Business analytics uses data to help organizations make better decisions and craft business strategies. As companies generate vast amounts of data, there is a need for professionals with data analysis skills. Leading companies are using analytics not just to improve operations but launch new business models. While some industries and digital natives have captured opportunities, much potential value from analytics remains untapped, especially in manufacturing, healthcare, and the public sector. For companies to succeed in an increasingly data-driven world, analytics must be incorporated strategically and supported by the right talent, processes, and infrastructure.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
This whitepaper from IBM shows how your organisation can implement a Big Data Analytics solution effectively and leverage insights that can transform your business.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
This whitepaper from IBM shows how your organisation can implement a Big Data Analytics solution effectively and leverage insights that can transform your business.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
Impact of Data Analytics in Changing the Future of Business and Challenges Fa...IJSRP Journal
Data Analytics refers to a comprehensive approach that makes use of both Qualitative and Quantitative Information in order to draw valuable insights and arriving at conclusions based on the extensive usage of statistical tools accompanied by explanatory and predictive models running over the data. It tries to understand the behavior and dynamics of businesses thereby leading to improved productivity and enhancing business gains by helping with appropriate decision making. Considering the intensified disruption caused by recent revolution in the field of Data Analytics, this articles aims to cover the potential impacts that Data Analytics could have over the already existing businesses and how new entrants, especially across the emerging economies, could make the best use of Data Analytics in gaining an edge over their competitors. It also aims to deep dive into the challenges faced by businesses while adopting or moving to Data Analytics and how they can overcome those challenging barriers for a successful future. .
The Fundamentals of Business Intelligence is a comprehensive overview of data and data analysis. The guide explains the types of data available to businesses and how these data types work with one another to provide insights to large companies. Look beyond the hype of big marketing to understand the role of all types of data and understand what big data is in the right context.
BUSINESS ANALYTICS, BACKBONE OF ORGANIZATIONS - A LITERATURE REVIEW.pdfAdheer A. Goyal
Business analytics is the process by which businesses use statistical methods and technologies based on historical data in order to attain organizational goals and make profit. Analytics are now regularly used in multiple areas of life. It should come as no surprise that business analytics is one of the fastest growing markets in enterprise software landscape. This article discusses about history and terminology of analytics. There is also a brief discussion about how business analytics gives opportunities not only to large scale and multinational companies but also to small and medium enterprises. In this conceptual paper major types of business analytics i.e., decision analytics, descriptive analytics, predictive analytics and prescriptive analytics are included. We also noted how business analytics can help you in supply chain management, analyze the key performance indicators which further helps in decision making, boost relationship with consumers and improve efficiency in the basis of product data. Then it consists of brief description about advantages and disadvantages of business analytics, difference between business analytics and business intelligence. This paper concludes with challenges in business analytics posed by the big data analytics, data scientists, business organization etc. and thoroughly researched the impact of business analytics on innovation.
Business analytics is a custom of transforming the data into business understandings enabling the end users for better decision-making. By using the modern tools and techniques, business analytics can help assess complex situations, consider all the available options, and predict outcomes and showcase critical risks for the decision makers.
Business Analytics can simply be described as a practice that includes the use of various techniques such as Data warehousing, Data mining, Programming in order to visualize and discover several patterns or trends in data. In simple, Analytics help convert the data into useful information, which can be used for decision-making. As a means of sorting through data to find useful information, the application of analytics has found new purpose
The Future of Analytics: Predict, Optimize, SucceedUncodemy
In today's data-driven world, the importance of analytics cannot be overstated. Businesses across industries are realizing the power of harnessing data to gain valuable insights, make informed decisions, and drive growth.
The new ‘A and B’ of the Finance Function: Analytics and Big Data - -Evolutio...Balaji Venkat Chellam Iyer
Published in 2013, this White Paper discusses how the finance function would evolve with the combined forces of Big Data and Analytics and the levers that could help catalyze the change and has drawn upon the Global Trend Study conducted by Tata Consultancy Services (TCS) on how companies were investing in Big Data and deriving returns from it.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
Big Data Update - MTI Future Tense 2014Hawyee Auyong
The Futures Group first wrote about the emerging phenomenon of Big Data in 2010 as it was about to enter the mainstream. It was envisaged that Big Data would create a demand for new skills (Google has identified statisticians as the “sexy job of the decade”) and generate new industries. This report updates on the industry value chain and business models for the data analytics industry, latest developments as well as the opportunities for Singapore.
Data science is the practice of extracting, analyzing, and interpreting large amounts of data to identify trends, correlations, and patterns. It combines machine learning, statistics, programming, and data engineering tools to uncover insights that can inform business decisions. Data scientists collect, organize, and analyze large amounts of data to find valuable insights and make predictions. Data science can be used in various industries, from finance and health care to retail and advertising. By leveraging data-driven decision-making, companies are able to gain a better understanding of their customers, identify new growth opportunities, and optimize their operations.
