Data analytics offers a wide variety of opportunities across all industries enabling improvements in all business operations. Data analytics adoption has several associated complexities making it cumbersome and challenging for organizations. In particular, small and medium businesses (SMB) and nonprofit organizations lag behind data analytics adoption, even though they are crucial for the economy and would benefit most from data-driven decisions. This paper aims to identify factors that influence data analytics adoption by organizations. We conduct a systematic literature review to identify articles relevant to data analytics adoption studies performed using survey methodology. We synthesize literature review results to propose a research model to investigate data analytics adoption in small & medium businesses and nonprofit organizations. The proposed research model was developed primarily based on Technology Organization Environment (TOE) framework, combined with other factors relevant to data analytics adoption found in the literature. The research model investigates the influences of data analytics, organization, and environment characteristics on data analytics adoption by SMBs and nonprofits. We hope that further progress with this research will provide insights into helping SMBs and nonprofits adopt data analytics technologies.
Tennessee Higher Education and the Use of Decision Support Systems in Strate...Jeff Hinds
Business Intelligence products and processes have helped public and private organization to identify opportunities and trends that are both internal and external. For years the concepts of Business Intelligence, data mining, “Big” data, have become an important part of strategic planning and decision making within many successful organizations. Higher education has been expressing the importance of research, statistical analysis, data modeling, and business decisions based on good information. In fact, higher education institutions have created entire academic programs around these topics. While this information is written and reported within professional periodicals and events, the question stands about how higher education is taking advantage of business intelligence as part of their strategic planning and decision making processes.
📊 Dive into the world of #DataAnalytics to unlock the secrets of information! 🚀 Understanding the basics is your gateway to data-driven success. 🌐 Explore foundational concepts, from data collection to interpretation, demystifying the data landscape. 📈 Master key techniques, empowering you to extract valuable insights and make informed decisions. 💡 Enhance your analytical skills and stay ahead in the fast-paced digital era. 🧠 Whether you're a beginner or looking for a refresher, this journey into data understanding is your stepping stone to a data-savvy future!
Tennessee Higher Education and the Use of Decision Support Systems in Strate...Jeff Hinds
Business Intelligence products and processes have helped public and private organization to identify opportunities and trends that are both internal and external. For years the concepts of Business Intelligence, data mining, “Big” data, have become an important part of strategic planning and decision making within many successful organizations. Higher education has been expressing the importance of research, statistical analysis, data modeling, and business decisions based on good information. In fact, higher education institutions have created entire academic programs around these topics. While this information is written and reported within professional periodicals and events, the question stands about how higher education is taking advantage of business intelligence as part of their strategic planning and decision making processes.
📊 Dive into the world of #DataAnalytics to unlock the secrets of information! 🚀 Understanding the basics is your gateway to data-driven success. 🌐 Explore foundational concepts, from data collection to interpretation, demystifying the data landscape. 📈 Master key techniques, empowering you to extract valuable insights and make informed decisions. 💡 Enhance your analytical skills and stay ahead in the fast-paced digital era. 🧠 Whether you're a beginner or looking for a refresher, this journey into data understanding is your stepping stone to a data-savvy future!
32 rcm.org.ukmidwivesTh e latest step-by-step practical g.docxtamicawaysmith
32 rcm.org.uk/midwives
Th e latest step-by-step practical guide...
PRACTICE
32
Write a
research
proposal
HOW TO...
Valerie Finigan
covers all aspects
of what a research
proposal needs to
include and where to
begin when writing it.
Writing a research proposal may be the most challenging part of the research
process – the document must
systematically recommend how
your study will be conducted
(Hollins-Martin and Flemming,
2010). It is the key to gaining ethical
approval, grant application success
and academic qualifi cation.
Yet the question is often asked:
‘What will a research proposal
contain and what should it look like?’
First of all, it is important that you
are passionate about the research
topic, have a vested interest in it, and
that it will add to the profession’s
body of scientifi c knowledge.
Discuss your idea with an
experienced researcher too, prior
to starting your proposal.
Th e proposals must be succinctly
written and clearly chronicle facts,
it must craft a convincing line of
reasoning and an argument for study
approval (Marshall, 2012).
Ask the questions:
1. What is the research about?
2. Why is it important?
3. What is the process that will be
taken to accomplish project goals
and objectives?
4. What will the project cost?
5. Who is the best person to conduct
this study? (Marshall, 2012).
