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The best HR & People Analytics articles of January 2024
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[The best HR & People Analytics articles of January 2024]
The best HR & People Analytics articles of January 2024 | David Green
The best HR & People Analytics articles of January 2024
- Report this article
David Green 🇺🇦
David Green 🇺🇦
Co-Author of Excellence in People Analytics | People Analytics leader | Director, Insight222 & myHRfuture.com | Conference speaker | Host, Digital HR Leaders Podcast
Published Feb 1, 2024
+ Follow
2024 is set to be a momentous year. With economic uncertainty, rising
geopolitical conflict, and rapid advances in technology, it is also set
to be a stormy 12 months for the world, for organisations, and for HR
professionals too.
Perhaps this explains the slew of insightful resources in January, which
has made compiling this month’s collection as challenging as it has been
enjoyable. One of the key focuses has been on ‘productivity’, and I’ve
brought together a number of resources on this topic. There are also new
studies from the likes of PwC, McKinsey, Glassdoor, Accenture, and
Deloitte as well as articles featuring practitioners from companies
including Spotify, Microsoft, Ericsson, Lloyds Banking Group, and
Standard Chartered. There’s lots to enjoy and learn from.
Join me for a webinar on February 21 to discover how Leading Companies shift People Analytics from insight to impact
Are you an HR or People Analytics Leader seeking to transform your
organisation’s People Analytics from mere insights to impactful business
outcomes? If so, I invite you to join me for a webinar that Insight222
is hosting on February 21. Naomi Verghese and I will walk through the
findings from the Insight222 People Analytics Trends research, unveiling
the distinctive characteristics of ABCD Teams that propel organisations
to new heights. Naomi and I will be joined by Alan Susi , VP and Global
Head of Organisational Analytics and People Insights at S&P Global. Alan
will share insights into how S&P Global successfully elevated their
approach to people analytics, turning data into tangible business
outcomes. You can register for the webinar here – or by clicking the
image below.
Jürgen Klopp – a study in leadership, culture, and analytics
As a fervent supporter, I’m still processing the totally unexpected news
that Jürgen Klopp will be leaving his post as the manager of Liverpool
at the end of the current football season. In his press conference on
taking the reins at Anfield in October 2015, Klopp stated his goal was
to turn Liverpool from “doubters to believers.” He has done this with
some aplomb amassing a haul of seven trophies (to date) including the
Champions League in 2019 and then, the following year, the Holy Grail of
Liverpool’s first league title in 30 years.
But Klopp is more than a brilliant football manager. He is the epitome
of an empathetic leader. His emotional intelligence and natural humility
not only endears Klopp to his players, but to supporters too for whom he
is adored. The reaction to the news reduced many Liverpool supporters to
tears. I’m still hoping – probably forlornly - that like Alex Ferguson
in 2002, Klopp will change his mind and stay.
In the likely event that he does depart, I’m sure that multiple studies
will be made on Klopp’s time at Anfield, and that his leadership skills,
use of data and analytics, and ability to build an inclusive winning
culture will be deservedly celebrated. YNWA.
Looking for a new role in people analytics or HR tech?
Before we get to this month’s collection of resources, I’d like to
highlight once again the wonderful resource created by Richard Rosenow
and the One Model team of open roles in people analytics and HR
technology, which now numbers over 500 roles.
Looking for a people analytics event to attend in 2024?
Richard Rosenow has also been busy compiling a study of People Analytics
Conferences to attend in 2024 with the data collected from practitioners
themselves. Society for Industrial and Organizational Psychology (SIOP)
, People Analytics World and the Wharton People Analytics Conference all
come out well as does the Insight222 Global Executive Retreat. Thanks to
Richard for putting this together.
Share the love!
Enjoy reading the collection of resources for January and, if you do,
please share some data driven HR love with your colleagues and networks.
Thanks to the many of you who liked, shared and/or commented on
December’s compendium (including those in the Comments below).
If you enjoy a weekly dose of curated learning (and the Digital HR
Leaders podcast), the Insight222 newsletter: Digital HR Leaders
newsletter is published every Tuesday – subscribe here.
------------------------------------------------------------------------
THE QUEST FOR PRODUCTIVITY
MCKINSEY - 2024 and beyond: Will it be economic stagnation or the advent
of productivity-driven abundance? | PwC - 27th Annual Global CEO Survey:
Thriving in an age of continuous reinvention | JOSH BERSIN - HR
Predictions for 2024: The Global Search For Productivity | ERIK
BRYNJOLFSSON - How AI Will Transform Productivity | BEN WABER AND
NATHANAEL J. FAST - Is GenAI’s Impact on Productivity Overblown?
...
When I talk with CHROs and People Analytics Leaders at the companies we
work with at Insight222, one of the words I’m hearing most at the moment
is ‘productivity’. Continuing economic and geopolitical uncertainty, the
promise of AI, and challenging talent demographics are all fuelling the
demand for productivity from CEOs. Here are five resources that can be
filed under the ‘productivity’ umbrella: (1) McKinsey’s Ezra Greenberg ,
Asutosh Padhi , and Sven Smit present a model for businesses to capture
the three-sided productivity opportunity (see FIG 1). (2) Amongst a ton
of takeaways, the standout theme from the annual PwC CEO survey is that
the vast majority of participating companies are already taking some
steps towards reinvention, while CEOs believe that 40% of their work is
wasted productivity (see FIG 2). (3) Josh Bersin draws from the PwC
survey in his 2024 predictions, where he outlines The Productivity
Advantage where “If you can help your company move faster (productivity
implies speed, not only profit), you can reinvent faster than your
competition.” (4) Stanford professor Erik Brynjolfsson offers leaders an
overview of how AI will transform productivity. (5) Finally, Ben Waber
and Nathanael Fast ’s absorbing essay in Harvard Business Review
cautions leaders on leaning into the hype on GAI’s supposed positive
impact on productivity too heavily. The authors break down two of the
key challenges with LLMs: a) their persistent ability to produce
convincing falsities and b) the likely long-term negative effects of
using LLMs on employees and internal processes.
FIG 1: The three-side productivity opportunity (Source: McKinsey)
FIG 2: CEOs estimate administrative inefficiency at 40% (Source: PwC)
GERGELY OROSZ AND ABI NODA - Measuring Developer Productivity:
Real-World Examples
Continuing the productivity theme, this is an invaluable resource by
Gergely Orosz and Abi Noda in The Pragmatic Engineer newsletter. It
provides detail on developer productivity metrics at 17 tech companies
including Google, Microsoft, Spotify, and Uber (see summary in FIG 3).
