Basic Concepts of Business Data Analytics, Evolution of Business Analytics, Data Analytics, Business Data Analytics Applications, Scope of Business Analytics.
Becoming an analytics-driven organization helps companies reduce costs, increase
revenues and improve competitiveness, and this is why business intelligence and
analytics continue to be a top priority for CIOs. Many business decisions, however,
are still not based on analytics, and CIOs are looking for ways to reduce time to value
for deploying business intelligence solutions so that they can expand the use of
analytics to a larger audience of users.
Companies are also interested in leveraging the value of information in so-called big
data systems that handle data ranging from high-volume event data to social media
textual data. This information is largely untapped by existing business intelligence
systems, but organizations are beginning to recognize the value of extending the
business intelligence and data warehousing environment to integrate, manage, govern
and analyze this information.
How relevant is Predictive Analytics relevant today?Steven Mugerwa
This is my view on how relevant is Predictive Analytics relevant today. Although its a high level view, it gives great insights to a person who is looking for somewhere to begin. This was an essay for the
Cognitive Solutions combine the power of mathematical algorithms and computing in collaboration with digital knowledge reasoning to enable intelligent insights and actions.
Basic Concepts of Business Data Analytics, Evolution of Business Analytics, Data Analytics, Business Data Analytics Applications, Scope of Business Analytics.
Becoming an analytics-driven organization helps companies reduce costs, increase
revenues and improve competitiveness, and this is why business intelligence and
analytics continue to be a top priority for CIOs. Many business decisions, however,
are still not based on analytics, and CIOs are looking for ways to reduce time to value
for deploying business intelligence solutions so that they can expand the use of
analytics to a larger audience of users.
Companies are also interested in leveraging the value of information in so-called big
data systems that handle data ranging from high-volume event data to social media
textual data. This information is largely untapped by existing business intelligence
systems, but organizations are beginning to recognize the value of extending the
business intelligence and data warehousing environment to integrate, manage, govern
and analyze this information.
How relevant is Predictive Analytics relevant today?Steven Mugerwa
This is my view on how relevant is Predictive Analytics relevant today. Although its a high level view, it gives great insights to a person who is looking for somewhere to begin. This was an essay for the
Cognitive Solutions combine the power of mathematical algorithms and computing in collaboration with digital knowledge reasoning to enable intelligent insights and actions.
Using Data Mining Techniques in Customer SegmentationIJERA Editor
Data mining plays important role in marketing and is quite new. Although this field expands rapidly, data mining is still foreign issue for many marketers who trust only their experiences. Data mining techniques cannot substitute the significant role of domain experts and their business knowledge. In the other words, data mining algorithms are powerful but cannot effectively work without the active support of business experts. We can gain useful results by combining these techniques and business expertise. For instance ability of a data mining technique can be substantially increased by combining person experience in the field or information of business can be integrated into a data mining model to build a more successful result. Moreover, these results should always be evaluated by business experts. Thus, business knowledge can help and enrich the data mining results. On the other hand, data mining techniques can extract patterns that even the most experienced business people may have missed. In conclusion, the combination of business domain expertise with the power of data mining techniques can help organizations gain a competitive advantage in their efforts to optimize customer management. Clustering algorithms, a group of data mining technique, is one of most common used way to segment data set according to their similarities. This paper focuses on the topic of customer segmentation using data mining techniques. In the other words, we theoretically discuss about customer relationship management and then utilize couple of data mining algorithm specially clustering techniques for customer segmentation. We concentrated on behavioral segmentation.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
Big Data Journeys: Review of roadmaps taken by early adopters to achieve thei...Krishnan Parasuraman
Implementing a Big Data program can be a long and arduous journey. Each organization has its own unique business drivers and technical considerations that drive their big data adoption roadmaps. Whatever be your organization's specific big data driver - be it managing a rapid surge of data, implementing a new set of analytic capabilities, incorporating unstructured data as part of your enterprise data platform or accessing real time information for actionable intelligence - the approach and roadmap that you put in place to reach that end goal becomes all the more critical in a space where early success stories are relatively rare, skill sets are hard to find and technologies are still evolving.
In this session we will chronicle the journeys of four different organizations that were early adopters of big data. Each of them charted a different path to achieve their big data goals. We will look at what were the key drivers behind their respective approaches, what worked and what did not work for them.
Big Data Forum at Salt River Fields (the spring training field for the Arizona Diamondbacks). Krishnan Parasuraman discusses how companies are using big data and analytics to transform their business.