Simple steps to follow:
1 Read the criteria for your proposal
If a format for writing is
given, use it. Check grammar, word
count and remember format and
brevity are important (12-point font,
legible and with a generous margin
will make the proposal easier to
read and comment on) to keep the
reviewer more engaged. Find a peer
or colleague to proofread the proposal
before submission, they may fi nd a
fl aw that you have overlooked. Submit
on time, or the work may be declined.
2 Underpin the study with a
research question
Th is enables you to choose the title
and design for your project and
identify the appropriate methodology
to answer the question of interest.
While the idea of the subject may be
in your mind, the question must be
focused and manageable to enable you
a purposeful and planned approach.
Th e title you choose for the
study should be used consistently
throughout all regulatory documents
(ethical approval consideration,
proposal and any grant applications).
Th e title needs to draw the attention
of the reviewer, so make it succinct
and exciting.
3 The abstractA brief description of your
research proposal, the
abstract should be a summary of the
entire project. It includes a statement
of the purpose of your research and
a brief description of its study design
and methodology.
4 Introduction sectionWithin the introduction
you should include some
background information about your
topic that is appropriate and to the
point. Here you convey the main
032-033_MID_summer OPINION_Practice_How_to v2.indd 32032-033_MID_summer OPINION_Practic ...
What Are the Challenges and Opportunities in Big Data Analytics.pdfMr. Business Magazine
Big data analytics is the use advanced analytic techniques for data that is very large and unstructured. The proliferation of digital information, coupled with advanced analytics capabilities, has ushered in an era where data isn’t just generated; it’s harnessed as a potent force for transformation.
Ethical Priniciples for the All Data RevolutionMelissa Moody
A presentation by Stephanie Shipp, from the Research Highlights session at the 2019 Women in Data Science Charlottesville Conference. Hosted by the UVA Data Science Institute.
A Big Picture in Research Data ManagementCarole Goble
A personal view of the big picture in Research Data Management, given at GFBio - de.NBI Summer School 2018 Riding the Data Life Cycle! Braunschweig Integrated Centre of Systems Biology (BRICS), 03 - 07 September 2018
Building Capacity for Evidence-Informed Policy-Making Lessons from Country Ex...OECD Governance
Presentation by Stéphane Jacobzone, Head of Unit on Evidence, Monitoring and Policy Evaluation. For more information see: http://www.oecd.org/gov/building-capacity-for-evidence-informed-policy-making-86331250-en.htm
Uncover Trends and Patterns with Data Science.pdfUncodemy
In today's data-driven world, the vast amount of information generated every second presents both challenges and opportunities for businesses and researchers alike. Harnessing this data effectively can provide valuable insights, unlock hidden trends, and identify patterns that drive innovation and strategic decision-making.
The 2023 Florida Data Science for Social Good (FL-DSSG) Big Reveal event was held on August 23 at the WJCT Studios, Jacksonville, FL. The DSSG interns presented findings from the Cathedral Arts Project, GrowFL, and Florida Philanthropic Network projects.
In this research, we analyzed voter registration and elections data re-leased by the Florida Division of Elections to investigate the profile of Florida voter participation. The utilized data was associated with federal general elec-tions from 2014 to 2020. Data preparation issues were resolved during the data merging, including exact duplicates, multiple associated vote types, and misclas-sified vote types. The merged data consisted of voter ID, registration county code, zip code, sex, ethnicity, age, vote type for each general election year, voting in-dicator for each general election year, and county code. Boosted Tree model (with a misclassification rate of 0.22) identified zip code, age, and voter status are key factors that influence voter participation. Based on voter eligibility and total vote counts in each general election held between 2014 and 2020, voters were classi-fied into the following profile categories: always-voted (participated in all elec-tions), increasing-in-voting (participated in recent elections but not in the past elections), intermittent-in-voting (participated in some elections but not all), de-creasing-in-voting (participated past elections but not recently), never-voted (didn’t participate in the elections), and not-eligible (registered but under 18 years of age). Voter profile counts information was merged with Census demo-graphic information at the zip code level. To find insights into the voter profiles, we created Tableau dashboards to view voter profiles, voting methods, and the effect of census variables on voter turnout at the zip code level. We hope this dashboard helps organizations like the League of Women Voters of Florida target their voter participation and engagement activities at the zip code level.