FIG 3: Developer productivity metrics at 17 tech companies (Source:
Pragmatic Engineer)
------------------------------------------------------------------------
2024 HR TRENDS AND PREDICTIONS
JASMINE PANAYIDES - Nine Ways to Put HR Trends and Predictions into
Practice in 2024
There has been a flood of articles advising what the key HR trends,
predictions, and opportunities for 2024 are, but how are HR
professionals supposed to make sense of these? In her article for the
myHRfuture blog, Jasmine Panayides provides actionable tips on how HR
professionals can apply the trends, predictions and opportunities to
their work, and their organisations so they can deliver value to the
company and the workforce. Jasmine also helpfully summarises the
trends/predictions from a variety of sources into one table (see FIG 4),
including from: Visier Inc. , Gartner , Bernard Marr , UNLEASH , Mercer
, and Culture Amp as well as my own 12 Opportunities for HR in 2024
article.
FIG 4: Analysis of HR Trends and Predictions for 2024 (Source:
myHRfuture)
KATARINA BERG - HR Trends for 2024 | GARTNER - 9 Future of Work Trends
for 2024 | GLASSDOOR – 2024 Workforce Trends | HUNG LEE - Forecasting
2024 in Recruitment Part 1, Part 2, Part 3, and Part 4 | KEVIN WHEELER -
What Does 2024 Hold in Store for Us? | STACIA GARR AND DANI JOHNSON –
2024 Mega Trends and how people leaders should respond (Webinar)
The deluge of commentators offering their HR trends and opportunities
continued in January. As such, it is a challenge to sort the wheat from
the chaff but in addition to those I highlighted in this compendium in
December, and in Jasmine’s article above, I recommend diving into the
following: (1) Spotify ’s chief people officer, Katarina Berg ,
highlights ten trends with the common theme being each trend is a
bridge, connecting the past with the future, and HR professionals are
the architects crafting these vital links – including “Staying Human in
the Age of AI – The Humanity Bridge”. (2) Gartner’s Jordan Turner and
Emily Rose McRae highlight nine future of work trends for the year ahead
(see FIG 5). (3) Aaron Terrazas and Daniel Zhao identify eight workforce
trends based on Glassdoor ’s data on workplace satisfaction, culture,
and conversations. (4) Hung Lee is at the cutting edge of recruiting and
HR tech, so his four-part series on recruiting in 2024 is definitely
worth checking out – two examples include: “Multi-generational replaces
neurodiversity as DEIB hot topic” and “Capital Allocation Shifts from
Sourcing & Engagement to Assessment & Verification Tech”. (5) Futurist
Kevin Wheeler offers seven insights and predictions together with his
self-assessed certainty rating including “Generative AI will dominate,
and every product will attempt to incorporate AI. 90% certainty” and
“More firms will embrace a four-day workweek 50% certainty”. (6)
Finally, I strongly recommend viewing the 2024 Mega Trends webinar
hosted by Stacia Sherman Garr and Dani Johnson for RedThread Research ,
which breaks down the key macro factors impacting the world of work and
how HR can respond.
FIG 5: 9 Future of Work Trends for 2024 (Source: Gartner)
GREG NEWMAN - 10 important topics that HR will likely ignore in 2024
Greg Newman takes an alternative, wry and contrarian approach by
focusing his list of “predictions” on ten things most HR teams will
continue to ignore in 2024. My favourite three are: (1) speaking the
language of the business, (2) focusing AI conversations on ethics before
technology, and (3) learning that good data is required to realise the
dreams of AI and analytics.
By aligning HR language with business terminology, we can more
effectively demonstrate the value of our initiatives in a way that
resonates with business stakeholders.
------------------------------------------------------------------------
GENERATIVE AI AND THE FUTURE OF WORK
ELLYN SHOOK AND PAUL DAUGHERTY - Work, workforce, workers: Reinvented in
the age of generative AI
A new study from Accenture , co-authored by Ellyn Shook and Paul
Daugherty , on how generative AI is impacting work, provides guidance on
how leaders can: “Set and guide a vision to reinvent work, reshape the
workforce and prepare workers for a generative AI world, while building
a resilient culture to navigate continuous waves of change.” The report
reveals a trust gap between workers and leaders on key elements related
to GAI’s impact on work, the workforce, and workers. The authors also
highlight four accelerators for leaders to navigate the journey ahead:
(1) Lead and learn in new ways, (2) Reinvent work, (3) Reshape the
workforce (see example in FIG 6), and (4) Prepare workers.
...
Jürgen Klopp – a study in leadership, culture, and analytics
As a fervent supporter, I’m still processing the totally unexpected news
that Jürgen Klopp will be leaving his post as the manager of Liverpool
at the end of the current football season. In his press conference on
taking the reins at Anfield in October 2015, Klopp stated his goal was
to turn Liverpool from “doubters to believers.” He has done this with
some aplomb amassing a haul of seven trophies (to date) including the
Champions League in 2019 and then, the following year, the Holy Grail of
Liverpool’s first league title in 30 years.
But Klopp is more than a brilliant football manager. He is the epitome
of an empathetic leader. His emotional intelligence and natural humility
not only endears Klopp to his players, but to supporters too for whom he
is adored. The reaction to the news reduced many Liverpool supporters to
tears. I’m still hoping – probably forlornly - that like Alex Ferguson
in 2002, Klopp will change his mind and stay.
In the likely event that he does depart, I’m sure that multiple studies
will be made on Klopp’s time at Anfield, and that his leadership skills,
use of data and analytics, and ability to build an inclusive winning
culture will be deservedly celebrated. YNWA.
Looking for a new role in people analytics or HR tech?
Before we get to this month’s collection of resources, I’d like to
highlight once again the wonderful resource created by Richard Rosenow
and the One Model team of open roles in people analytics and HR
technology, which now numbers over 500 roles.
Looking for a people analytics event to attend in 2024?
Richard Rosenow has also been busy compiling a study of People Analytics
Conferences to attend in 2024 with the data collected from practitioners
themselves. Society for Industrial and Organizational Psychology (SIOP)
, People Analytics World and the Wharton People Analytics Conference all
come out well as does the Insight222 Global Executive Retreat. Thanks to
Richard for putting this together.
Share the love!
Enjoy reading the collection of resources for January and, if you do,
please share some data driven HR love with your colleagues and networks.
Thanks to the many of you who liked, shared and/or commented on
December’s compendium (including those in the Comments below).
If you enjoy a weekly dose of curated learning (and the Digital HR
Leaders podcast), the Insight222 newsletter: Digital HR Leaders
newsletter is published every Tuesday – subscribe here.
