2017 the year of data literacy
TRANSCRIPT
2017 The Year of Data LiteracyOutsource UK Business Intelligence Team looks at
what 2017 holds for the world of Business Intelligence.
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The Outsource
Business Intelligence Team
Reece is an
avid festival
goer
Anna is a
chocolate
lover
Jen’s
weakness is
‘dark n
stormy’
Claire is half
way through dry
January
The concept of visualization will move from
“analysis only” to the whole information supply
chain
Improved semantics will shift big data from size
to combinations
Visual
Rendering
The Year of Information
Activism
The Year of Data
Literacy
Context Driven visualization
Context Driven
Visualization
2016: The ‘post-fact era’
• Identifying that there are actual real-life
people who can take BIG data and do
something with it!
• Where Data Scientists & Engineers were
noted as the people who really understood
what Data Insights were:
• Qualified enough to advise on high-
level business decisions
• Worth their weight in gold!
Data literacy includes the ability to read, work with, analyse & argue to data.
2017: The year of growth and ‘Data
Literacy’:
• Spreading insights and literacy
throughout different organisational
functions, ensuring that all areas of
business benefit from business insights,
and ultimately becoming a critical skill of
the future
• With the looming redundancy of Data
Warehouses and the push forward with
more affordable, Cloud enterprise
processes.
• ‘Whole’ information will become the
master input to what is currently mined
during the process of Data Visualization.
Therefore Data-Vis will naturally move
away from ‘analysis’, and move toward
inputs from ‘whole’ supply chain offering
wider visibility across multiple business
functions.
Data
Semantics
and
Catalogues
Data Prep
Visual
Analysis
Storytelling
and Collab
2017 & Data Literacy
Data Pollution
- News travels like wildfire through media. Online algorithms
plus an overload of counter productive data cause risk of
producing ‘data pollution’, ‘biased’ or ‘fatigued’ insights. For
example, Insight consumers can be led in ignorance due to
experts such as econometrist's, who are using earlier measured
ways of collecting data like voting, poll information and historical
data to draw up trends and analysis. An example was the recent
election and campaigns where over 80% of the ‘Factual
information’ provided to the UK was incorrect based on incorrect
sources and bias data.
‘Solution Orientated’ Data
Visualization – where improved
semantics will shift ‘Big Data’ to ‘Big
Insight’. Big Data analytics become
more solution orientated and focused
on the possible, providing better data
quality, insights and outcomes.
Visualization the Commodity – Personal Analytics
become more and more readily available with the
introduction of ‘Freemium’ access to visualization tools
and removes barriers to entry enabling people to learn
and utilise their own data and become more Data
Literate. With the entry of Geospacial Augmented
Reality (eg.Pokemon Go!) and heavier investment in
Internet of Things throughout 2017. IoT will enable us
to contextualize analytics in a physical world and
capture “moments” to build from in the future.
Machine learning
Human Reasoning
& Questions
Data Science
Augmented Intelligence
Sweet
spot
Advancing Analytics
Man and Machine are
more widely developed
over Machine and
Machine. Throughout
2017 “Advanced Analytics”
mature into “Advancing
Analytics” through the
optimisation of Machine
Learning and Data
Science against Human
Reasoning and Questions,
to find the optimal area for
research and
development, predictive
models and shared
findings.
‘Operational Analytics’
Traditional BI
Management
Layers
Modern BI
Information
Activities
Custom Apps
Info Workers
Embedded
Developer
Operational
Workers
Operational Analysts
A wave of people who do
not build applications, but
are simply able to use them
effectively and with ease in
order to maximise business
decisions and strategies.
“WWI - Workers with
information”
The concept of embedded
BI takes Analytics ‘as a
destination’ (or process of
doing) to a much simpler
analytics that ‘come to you’-
type approach. Where
applications are pre-
determined and user
access is widespread. This
allows all areas of an
organisation to benefit from
operational analytics and
growth.
Weblogs
Open
Data
Sensors
Industry
Data
Dark
Data
Geospacial
Data
Customer Data
Financial Data / ERP
Human Resources
Data
Warehouse
Lake
100% On-Premises 100% Cloud / External
Cloud
Cloud uptake across business, both traditional and new data-ready, will
increase by close to 50%, with a potential plateau and then eventual decline in the
use of warehousing or data lake ‘on premise’ solutions. The uptake in transient data
and agile methods will become more and more widely used within the industry.
Freemium licensing and newer enterprise scale options available to businesses and
individual users will force the cost of on-premise to external to go down, enabling IT
to benefit across a wider range of business functions.
IT
Eco Systems
Compute
Data
People
Data
People
Ideas
Governance and Quality - is a key part in the
purpose of analytics and will be something to watch. If
too much governance is enforced by a single party,
the quality of the analysis can be altered, bias or
unjust. Driving business forwards with a mentality of
‘none of us are as smart as all of us’ will maximise
innovation and ideas.
Storytelling – 2016 identified Data Scientist as the role to aspire to,
with higher-level thinking and influence, combined with a deeper
understanding of analytics, methodologies and organisational change.
Data Scientists rose up throughout the year as a fashionable and
highly-respected position. This year brings new challenges. With the
wider use of machine learning and human intervention, coupled with
the skills encompassed within data science, interpreting data will be
essential as we move closer toward augmented intelligence. Data
Artisan, Data Journalist and Storyteller will be the roles to supercede
scientists through 2017.
Storytelling
• Open and Secure
• Open platforms & ecosystems
• Stack vendors
• Specialist tools
• On premise, public, private and edge computing
• Internal and External
• Centrally published, embedded and personally created
User Expansion
Data Expansion
Business Expansion
Computing Expansion
Fewer compromises
IT
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