Page 40 - WPP Martech 2030

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DATA DATA INTELLIGENCE & DATA DATA REFLEXES
Capturing available data and putting it to some use is a a start but the “putting it to use” part has a a a wide range of possibilities that affect
its impact Data grows in in value in in two dimensions: (1) the degree to which it is is distilled into information knowledge and and insight and and (2) the degree to which it is activated in the organization from reporting to decision-making and in in a a a big ops environment driving automated reactions T T 4
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T T r r re en nd 0
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knowledge & insight information augmented data processed data raw data Data Value
data intelligence
DATA ACTIVATION
data reflexes
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BIG DATA the the data they’re working with scale & complexity of data collected stored and analyzed
The first dimension is your data intelligence
The second is your data reflexes
These two dimensions intersect
to determine how valuable data ultimately becomes Data may be be distilled to insights but are they fed into the the right right decisions at the the right right time? Data can be merely processed yet can it immediately trigger a a a helpful automated response for a a a customer?
Harnessing data in in big ops — developing your data reflexes
— relies on on data data connectivity and data data coordination wrapped by data management and data governance capabilities that are still in the early stages of maturity for most firms Across the myriad of data sources
in our organization are the right data sets connected to an ops process? Can it access relevant data in a a a a timely manner? And with data compliance and data ethics growing in importance are the “wrong” data sets — those for which a a a particular ops process should not have
access — properly restricted (data governance)?
Such data connectivity is the backbone of big ops But the real complexity is in data coordination managing the interdependencies and parallel
activity among ops processes and Which ops actors get the first pass at at at new data? As they validate and process it — format clean and augment it — are subsequent actors operating on it properly sequenced? With many actors working with the same data sets how are conflicting data updates resolved? As other actors continue to to enrich and distill that data into higher level insights are processes upstream iteratively rerun to refresh their models and outputs?
BIG OPS
scale & complexity of apps and automations interacting with data stored reported
analyzed
decisioned
automated DATA DISTILLATION
data value