Impact of Data Analytics in Changing the Future of Business and Challenges Fa...IJSRP Journal
Data Analytics refers to a comprehensive approach that makes use of both Qualitative and Quantitative Information in order to draw valuable insights and arriving at conclusions based on the extensive usage of statistical tools accompanied by explanatory and predictive models running over the data. It tries to understand the behavior and dynamics of businesses thereby leading to improved productivity and enhancing business gains by helping with appropriate decision making. Considering the intensified disruption caused by recent revolution in the field of Data Analytics, this articles aims to cover the potential impacts that Data Analytics could have over the already existing businesses and how new entrants, especially across the emerging economies, could make the best use of Data Analytics in gaining an edge over their competitors. It also aims to deep dive into the challenges faced by businesses while adopting or moving to Data Analytics and how they can overcome those challenging barriers for a successful future. .
The Fundamentals of Business Intelligence is a comprehensive overview of data and data analysis. The guide explains the types of data available to businesses and how these data types work with one another to provide insights to large companies. Look beyond the hype of big marketing to understand the role of all types of data and understand what big data is in the right context.
BUSINESS ANALYTICS, BACKBONE OF ORGANIZATIONS - A LITERATURE REVIEW.pdfAdheer A. Goyal
Business analytics is the process by which businesses use statistical methods and technologies based on historical data in order to attain organizational goals and make profit. Analytics are now regularly used in multiple areas of life. It should come as no surprise that business analytics is one of the fastest growing markets in enterprise software landscape. This article discusses about history and terminology of analytics. There is also a brief discussion about how business analytics gives opportunities not only to large scale and multinational companies but also to small and medium enterprises. In this conceptual paper major types of business analytics i.e., decision analytics, descriptive analytics, predictive analytics and prescriptive analytics are included. We also noted how business analytics can help you in supply chain management, analyze the key performance indicators which further helps in decision making, boost relationship with consumers and improve efficiency in the basis of product data. Then it consists of brief description about advantages and disadvantages of business analytics, difference between business analytics and business intelligence. This paper concludes with challenges in business analytics posed by the big data analytics, data scientists, business organization etc. and thoroughly researched the impact of business analytics on innovation.
Business analytics is a custom of transforming the data into business understandings enabling the end users for better decision-making. By using the modern tools and techniques, business analytics can help assess complex situations, consider all the available options, and predict outcomes and showcase critical risks for the decision makers.
Business Analytics can simply be described as a practice that includes the use of various techniques such as Data warehousing, Data mining, Programming in order to visualize and discover several patterns or trends in data. In simple, Analytics help convert the data into useful information, which can be used for decision-making. As a means of sorting through data to find useful information, the application of analytics has found new purpose
The Future of Analytics: Predict, Optimize, SucceedUncodemy
In today's data-driven world, the importance of analytics cannot be overstated. Businesses across industries are realizing the power of harnessing data to gain valuable insights, make informed decisions, and drive growth.
The new ‘A and B’ of the Finance Function: Analytics and Big Data - -Evolutio...Balaji Venkat Chellam Iyer
Published in 2013, this White Paper discusses how the finance function would evolve with the combined forces of Big Data and Analytics and the levers that could help catalyze the change and has drawn upon the Global Trend Study conducted by Tata Consultancy Services (TCS) on how companies were investing in Big Data and deriving returns from it.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
Big Data Update - MTI Future Tense 2014Hawyee Auyong
The Futures Group first wrote about the emerging phenomenon of Big Data in 2010 as it was about to enter the mainstream. It was envisaged that Big Data would create a demand for new skills (Google has identified statisticians as the “sexy job of the decade”) and generate new industries. This report updates on the industry value chain and business models for the data analytics industry, latest developments as well as the opportunities for Singapore.
Data science is the practice of extracting, analyzing, and interpreting large amounts of data to identify trends, correlations, and patterns. It combines machine learning, statistics, programming, and data engineering tools to uncover insights that can inform business decisions. Data scientists collect, organize, and analyze large amounts of data to find valuable insights and make predictions. Data science can be used in various industries, from finance and health care to retail and advertising. By leveraging data-driven decision-making, companies are able to gain a better understanding of their customers, identify new growth opportunities, and optimize their operations.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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3. Business analytics is a powerful tool in today’s
marketplace that can be used to make
decisions and craft business strategies. Across
industries, organizations generate vast amounts
of data which, in turn, has heightened the need
for professionals who are data literate and
know how to interpret and analyze that
information.
4. According to a study by MicroStrategy,
companies worldwide are using data to:
•Improve efficiency and productivity (64
percent)
•Achieve more effective decision-making (56
percent)
•Drive better financial performance (51
percent)
5. The research also shows that 65 percent of global enterprises plan to
increase analytics spending.