The business value of consumer analytics and big data is not just about what you can discover or infer about the consumer, but how you can use this insight promptly and effectively across multiple touchpoints (including e-Commerce systems and CRM) to create a powerful and truly personalized consumer experience.
For most organizations, mobilizing this kind of intelligence raises organizational challenges as well as technical ones.
This presentation reveals how some leading companies are starting to address these challenges, and describes the vital role of enterprise architecture in supporting such initiatives.
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
The pioneers in the big data space have battle scars and have learnt many of the lessons in this report the hard way. But if you are a general manger & just embarking on the big data journey, you should now have what they call the 'second mover advantage’. My hope is that this report helps you better leverage your second mover advantage. The goal here is to shed some light on the people & process issues in building a central big data analytics function
Accelerate Confident Decision-Making with Data EnrichmentPrecisely
Data powers all analytics today—driving industry workflows from customer and market intelligence to property underwriting, risk management, and resource allocation. Internal structured data alone is not sufficient—it is imperative to enrich your data.
Data enrichment helps leaders make intelligent decisions with contextual information to help extract deeper business insights. Most importantly, data enrichment can help better utilize existing tools and resources to gain competitive advantages through confident decision-making.
Understanding your data in the context of location opens the door to more intelligent business decisions. Organizations can gain powerful new insights from curated data sets, adding value by linking business data records to real-life locations.
Join this TDWI Webinar for presentations and a roundtable discussion about how to realize the value of incorporating data enrichment into current business processes for better-informed, data-driven decisions.
Join this on-demand webinar to learn:
- Data enrichment practices for driving better decisions in telco, property technology (proptech), and insurance
- Leveraging trusted attributes to inform machine learning and other analytics models
- How to easily incorporate accurate, complete, and current data into business processes
The elements of the development plan
Elements of the quality plan
Development and quality plans for small and for internal projects
Software development risks a
Using Data Mining Techniques in Customer SegmentationIJERA Editor
Data mining plays important role in marketing and is quite new. Although this field expands rapidly, data mining is still foreign issue for many marketers who trust only their experiences. Data mining techniques cannot substitute the significant role of domain experts and their business knowledge. In the other words, data mining algorithms are powerful but cannot effectively work without the active support of business experts. We can gain useful results by combining these techniques and business expertise. For instance ability of a data mining technique can be substantially increased by combining person experience in the field or information of business can be integrated into a data mining model to build a more successful result. Moreover, these results should always be evaluated by business experts. Thus, business knowledge can help and enrich the data mining results. On the other hand, data mining techniques can extract patterns that even the most experienced business people may have missed. In conclusion, the combination of business domain expertise with the power of data mining techniques can help organizations gain a competitive advantage in their efforts to optimize customer management. Clustering algorithms, a group of data mining technique, is one of most common used way to segment data set according to their similarities. This paper focuses on the topic of customer segmentation using data mining techniques. In the other words, we theoretically discuss about customer relationship management and then utilize couple of data mining algorithm specially clustering techniques for customer segmentation. We concentrated on behavioral segmentation.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
Big Data Journeys: Review of roadmaps taken by early adopters to achieve thei...Krishnan Parasuraman
Implementing a Big Data program can be a long and arduous journey. Each organization has its own unique business drivers and technical considerations that drive their big data adoption roadmaps. Whatever be your organization's specific big data driver - be it managing a rapid surge of data, implementing a new set of analytic capabilities, incorporating unstructured data as part of your enterprise data platform or accessing real time information for actionable intelligence - the approach and roadmap that you put in place to reach that end goal becomes all the more critical in a space where early success stories are relatively rare, skill sets are hard to find and technologies are still evolving.
In this session we will chronicle the journeys of four different organizations that were early adopters of big data. Each of them charted a different path to achieve their big data goals. We will look at what were the key drivers behind their respective approaches, what worked and what did not work for them.
Big Data Forum at Salt River Fields (the spring training field for the Arizona Diamondbacks). Krishnan Parasuraman discusses how companies are using big data and analytics to transform their business.
The business value of consumer analytics and big data is not just about what you can discover or infer about the consumer, but how you can use this insight promptly and effectively across multiple touchpoints (including e-Commerce systems and CRM) to create a powerful and truly personalized consumer experience.
For most organizations, mobilizing this kind of intelligence raises organizational challenges as well as technical ones.
This presentation reveals how some leading companies are starting to address these challenges, and describes the vital role of enterprise architecture in supporting such initiatives.