------------------------------------------------------------------------
THE QUEST FOR PRODUCTIVITY
MCKINSEY - 2024 and beyond: Will it be economic stagnation or the advent
of productivity-driven abundance? | PwC - 27th Annual Global CEO Survey:
Thriving in an age of continuous reinvention | JOSH BERSIN - HR
Predictions for 2024: The Global Search For Productivity | ERIK
BRYNJOLFSSON - How AI Will Transform Productivity | BEN WABER AND
NATHANAEL J. FAST - Is GenAI’s Impact on Productivity Overblown?
When I talk with CHROs and People Analytics Leaders at the companies we
work with at Insight222, one of the words I’m hearing most at the moment
is ‘productivity’. Continuing economic and geopolitical uncertainty, the
promise of AI, and challenging talent demographics are all fuelling the
demand for productivity from CEOs. Here are five resources that can be
filed under the ‘productivity’ umbrella: (1) McKinsey’s Ezra Greenberg ,
Asutosh Padhi , and Sven Smit present a model for businesses to capture
the three-sided productivity opportunity (see FIG 1). (2) Amongst a ton
of takeaways, the standout theme from the annual PwC CEO survey is that
the vast majority of participating companies are already taking some
steps towards reinvention, while CEOs believe that 40% of their work is
wasted productivity (see FIG 2). (3) Josh Bersin draws from the PwC
survey in his 2024 predictions, where he outlines The Productivity
Advantage where “If you can help your company move faster (productivity
implies speed, not only profit), you can reinvent faster than your
competition.” (4) Stanford professor Erik Brynjolfsson offers leaders an
overview of how AI will transform productivity. (5) Finally, Ben Waber
and Nathanael Fast ’s absorbing essay in Harvard Business Review
cautions leaders on leaning into the hype on GAI’s supposed positive
impact on productivity too heavily. The authors break down two of the
key challenges with LLMs: a) their persistent ability to produce
convincing falsities and b) the likely long-term negative effects of
using LLMs on employees and internal processes.
FIG 1: The three-side productivity opportunity (Source: McKinsey)
FIG 2: CEOs estimate administrative inefficiency at 40% (Source: PwC)
GERGELY OROSZ AND ABI NODA - Measuring Developer Productivity:
Real-World Examples
Continuing the productivity theme, this is an invaluable resource by
Gergely Orosz and Abi Noda in The Pragmatic Engineer newsletter. It
provides detail on developer productivity metrics at 17 tech companies
including Google, Microsoft, Spotify, and Uber (see summary in FIG 3).
FIG 3: Developer productivity metrics at 17 tech companies (Source:
Pragmatic Engineer)
------------------------------------------------------------------------
2024 HR TRENDS AND PREDICTIONS
JASMINE PANAYIDES - Nine Ways to Put HR Trends and Predictions into
Practice in 2024
There has been a flood of articles advising what the key HR trends,
predictions, and opportunities for 2024 are, but how are HR
professionals supposed to make sense of these? In her article for the
myHRfuture blog, Jasmine Panayides provides actionable tips on how HR
professionals can apply the trends, predictions and opportunities to
their work, and their organisations so they can deliver value to the
company and the workforce. Jasmine also helpfully summarises the
trends/predictions from a variety of sources into one table (see FIG 4),
including from: Visier Inc. , Gartner , Bernard Marr , UNLEASH , Mercer
, and Culture Amp as well as my own 12 Opportunities for HR in 2024
article.
...
NAOMI VERGHESE - How to Measure the Value of People Analytics
My Insight222 colleague Naomi Verghese digs how to measure the
commercial value of people analytics, highlighting a powerful case study
from Jaesun HA and LG Electronics. Naomi provides detail on four key
areas where people analytics adds value (business performance, workforce
experiences, driving an analytics culture and societal benefit) as well
as providing data on the characteristics of companies that ARE creating
commercial value from people analytics (see FIG 8).
FIG 8: Characteristics of people analytics that disclosed and measured
commercial value of people analytics solutions (Source: Insight222
People Analytics Trends, 2023)
ANDRÉS GARCIA AYALA - 5 Change Drivers Impacting People Analytics & How
To Thrive In Them | WILLIS JENSEN - Attrition versus Retention: Which
Should I Use? | KEITH McNULTY – Regression Modeling in People Analytics:
Survival Analysis | LYDIA WU - The Market Sucks and You are Looking for
a Job, Now What? | SEBASTIAN SZACHNOWSKI - 16 HR Metrics for IT | ERIN
FLEMING AND NICK JESTEADT - People Analytics Perspectives from the
Fringe: Current Priorities and a View on Optimized Teams in 2024
January saw a slew of articles from current and recent people analytics
leaders, which typically act as a spur and inspiration for the field.
Six are highlighted here: (1) Andrés García Ayala highlights some of the
key change drivers impacting people analytics and ways to incorporate
them into our work. (2) Willis Jensen builds on the recent primer on
attrition metrics by Ben Teusch that I highlighted in December’s
edition. He explains why we should be using attrition and retention as
separate terms that lead to distinct metrics with different objectives
(see also FIG 9). (3) Keith McNulty provides another indispensable
practical guide for people analysts with a step-by-step tutorial to
conducting survival analysis in R. (4) The prolific Lydia Wu turns her
attention to providing some handy guidance for those looking for their
next people analytics / HR tech role. (5) Sebastian Szachnowski provides
a useful breakdown of 16 HR metrics for technology companies. (6) Last
but definitely not least, Erin Fleming and Nick Jesteadt provide
insights from their survey of fellow people analytics practitioners.
Insights include a) 41% of respondents (n=49) operate as a one-person
people analytics team, and ii) the main current focus areas of work
include employee turnover, cultural engagement, return to office, and
restructuring.
FIG 9: When to use Attrition and Retention (Source: Willis Jensen)
MAX BLUMBERG - The Big List of GPTs to Revolutionize Your People
Processes | JOHANNES SUNDLO - GenAI for People Analytics
Two articles addressing the opportunity for generative AI in the people
space. (1) Max Blumberg (JA) 🇺🇦 sets out 93 potential ways to upgrade
your People Processes with AI and GPTs across four categories –
workforce planning and strategy, recruitment, learning and development,
and employee wellbeing. (2) Johannes Sundlo provides examples of
companies using GAI in their people analytics work to support analyses
on engagement data, skills, and tailoring training recommendations.
GPTs are an amazing tool for scenario planning, forecasting future
workforce needs, identifying talent gaps, and developing integrated
talent strategies.