More Related Content
Similar to Systematic Literature Review and Research Model to Examine Data Analytics Adoption in Organizational Contexts
32 rcm.org.ukmidwivesTh e latest step-by-step practical g.docxtamicawaysmith
32 rcm.org.uk/midwives
Th e latest step-by-step practical guide...
PRACTICE
32
Write a
research
proposal
HOW TO...
Valerie Finigan
covers all aspects
of what a research
proposal needs to
include and where to
begin when writing it.
Writing a research proposal may be the most challenging part of the research
process – the document must
systematically recommend how
your study will be conducted
(Hollins-Martin and Flemming,
2010). It is the key to gaining ethical
approval, grant application success
and academic qualifi cation.
Yet the question is often asked:
‘What will a research proposal
contain and what should it look like?’
First of all, it is important that you
are passionate about the research
topic, have a vested interest in it, and
that it will add to the profession’s
body of scientifi c knowledge.
Discuss your idea with an
experienced researcher too, prior
to starting your proposal.
Th e proposals must be succinctly
written and clearly chronicle facts,
it must craft a convincing line of
reasoning and an argument for study
approval (Marshall, 2012).
Ask the questions:
1. What is the research about?
2. Why is it important?
3. What is the process that will be
taken to accomplish project goals
and objectives?
4. What will the project cost?
5. Who is the best person to conduct
this study? (Marshall, 2012).
Simple steps to follow:
1 Read the criteria for your proposal
If a format for writing is
given, use it. Check grammar, word
count and remember format and
brevity are important (12-point font,
legible and with a generous margin
will make the proposal easier to
read and comment on) to keep the
reviewer more engaged. Find a peer
or colleague to proofread the proposal
before submission, they may fi nd a
fl aw that you have overlooked. Submit
on time, or the work may be declined.
2 Underpin the study with a
research question
Th is enables you to choose the title
and design for your project and
identify the appropriate methodology
to answer the question of interest.
While the idea of the subject may be
in your mind, the question must be
focused and manageable to enable you
a purposeful and planned approach.
Th e title you choose for the
study should be used consistently
throughout all regulatory documents
(ethical approval consideration,
proposal and any grant applications).
Th e title needs to draw the attention
of the reviewer, so make it succinct
and exciting.
3 The abstractA brief description of your
research proposal, the
abstract should be a summary of the
entire project. It includes a statement
of the purpose of your research and
a brief description of its study design
and methodology.
4 Introduction sectionWithin the introduction
you should include some
background information about your
topic that is appropriate and to the
point. Here you convey the main
032-033_MID_summer OPINION_Practice_How_to v2.indd 32032-033_MID_summer OPINION_Practic ...
What Are the Challenges and Opportunities in Big Data Analytics.pdfMr. Business Magazine
Big data analytics is the use advanced analytic techniques for data that is very large and unstructured. The proliferation of digital information, coupled with advanced analytics capabilities, has ushered in an era where data isn’t just generated; it’s harnessed as a potent force for transformation.
Ethical Priniciples for the All Data RevolutionMelissa Moody
A presentation by Stephanie Shipp, from the Research Highlights session at the 2019 Women in Data Science Charlottesville Conference. Hosted by the UVA Data Science Institute.
A Big Picture in Research Data ManagementCarole Goble
A personal view of the big picture in Research Data Management, given at GFBio - de.NBI Summer School 2018 Riding the Data Life Cycle! Braunschweig Integrated Centre of Systems Biology (BRICS), 03 - 07 September 2018
Building Capacity for Evidence-Informed Policy-Making Lessons from Country Ex...OECD Governance
Presentation by Stéphane Jacobzone, Head of Unit on Evidence, Monitoring and Policy Evaluation. For more information see: http://www.oecd.org/gov/building-capacity-for-evidence-informed-policy-making-86331250-en.htm
Uncover Trends and Patterns with Data Science.pdfUncodemy
In today's data-driven world, the vast amount of information generated every second presents both challenges and opportunities for businesses and researchers alike. Harnessing this data effectively can provide valuable insights, unlock hidden trends, and identify patterns that drive innovation and strategic decision-making.
The 2023 Florida Data Science for Social Good (FL-DSSG) Big Reveal event was held on August 23 at the WJCT Studios, Jacksonville, FL. The DSSG interns presented findings from the Cathedral Arts Project, GrowFL, and Florida Philanthropic Network projects.
