analytics are completed with that new data source, things become much more streamlined and efficient.

Revisiting A Well-Worn Path

Once a data source has been utilized for a range of analytics and is well understood, developing a new analytic process with it starts to drift towards the 20/80 ratio. By making use of things like Enterprise Analytic Datasets, it is possible to jump almost directly into a new analysis as long as that analysis can utilize the same type of metrics that past analysis made use of.

In fact, many large organizations have greatly standardized and streamlined the use of traditional data sources for analytics. For example, transactional data is utilized to analyze customer behavior in a wide range of industries. Many organizations have a large number of standardized customer metrics available that can feed analytics both new and old. I know of companies with tens of thousands of metrics for each customer based on transactional history. Spinning up a new analytic process with these metrics is not that difficult and can often be more of a 20% prep/80% analysis proposition than an 80/20 proposition.

Even if you accept all of the points above, doesn’t it still seem like your analytics organization is spending a ton of time on data preparation today? Well, your instinct is probably on target, but not for the reasons you may initially think of.

The Big Challenge of Big Data

The rise of big data has led to a proliferation of data sources over the past few years. Simultaneously, analytics have become a major focus and there is demand for analytics to address an ever-widening range of business problems. When combining these two trends, we are left with a large amount of new ground to break, which drives us back to the need for an abundance of work to understand, cleanse, and assess data. We, therefore, end up spending much of our time on data preparation and still see an 80/20 ratio.

However, it is important to look backward and recognize the progress that has been made. The data that required a lot of work a few years ago likely does NOT require a lot of work today. The ratio of data prep to analysis may well be nearing the 20/80 target ratio in those cases. We tend to lose sight of this progress when we are inundated with the data issues of today. Even though we have made a lot of progress with our old data and analytics, we’re simply facing a huge amount of new data and problems to work on.

Keep The Right Perspective

It can certainly be frustrating to feel like your organization is forever stuck doing more data preparation than analysis. However, it is critical to recognize that the data and problems for which you’re doing that prep are constantly changing. It is simply impossible to analyze new data for a new problem without going through a bunch of grunt work and data prep at the outset. There is nothing wrong with this.

In fact, if your organization is breaking enough new ground with analytics to feel stuck in a data preparation mode, then you should be happy because it means you are likely making progress. The key is to ensure that once you’ve solved today’s problems and understand today’s data sources that you drive to a higher level of automation and standardization for those data sources and processes. By making analytics easier for the data and problems you already understand, you free up time to prepare the data for your next analytics adventure.

Original Article

BillFranks October 14, 2016
Share This Article
Facebook Twitter Pinterest LinkedIn
Share
By BillFranks
Follow:
Bill Franks is Chief Analytics Officer for The International Institute For Analytics (IIA). Franks is also the author of Taming The Big Data Tidal Wave and The Analytics Revolution. His work has spanned clients in a variety of industries for companies ranging in size from Fortune 100 companies to small non-profit organizations. You can learn more at http://www.bill-franks.com.

Follow us on Facebook

Latest News

smart home data
7 Mind-Blowing Ways Smart Homes Use Data to Save Your Money
Big Data
ai low code frameworks
AI Can Help Accelerate Development with Low-Code Frameworks
Artificial Intelligence
data Analytics instagram stories
Data Analytics Helps Marketers Make the Most of Instagram Stories
Analytics
data breaches
How Hospital Security Breaches Devastate Local Communities
Policy and Governance

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

data Analytics instagram stories
Analytics

Data Analytics Helps Marketers Make the Most of Instagram Stories

15 Min Read
data breaches
Policy and Governance

How Hospital Security Breaches Devastate Local Communities

7 Min Read
analyst,women,looking,at,kpi,data,on,computer,screen
Analytics

What to Know Before Recruiting an Analyst to Handle Company Data

6 Min Read
Data Ethics: Safeguarding Privacy and Ensuring Responsible Data Practices
Best PracticesBig DataData CollectionData ManagementPrivacy

Data Ethics: Safeguarding Privacy and Ensuring Responsible Data Practices

7 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?