Thursday, April 22, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Data Science

Adapt or Die: why your business strategy is failing your data strategy

November 16, 2019
in Data Science
Adapt or Die: why your business strategy is failing your data strategy
585
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter

A few years ago I took a call from an analyst at a hedge fund who was looking for external data that would, in his words, provide “alpha.” I explained that our company was connected to thousands of data sources and hundreds of thousands of public datasets; I told him that we were continuously pulling in open data from 70 countries, standardizing it through an ingestion pipeline trained against the largest catalogue of public data in the world, and serving it up via a suite of APIs that plug directly into any ecosystem.

You might also like

Top Python Operator – Data Science Central

6 Ways AI is Changing The Learning And Development Landscape

How TensorFlow Works? – Data Science Central

There was a brief pause and the analyst said, “I have all that. Do you have anything I don’t?”

This surprised me. “Are you telling me that you’re already pulling down and processing data from every public agency and municipality in the USA?”

“No, but I could. It’s easy to get that stuff. I want something no one else has.”


This wouldn’t be the last time I’d hear someone make this claim. The misguided belief that public data is easy to access has defined the first ten years of the open data movement, much to the detriment of people who are actually trying to use it to make intelligent business decisions. In practice, public data is not easy to access and it’s not easy to use. We refer to this as the difference between available and accessible, and it’s a significant barrier to adoption.

For modern businesses, accessing and using data effectively is undeniably important. Whether it’s the data generated within the four walls of your organization, government open data, or even the data being released by other businesses, there’s lots of value that can be unlocked by piping these streams of information into analytical models or applications. With so much out there, executives are champing at the bit to gain access to new data, knowing that whoever innovates fastest can steal market share from whoever doesn’t adapt quickly enough.

This focus on innovation has led organizations to hire a lot of data scientists and analysts who are being tasked with creating new products, services, and applications that can be used to fuel business intelligence.

So what’s the problem?

The problem is the disconnect between a business division’s outcome-based thinking and a data scientist’s reality. When you’ve hired a fleet of data scientists, “innovation” is expected, but at ground level only a fraction of the work they’re doing can be considered groundbreaking. The majority of the time, data scientists are struggling with all of the things that my quant friend at the hedge fund thinks are simple: sourcing data, refining it, and plugging it into applications.

Because of the difficulty of working with data, we’re seeing a growing dissatisfaction with data projects. They hype is there, the talent is there, but the results aren’t. Months or even years of work are going into ideas that aren’t being productionalized due to the operational hurdles involved in using data effectively. It’s demoralizing for data scientists, and it’s a pressing business concern for executives.

Things are looking grim. In 2016 Gartner estimated that 60% of big data projects failed, and a year later Gartner analyst Nick Heudecker said the number was likely closer to 85%. No matter how deep your pockets are, no sane business is going to keep throwing money at something that fails four out of every five times.

The reality is that in order to innovate, all businesses have to optimize their data strategy.

From a management perspective, optimization starts with communication. There needs to be a better line of dialogue between the C-suite and data science divisions. Operational objectives should be clear, and data scientists have to be given the resources they need to do their job effectively. Having a data strategy is a step in the right direction, but has it been implemented? It’s not enough to want to be data-driven, you also need to understand what that entails and provide your team with the tools and support that enables them to put ideas into production.

The second way to optimize your data infrastructure is to speed up the time-consuming data management tasks that are plaguing data divisions everywhere.

In an ideal scenario, data scientists are empowered to experiment and try out ideas, but operationally this is often impossible. Sourcing, scraping, standardizing, refining, and integrating data simply takes too long. The result of this operational pitfall is one of two scenarios:

  1. Ideas come from the top, where executives decide on key objectives and throw their data division at them; or
  2. Data scientists work in controlled environments, using synthetic, small data to test out models.

In the first case, data scientists lack ownership and are handcuffed to projects that may not be feasible. In the second case, good ideas and decent models often fail the moment they’re put in a real-world environment.

By adopting DataOps frameworks and finding ways to automate the prep and process phase of gathering data, data scientists will be able to test and evaluate ideas faster and ensure their models are production-ready. This will lead to increased output, which will lead to better business outcomes.  Optimization will lead to innovation.

Recently, I was in a meeting with someone who wanted to start using public data. I walked him through the platform and explained the steps involved in sourcing and integrating data.

He shrugged. “My data team can do all this.”

This time I was ready. “So why haven’t they?”

                                                **********************************************

You can view the original posted here 


Credit: Data Science Central By: Lewis Wynne-Jones

Previous Post

How To Prepare For Human-Machine Partnerships At Work

Next Post

Kaspersky to launch transparency center in Brazil

Related Posts

Top Python Operator – Data Science Central
Data Science

Top Python Operator – Data Science Central

April 22, 2021
6 Ways AI is Changing The Learning And Development Landscape
Data Science

6 Ways AI is Changing The Learning And Development Landscape

April 21, 2021
How TensorFlow Works? – Data Science Central
Data Science

How TensorFlow Works? – Data Science Central

April 21, 2021
Limitations Of Power Bi – Data Science Central
Data Science

Limitations Of Power Bi – Data Science Central

April 21, 2021
How to Leverage Data Science For Customer Management
Data Science

How to Leverage Data Science For Customer Management

April 21, 2021
Next Post
Kaspersky to launch transparency center in Brazil

Kaspersky to launch transparency center in Brazil

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

Hackers threaten to leak stolen Apple blueprints if $50 million ransom isn’t paid
Internet Privacy

Hackers threaten to leak stolen Apple blueprints if $50 million ransom isn’t paid

April 22, 2021
Top Python Operator – Data Science Central
Data Science

Top Python Operator – Data Science Central

April 22, 2021
Machine Learning Tacks Evolution of COVID-19 Misinformation
Machine Learning

Machine Learning Tacks Evolution of COVID-19 Misinformation

April 22, 2021
How AI Is Disruptive Innovation For OCR | by Infrrd | Apr, 2021
Neural Networks

How AI Is Disruptive Innovation For OCR | by Infrrd | Apr, 2021

April 22, 2021
Instagram debuts new tool to stop abusive message salvos made through new accounts
Internet Security

Instagram debuts new tool to stop abusive message salvos made through new accounts

April 21, 2021
Improve Your Cyber Security Posture by Combining State of the Art Security Tools
Internet Privacy

Improve Your Cyber Security Posture by Combining State of the Art Security Tools

April 21, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • Hackers threaten to leak stolen Apple blueprints if $50 million ransom isn’t paid April 22, 2021
  • Top Python Operator – Data Science Central April 22, 2021
  • Machine Learning Tacks Evolution of COVID-19 Misinformation April 22, 2021
  • How AI Is Disruptive Innovation For OCR | by Infrrd | Apr, 2021 April 22, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates