Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
No Result
View All Result

Is Your Big Data Vision Focused on Designing Everyday Apps?

by Allen Bonde
September 10, 2014
in Data Science
Home Topics Data Science
Share on FacebookShare on TwitterShare on LinkedInShare on WhatsAppShare on e-mail

There’s huge market and business investment in the Big Idea that is known as Big Data. But missing from the conversation is the key point: how do we convert data – at scale and from multiple sources, into consumer style, simple yet truly smart data-driven apps that deliver value to all types of users in their everyday roles?

That’s the Big Problem and, without addressing it, we simply can’t offer the kind of effective data-driven apps that analysts like IDC see as “the next big thing” in marketing and sales.

These are the kinds of “everyday” apps customers are crying out for: apps that offer useful insights or connect them to essential resources. Apps that may tap massive amounts of data, but present it in highly visual, easy to manipulate interfaces that end-users can use intuitively with zero training. Think about it like this: people want apps that add value
to their lives – professional and personal. They want apps that are personalised for them and their daily tasks. They want highly responsive apps that change as their needs do – apps that, in fact, adapt to location or how they are accessed, whether on a laptop, mobile or even wearable device.

So how do we funnel all that Big Data horsepower into smaller, more bite-sized functionality? It starts by thinking “Small.” More precisely, we need to create new “information hubs” that will filter multi-source Big Data into the meaningful Small Data that will add the kind of value end-users are looking for – and enterprises will need to deliver if they are to become more customer focused.Screen Shot 2014-09-10 at 17.51.55

So the challenge is to deliver this Small Data to our customers – internal and external – via new types of data-driven apps and experiences.


Join the Partisia Blockchain Hackathon, design the future, gain new skills, and win!


What will those data-driven apps look like? For starters, they’ll incorporate a mix of historical, real time and trend-based data. Real-time data can support daily deal or target selling services for consumers as much as it can support industrial functions like inventory optimisation. Historical or trend data can power competitive intelligence or online
merchandising apps, and so on. A great example is the Kayak when-to-book tool. What makes it special is that it takes literally billions of data points at the back end from pricing and trend information, and transforms this massively Big Data into Small Data via a simple dashboard that provides actionable purchasing guidance (“Buy” or “Wait”).

In fact, there are many compelling information apps we could create by accessing all our Big Data and serving it up as super useful, actionable Small Data. But to get to the full potential of data-driven customer facing apps, we’re going to need effective technology to help us in the collection and processing phase of all that data, as well as tools to compose it and visualise it for easy consumption and decision making.

The good news is that a set of clear principles are starting to emerge that data-driven app designers can take advantage of. These best practice guidelines started to become clear after years of tracking the move from Big Data to practical Small Data, in my time as an analyst and researcher at firms such as Yankee Group, McKinsey, and most recently the Digital Clarity Group, where I and my colleagues defined the notion of Small Data in a marketing context.

Supporting these emerging principles will be better business analytics and superior APIs.

At our company Actuate – working with our 200+ OEMs and millions of BIRT and BIRT iHub developers – we’ve found some clear ways forward:

  • Start with the customer – it’s their journey that you are going to streamline, so the apps you build have to useful at each step.
  • Think about the ‘last mile’ of Big Data – don’t concentrate all your effort on the macro picture. Think about what your customers – whether internal or external – are going to need and how the right information can be delivered, and work upstream from there.
  • Don’t forget scale – you still need to have the horsepower to roll out your new data-driven apps to (potentially) millions of people, on potentially dozens of different types of devices.
  • Personal is the new interactive – your data-driven apps need to be customisable by each single user, even if you are rolling them out to many people at once.

In this ongoing wave of change, what is certain is that methods exist to make the power of Big Data practical for everyday tasks – via new, consumer style data-driven apps. Creating the design environments and ecosystem to foster those methods and the developers that will bring them to life can really help you create your next competitive advantage.

Follow @DataconomyMedia


Allen BondeAllen Bonde is VP of Product Marketing & Innovation at Actuate, an advisor to several start-ups, and a former digital media and Internet CMO. An early proponent of self-service apps and data-driven marketing, he has helped global leaders with their big data and customer experience strategies, and shares his perspectives as a contributor to Forbes.com, DM News, and Small Data Group – his top-rated blog.  He started his career as a researcher and data scientist in the telecom sector, and was part of teams at McKinsey and Yankee Group that did early work in online advertising and e-commerce. Allen attended Brown University, and holds degrees from the University of Virginia and Worcester Polytechnic Institute.

Follow Allen on Twitter at @abonde or email him at abonde@actuate.com


 

Related Posts

AI Text Classifier: OpenAI's ChatGPT detector can distinguishes AI-generated text

AI Text Classifier: OpenAI’s ChatGPT detector indicates AI-generated text

February 1, 2023
BuzzFeed ChatGPT integration: Buzzfeed stock surges in enthusiasm over OpenAI

BuzzFeed ChatGPT integration: Buzzfeed stock surges after the OpenAI deal

January 31, 2023
Adversarial machine learning 101: A new frontier in cybersecurity

Adversarial machine learning 101: A new cybersecurity frontier

January 31, 2023
What is the Nvidia Eye Contact AI feature? Learn how to get and use the new Nvidia Broadcast feature. Zoom meetings and streams get easier.

Nvidia Eye Contact AI can be the savior of your online meetings

January 30, 2023
How did ChatGPT passed an MBA exam

How did ChatGPT passed an MBA exam?

January 27, 2023
What is AI prompt engineering? Learn how to write a prompt with examples. ChatGPT prompt engineering and more explained in this article.

AI prompt engineering is the key to limitless worlds

January 27, 2023

Leave a Reply Cancel reply

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

LATEST ARTICLES

AI Text Classifier: OpenAI’s ChatGPT detector indicates AI-generated text

A journey worth taking: Shifting from BPM to DPA

BuzzFeed ChatGPT integration: Buzzfeed stock surges after the OpenAI deal

Adversarial machine learning 101: A new cybersecurity frontier

Fostering a culture of innovation through digital maturity

Nvidia Eye Contact AI can be the savior of your online meetings

Dataconomy

COPYRIGHT © DATACONOMY MEDIA GMBH, ALL RIGHTS RESERVED.

  • About
  • Imprint
  • Contact
  • Legal & Privacy
  • Partnership
  • Writers wanted

Follow Us

  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
No Result
View All Result
Subscribe

This website uses cookies. By continuing to use this website you are giving consent to cookies being used. Visit our Privacy Policy.