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agility for businesses of the future - Coggle Diagram
agility for businesses of the future
overview of data and data management
era of big data
taking advantage of big data
acknowledge big data importance to enable competitive advantage
consciously and strategically leverage big data by building capability in business analytics and data infrastructure
using and embedding analytics in every facet of the business, particularly in high-level decision making activities.
analytics level
Gartner's Analytic Continuum's 4 analytics level
descriptive i.e. what happened
hindsight
diagnostic i.e. why it happened
insight
predictive i.e. what will happen
foresight
prediction based on
past events
knowledge about participants
forecasting vs predictive
forecasting: more to working with aggregate values (macro level) e.g, 70% of customers will purchase an item, but we don't know which customers
predictive: working with individual's behavior (micro level) e.g, Customer A has a 70% likelihood of purchasing an item.
lesser value to computer based prediction
guess work
expert instinct
managerial gut feeling
predictive analytics is not based on a pre-determined set of outcomes. but the attempt to make sense of various data to produce multiple likely future scenarios. 100% accurate predictions are not possible
prescriptive i.e. how we can make it happen
foresight
increase in level, increase in difficulty and complexity in accessing and utilizing data. infrastructure wise, skill sets wise, overall investment and cost of maintaining the capability
business challenges
fast changing market, and shorter lead time
fickle-minded, highly demanding and increasingly sophisticated customer base
increasing intensity of competitions & competitors
globalisation & borderless organisational activities
maximising value from investments & assets, also know as return on investment (ROI)
optimising opportunities, reducing risk, lowering cost and improving profitability.
business transformation
prospects journey and points of capture
metadata
touchpoint
user's entry
differing rules and frameworks (legal ethical dilemmas)
data ownership
data collection
data storage location
data dissemination
data usage
Businesses and organisations need to use appropriate IS/IT (data infrastructure, information, systems), analytics tools, modelling and techniques to draw inferences from data and simulate possible scenarios based on certain parameters.
It is impossible to have good analytics without having a good understanding of the business and its objectives