In this research, we analyzed voter registration and elections data re-leased by the Florida Division of Elections to investigate the profile of Florida voter participation. The utilized data was associated with federal general elec-tions from 2014 to 2020. Data preparation issues were resolved during the data merging, including exact duplicates, multiple associated vote types, and misclas-sified vote types. The merged data consisted of voter ID, registration county code, zip code, sex, ethnicity, age, vote type for each general election year, voting in-dicator for each general election year, and county code. Boosted Tree model (with a misclassification rate of 0.22) identified zip code, age, and voter status are key factors that influence voter participation. Based on voter eligibility and total vote counts in each general election held between 2014 and 2020, voters were classi-fied into the following profile categories: always-voted (participated in all elec-tions), increasing-in-voting (participated in recent elections but not in the past elections), intermittent-in-voting (participated in some elections but not all), de-creasing-in-voting (participated past elections but not recently), never-voted (didn’t participate in the elections), and not-eligible (registered but under 18 years of age). Voter profile counts information was merged with Census demo-graphic information at the zip code level. To find insights into the voter profiles, we created Tableau dashboards to view voter profiles, voting methods, and the effect of census variables on voter turnout at the zip code level. We hope this dashboard helps organizations like the League of Women Voters of Florida target their voter participation and engagement activities at the zip code level.
A Systematic Review of Affordable Homeownership using Data Science MethodsKarthikeyan Umapathy
Interest in the global unaffordable housing dilemma is manifest in its growing publications. However, there is a limited systematic review of the literature concerning data science approaches to address the social issues of owning affordable homes through Housing and Urban Development (HUD) programs. The systematic literature review was performed using Google Scholar and followed the phases prescribed in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study synthesizes data sources, tools, analytical approaches, and theoretical frameworks from the literature on affordable housing issues using data science methods. Our findings indicate that researchers have approached the issue completely differently from each other, with census data and usage of mapping visualizations as being a common trend.
Identifying Communities with Opportunities for Positive Youth DevelopmentKarthikeyan Umapathy
Game Face 4:13 Training Academy is a strategic camp developed by Mrs. Ashanti Jackson that exists to develop basketball skills, academic excellence, and ethical character within young people in the Jacksonville community. To identify the areas where GameFace can offer its programs, we collected data from the Census, Florida Department of Juvenile Justice, Florida Department of Health, Duval County Public Schools, Food Deserts, and Churches with Food Banks. We performed Factor Analysis and Correlation Analysis on the gathered data. We utilized the statistical analysis results to create a geographic information systems decision tool using Tableau.
Based on our analysis, we understand that youths are facing major issues in Duval County are Juvenile Arrests and Disciplinary Actions within schools. Zip Codes 32210, 32209, and 32208 have a higher number of all the issues analyzed. Game Face is on the right track to providing Physical, Mental, and Spiritual Training to youth in Duval County as these would help tackle these issues and create a positive impact on the lives of youth. We recommend Game Face collaborate with schools and churches in these communities to set up their training camp. We recommend Game Face gather data on youth involvement in training, leadership coaching, and healthy habits programs. We recommend Game Face to gather data related to student academic outcomes beyond Game Face training, such as college scholarships and high school graduations.
2022 Florida Data Science for Social Good (FL-DSSG) Big Reveal SlidesKarthikeyan Umapathy
The 2022 Florida Data Science for Social Good (FL-DSSG) Big Reveal event was held on August 23 at the WJCT Studios, Jacksonville, FL. The DSSG interns presented findings from the Cathedral Arts Project, League of Women Voters of Florida, and GameFace 4:13 Training Academy projects.
Longitudinal Study on the Generational Impacts of Habitat for Humanity: A Res...Karthikeyan Umapathy
Habitat for Humanity addresses the challenging social issue of providing affordable housing to income-constrained families. There isn’t much research on the generational impact of affordable homeownership on the families served by Habitat. In this research, we propose a longitudinal research design that will collect and analyze data gathered from families who received affordable housing. This study will be performed as a collaborative inquiry with a partnership from HabiJax, Jacksonville, FL, affiliated with Habitat for Humanity. We will be conducting semi-structured interviews focused on education, employment, wealth building, safety, neighborhood, health, and critical life-changing impacts. We will be collecting data every 5 years over the next 30 years. Collected data would be analyzed to identify the generational impacts of affordable housing. We will be sharing the findings with HabiJax decision-makers who could improve program strategies.