In light of these market trends, gaining an in-depth understanding of
business analytics can be a way to advance your career and make
better decisions in the workplace.
“Using data analytics is a very effective way to have influence in an
organization,” said Harvard Business School Professor Jan
Hammond, who teaches the online course Business Analytics, in
a previous interview. “If you’re able to go into a meeting and other
people have opinions, but you have data to support your arguments
and your recommendations, you’re going to be influential.”
Before diving into the benefits of data analysis, it’s important to
understand what the term “business analytics” means.
6. Business analytics is the process of using quantitative methods to derive
meaning from data to make informed business decisions.
Business Analytics may be defined as refining past or present business
data using modern technologies. They are used to build sophisticated
models for driving future growth. A general Business Analytics process may
include Data Collection, Data Mining, Sequence Identification, Text Mining,
Forecasting, Predictive Analytics, Optimization, and Data Visualization.
Every business today produces a considerable amount of data in a specific
way. Business Analytics now are leveraging the benefits of statistical
methods and technologies to analyze their past data. This is used to
uncover new insights to help them make a strategic decision for the future.
7. Business Intelligence, a subset of the Business Analytics field, plays
an essential role in utilizing various tools and techniques such
as machine learning and artificial intelligence technologies to predict
and implement insights into daily operations.
Thus, Business Analytics brings together fields of business
management, and computing to get actionable insights. These
values and inputs are then used to remodel business procedures to
generate more efficiency and build a productive system.
After going through What is Business Analytics, let us understand
more about its evolution.
8. Evolution of Business Analytics
Technologies have been used as a measure to improve business
efficiency since the beginning. Automation has played a considerable
role in managing and performing multiple tasks for large
organizations. The unprecedented rise of the internet and information
technology has further boosted the performance of businesses.
With advancement today, we have Business Analytics tools that
utilize past and present data to give businesses the right direction for
their future.
As we now have a stronghold on What is Business Analytics, let us
next look into the types of business analytics techniques.
9. Types of Business Analytics Techniques
Business analytics techniques can be segmented in the following
four ways:
1.Descriptive Analytics: This technique describes the past or present
situation of the organization's activities.
2.Diagnostic Analytics: This technique discovers factors or reasons
for past or current performance.
3.Predictive Analytics: This technique predicts figures and results
using a combination of business analytics tools.
4.Prescriptive Analytics: This technique recommends specific
solutions for businesses to drive their growth forward.
10. A complete business analytics life cycle
starts from raw data received from the
devices or services, then collecting data in
an unstructured type, then processing and
analyzing data to draw actionable insights.
These are then integrated into business
procedures to deliver better outcomes for
the future.
11. Business Analytics Applications
Business Analytics is now systematically
integrated across several applications in
the field of supply chain management,
customer relationship management,
financial management, human resources,
manufacturing, and even build smart
strategies for sports too.
12. Importance of Business Analytics
•Business analytics can transform raw data into more
valuable inputs to leverage this information in decision
making.
•With Business Analytics tools, we can have a more
profound understanding of primary and secondary data
emerging from their activities. This helps businesses refine
their procedures further and be more productive.
•To stay competitive, companies need to be ahead of their
peers and have all the latest toolsets to assist their decision
making in improving efficiency as well as generating more
profits.
13. The Scope of Business Analytics
Business Analytics has been applied to a wide variety of
applications. Descriptive analytics is thoroughly used by businesses
to understand the market position in the current scenarios.
Meanwhile, predictive and prescriptive analytics are used to find
more reliable measures for businesses to propel their growth in a
competitive environment.
In the last decade, business analytics is among the leading career
choices for professionals with high earning potential and assisting
businesses to drive growth with actionable inputs.
We have understood well about what Business Analytics is, let us
next understand its benefits.
14. The Benefits of Business Analytics
To club in one phrase: Business Analytics brings actionable insights
for businesses. However, here are the main benefits of Business
Analytics:
1.Improve operational efficiency through their daily activities.
2.Assist businesses to understand their customers more precisely.
3.Business uses data visualization to offer projections for future
outcomes.
4.These insights help in decision making and planning for the future.
5.Business analytics measures performance and drives growth.
6.Discover hidden trends, generate leads, and scale business in the
right direction.
15. Difference Between Business Intelligence and Business
Analytics
Business Intelligence(BI) uses the past and present to
identify trends and patterns in the organizational
procedures, while Business Analytics determines the
reasons and factors that led to present situations. Business
Intelligence focuses mainly on descriptive analysis, while
Business Analytics deals with predictive analysis. BI tools
are part of Business Analytics that helps understand the
Business Analytics process better.
16. A Career in Business Analytics
The role of Business Analytics professionals may change accordingly to meet
organizational goals and objectives. Several individual profiles are closely
associated with business analytics when dealing with data.
