We use cookies, including third-party cookies from Google to serve personalized ads through AdSense, to operate this site and understand how it is used. By continuing to browse, you accept this use. See our Privacy Policy and Terms of Use for details, including how to opt out of personalized advertising.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
    7 Min Read
    chatgpt image jul 21, 2026, 04 34 30 pm
    4 Core Benefits of Predictive Maintenance after Vibration Analysis
    10 Min Read
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results -- AI-generated illustration
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results
    11 Min Read
    chatgpt image jul 13, 2026, 04 23 45 pm
    How Data Analytics Helps Companies Improve User Engagement
    19 Min Read
    chatgpt image jul 13, 2026, 03 59 46 pm
    How Data Analytics Improves Multi-Location Search Strategies
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Taming Big Data Is Not a Technology Issue
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Data Management > Policy and Governance > Taming Big Data Is Not a Technology Issue
Big DataITPolicy and Governance

Taming Big Data Is Not a Technology Issue

BillFranks
BillFranks
7 Min Read
Taming Big Data Is Not a Technology Issue
Illustration generated with FLUX.2 [klein 4B] via Cloudflare Workers AI.
SHARE

One thing that has struck me recently is that most of the focus when discussing big data is upon the technologies involved. The consensus seems to be that the biggest challenge with big data is a technological one, yet I don’t believe this to be the case.

One thing that has struck me recently is that most of the focus when discussing big data is upon the technologies involved. The consensus seems to be that the biggest challenge with big data is a technological one, yet I don’t believe this to be the case. Sure, there are challenges today for organizations using big data, but, I would like to submit to you that technology is not the biggest problem. In fact, technology may be one of the easiest problems to solve when it comes time to tame big data.

The fact is that there are tools and technologies out there that can handle virtually all of the big data needs of the vast majority of organizations. As of today, you can find products and solutions that do whatever you need to do with big data. Technology itself is not the problem.

Then, what are the issues? The real problems are with resource availability, skills, process change, politics, and culture. While the technologies to solve your problems may be out there just waiting for you to implement them, it isn’t quite that easy, is it? You have to get budget, you have to do an implementation, you have to get your people up to speed on how to use the tools, you have to get buy in from various stakeholders, and you have to push against a culture averse to change.

More Read

Data Governance & Quality Rock Stars
Data Governance & Quality Rock Stars
6 Top Data-Driven SEO Tools To Track Your Marketing Efforts
Tips for Executives – How to Create a Culture of Evidence
4 Ways to Increase the Security of Wearable Devices
The Cloud’s Role in Standardizing Business Processes

The technology is right there, but you are unable to effectively put it to work. It FEELS like a technology issue since technology is front and center. However, it is really the cultural, people, and political issues surrounding the technology that are the problem. Let me illustrate with an example.

A few weeks back, I was at the annual Teradata Partners conference having a really good discussion with someone from one of our larger customers. When I suggested to him that his struggle with big data isn’t a technology problem, he challenged me. He pointed out that the network protocols they have in place just can’t support the level of data flow required to handle the big data they need to utilize. He felt he had a very real technological issue.

I pushed back on him and asked if there weren’t widely available products that would solve his network bandwidth problem tomorrow if he were to implement them. He said that was true but that he couldn’t just go and implement them tomorrow. I then pointed out to him that he had just proven he didn’t have a technology problem. The technology exists. He knows what it is and how to put it in place. He just can’t get it in his hands.

His real problem is with a culture that doesn’t want to invest in upgrading the network at this time. His real problem is a bureaucratic budgeting process that takes a long time to navigate. His real problem is building a business case to prove the need and value of making the upgrade. All of these problems are very real for my client and are also very valid. But, they aren’t about technology. Brush aside corporate policies, give him cash, and give him purchase authority today, and his problem will be solved very quickly.

I suspect that as many readers consider the story above, they will realize that they don’t really have a technology problem either. Technology is the easy part. It exists and it can be put in place as soon as you’re ready. However, those pesky budgets, policies, skill gaps, and cultural realities get in the way of that happening.

As your organization proceeds with the process of incorporating big data, be sure to give appropriate attention to the non-technical hurdles that you’ll face along the way. Don’t mistake them for a technology problem and don’t underestimate them. These hurdles will often be much more difficult to clear than finding a set of tools and technologies that will do what you require.

A few things to keep top of mind:

  1. Think through the challenges you’ll face in getting your organization to effectively use the big data tools you’ve pushed so hard to acquire.
  2. Plan to help your organization embrace the business model and process changes that might be suggested by results that your new tools and analytics uncover within the new data sources.

The fact that the tools and technologies to tame big data are readily available today is a terrific thing and you need not lose sleep on that front. However, there may be a lot of sleep loss as you figure out how to get your organization to actually acquire, implement, and put the tools and technologies to use. The people, cultural, and political issues surrounding big data are very big and very real.

To see a video version of this blog, visit my YouTube channel.

Originally published by the International Institute for Analytics

Share This Article
Facebook Pinterest LinkedIn
Share
ByBillFranks
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

Flat editorial illustration: The article's core relationship is the gap between raw bank-feed records and reliable subscription i
Subscription Trackers Have a Transaction Data Problem
Big Data Exclusive
Flat editorial illustration: The article's core relationship is the hypervisor as the abstraction layer that makes hybrid cloud p
7 Best Virtualization Platforms for Hybrid Cloud Environments
Big Data
Flat editorial illustration: The article's core relationship is between aggregated workplace data (occupancy, environmental, feed
How Businesses Can Use Workplace Data to Create Better Office Environments
Big Data Business Intelligence Exclusive
Flat editorial illustration: The article's core relationship is the engineering transition from an AI prototype to a production-r
From AI Prototype to Production: Observability and Scalability Requirements for Data-Heavy SaaS Applications
Artificial Intelligence Exclusive

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Top 10 Root Causes of Data Quality Problems: Part 4
Data MiningData QualityData Visualization

Top 10 Root Causes of Data Quality Problems: Part 4

4 Min Read
4 Ways to Boost Social Media Engagement With Big Data
Big DataExclusiveSocial Data

4 Ways to Boost Social Media Engagement With Big Data

10 Min Read
Analytics Moves To The Core: Reporting from the Gartner Summit
AnalyticsBusiness IntelligenceData WarehousingPredictive Analytics

Analytics Moves To The Core: Reporting from the Gartner Summit

5 Min Read
Customer Service
ITSecurity

Integrating Cybersecurity Responses into Your Customer Service Approach

5 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 chatbot
How AI Website Chatbots Improve Customer Support and Lead Generation
Chatbots Exclusive
AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-26 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?