------------------------------------------------------------------------
THE EVOLUTION OF HR AND DATA DRIVEN CULTURE
DAVE ULRICH, NORM SMALLWOOD, AND JOE GROCHOWSKI - Why and How to Move HR
to an Outside-In Approach
When asked the question, “What is the biggest challenge in your job
today?” HR professionals will typically provide answers such as: “Build
a skills-based organisation” or “Help our employees have a better
experience”. As Dave Ulrich , Norm Smallwood , and Joe Grochowski write,
these answers would be far more powerful when a “so that” is applied
e.g. “Help employees have a better experience so that customer
experience improves.” The article demonstrates that greater value is
created with an outside-in approach that starts with the needs of
external stakeholders (customers, investors, community) and then
figuring out the implications inside the company for meeting those
needs. Dave, Norm, and Joe also present their Human Capability Framework
and a tool that provides an assessment of an organisation’s outside-in
performance (see FIG 10).
FIG 10: Human capability from the outside-in - diagnostic questions
(Source: Dave Ulrich et al)
------------------------------------------------------------------------
WORKFORCE PLANNING, ORG DESIGN, AND SKILLS-BASED ORGANISATIONS
AMY WEBB - Bringing True Strategic Foresight Back to Business
In her article for Harvard Business Review, Amy Webb defines strategic
foresight as “a disciplined and systematic approach to identify where to
play, how to win in the future, and how to ensure organizational
resiliency in the face of unforeseen disruption.” Her article also
advocates for the integration of strategic foresight as a core
competency in every organisation, regardless of size. Moreover, Amy
provides guidance on how to operationalise strategic foresight by
unveiling a ten-step process. Read alongside another article authored by
Amy for HBR: How to Do Strategic Planning Like a Futurist, which
includes Amy’s Futurist’s Framework for Strategic Planning (see FIG 11).
FIG 11: A Futurist’s Framework for Strategic Planning (Source: Amy Webb)
WORLD ECONOMIC FORUM AND PwC - Putting Skills First: Opportunities for
Building Efficient and Equitable Labour Markets
As the introduction to this compelling collaboration between the World
Economic Forum and PwC begins: “Skills and talent shortages are critical
challenges facing societies and economies today. The absence of relevant
skills impedes business growth, hinders economic prosperity, and
inhibits individuals from realizing their full potential.” The report
identifies five specific opportunities for intervention where the gains
from skills-first solutions are most likely for employers and workers
alike (see ‘Skills-first Framework’ in FIG 12). Additionally, the report
also showcases 13 Skills First “Lighthouses”, including IBM, Siemens,
Standard Chartered and Sanofi. It concludes by offering key takeaways
regarding six success factors in implementing skills-first approaches
including (1) Sponsorship from leadership, (2) Alignment with business
needs, and (3) Data and evaluation for iteration. (Authors: Genesis
Elhussein , Mark Rayner , Aarushi Singhania , Saadia Zahidi , Peter
Brown MBE , Miral Mir , and Bhushan Sethi ).
2. Data Science Statistics 2024 By Best Solution, Easy Insights | 75% of businesses using AI-based predictive analytics report increased sales and customer satisfaction. (Source: McKinsey, MIT Sloan Management Review, PwC). AI ...
(Source: Stanford University)
[]
[]
Data Science Statistics – Future Outlook
- The demand for data scientists is projected to grow by 16% from 2020
to 2028.
- By 2025, the global AI market is estimated to reach $190.61 billion.
- The ML market is expected to reach $96.7 billion by 2025, growing at
a CAGR of 43.8% between 2020 and 2025.
- The global big data analytics market is forecasted to reach $103
billion by 2027, with a CAGR of 10.9% from 2020 to 2027.
- By 2025, it is estimated that 97.2 zettabytes (ZB) of data will be
generated globally.
- 84% of customers consider data privacy as a significant concern.
- By 2024, organizations leveraging AI for decision-making will face
ethical challenges, resulting in reputational damage or financial
penalties for 60% of organizations.
- The number of IoT devices is projected to reach 38.6 billion by
2025.
- IoT-generated data is estimated to reach 79.4 zettabytes (ZB) by
2025.
- By 2023, augmented analytics will be pervasive in data science
platforms, with more than 40% of data science tasks automated.
- The global market size of augmented analytics is expected to reach
$18.4 billion by 2027, with a CAGR of 26.7% from 2020 to 2027.
(Source: U.S. Bureau of Labor Statistics, Statista, IDC, Salesforce,
Gartner, Statista)
Data Science Statistics – Challenges
- According to a study by IBM, poor data quality costs the US economy
around $3.1 trillion per year.
- Research by Gartner suggests that data quality issues can result in
a 40% loss of revenue for businesses.
- A survey conducted by O’Reilly found that 53% of respondents listed
data privacy and security as their top concerns in data science
projects.
- In 2020, the International Association of Privacy Professionals
(IAPP) reported that the average cost of a data breach reached $3.86
million.
- The World Economic Forum estimates that by 2022, there will be a
shortage of 1.5 million data scientists worldwide.
- According to LinkedIn’s Workforce Report, data science roles have
been one of the fastest-growing job categories, with a 37% annual
growth rate.
- The Data & Marketing Association (DMA) reported that 71% of
consumers are concerned about how brands use their data.
- IDC predicts that the global data sphere will reach 175 zettabytes
by 2025, posing significant challenges in terms of processing,
storage, and analysis.
- A survey conducted by NewVantage Partners found that 92.2% of
executives face challenges scaling their big data and AI
initiatives.
(Source: IBM, Gartner, O’Reilly, IAPP, World Economic Forum, LinkedIn,
DMA, IDC, NewVantage Partners)
Recent Developments
Merger and Acquisition:
- Microsoft’s Acquisition of Nuance Communications: Microsoft, a
global technology company, finalized the acquisition of Nuance
Communications, a leading provider of conversational AI and speech
recognition technology.
- The acquisition, valued at $19.7 billion, strengthens Microsoft’s
capabilities in natural language processing and enhances its
position in the data science and artificial intelligence market.
New Product Launches:
- Google’s Launch of TensorFlow 2.0: Google introduced TensorFlow 2.0,
the latest version of its open-source machine learning platform.
- TensorFlow 2.0 offers improved usability, performance, and
flexibility for data scientists and developers, enabling faster
development and deployment of machine learning models.
- Within the first month of its release, TensorFlow 2.0 attracted over
1 million downloads, demonstrating strong industry interest in
advanced machine learning tools.
Funding:
- Funding Round for Data Science Startup: DataRobot, a data science
and machine learning platform, raised $300 million in Series G
funding led by Altimeter Capital and Tiger Global Management.