In this competitive age, business analytics has revolutionized the procedures to
discover intelligent insights and gain more profits using their existing methods
only. Business Analytics Techniques also help organizations personalize
customers with more optimized services and even include their feedback to
create more profitable products. Large organizations today are now competing to
stay top in the markets by utilizing practical business analytics tools.
Several business analytics tools are available in the market that offers specific
solutions to match requirements. Professionals might need business analytics
skills, like understanding and expertise of statistics or SQL to manage them.
17. The age of analytics: Competing in a data-driven world
18. Big data’s potential just keeps
growing. Taking full advantage
means companies must incorporate
analytics into their strategic vision
and use it to make better, faster
decisions.
19. Is big data all hype? To the contrary: earlier research
may have given only a partial view of the ultimate
impact. A new report from the McKinsey Global Institute
(MGI), The age of analytics: Competing in a data-driven
world, suggests that the range of applications and
opportunities has grown and will continue to expand.
Given rapid technological advances, the question for
companies now is how to integrate new capabilities into
their operations and strategies—and position
themselves in a world where analytics can upend entire
industries.
20. A 2011 MGI report highlighted the transformational
potential of big data. Five years later, we remain
convinced that this potential has not been oversold. In
fact, the convergence of several technology trends is
accelerating progress. The volume of data continues to
double every three years as information pours in from
digital platforms, wireless sensors, virtual-reality
applications, and billions of mobile phones. Data-storage
capacity has increased, while its cost has plummeted.
Data scientists now have unprecedented computing
power at their disposal, and they are devising algorithms
that are ever more sophisticated.
21. Earlier, we estimated the potential for big data and
analytics to create value in five specific domains.
Revisiting them today shows uneven progress and a great
deal of that value still on the table (exhibit). The greatest
advances have occurred in location-based services and in
US retail, both areas with competitors that are digital
natives. In contrast, manufacturing, the EU public sector,
and healthcare have captured less than 30 percent of the
potential value we highlighted five years ago. And new
opportunities have arisen since 2011, further widening the
gap between the leaders and laggards.
22.
23. Leading companies are using their capabilities not only
to improve their core operations but also to launch
entirely new business models. The network effects of
digital platforms are creating a winner-take-most
situation in some markets. The leading firms have
remarkably deep analytical talent taking on various
problems—and they are actively looking for ways to
enter other industries. These companies can take
advantage of their scale and data insights to add new
business lines, and those expansions are
increasingly blurring traditional sector boundaries.
24. Where digital natives were built for analytics, legacy
companies have to do the hard work of overhauling
or changing existing systems. Adapting to an era of
data-driven decision making is not always a simple
proposition. Some companies have invested heavily
in technology but have not yet changed their
organizations so they can make the most of these
investments. Many are struggling to develop the
talent, business processes, and organizational
muscle to capture real value from analytics.
25. The first challenge is incorporating data and analytics
into a core strategic vision. The next step is developing
the right business processes and building capabilities,
including both data infrastructure and talent. It is not
enough simply to layer powerful technology systems on
top of existing business operations. All these aspects of
transformation need to come together to realize the full
potential of data and analytics. The challenges
incumbents face in pulling this off are precisely why
much of the value we highlighted in 2011 is still
unclaimed.
26. The urgency for incumbents is growing, since leaders are staking out
large advantages, and hesitating increases the risk of being
disrupted. Disruption is already happening, and it takes multiple
forms. Introducing new types of data sets (“orthogonal data”) can
confer a competitive advantage, for instance, while massive
integration capabilities can break through organizational silos,
enabling new insights and models. Hyperscale digital platforms can
match buyers and sellers in real time, transforming inefficient
markets. Granular data can be used to personalize products and
services—including, most intriguingly, healthcare. New analytical
techniques can fuel discovery and innovation. Above all, businesses
no longer have to go on gut instinct; they can use data and analytics
to make faster decisions and more accurate forecasts supported by
a mountain of evidence.
27. The next generation of tools could unleash
even bigger changes. New machine-
learning and deep-learning capabilities
have an enormous variety of applications
that stretch into many sectors of the
economy. Systems enabled by machine
learning can provide customer service,
manage logistics, analyze medical records,
or even write news stories.
28. These technologies could generate productivity
gains and an improved quality of life, but they
carry the risk of causing job losses and
dislocations. Previous MGI research found
that 45 percent of work activities could be
automated using current technologies; some
80 percent of that is attributable to existing
machine-learning capabilities. Breakthroughs in
natural-language processing could expand that
impact.
29. Data and analytics are already shaking up
multiple industries, and the effects will only
become more pronounced as adoption reaches
critical mass—and as machines gain
unprecedented capabilities to solve problems
and understand language. Organizations that
can harness these capabilities effectively will
be able to create significant value and
differentiate themselves, while others will find
themselves increasingly at a disadvantage.