We analyzed voter registration and elections data released by the Florida Division of Elections to investigate the profile of Florida voter participation. We utilized data associated with general elections from 2012 to 2020. We merged an individual’s January 2021 voter registration data with elections data for above listed years. Several data preparation issues were resolved during the data merging process, including exact duplicates, multiple associated vote types, and misclassified vote types. The merged data consisted of the following columns: voter ID, registration county code, zip code, sex, ethnicity, age, [2012 to 2020] vote type, [2012 to 2020] voting indicator, and [2012 to 2020] county code. As it is computationally intensive to process data of entire Florida voters, county-wise data were merged into associated metropolitan, micropolitan, and rural areas (MSA). Based on voter eligibility and total vote counts on each general election held between 2012 and 2020, voters were classified into the following profile categories: always-voted (participated in all elections), increasing-in-voting (participated in recent elections but not in the past elections), intermittent-in-voting (participated in some elections but not all), decreasing-in-voting (participated past elections but not recently), never-voted (didn’t participate in the elections), and not-eligible (registered but under 18 years of age). Our analysis indicates that there is a considerable number of never-voted individuals regardless of MSA regions. Profile analysis reveals that metropolitan and micropolitan regions tend to have more individuals categorized as increasing-in-voting than always-voted. In contrast, rural regions tend to have more individuals categorized as always-voted than increasing-in-voting.
FL-DSSG Big Reveal Event was held on August 17th, 2021, from 4:30 PM to 6:30 PM as a Zoom webinar event. At the event, DSSG interns presented findings and revealed insights gained from the Barnabas Center, League of Women Voters of Florida, and Jewish Family and Community Services data science projects.
At the event, DSSG interns presented findings and revealed insights gained from the Center for Children’s Rights, Episcopal Children’s Services, and Literacy Alliance of Northeast Florida projects.
Dashboard for Extracting Regional Insights and Ranking Food Deserts in Northe...Karthikeyan Umapathy
2019 Florida Data Science for Social Good (FL-DSSG) Feeding Northeast Florida project results presented as a poster at the University of North Florida (UNF) Digital Humanities Initiative (DHI) Digital Projects Showcase event on November, 15, 2019.
Developing a GIS Dashboard Tool to Inform Non-Profit Hospitals of Community H...Karthikeyan Umapathy
Slide deck for the paper presented at the 2019 Conference on Information Systems Applied Research (CONISAR), Cleveland, OH, on November 8, 2019.
The objective of this paper is to describe the methods used to develop geographic information systems (GIS) dashboard tool and explain how it can assist nonprofit hospitals to identify priority neighborhoods. Multiple data sources from the 500 Cities Project databases were analyzed, and two online dashboards were created. The first dashboard is a hospital-specific composite dashboard, and the second is a comparison dashboard of health outcomes identified by both the hospital and the county’s community health needs assessment focused on neighborhood-level disparities. Hospital-specific health outcomes were Stroke, Diabetes, and Coronary Heart Disease. County-specific health outcomes were Obesity, Dental, and Mental Health. All of the six health outcomes were standardized, rescaled, and weighted within the final composite score. Tableau was used for developing the dashboards and geographically mapping the analyzed data. The maps were developed specifically for a large hospital in Florida; however, this methodology can be utilized by other hospitals across the US. City-specific data is essential to ensure the accuracy of community health needs. The development of an interactive, comprehensive map using Tableau is a useful tool for visualizing target neighborhoods for community health outreach. The integration of community needs assessment findings into the development of composite scores allows hospitals in the US to use this tool to inform community health outreach strategy adequately.
Collaborative Community Engagement: Bringing Data Science to Societal Challen...Karthikeyan Umapathy
The collaborative community engagement triad model involves a partnership between the university, private, and nonprofit sectors to enhance the student learning experience while creating community impacts. This talk will introduce the triad model, and describe how it was implemented at the College of Computing at the University of North Florida under the Data Science for the Social Good (DSSG) umbrella. The talk will describe the challenges faced, how they were addressed, and the solutions developed in response. The triad model and the outcomes from the model will be demonstrated with example implementations from a capstone that leads to students producing software and other artifacts incorporating data science techniques in response to important societal problems. The talk will also discuss questions of scaling such efforts, and the next steps in the journey at the University of North Florida.
