{"id":1110,"date":"2019-02-15T10:31:58","date_gmt":"2019-02-15T10:31:58","guid":{"rendered":"http:\/\/kusuaks7\/?p=715"},"modified":"2023-07-27T15:58:11","modified_gmt":"2023-07-27T15:58:11","slug":"data-products-in-2016","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/data-products-in-2016\/","title":{"rendered":"Data Products in 2016"},"content":{"rendered":"

At breakfast last week, my wife and I noticed water dripping out from under our coffee maker.<\/span><\/span><\/p>\n

We\u2019ve had our coffee maker for over 10 years, and as coffee zealots, it\u2019s faithfully poured our morning dose nearly every day we\u2019ve been home. This coffee maker is old but great: it automatically grinds, tamps, pours and disposes of coffee, requiring only a single button push to get a cup. \u00a0I\u2019m also partially convinced that this coffee maker, which also makes lattes and mochas, is a primary driver for Grandma and Grandpa\u2019s visits.<\/span><\/span><\/p>\n

The convenience of this machine is great, but the machine still requires human intervention \u2013 requiring near-daily refills of water and beans and emptying of the grind pucks, adjustments, and maintenance. However, you can also fine-tune the amount and grind of the coffee and the size and strength of the pour. \u00a0As a result, it pours a great cup.<\/span><\/span><\/p>\n

During our research for a suitable replacement, we tried one of the little plastic cup systems that have replaced drip and espresso machines since we were last shopping for coffee makers. \u00a0This new coffee maker promised to be even more efficient since you never actually have to *see* the coffee \u2013 it remains clean and safe inside its capsules. \u00a0The downside? \u00a0We have yet to pour a decent cup from it. We miss the customizations afforded by the near- but not fully-automatic maker it replaced.<\/span><\/span><\/p>\n

At the Data Guild our love of coffee is bested only by our love of data products. The same challenges that confront coffee drinkers in optimizing the continuum between fully-automatic and fully-crafted are similar to what we see every day in the data marketplace: finding the balance between bespoke solutions and one-size fits all products. \u00a0<\/span><\/span><\/p>\n

At the Data Guild <\/span>we’re shifting more energy to data product development,<\/span> which will address key market pains we\u2019ve observed while building custom data products in various industries. <\/span><\/span><\/p>\n

A Typical Dialog<\/span><\/p>\n

In the last two years of strategic data consulting engagements with several Fortune 100 companies, we\u2019ve had several similar initial conversations. \u00a0While there are variations, typically they go something like this:<\/span><\/span><\/p>\n

Client:<\/span> \u201cWe\u2019re looking to turn our data into actionable insights and competitive advantages.\u201d<\/span><\/span><\/p>\n

Data Guild: <\/span>\u201cGreat, we\u2019ve got some experience in that and would love to help.\u201d<\/span><\/span><\/p>\n

Client: <\/span>\u201cOK I need to hire a team of 12 data scientists from you to work on-site for the next 6 months and help me build a hadoop [sic]\u201d <\/span><\/span><\/p>\n

Data Guild:<\/span> \u201cOK, but first we\u2019d love to explore your needs and determine what it is you actually need before you start spending money.\u201d<\/span><\/span><\/p>\n

Client:<\/span> \u201cCan we do this in parallel? \u00a0I need to get some people and products deployed in the next three months.\u201d<\/span><\/span><\/p>\n

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So What is a \u201cData Product\u201d?<\/span><\/p>\n

Data products can take two forms: algorithms and data source. \u00a0\u00a0<\/span><\/span><\/p>\n

On the algorithmic side, there are opportunities to integrate theory from existing literature to develop systems which can enable new, smarter and faster capabilities that streamline existing business processes. Nearly every industry has gone through a major shift relative to the integration of algorithmic systems into workflow.\u00a0Product\/media suggestions, healthcare predictive capabilities, energy optimization are just a few that we\u2019ve been fortunate enough to help develop. \u00a0<\/span><\/span><\/p>\n

On the data source side, we see the need for secure, reliable data sources that may be used by many to develop a disparate set of solutions. \u00a0Real estate, government, electronic health records and national health registries are a few examples where data \u2013 open or otherwise \u2013 can have a significant improvement in impact when centralized and shared.<\/span><\/span><\/p>\n

However, in either of these cases, we believe that data products must deliver value <\/span>on implementation<\/span>: it\u2019s not enough to have the \u201cpromise\u201d of value creation; rather, demonstrable ROI in its first deployment is the only path to sustainability.<\/span><\/span><\/p>\n

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A Data Product For Everyone?<\/span><\/p>\n

It\u2019s no surprise given the current hype over big data, IIoT (Industrial Internet of Things), data science, predictive analytics, machine learning, deep learning, etc., that traditional industries are feeling the pressure to make headway or risk lagging their competition in putting their data to work.<\/span><\/span><\/p>\n

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However, in the new age of data-driven software, there is a new rule: <\/span>one-size-fits-all fits no one<\/span>. \u00a0This is true generally of software products, where even the most widespread consumer apps and services are experienced completely differently by each individual:<\/span><\/span><\/p>\n