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Analytics at The Edge of the Modern Organization
Neda Hitsova,
Business Development Lead at B EYE
Dimitar Dekov,
Founder and CEO at B EYE
Fact-based Decision Culture
The Best/ The Winners
51% of the managers of the Best ALWAYS make fact-based decisions
Often
Often
Always
Always
Seldom
Never
Forrester found that between 60 and 73 % of all enterprise data is never
analyzed. (2019)
Sources: Closing the Data-Value Gap | How to become data-driven and pivot to the new, Accenture, 2019 ; Data and Analytics Impact Index – Forbes Insights (2015)
Everyone Else
Why Data Analytics? Simply because…
"Analytics will define the difference between the losers and winners going forward."
Tim McGuire,
a McKinsey director
Which Countries Are Leading the Data Economy? (HBR)
Gross Data Product– HBR version
of the new GDP
• Volume: Absolute amount of
broadband consumed by a country
• Usage: Number of users active on the
internet
• Accessibility: Institutional openness to
data flows
• Complexity: Volume of broadband
consumption per capita
Measuring Data Literacy Government of Canada
Measuring Data Literacy - Government of
Canada
Bid #: J036687
Published By: Statistics Canada
Bid Type: Bid
Value Range: Not Available
Published Date: 12/12/2019 (MM/DD/YYYY)
Bid Closing Date: 01/10/2020 2:00:00 PM ET (28 days left) (MM/DD/YYYY)
Bid Status: Open for Bidding
NOI Date: Not Applicable
Other Bids From Us Email This To Add to MyFavourite Bids
…………One of the barriers
identified to realizing this
vision is the lack of data
literacy skills among the public
servants………………
Democratizing AI – Finland offers free AI education to every EU citizen
Finland provides European citizens with free access to the
Elements of AI, https://www.elementsofai.com/
The Buzz Words
If we use it on PowerPoint it is
Artificial Intelligence. Otherwise, if we
use it in Python we call it Machine
Learning.
What is the difference between Machine Learning and Artificial Intelligence?
The Buzz Words –They all support the same goal
• Big Data
• Data Analytics
• Data Science
• Machine Learning (ML)
• Business Intelligence (BI)
• Artificial intelligence (AI)
• Augmented Analytics/ Augmented Intelligence (AI)
Using tools and techniques to turn data into valuable business insights
Are you ready for BI?
• Data Literacy
• The ability to read, work with, analyze and argue with data (MIT)
• Data Culture
• Data Democratization
• Self-service Analytics
https://b-eye.com/news/business-intelligence-quiz
Making Data Analytics Work
What is a MUST?
Where to start from?
KPIs in advance?
The Data warehouse
mythQuality of Data
Software tools
Internal promotion
What is a Must?
1. To have the right management
culture that embraces data
analytics
• To prioritize the project at the C-Level
(Executive Project Sponsor)
2. This is NOT an IT project
3. Start new things, brake things and make
mistakes
Mark Zuckerberg, Facebook
Where to start from?
Start with a Hypothesis - What kind of problem are you trying to solve?
Do we need KPIs in Advance?
• Good to have them, not a showstopper
• KPIs could be defined during the project, in parallel
BankingBranches: Customer Sat, Profitability, Risk profile
Call Center/Operations: SLA, Customer Satisfaction
e-CommerceKeyword Performance
% New Visits
Website Traffic Lead Ratio
Conversion Rate
ManufacturingProduction Volume
Maintenance Costs
Capacity Utilization
Production Costs
Credits Dashboard- KPIs
Quality of Data
• Good analytics doesn’t solve bad business process
• You can’t clear the data unless you are using Analytics!
• Better to base decisions on 95% accuracy of Data than on NO Data
Garbage In Garbage Out
The Data Warehouse Myth
Key to a company’s success is being able to process raw data fast and quickly
How important is the Software Tool?
• Take a Test Drive (Proof of Concept)
• Things to consider
• Fast building capabilities
• Self-service
• Visualization capabilities
• Mobile-enabled
• Automated Reporting
• Cloud-enabled
How to Promote the Project Internally?
• It is a management change project!
• Enlighten the Data Knights/Data Jedis in your organization
• Create a good example and a success story
How Data Democratization
Looks like?
• A live example form a global leader
One Universally Successful Company
1billion+ patients cured due to their
philanthropic programs
X64 times increase in stock shares
value since 2000
12 years double digit
revenue growth
44billion+ USD Revenue
13,000 + employees
10ns of revolutionary
developed products
Data Democratization in ActionTh
e co
mp
any • 6 continents
• >13,000 employees
• >6000 outside of manufacturing
• >3000 interactive dashboard users
• >200 software systems
• ~4% Self-Service users
• Cloud mainly
• Mobile everywhere
KP
Is • 1700 KPIs
• 445 Time Frame variables
• 699 forecasts, budgets, targets
• 607 dimensions
• 724.6 GB data(compressed)
• 5.18 bill rows Dat
a an
alyt
ics • 150+ dashboards
• 100+ automated daily e-mails
• 50+ PMs, analytics developers, and support
• 150+ projects, new functionalities and improvements/ month
• AGILE development
About B EYE
Tech Company of the Year award 2019
Tech Growth Business of the Year award 2018
B EYE is visual analytics and automated reporting development factory
Fortune 500 companies Trusted partner
7+ years on the
market
50+ Data
Developers
2000+ active users
per day
1500 + automated
reports
per day
About
What were the most valuable insights for you?
2 minutes wrap up
1. Data-driven MANAGEMENT CULTURE is a must
2. Data Analytics is a BUSINESS PROJECT
3. DATA DEMOCRATIZATION and DATA LITERACY
are key for success
4. AGILE approach – quick wins
5. The tool is also important!
Dimitar Dekov
+359 898 223 304
Thank you for Your
attention!
Neda Hitsova
+359 896 716 535