- The funding will support DataRobot’s expansion efforts and further
development of its automated machine learning platform, empowering
organizations to accelerate AI adoption and drive business insights
from data.
Innovation in Data Science Tools and Technologies:
- Advancements in Automated Machine Learning: Companies are investing
in automated machine learning (AutoML) tools to democratize data
science and enable non-experts to build and deploy machine learning
models with ease.
- AutoML platforms like H2O.ai and DataRobot leverage automation and
AI to streamline the machine learning workflow, from data
preprocessing to model selection and deployment, reducing the time
and expertise required for model development.
Expansion of Data Science Applications:
- Diversification of Data Science Use Cases: Data science is
increasingly being applied across diverse domains, including
healthcare, finance, retail, and manufacturing, to solve complex
challenges and drive innovation.
- Applications of data science range from predictive analytics for
disease diagnosis and treatment optimization to fraud detection in
financial transactions and demand forecasting in supply chain
management.
- As organizations recognize the value of data-driven insights, the
scope and impact of data science continue to expand across
industries.
Key Takeaways
Data Science Statistics – Data science has emerged as a powerful field
that leverages large volumes of data to extract valuable insights and
drive informed decision-making.
Through the application of statistical analysis, machine learning
algorithms, and visualization techniques, data scientists can uncover
patterns, trends, and correlations that were previously hidden.
This field has revolutionized industries ranging from healthcare and
finance to marketing and technology, enabling organizations to optimize
their operations, enhance customer experiences, and achieve competitive
advantages.
As the world continues to generate vast amounts of data, data science
will remain crucial in extracting meaningful knowledge and driving
innovation in various domains.
FAQs
What is data science?
Data science is an interdisciplinary field that combines techniques from
statistics, mathematics, and computer science. Domain knowledge to
extract insights and knowledge from structured and unstructured data.
What programming languages are commonly used in data science?
Python and R are the two most popular programming languages for data
science. Python has a vast ecosystem of libraries and frameworks like
NumPy, Pandas, and TensorFlow. While R is known for its libraries and
visualization capabilities.
What are some common machine learning algorithms?
Common machine learning algorithms include linear regression, logistic
regression, decision trees, and random forests. Support vector machines
(SVM), k-nearest neighbors (KNN), naive Bayes, and neural networks.
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...
(Source: IDC, IBM, McKinsey & Company, PwC, Accenture, McKinsey &
Company, NewVantage Partners, Capgemini)
Data Science Statistics – In Industries
Finance and Banking
- The global market for big data in the banking sector is projected to
reach $14.83 billion by 2026, growing at a CAGR of 18.8% from 2019
to 2026.
- According to a survey by Deloitte, 88% of financial institutions
believe that artificial intelligence (AI) will revolutionize the way
they gather information and interact with customers.
- In a study by McKinsey, it was found that data-driven banks have the
potential to achieve a 5-10% increase in return on equity (ROE).
- Machine learning algorithms are increasingly used in fraud detection
and prevention in the banking industry. According to the Association
for Financial Professionals, 74% of organizations use AI and machine
learning for fraud prevention.
- The adoption of advanced analytics, including data science
techniques. This can lead to a 1-3% increase in loan approval rates
for banks.
- According to a study by PwC, 61% of financial institutions have
invested in robotic process automation (RPA) and machine learning
for risk management and compliance.
(Source: Deloitte, McKinsey & Company, Association for Financial
Professionals, McKinsey & Company, PwC)
Healthcare and Medicine
- The global healthcare analytics market is expected to reach $84.2
billion by 2027. Growing at a CAGR of 25.2% from 2020 to 2027.
- The adoption of big data analytics in healthcare can potentially
save the industry $300 billion per year in the United States alone.
- According to a survey by HealthITAnalytics, 89% of healthcare
executives have reported that they have invested in big data
analytics and artificial intelligence (AI) for their organizations.
- The use of machine learning algorithms has demonstrated high
accuracy in diagnosing diseases from medical imaging data. For
example, a deep learning algorithm achieved 94.5% accuracy in
identifying lung cancer from CT scans.
- Electronic Health Records (EHRs) and patient data provide valuable
insights for data science applications. According to a study
published in the Journal of Medical Internet Research. Using EHR
data for predictive modeling improved the prediction of patient
outcomes by 12-14%.
(Source: McKinsey & Company, HealthITAnalytics, Nature, Journal of
Medical Internet Research)
Retail and E-commerce
- E-commerce companies that effectively use data science techniques to
personalize customer experiences can see a 6% increase in revenue.
- According to a study by McKinsey, companies that extensively use
customer analytics are more likely to generate higher profits than
their competitors.
- E-commerce companies that effectively use data science techniques to
personalize customer experiences can see a 6% increase in revenue.
- By 2022, 35% of leading global retailers are expected to adopt AI
for personalized product recommendations, leading to a 25% increase
in revenue.
- Data-driven pricing strategies in retail can result in a 2-5%
increase in sales and a 2-4% increase in profit margins.
- According to a study by Segment, 49% of consumers have made impulse
purchases after receiving a personalized recommendation from an
e-commerce store.
- Retailers using AI-powered chatbots for customer service have
reported a 70-80% reduction in customer support costs.
(Source: McKinsey & Company, Gartner, Segment, IBM)
Manufacturing and Supply Chain
- The predictive analytics market in the manufacturing sector is
projected to reach $3.55 billion by 2026, growing at a CAGR of 21.6%
from 2019 to 2026.
- Data-driven supply chains can reduce inventory holding costs by up
to 20% and increase order fulfillment rates by up to 7%.
- According to a survey by PwC, 40% of manufacturing companies are
already using big data analytics to improve their supply chain
operations.
- The adoption of artificial intelligence (AI) in the manufacturing
sector is expected to lead to a 20% increase in production capacity
by 2025.
- The implementation of advanced analytics in supply chain management
can lead to a 10% reduction in supply chain costs and a 10% increase
in revenue.
- According to a report by MHI and Deloitte, 80% of supply chain
professionals believe that digital supply chain technologies,
including data analytics, will be the dominant force shaping the
future of supply chains.
- Machine learning algorithms applied to supply chain data can improve
demand forecasting accuracy by up to 20%.
(Source: Accenture, PwC, McKinsey & Company, Forbes, MHI and Deloitte,
Supply Chain Dive)
Marketing and Advertising
- In a survey by Adobe, 69% of marketers stated that data-driven
marketing is crucial for success in a competitive global economy.
