PredictiveEverywhere

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#Predictiveanalytics is about making actionable information out of #BigData

It can help the manager @mikejulietbravo was talking about earlier -- #bedataconfident -- maybe?

@jane_hendricks Yes indeed :-)

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Predictive Analytics deliver insights from Big Data

how about making #machinelearning #alogrithms more powerful and scalable such that a Datamodel or MLmodel can deliver the accurate results from unstructured data #predictiveeverywhere

#predictiveanalytics is #analyticswithpurpose -- big data gives it the additional power and insight like sentiment analysis, geo spatial, and more

Key issue here is not only BIG data - but the sheer speed at which it appears

using predictive analytics companies can use big data proactively ..lot of wht if problems can be solved using predictive techniques

If you have a larger pool of historical, profile, transaction, & other customer data, you can build far more nuanced segmentation models and drive more accurate predict models regarding their propensity to churn, accept offers, etc

Higher velocity of updates of customer data drives your ability to update your predictive model of what they might do next. That's a revenue-boosting advantage when you're driving next best offers.

@mikejulietbravo agree so the challenge is with the scalability to handle the performance and manage the data

@sourcingpower Spot on!

Higher variety of customer data types (e.g., transactions, sentiment, clickstreams, social) can drive a more well-rounded predictive portrait of their propensities. Revenue-boosting advantages there too.

Scale is key. Need to think scale from the beginning

also the sheer volume of data #bigdata can be used to train #predictive models to be more accurate hence giving better results

Retail and Predictive is an easy concept to grasp: speed, volume, sentiment etc etc

Higher volume of customer data--such as you gain from a deep historical record of their transactions and interactions--can help you develop and validate predictive models of them under diverse scenarios. Makes predictions more robust

B2B is a different paradigm

Gotta sign out - good chat so far :-)

Time for question #4 at the top of your screen

big data is big win on massive dataset to capture modern art. But all modern art is not understandable. So it requires additional catalyst like machine learning+data scientst need to make it happen.

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These days, it's a team sport http://www.ibmbigdat...

Understanding the business, asking the right questions, ability to read the data, using correct techniques and successful deployment

out of box thinking... clear concept of basic

critical thinking, statistics, machine learning, knowledge of business domain, storytelling, programming language, creativity and understanding of customer needs (through research and analysis)

To succeed with analytics, you need to understand what decisions might be improved with data, structured reports, visualizations, predictions, etc. And you need to deliver analytic insights to right decision makers at right times.

You need a team with different capabilities and expertise.

To succeed with analytics, you need a center of excellence that recruit, cultivates, and manages your skills, tools, and practices in data mining, predictive modeling, visualization, etc.

Astute Number-savvy Adaptable Listener Yeasty Tool-savvy Inquisitive Collaborative Speedy

To succeed with analytics, you need to instill a passion for data-driven decision making into every level of the organization. It needs to be infused into your business culture.

I think the capability to collaborate between expert #ML #Datascience #engineering team with #Sales and #marketing will help to achieve business goals by leveraging analytics :) #predictiveeverywhere

ANALYTICS :Astute Number-savvy Adaptable Listener Yeasty Tool-savvy Inquisitive Collaborative Speedy

ability to look at the bigger picture, move from data processing to insight mining , having the best technologies/solutions to take care of your data and the ability to communicate the insights to all stakeholders

Aske the right questions, know where to look, know what to look for, ability to contextualize data

@jameskobielus Absolutely agree with you . Bang on !

stakeholders buy-in in very important for organizations who really want to benefit from analytics

An adaptive analytical leadership with an enthusiasm for data who can think outside of the box and drive a team

sometimes we neglect to consider culture. There needs to a culture of curiosity and trust in data for #predictiveanalytics to succeed

A collaborative environment with technologies that can support different needs and capabilities of the team

@jane_hendricks yes. it's also to do with growth mindset and be with solution oriented approach #predictiveeverywhere

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"You're only as good as the future you foresee"!

future gazing is now within reach for all and with the changing business dynamics and pressure to stay ahead, inability to forecast and be prepared for future is a must!

Without the ability to forecat fututre events you keep working in the dark.

http://deepaksethspe...

It's not just forecast but forecast with confidence that's key. Anyone can guess at a future, #predictiveanalytics can #measurethefuture

Inability to forecast demand means you risk not delivering sufficient amounts of the right kinds of products into the market to realize revenues when demand materializes. That's a predictive weakness that can doom any business.

Companies which do not foresee/predict the future so will feel the impact of what was actually an evolutionary process as if it was a "Big Bang".

@jane_hendricks absolutely agree. #prediction needs to be #data driven not just based on intuition

Guys who see an oncoming bus and prepare for it can run alongside it a bit and then board it ; guys who are oblivious will be "hit by a bus".

Inability to predict key events--e.g, customer retention, offer acceptances, etc.--means you may be missing out on "low hanging fruit" revenue opportunities.

#data driven predictions give people an opinion-neutral starting point #beyondguesswork

#machinelearning and #datascience have become important aspects of any org.without it there will be high risk of loosing competition,customer trust in a fast pace world.understanding customer choice is an important element of growth strategy

Inability to predict how your competitors might respond to various marketplace conditions, including your own moves, means they might gain a strategic advantage over you.

By forecasting future events you can put the personnel, time and money where it is needed and stay ahead, the opposite becomes true if you don't have a forecast & #predictiveanalytics

@sourcingpower yes there are too many data points available and without #predictiveanalytics you are likely to miss the answer that's right in front of you

Time for question #6 at the top of your screen..

Inability to predict marketplace disruptors and other "black swans"--or at least to mitigate the risks of those "unforeseeables"--means you're strategically vulnerable, though you may in fact still be growing revenues.

Inability to forecast can also increase costs and decrease revenue

Revenue depends upon sales, market segmentation, profit per unit and availability. Analytics allows us to quantify demand, do market segmentation and match market's needs. Without analytics we are back to the 90's.