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
The pioneers in the big data space have battle scars and have learnt many of the lessons in this report the hard way. But if you are a general manger & just embarking on the big data journey, you should now have what they call the 'second mover advantage’. My hope is that this report helps you better leverage your second mover advantage. The goal here is to shed some light on the people & process issues in building a central big data analytics function
Accelerate Confident Decision-Making with Data EnrichmentPrecisely
Data powers all analytics today—driving industry workflows from customer and market intelligence to property underwriting, risk management, and resource allocation. Internal structured data alone is not sufficient—it is imperative to enrich your data.
Data enrichment helps leaders make intelligent decisions with contextual information to help extract deeper business insights. Most importantly, data enrichment can help better utilize existing tools and resources to gain competitive advantages through confident decision-making.
Understanding your data in the context of location opens the door to more intelligent business decisions. Organizations can gain powerful new insights from curated data sets, adding value by linking business data records to real-life locations.
Join this TDWI Webinar for presentations and a roundtable discussion about how to realize the value of incorporating data enrichment into current business processes for better-informed, data-driven decisions.
Join this on-demand webinar to learn:
- Data enrichment practices for driving better decisions in telco, property technology (proptech), and insurance
- Leveraging trusted attributes to inform machine learning and other analytics models
- How to easily incorporate accurate, complete, and current data into business processes
The elements of the development plan
Elements of the quality plan
Development and quality plans for small and for internal projects
Software development risks a
Dear students get fully solved assignments
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Data set Improve your business with your own business dataData-Set
The objective of this module is to gain an overview of how to use the data you already have available in order to improve your business.
Upon completion of this module you will:
-Gain an understanding of how to take advantage of the existing data you already have
-Comprehend the location of where internal data already lies within your company
-Improve your knowledge on how data can help build your brand
Age Friendly Economy - Improving your business with dataAgeFriendlyEconomy
The objective of this module is to gain an overview how you can use the data you already have available to improve your business.
Upon completion of this module you will:
- Learn the tips of how take advantage of the existing data you already have
- Be able to locate where internal data already lies within your company
- See how data can help you to build your brand
Lecture 1.13 & 1.14 &1.15_Business Profiles in Big Data.pptxRATISHKUMAR32
The presentation contain the business profiles in big data analytics. through this ppt user can learn about the different case studies such as facebook and walmart. This ppt contain the information and seven characteristics that are required to learn the basics of big data.
The objective of this module is to gain an overview of how to use the data you already have available in order to improve your business.
Upon completion of this module you will:
Gain an understanding of how to take advantage of the existing data you already have
Comprehend the location of where internal data already lies within your company
Improve your knowledge on how data can help build your brand
IMEX Frankfurt - BIG DATA session - Human EquationHuman Equation
Bid data session given at IMEX Frankfurt, on May 2014. Human Equation has been invited by ICCA for this training on how to use big data for associations, convention bureau, tourism office and venues. Open data, big data, data governance & performance.
Data strategy - The Business Game ChangerAmit Pishe
This blog highlights the basics of Data Strategy and its application in real-time business scenarios. Components of Data strategy, Data Analytics have been explained crisply. How Insights and Data Stories can be used to create powerful impact on the Business decisions.
Data analytics and digital transformation go hand in hand. Data analytics provides the foundation upon which digital transformation can thrive. By harnessing the power of data, organizations can make informed decisions and create personalized experiences for their customers.
Big Data, Big Thinking: Untapped OpportunitiesSAP Technology
In this webinar factsheet, SAP’s Rohit Nagarajan and Suni Verma from Ernst & Young explore Big Data in India, adoption patterns across the globe, and how you can embark on your own Big Data journey.
Open Source in the Energy Industry - Creating a New Operational Model for Dat...DataWorks Summit
The energy industry is well known to be laggard adopters of new technology. However, industry challenges such as aging assets & workforce, increased regulatory scrutiny, renewable energy sources, depressed commodity prices, changing customer expectations, and growing data volumes are pushing companies to explore new technologies to help solve these problems. Learn how Io-Tahoe’s platform built on open source technologies from Hortonworks, is helping organizations in the energy vertical transform into data driven enterprises.
Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic...DATAVERSITY
The Digital Economy is changing the way organizations do business across the globe, and is set to transform the economy on an unprecedented scale. Business optimization, and entirely new business models are emerging as data-driven technology provides unprecedented opportunity for innovation and change. In many organizations, data not only supports business profitability, but data itself has become the critical business asset.
What does it mean to leverage data as a business asset? And how can today’s data-centric technologies support the data-driven revolution? Join our expert panelists as they discuss the latest innovations in the data landscape.
Data Strategy - Executive MBA Class, IE Business SchoolGam Dias
For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way.
Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers.
This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.
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
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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Instructions for Submissions thorugh G- Classroom.pptx
Smart Data Module 2 d drive_own data
1. D: DRIVE
How to become Data Driven?
This programme has been funded with
support from the European Commission
Module 2: Improving current
business with own data
2. Smart Data Smart Region | www.smartdata.how
This programme has been funded with support from the European Commission. The author is
solely responsible for this publication (communication) and the Commission accepts no
responsibility for any use that may be made of the information contained therein.
The objective of this module is to gain an overview how
you can use the data you already have available to
improve your business.
Upon completion of this module you will:
- Learn the tips of how take advantage of the existing
data you already have
- Be able to locate where internal data already lies
within your company
- Recognize the importance of implementing data
enrichment into your big data projects
- See how data can help you to build your brand
Duration of the module: approximately 1 – 2 hours
Module 2: Improving
current business
with own data
3. 1 Advantages of Smart Data
2
Data Enrichment3
Smart Data Smart Region | www.smartdata.how
This programme has been funded with support from the European Commission. The author is solely responsible for this
publication (communication) and the Commission accepts no responsibility for any use that may be made of the information contained
therein.
– Sources of Data
– Sources of Internal Data
Data Collection
– Why Data Enrichment is a must
– How does it work?
– The Steps of Integration
– The Principles of Data Enrichment
– Why is Smart Data smart?
– How to turn your Data into
Competitve Advantage?
Using Data to build your Brand4
– How can Data help you build your
Business
– The Benefits of using Internal Data in
Marketing
5. It's not important how much data you have,
it's about how well you use it. Big Data could
potentially be just a big a problem. Smart
Data is a solution that changes the game of
marketing, and how we deliver better
solutions for customers from this point
forward.
Smart Data Smart Region | www.smartdata.how
6. • Smart in what data to collect, validate and transform
• Smart in how data is stored, managed, operated and used
• Smart in taking actions based on results of data analysis including
organisation structures, roles, devolution and delegation of decision-
making, processes and automation
• Smart in being realistic, pragmatic and even sceptical about what can be
achieved and knowing what value can be derived and how to maximise
value obtained
• Smart in defining achievable, benefits-lead strategy integrated with the
needs business and in its implementation
• Smart in selecting the channels and interactions to include smart data
use cases
More focussed investment in achieving better
business and organisation results
Greater confidence by the business and organisation
in justifying and approving investment and resource
allocation
Quick delivery of results
WHY IS SMART DATA SMART?
SMART DATA MEANS BEING...
Smart Data Smart Region | www.smartdata.how
8. The real challenge of Big Data is
not technological: it is "business".
It will necessarily involve human
expertise to enrich the data and
get it to "speak". When turning big
into smart, be aware of next tips!
HOW TO TURN
YOUR DATA
INTO COMPETITIVE
ADVANTAGE
Big Data is a project, not a tool!
Ask the right questions
Start with internal data
Enrich and cross-reference existing data
Enrich the models with external data
Involve business experts
Do not presume what the outcome will be
Remain agnostic
1
2
3
4
5
6
7
8
9. 1. Sources of Data
– Internal
• Sources of Internal Data
– External
DATA COLLECTION
10. Big Data is not
neccessarily big. The
most „magical“ aspect
of big data is what I
call „Smart Data“.
Philip Brittan
11. The data collection component of research is common to all fields of study including
physical and social sciences, humanities, business, etc. The goal for all data collection is to
capture quality evidence so as to translate into rich data analysis.
Smart Data Smart Region | www.smartdata.how
DATA COLLECTION
Data collection is the process
of gathering and measuring
information on variables of
interest, in an established
systematic fashion that
enables one to answer stated
research questions and
evaluate outcomes.
Smart Data Smart Region | www.smartdata.how
Need of Data Collection
• To get information for analysis
• To get idea about real time situation
• To compate between two situations
Factors to be considered before collection of Data
• Scope of the enquiry
• Sources of information
• Quantitive expression
• Techniques of data collection
• Unit of collection
12. Smart Data Smart Region | www.smartdata.how
SOURCES OF DATA
DATA
INTERNAL SOURCES EXTERNAL SOURCES
PRIMARY SOURCES
SECONDARY
SOURCES
13. Sources of Internal Data
Before decision-makers and data scientists look for external sources, it’s critical to ensure that all of a business’s internal data sources are mined,
analyzed and leveraged for the good of the company. While external data can offer a range of benefits, internal data sources are typically easier to
collect and can be more relevant for the company’s own purposes and insights.