- Personalized emails generated through data-driven segmentation have
a 26% higher open rate than generic emails.
- In a survey by Econsultancy, 77% of marketers stated that
data-driven marketing was their most exciting opportunity in 2021.
- According to a report by Forbes, 72% of marketers believe that data
analysis and interpretation are the most critical skills for their
organization’s success.
- Data-driven marketing campaigns can result in a 20% increase in
sales on average.
- Personalized marketing campaigns driven by data analysis can lead to
a 10% increase in customer satisfaction.
- According to a survey by Adobe, 57% of marketers reported that data
science and analytics are vital for understanding customer behavior.
- Companies that effectively utilize data science in their marketing
strategies are 6 times more likely to achieve a higher customer
retention rate.
- Data-driven segmentation and targeting can result in a 760% increase
in email revenue.
(Source: Adobe, Campaign Monitor, Econsultancy, Forbes, McKinsey &
Company)
Ethical Challenges in Data Analytics
Bias and Fairness
- Over 80% of data scientists and AI researchers believe that
addressing bias in AI and machine learning algorithms is a
significant challenge.
- A study found that commercial facial recognition systems had higher
error rates in classifying the gender of darker-skinned females,
with error rates ranging from 20% to 34.7%, compared to
lighter-skinned males with an error rate of 0.8%.
(Source: O’Reilly’s)
Privacy and Data Protection
- According to a survey, 79% of consumers in the United States are
concerned about how their data is being used by companies.
- In 2020, data breaches exposed over 36 billion records, with the
average cost of a data breach being $3.86 million.
...
(Source: McKinsey & Company, Gartner, Segment, IBM)
Manufacturing and Supply Chain
- The predictive analytics market in the manufacturing sector is
projected to reach $3.55 billion by 2026, growing at a CAGR of 21.6%
from 2019 to 2026.
- Data-driven supply chains can reduce inventory holding costs by up
to 20% and increase order fulfillment rates by up to 7%.
- According to a survey by PwC, 40% of manufacturing companies are
already using big data analytics to improve their supply chain
operations.
- The adoption of artificial intelligence (AI) in the manufacturing
sector is expected to lead to a 20% increase in production capacity
by 2025.
- The implementation of advanced analytics in supply chain management
can lead to a 10% reduction in supply chain costs and a 10% increase
in revenue.
- According to a report by MHI and Deloitte, 80% of supply chain
professionals believe that digital supply chain technologies,
including data analytics, will be the dominant force shaping the
future of supply chains.
- Machine learning algorithms applied to supply chain data can improve
demand forecasting accuracy by up to 20%.
(Source: Accenture, PwC, McKinsey & Company, Forbes, MHI and Deloitte,
Supply Chain Dive)
Marketing and Advertising
- In a survey by Adobe, 69% of marketers stated that data-driven
marketing is crucial for success in a competitive global economy.
- Personalized emails generated through data-driven segmentation have
a 26% higher open rate than generic emails.
- In a survey by Econsultancy, 77% of marketers stated that
data-driven marketing was their most exciting opportunity in 2021.
- According to a report by Forbes, 72% of marketers believe that data
analysis and interpretation are the most critical skills for their
organization’s success.
- Data-driven marketing campaigns can result in a 20% increase in
sales on average.
- Personalized marketing campaigns driven by data analysis can lead to
a 10% increase in customer satisfaction.
- According to a survey by Adobe, 57% of marketers reported that data
science and analytics are vital for understanding customer behavior.
- Companies that effectively utilize data science in their marketing
strategies are 6 times more likely to achieve a higher customer
retention rate.
- Data-driven segmentation and targeting can result in a 760% increase
in email revenue.
(Source: Adobe, Campaign Monitor, Econsultancy, Forbes, McKinsey &
Company)
Ethical Challenges in Data Analytics
Bias and Fairness
- Over 80% of data scientists and AI researchers believe that
addressing bias in AI and machine learning algorithms is a
significant challenge.
- A study found that commercial facial recognition systems had higher
error rates in classifying the gender of darker-skinned females,
with error rates ranging from 20% to 34.7%, compared to
lighter-skinned males with an error rate of 0.8%.
(Source: O’Reilly’s)
Privacy and Data Protection
- According to a survey, 79% of consumers in the United States are
concerned about how their data is being used by companies.
- In 2020, data breaches exposed over 36 billion records, with the
average cost of a data breach being $3.86 million.
(Source: Pew Research Center, IBM Securities)
Algorithmic Accountability
- An investigation revealed that Amazon’s recruiting algorithm
discriminated against women by downgrading their resumes, leading to
a bias against female applicants.
- In the United States, 67% of respondents in a survey expressed
concern about using automated decision-making systems for criminal
justice purposes.
(Source: Reuters, Data & Societies)
Transparency and Explainability
- Only 20% of organizations report having a framework in place to
ensure the ethical use of AI and data analytics.
- A study found that 64% of people would like to know why an AI system
made a particular decision.
(Source: Gartner’s, Capgemini’s)
[Data Science Statistics]
[Data Science Statistics]
Data Science Statistics – AI Implementation
AI Adoption in Data Science
- By 2022, 85% of all big data analytics will leverage AI
capabilities.
- In a survey of data professionals, 81% reported using AI and machine
learning techniques in their data science projects.
- 90% of data science projects will incorporate automated machine
learning by 2025.
(Source: Gartner, O’Reilly, Gartner)
AI and Automation in Data Science
- AI automation can reduce the time spent on data preparation by up to
80%.
- According to a survey, 43% of data scientists consider automation as
the most important skill to develop for the future.
- By 2025, 40% of data science tasks will be automated, resulting in
increased productivity and efficiency.
(Source: Forbes)
AI in Predictive Analytics
- AI-based predictive analytics can achieve an accuracy rate of 95% or
higher in some industries.
- Companies that leverage AI in predictive analytics have a 12% higher
profit margin than companies that don’t.
- 75% of businesses using AI-based predictive analytics report
increased sales and customer satisfaction.
(Source: McKinsey, MIT Sloan Management Review, PwC)
AI and Natural Language Processing (NLP) in Data Science
- NLP is the most widely used AI technology among data scientists,
with 49% utilizing NLP techniques.
- By 2024, the NLP market is projected to reach $26.4 billion, driven
by the increasing demand for AI-powered language processing.
- NLP is employed in various data science applications, including
sentiment analysis, chatbots, and text classification.
(Source: O’Reilly)
AI and Computer Vision in Data Science
- Computer vision, a subfield of AI, is gaining prominence in data
science, with applications in image recognition, object detection,
and autonomous vehicles.
