Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Analytics: Not About Saving Time
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Analytics: Not About Saving Time
Analytics

Analytics: Not About Saving Time

nraden
nraden
7 Min Read
SHARE

 We hear a lot these days about how analytics can accelerate a calculation that used to take ten hours and do it in six minutes. For me, this is a very poor argument for analytics. After all, is an analyst going to stare at a screen for ten hours waiting for a response? Of course not, she will be busy doing other things, so the time lag, or latency as it’s referred to these days, is not really an issue. However, if that bit of analysis is so timely that it needs to be done in six minutes, then that is probably a good case, but I doubt that it is that timely.

 We hear a lot these days about how analytics can accelerate a calculation that used to take ten hours and do it in six minutes. For me, this is a very poor argument for analytics. After all, is an analyst going to stare at a screen for ten hours waiting for a response? Of course not, she will be busy doing other things, so the time lag, or latency as it’s referred to these days, is not really an issue. However, if that bit of analysis is so timely that it needs to be done in six minutes, then that is probably a good case, but I doubt that it is that timely. A ten hour query was developed to solve a problem that did not need to be solved in six minutes.

There are many problems begging to be addressed with extremely fast response times, but my suspicion is that these are new solutions that couldn’t be done before. Hence, the time-saving pitch for existing analytics seems a little shallow.

There are great reasons for applying today’s amazing tools, however. A few years ago, I was engaged to solve a supply chain problem for a company that manufactured their products in Asia and shipped them via container to the US. The main warehouse was near Seattle and there were satellite warehouses across the US. My client was a Senior Vice President of Logistics with a nasty problem. Clients were unhappy because they were frequently out of stock. The existing solution was called (incorrectly) )the On the Water Report.

More Read

Crossing the Language Chasm: Extracting Information from Foreign-Language Text
Collecting Analytic Data by Tracking Mobile Visitors: A Guide for Mobile Insights
Decision management and the top 4 concerns of CIOs
Will Pay-Per-Use Pricing Become the Norm?
Interactive Analysis and Relate Tools – Part I

The report was a three-inch thick greenbar report that detailed all products ready for shipment at the plants in Thailand and Malaysia, product on ships (hence, on the water) in containers and inventory at the warehouses. My client would get the report once per week, scan the entire thing and combine, in his head, what he knew about orders, and highlight every instance where he felt a problem could occur.  This took almost a full day of his time. When he was finished, the report would go to an analyst who would build an “issues” spreadsheet and from there, various people in the organization were alerted to potential problems.

The key word here is “alerted.” No solution was devised.  The only response was damage control.

In my naïveté, I thought I would be able to design a system to skip the greenbar by eliciting his explicit and tacit knowledge, and generate the spreadsheet automatically, saving him a day a week, and another day of work for an analyst. Based on this, the project had a very positive ROI and nice corollary benefits, such as integrated data for many other uses.

His response when I presented the solution to him was, “Neil, you don’t get it, do you?” I have to admit that this wasn’t the first time in my consulting career I heard this. I was about to get a lesson about how things really work (For me, this is the best part of consulting, learning from experts how things really work and what is really important).

He said, “You can save a day a week of my time and day a week of an analyst’s time, but that isn’t going to mean a damned thing in the greater scheme of things. Here is where you can really do some good. Save this company the expense of sending a helicopter to a ship at sea to break open a container to satisfy a major client. Save our sales force the time they spend on the phone apologizing for missed shipments and for putting clients on allocation because we can’t get the products to the right place at the right time, and repurpose that time getting them in front of customers in a good mood and selling them things.”

That’s what we set out to do, to build an optimizing system linking sales forecasts, contract compliance, manufacturing and transportation. In uncharacteristically candid disclosure for a consultant, I regret to say the project wasn’t a success. Senior management got involved in a scandal, business deteriorated and a very injured company was sold and largely disappeared. But the lesson for me was clear.

The moral of this story is that informing people with analytics isn’t worth a bucket of spit (as they say in Texas) if you can’t take it all the way to presenting a solution. Making things go faster, or “saving time” of professional staff is not a very compelling proposition. Changing a process to provide better service to customers, making an entire sales force more productive or fine-tuning manufacturing forecasts are.

 

 

TAGGED:analyticsbest practicesbig dataBigDatabusiness analyticsbusiness intelligencebusiness valuedata governancedata importdata integrationdata managementdata miningdata qualitydata reductiondata retentiondata retrievaldata stewardshipdata visualizationdata warehousedata warehousingdatabasedecision managementdecision servicesneil radenoptimizationpredictive analyticsroisentiment analyticssocial datasocial media analyticssoftwarestatisticstext analyticsunstructured dataweb analyticsworkforce analyticsworkforce data
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

sales and data analytics
How Data Analytics Improves Lead Management and Sales Results
Analytics Big Data Exclusive
ai in marketing
How AI and Smart Platforms Improve Email Marketing
Artificial Intelligence Exclusive Marketing
AI Document Verification for Legal Firms: Importance & Top Tools
AI Document Verification for Legal Firms: Importance & Top Tools
Artificial Intelligence Exclusive
AI supply chain
AI Tools Are Strengthening Global Supply Chains
Artificial Intelligence Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

NBC’s Olympics: Real time vs. prime time

4 Min Read

Is outsourcing business intelligence a good idea?

20 Min Read
data collection procedures for 2019
Big DataExclusive

Be On The Look Out For These Top Data Collection Procedures For 2019

6 Min Read

Continuously Crossing Channels while Crossing the Continent

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.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?