There are a number of impactful, internal places that companies can look to mine data. These include:
TRANSACTIONAL
DATA AND POS
INFORMATION
CUSTOMER
RELATIONSHIP
MANAGEMENET
SYSTEM
INTERNAL
DOCUMENTS
ARCHIVES
OTHER BUSINESS
APPLICATIONS
DEVICE SENSORS
Find out how Amazon gathers its own
internal data and how it uses it in Exercise
1 of Learners workbook #2
14. Overall, internal sources of big data can offer
numerous advantages for today’s
businesses. Not only are these sources
incredibly telling and relevant, but they’re
free of cost to the company, as this is
information that the organization already
owns. In this way, enterprises can launch an
array of big data initiatives without ever
looking beyond their own walls.
Smart Data Smart Region | www.smartdata.how
15. DATA ENRICHMENT
1. Why Data Enrichment is a must
2. How does it work?
3. The Steps of Integration
4. The Principles of Data Enrichment
16. The data integration process is traditionally thought of in three steps:
1. Extract
2. Transform
3. Load
Smart Data Smart Region | www.smartdata.how
DATA ENRICHMENT
Data enrichment refers to
processes used to enhance, refine
or otherwise improve raw data.
This idea and other similar
concepts contribute to making
data a valuable asset for almost
any modern business or
enterprise.
Smart Data Smart Region | www.smartdata.how
WHY DATA ENRICHMENTS IS A NECESSARY 4th STEP
25%
74%
88%
36%
0
10
20
30
40
50
60
70
80
90
100
of the average
B2B marketer's
database contains
critical data errors
of companies do
not have a
sophisticated
approach to data
quality
of records
analyzed lack
firmographic data
of marketers say
that data quality is
the biggest
obstacle to
marketing
automation
success
17. HOW DOES IT WORK?
Data integrators traditionally bring data from source
to target unchanged. It's as if ETL developers were
movers who prided themselves on putting your
furniture in the new place unbroken. Businesses
today are asking the movers to repair and improve
the furniture before landing it in the new house.
The types of information that can be added, or
"augmented„ to a demographics database:
GEOGRAPHIC
BEHAVIORAL
DEMOGRAPHIC
PSYCHOGRAPHIC
CENSUS
Smart Data Smart Region | www.smartdata.how
Complete Exercise 2 of Learners workbook
#2, to test your knowledge on best
practices of collecting Big Data
18. First Last Income
John Smith 32,000 $
Henry White 88,000 $
Andy Brown 120,000 $
Steve Brook 54,000 $
Income L Income U Target
20000 39999 A
40000 59999 B
60000 79999 C
80000 99999 D
100000 119999 E
120000 139999 F
First Last Income Target
John Smith 32,000 $ A
Henry White 88,000 $ D
Andy Brown 120,000 $ F
Steve Brook 54,000 $ B
Example
19. Enrichment isn't limited
only to demographics. Data
quality tools allow
definition of rules that
integrate into the ETL
stream for any data source.
Smart Data Smart Region | www.smartdata.how
MATCHING CORRECTING INTERPOLATING
THE STEPS OF INTEGRATION
1 2 3
20. THE PRINCIPLES OF DATA ENRICHMENT
Operations that automatically match, correct, or interpolate data values operate with some "confidence" level, meaning that sometimes they are wrong. That means
that hundreds of thousands of matches may have been incorrect - not necessarily an issue for the particular application involved, but something for those implementing
enrichment to consider.
The business should drive and manage enrichment definition
Enriched data must be identifiable and audit-able in the target database
Data replaced by enrichment must be available alongside the enriched data
1
3
2
Smart Data Smart Region | www.smartdata.how
21. USING DATA TO
BUILD YOUR BRAND
1. How can Data help you build your
Business
2. The Benefits of using Internal Data in
Marketing
Smart Data Smart Region | www.smartdata.how
22. Smart Data Smart Region | www.smartdata.how
HOW CAN DATA HELP
YOU BUILD YOUR
BUSINESS
23. Identifying trends
Smart Data Smart Region | www.smartdata.how
2
Checking out the competition3
Improving operations4
Recruiting and managing talent5
Understanding what makes your customers tick1
Tweaking your business model6
24. Smart Data Smart Region | www.smartdata.how
THE BENEFITS OF
USING INTERNAL DATA
IN MARKETING
25. Smart Data Smart Region | www.smartdata.how
ORIGINALITY
CONSUMER
VALUE
OPERATIONAL
TRANSPARENCY
CONSUMER
TRUST
BRAND
RECOGNITION
COMPANY
WORTH
Integrate Big Data into your own company
in Exercise 3 of Learners workbook #2