- The global computer vision market is expected to reach $48.32
billion by 2023, driven by advancements in AI and deep learning.
- Computer vision models have achieved human-level accuracy in tasks
such as image classification and object detection.
...
(Source: O’Reilly’s)
Privacy and Data Protection
- According to a survey, 79% of consumers in the United States are
concerned about how their data is being used by companies.
- In 2020, data breaches exposed over 36 billion records, with the
average cost of a data breach being $3.86 million.
(Source: Pew Research Center, IBM Securities)
Algorithmic Accountability
- An investigation revealed that Amazon’s recruiting algorithm
discriminated against women by downgrading their resumes, leading to
a bias against female applicants.
- In the United States, 67% of respondents in a survey expressed
concern about using automated decision-making systems for criminal
justice purposes.
(Source: Reuters, Data & Societies)
Transparency and Explainability
- Only 20% of organizations report having a framework in place to
ensure the ethical use of AI and data analytics.
- A study found that 64% of people would like to know why an AI system
made a particular decision.
(Source: Gartner’s, Capgemini’s)
[Data Science Statistics]
[Data Science Statistics]
Data Science Statistics – AI Implementation
AI Adoption in Data Science
- By 2022, 85% of all big data analytics will leverage AI
capabilities.
- In a survey of data professionals, 81% reported using AI and machine
learning techniques in their data science projects.
- 90% of data science projects will incorporate automated machine
learning by 2025.
(Source: Gartner, O’Reilly, Gartner)
AI and Automation in Data Science
- AI automation can reduce the time spent on data preparation by up to
80%.
- According to a survey, 43% of data scientists consider automation as
the most important skill to develop for the future.
- By 2025, 40% of data science tasks will be automated, resulting in
increased productivity and efficiency.
(Source: Forbes)
AI in Predictive Analytics
- AI-based predictive analytics can achieve an accuracy rate of 95% or
higher in some industries.
- Companies that leverage AI in predictive analytics have a 12% higher
profit margin than companies that don’t.
- 75% of businesses using AI-based predictive analytics report
increased sales and customer satisfaction.
(Source: McKinsey, MIT Sloan Management Review, PwC)
AI and Natural Language Processing (NLP) in Data Science
- NLP is the most widely used AI technology among data scientists,
with 49% utilizing NLP techniques.
- By 2024, the NLP market is projected to reach $26.4 billion, driven
by the increasing demand for AI-powered language processing.
- NLP is employed in various data science applications, including
sentiment analysis, chatbots, and text classification.
(Source: O’Reilly)
AI and Computer Vision in Data Science
- Computer vision, a subfield of AI, is gaining prominence in data
science, with applications in image recognition, object detection,
and autonomous vehicles.
- The global computer vision market is expected to reach $48.32
billion by 2023, driven by advancements in AI and deep learning.
- Computer vision models have achieved human-level accuracy in tasks
such as image classification and object detection.
(Source: Stanford University)
[]
[]
Data Science Statistics – Future Outlook
- The demand for data scientists is projected to grow by 16% from 2020
to 2028.
- By 2025, the global AI market is estimated to reach $190.61 billion.
- The ML market is expected to reach $96.7 billion by 2025, growing at
a CAGR of 43.8% between 2020 and 2025.
- The global big data analytics market is forecasted to reach $103
billion by 2027, with a CAGR of 10.9% from 2020 to 2027.
- By 2025, it is estimated that 97.2 zettabytes (ZB) of data will be
generated globally.
- 84% of customers consider data privacy as a significant concern.
- By 2024, organizations leveraging AI for decision-making will face
ethical challenges, resulting in reputational damage or financial
penalties for 60% of organizations.
- The number of IoT devices is projected to reach 38.6 billion by
2025.
- IoT-generated data is estimated to reach 79.4 zettabytes (ZB) by
2025.
- By 2023, augmented analytics will be pervasive in data science
platforms, with more than 40% of data science tasks automated.
- The global market size of augmented analytics is expected to reach
$18.4 billion by 2027, with a CAGR of 26.7% from 2020 to 2027.
(Source: U.S. Bureau of Labor Statistics, Statista, IDC, Salesforce,
Gartner, Statista)
Data Science Statistics – Challenges
- According to a study by IBM, poor data quality costs the US economy
around $3.1 trillion per year.
- Research by Gartner suggests that data quality issues can result in
a 40% loss of revenue for businesses.
- A survey conducted by O’Reilly found that 53% of respondents listed
data privacy and security as their top concerns in data science
projects.
- In 2020, the International Association of Privacy Professionals
(IAPP) reported that the average cost of a data breach reached $3.86
million.
- The World Economic Forum estimates that by 2022, there will be a
shortage of 1.5 million data scientists worldwide.
- According to LinkedIn’s Workforce Report, data science roles have
been one of the fastest-growing job categories, with a 37% annual
growth rate.
- The Data & Marketing Association (DMA) reported that 71% of
consumers are concerned about how brands use their data.
- IDC predicts that the global data sphere will reach 175 zettabytes
by 2025, posing significant challenges in terms of processing,
storage, and analysis.
- A survey conducted by NewVantage Partners found that 92.2% of
executives face challenges scaling their big data and AI
initiatives.
(Source: IBM, Gartner, O’Reilly, IAPP, World Economic Forum, LinkedIn,
DMA, IDC, NewVantage Partners)
Recent Developments
Merger and Acquisition:
- Microsoft’s Acquisition of Nuance Communications: Microsoft, a
global technology company, finalized the acquisition of Nuance
Communications, a leading provider of conversational AI and speech
recognition technology.
- The acquisition, valued at $19.7 billion, strengthens Microsoft’s
capabilities in natural language processing and enhances its
position in the data science and artificial intelligence market.
...
(Source: Statista)
[]
[]
Data Science Statistics – Roles of Data Scientists
- Data scientists spend about 80% of their time on data preparation,
cleaning, and integration tasks, also known as data wrangling.
- According to Glassdoor, data scientist was ranked as the best job in
the United States in 2021 based on job satisfaction, salary, and job
openings.
- Data scientists are proficient in programming languages, with Python
being the most commonly used language, followed by R and SQL.
- The demand for data scientists has increased by 31% since 2019, with
a growing number of industries recognizing the value.
- According to a survey by O’Reilly, 74% of respondents stated that
their organizations were investing in or planning to invest.
- The average salary for a data scientist in the United States is
around $120,000 per year. Making it one of the highest-paying job
roles in the field of technology.
- LinkedIn identified data science skills as one of the top skills
that can get you hired in 2021.
- A report by IBM estimated that the demand for data scientists would
increase by 28% by 2022.