2019 Florida Data Science for Social Good (FL-DSSG) Big RevealKarthikeyan Umapathy
At the 2019 Big Reveal event, FL-DSSG interns presented findings and revealed insights gained from the Cathedral Arts Project, Children's Services Council, Feeding Northeast Florida, GTM Research Reserve, and Starting Point Behavioral Healthcare projects. The UNF Foundation funded 2019 FL-DSSG Internship program. Big Reveal presentations were held at the WJCT Studio A, 100 Festival Park Ave., Jacksonville, FL - 32202. For more information about the 2019 FL-DSSG program visit http://dssg.unf.edu/2019program.html.
2018 Florida Data Science for Social Good (FL-DSSG) Big Reveal PresentationKarthikeyan Umapathy
At the 2018 Big Reveal event, FL-DSSG interns presented findings and revealed insights gained from the Baptist Health, Family Support Services, Girls Inc. of Jacksonville, and Performers Academy projects. 2018 FL-DSSG Internship program was funded by the Nonprofit Center for Northeast Florida and the University of North Florida. 2018 Big Reveal event was sponsored by AgileThought, Tampa based software consulting firm. Big Reveal presentations were held at the WJCT Studio A, 100 Festival Park Ave., Jacksonville, FL - 32202. For more information about the 2018 FL-DSSG program visit http://dssg.unf.edu/2018program.html.
Security and User Experience: A Holistic Model for CAPTCHA Usability IssuesKarthikeyan Umapathy
CAPTCHA is a widely adopted security measure on the Web and is designed to effectively distinguish humans and bots by exploiting human’s ability to recognize patterns that an automated bot is incapable of. To counter this, bots are being designed to recognize patterns in CAPTCHAs. As a result, CAPTCHAs are now being designed to maximize the difficulty for bots to pass human interaction proof tests, while making it quite an arduous task even for humans as well. The approachability of CAPTCHA is increasingly being questioned because of the inconvenience it causes to legitimate users. Irrespective of the popularity, CAPTCHA is indispensable if one wants to avoid potential security threats. We investigated the usability issues associated with CAPTCHA. We built a holistic model by identifying the important concepts associated with CAPTCHAs and its usability. This model can be used as a guide for the design and evaluation of CAPTCHAs.
Florida Data Science for Social Good (FL-DSSG) Big Reveal event was held on August 7 (Monday) from 4:30 PM to 6:30 PM at the Nonprofit Center (40 E Adams St., Jacksonville). At the event, FL-DSSG interns presented findings and revealed insights gained from the Mayo Clinic, Changing Homelessness, and Yoga 4 Change projects.
A Research Plan to Study Impact of a Collaborative Web Search Tool on Novice'...Karthikeyan Umapathy
In the past decade, research efforts dedicated to studying the process of collaborative web search have been on the rise. Yet, limited number of studies have examined the impact of collaborative information search process on novice’s query behaviors. Studying and analyzing factors that influence web search behaviors, specifically users’ patterns of queries when using collaborative search systems can help with making query suggestions for group users. Improvements in user query behaviors and system query suggestions help in reducing search time and increasing query success rates for novices. In this paper, we present an empirical study plan designed to investigate the influence of collaboration between experts and novices as well as use of a collaborative web search tool on novice’s query behavior. In this research-in-progress study, we intend to use SearchTeam as our collaborative search tool. The results of this study are expected to provide information that could help collaborative web search tool designers to find ways to improve the query suggestions feature for group users. Additionally, this study will test the hypothesis that – having domain experts working with non-experts using collaborative search systems would immensely increase the query success rates for non-expert users, and help them learn querying strategies over the course of time. If the above hypothesis is proven, then use of collaborative web search tools during training of interns would be highly recommended.
Leveraging Service Computing and Big Data Analytics for E-CommerceKarthikeyan Umapathy
Panel discussions on Leveraging Service Computing and Big Data Analytics for E-Commerce at the Workshop on e-Business (WeB) 2015 held on December 12, 2015 at Fort Worth, Texas, USA.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
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Systematic Literature Review and Research Model to Examine Data Analytics Adoption in Organizational Contexts
1. Systematic Literature Review
and Research Model to Examine
Data Analytics Adoption in
Organizational Contexts
ARIELLE O’NEAL
M.S. Data Science
KARTHIKEYAN UMAPATHY
(Presenter)
Co-Director of Florida Data Science for Social Good
Associate Professor
School of Computing
University of North Florida
Jacksonville, FL
University of North Florida
Jacksonville, FL
2. Data-Driven Culture
Development ofWeb and Mobile application systems has helped organizations gather
data on every aspect of our life
Social activities, science, work, health, and infrastructure
As we transform into a data-centric economy, becoming a data-driven organization is
fundamental for survival
Organizations are seeking to infuse data-driven culture
Data-driven culture can be interpreted as a process involving data collection, data
analysis, model building, gaining actional insights, converting insights into business
value & competitive edge.