- Data scientists play a crucial role in driving business value
through data analytics. With organizations using analytics reporting
a median return on investment of 10 times their analytics spending.
- According to LinkedIn’s 2021 Emerging Jobs Report, It is one of the
fastest-growing jobs, with a 37% annual growth rate.
(Source: Forbes, Glassdoor, Kaggle, LinkedIn, IBM, MIT Sloan Management
Review, O’Reilly, Indeed)
[Data Science Statistics]
[Data Science Statistics]
Data Science Statistics – Growth and Impact
- The worldwide revenue from big data and business analytics is
forecasted to reach $274.3 billion in 2022, with a CAGR of 13.2%
from 2017 to 2022.
- According to a report by IBM, data science-related job postings have
increased by 650% since 2012, indicating the rapid growth.
- The healthcare analytics market is projected to reach $84.2 billion
by 2027. Driven by the increasing need for advanced analytics in
healthcare organizations.
- Data-driven organizations are 23 times more likely to acquire
customers, and six times more likely to retain customers compared to
their non-data-driven counterparts.
- A study by PwC estimates that artificial intelligence (AI) and
machine learning (ML) could contribute up to $15.7 trillion to the
global economy by 2030.
- The financial sector has experienced significant benefits from data
science. With a potential annual value of $1.3 trillion in the form
of cost savings and additional revenue.
- Data science has helped reduce maintenance costs in the
manufacturing industry by up to 40% and decrease unplanned downtime
by up to 50%.
- According to a survey by NewVantage Partners, 97.2% of executives
report that their organizations are investing in or planning.
- The transportation and logistics industry can achieve operational
cost savings of 10% to 20% by leveraging data science.
(Source: IDC, IBM, McKinsey & Company, PwC, Accenture, McKinsey &
Company, NewVantage Partners, Capgemini)
Data Science Statistics – In Industries
Finance and Banking
- The global market for big data in the banking sector is projected to
reach $14.83 billion by 2026, growing at a CAGR of 18.8% from 2019
to 2026.
- According to a survey by Deloitte, 88% of financial institutions
believe that artificial intelligence (AI) will revolutionize the way
they gather information and interact with customers.
- In a study by McKinsey, it was found that data-driven banks have the
potential to achieve a 5-10% increase in return on equity (ROE).
- Machine learning algorithms are increasingly used in fraud detection
and prevention in the banking industry. According to the Association
for Financial Professionals, 74% of organizations use AI and machine
learning for fraud prevention.
- The adoption of advanced analytics, including data science
techniques. This can lead to a 1-3% increase in loan approval rates
for banks.
- According to a study by PwC, 61% of financial institutions have
invested in robotic process automation (RPA) and machine learning
for risk management and compliance.
(Source: Deloitte, McKinsey & Company, Association for Financial
Professionals, McKinsey & Company, PwC)
Healthcare and Medicine
- The global healthcare analytics market is expected to reach $84.2
billion by 2027. Growing at a CAGR of 25.2% from 2020 to 2027.
- The adoption of big data analytics in healthcare can potentially
save the industry $300 billion per year in the United States alone.
- According to a survey by HealthITAnalytics, 89% of healthcare
executives have reported that they have invested in big data
analytics and artificial intelligence (AI) for their organizations.
- The use of machine learning algorithms has demonstrated high
accuracy in diagnosing diseases from medical imaging data. For
example, a deep learning algorithm achieved 94.5% accuracy in
identifying lung cancer from CT scans.
- Electronic Health Records (EHRs) and patient data provide valuable
insights for data science applications. According to a study
published in the Journal of Medical Internet Research. Using EHR
data for predictive modeling improved the prediction of patient
outcomes by 12-14%.
(Source: McKinsey & Company, HealthITAnalytics, Nature, Journal of
Medical Internet Research)
Retail and E-commerce
- E-commerce companies that effectively use data science techniques to
personalize customer experiences can see a 6% increase in revenue.
- According to a study by McKinsey, companies that extensively use
customer analytics are more likely to generate higher profits than
their competitors.
- E-commerce companies that effectively use data science techniques to
personalize customer experiences can see a 6% increase in revenue.
- By 2022, 35% of leading global retailers are expected to adopt AI
for personalized product recommendations, leading to a 25% increase
in revenue.
- Data-driven pricing strategies in retail can result in a 2-5%
increase in sales and a 2-4% increase in profit margins.
- According to a study by Segment, 49% of consumers have made impulse
purchases after receiving a personalized recommendation from an
e-commerce store.
- Retailers using AI-powered chatbots for customer service have
reported a 70-80% reduction in customer support costs.3. The Shift from Analytics to AI: Implications for Strategic and ... | According to a report by IDC, global spending on AI systems is expected to reach $97.9 billion in 2023, a 44% increase from the previous year.
Real-World Examples and Data Points
To illustrate the impact of AI and the continued relevance of analytics,
consider the following examples and data points:
- Healthcare: AI-powered analytics are revolutionizing healthcare by
enabling personalized treatment plans and early disease detection.
For instance, the Mayo Clinic uses AI to analyze patient data and
predict disease progression, leading to more effective treatment
strategies.
- Finance: Financial institutions are leveraging AI for fraud
detection, risk management, and customer service. JPMorgan Chase's
COiN platform uses AI to analyze legal documents, saving over
360,000 hours of work annually. Meanwhile, analytics remains crucial
for portfolio management and market analysis.
- Retail: Retailers like Walmart use AI-driven analytics to optimize
supply chain operations, forecast demand, and enhance customer
experiences. Walmart's use of AI for inventory management has
resulted in a 30% reduction in stockouts and increased sales.
The rise of AI represents a transformative shift in the technology
landscape, but it should not come at the expense of analytics. For
strategic and management consulting firms, the challenge lies in
balancing the excitement of AI with the enduring value of analytics. By
integrating these complementary disciplines, upskilling their workforce,
and guiding clients through the complexities of AI adoption, consultants
can continue to provide unparalleled value and drive meaningful business
transformation. The future belongs to those who can harness the power of
AI while staying grounded in the insights and rigor that analytics
provides. The convergence of AI and analytics will shape the next wave
of innovation, creating opportunities for consulting firms to lead the
charge in this dynamic and evolving landscape.
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[1] https://www.linkedin.com/pulse/best-hr-people-analytics-articles-january-2024-david-green--mktxe
[2] https://scoop.market.us/data-science-statistics/
[3] https://www.linkedin.com/pulse/shift-from-analytics-ai-implications-strategic-abhay-gupta-ph-d--7d88f