Organizations are trying to stay on cutting edge of competition as many large
organizations have already adopted capability to implement data analytics tools
3. Research Focus: Data Analytics Adoption
Data analytics tools allow businesses to identify and predict trends and keep up with
supply and demand
Access to the tools and talents are not universally accessible to all organizations
McKinsey Analytics and NewVantage Partners have investigated data analytics adoption
by large organizations
While facing considerable challenges, large organizations are ramping up investments to adopt data
analytic tools
Small & Medium business and non-profit organizations have not been thoroughly
studied in the context of data analytics adoption
Addressing this gap is the key research focus
4. Systematic Literature Review of Data Analytics Adoption
Studies: Data Collection
Google Scholar was chosen as key scientific database for data collection
The keywords used in the search were a combination of “data analytics adoption”,
“business analytics adoption”, “data science adoption”, “survey method”, and
“benchmark” to identify relevant articles
The search was restricted to articles published from 2010 to 2022
As the data analytics topic emerged in the late 2000s and early 2010s
The base inclusion criteria consisted of articles that were free to access, written in
English, and published through a peer-review process.
Articles that were journals, conference proceedings, workshop papers, and book
chapters were considered. Articles that were unrelated to data analytics adoption or their
full texts were not available were excluded.
5. Data Extraction
We extracted data on article title,
first author affiliation location,
research method, study focus,
whether survey questions were
included, theoretical framework,
statistical analysis, and
organization focus
Data were extracted and documented
using Microsoft Excel files
7. Methodology of Studies Identified
0 2 4 6 8 10 12
Quantative survey method
Interview and case study
Mixed method - survey and case study
8. Survey Method
0 1 2 3 4 5 6 7 8 9
5-point
7-point
Did not specify
Likert Scales Of the 11 studies, 9 used survey
instruments for gathering data
on data analytics adoption in the
published article
9. Theoretical Framework
Technology Acceptance Model (TAM)
Motivation Model (MM)
Diffusion of Innovations (DOI)
UnifiedTheory ofAcceptance and Use ofTechnology (UTAUT)
Technology Organization Environment (TOE) – 5 out of 13
Resourced-based
Situation Complication Question and Answer (SCQA)
ComplexityTheory
InstitutionalTheory
10. Analysis Performed
Quantitative Analysis
7 studies used Partial Least Squares (PLS) or PLS Structural Equation Model (PLS-SEM)
Factor analysis
Regression
Neural networks
Qualitative Analysis
Thematic analysis
11. Organization Size and Industry Sector
Many studies were not focused on the business size
Various organization sizes were most frequent in the selected review studies
3 studies focused on Small & Medium Enterprises, one study focused on medium and
large organizations
Most studies were not focused on particular industry sector
Manufacturing – 2 studies
Construction
Healthcare
Retail
Governance services
12. Country of Study
Country Frequency
Malaysia 3
India 2
Pakistan 1
Taiwan 1
France 1
Greece 1
Spain 1
United Kingdom 1
Colombia 1
Cameroon 1
Asia, 7
Europe, 4
Africa, 1
South America, 1
Count
The lack of studies on
North American countries
is a research gap that must
be addressed.
13. Research Focus of Studies Observed Factors studied:
Firm performance
COVID-19 crisis
Organizational context
Strategic determinants
Continual usage
Process innovation
Predicting adoption
Technology readiness
Project management
Sustainability
Transformational leadership
Data Analytics Focus Frequency
Big data 9
Business analytics 2
Social media 1
Technology implementation 1
15. Proposed Research Model
The purpose of this
study is to
inductively develop
a research model
for data analytics
adoption for SMBs
and nonprofit
organizational
contexts through
articles found using
the systematic
literature review
16. Research Plan
Create survey instrument that will be used for benchmarking data analytics adoption by
SMBs and nonprofits
SMB and nonprofit subject matter experts would be contacted for validating survey
instrument
After obtaining IRB permits, survey will be distributed to SMBs registered with
Jacksonville Chamber and nonprofits registered with Nonprofit Center for Northeast
Florida
The survey data collection would be conducted for three months
Descriptive and confirmatory factor analysis would be performed on survey responses
from SMBs and nonprofits