Sep 24, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Statistically Significant  Source

[ AnalyticsWeek BYTES]

>> Data APIs: Gateway to Data-Driven Operation and Digital Transformation by analyticsweek

>> Decoding Agritech – Current & Upcoming Trends by analyticsweekpick

>> Building an AI startup to solve #Productivity @DennisMortensen #JobsOfFuture #Podcast by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

CS229 – Machine Learning

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This course provides a broad introduction to machine learning and statistical pattern recognition. … more

[ FEATURED READ]

On Intelligence

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Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:Define: quality assurance, six sigma?
A: Quality assurance:
– A way of preventing mistakes or defects in manufacturing products or when delivering services to customers
– In a machine learning context: anomaly detection

Six sigma:
– Set of techniques and tools for process improvement
– 99.99966% of products are defect-free products (3.4 per 1 million)
– 6 standard deviation from the process mean

Source

[ VIDEO OF THE WEEK]

Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

 Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The goal is to turn data into information, and information into insight. – Carly Fiorina

[ PODCAST OF THE WEEK]

Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

 Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data.

Sourced from: Analytics.CLUB #WEB Newsletter

Datumbox Machine Learning Framework 0.6.0 Released

The new version of Datumbox Machine Learning Framework has been released! Download it now from Github or Maven Central Repository. What is new? The main focus of version 0.6.0 is to extend the Framework to handle Large Data, improve the code architecture and the public APIs, simplify data parsing, enhance the documentation and move to […]

Source: Datumbox Machine Learning Framework 0.6.0 Released by administrator

Sep 17, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data Storage  Source

[ FEATURED COURSE]

R, ggplot, and Simple Linear Regression

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Begin to use R and ggplot while learning the basics of linear regression… more

[ FEATURED READ]

Rise of the Robots: Technology and the Threat of a Jobless Future

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What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence… more

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:What does NLP stand for?
A: * Interaction with human (natural) and computers languages
* Involves natural language understanding

Major tasks:
– Machine translation
– Question answering: “what’s the capital of Canada?”
– Sentiment analysis: extract subjective information from a set of documents, identify trends or public opinions in the social media

– Information retrieval

Source

[ VIDEO OF THE WEEK]

Using Topological Data Analysis on your BigData

 Using Topological Data Analysis on your BigData

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The world is one big data problem. – Andrew McAfee

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Brands and organizations on Facebook receive 34,722 Likes every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

A Look Into Trust in the Australian & New Zealand Construction Industry  

We can’t emphasise the importance of trust within the construction industry enough. But while there has always been an assumption that trust plays a large part in construction, it’s difficult to measure just how critical the value correlates to project success. That is, until recently. This month, Autodesk and FMI published a report on the impact of trust in the construction industry in a new study, “Trust Matters: The High Cost of Low Trust.” 

The report noted trust and collaboration patterns across the construction industry, including Australia and New Zealand. While a large percentage of respondents report above-average levels of trust, far less report “very high trust” levels, but higher levels of trust could provide millions of dollars in benefits for firms. This includes lower turnover rates, fewer missed schedules, and more repeat business. 

In countries like Australia and New Zealand, where communities are especially tight-knit, the impact of trust expands beyond personal relationships and into daily business life. Not surprisingly, construction companies in both Australia and New Zealand are out-performing their global peers when it comes to high levels of trust. In fact, 38% of Australian and 40% of New Zealand firms reported very high levels of trust compared to 37% global average.  

Nevertheless, maintaining and even increasing these trust levels will be essential for the lifeblood of the industry in the coming years. In Australia, construction is one of the fastest-growing industries, and it represents 9% of the GDP. In New Zealand, the industry has grown exorbitantly in the last few years, and it’s projected to grow by another 20% in the next few years. To keep up with the high demands of the future, firms will need to continue to build trust and collaboration internally, with partners, and with clients.

So, what are the key insights construction professionals can learn from the report, “Trust Matters: The High Cost of Low Trust?” Here are the top takeaways about trust in the Australian and New Zealand construction industries.

Takeaway 1: Autonomy Supports Trust

Micromanagement is often seen as a negative in the construction industry, but it’s viewed as especially detrimental to work in Australia and New Zealand. In these countries, the best way to encourage a loyal and innovative team is by supporting individual workers’ professional growth and allowing them the freedom to make key decisions for their position. In a basic sense, management hires skilled workers and trains them as needed but also trusts them to know and complete their job requirements to the high level of quality they desire.

This dynamic allows workers to feel supported by their employers while still contributing in meaningful ways to the project based on their expertise. According to the Australian respondents, this is an essential aspect of building trust among their employees, which means higher quality and work, which also translates to more satisfactory job completion. Because the management style is such that it encourages growth in the workers, there is a higher job satisfaction rate and better employee retention.

The general feeling among New Zealand construction professionals runs concurrent with those in Australia. Micromanagement of staff leads to less job satisfaction and curtails workers’ ability to bring innovation and excitement to the job. However, trusting workers to know their job and add their own independent solutions to a project increased excitement, job satisfaction, and overall productivity.

Creating this level of trust between leadership and staff helps to diminish the need or want to micromanage, which also frees the executive staff time for more worthwhile processes. It’s a better use of time overall. Hire great staff. Trust them to know their job. Train them for more responsibilities. Let them work up to their potential. Productivity in these scenarios soars because there’s no need for management to work on the same tasks as their staff and projects can be addressed in a more proactive way.

Another benefit of autonomy was that it encouraged workers to become more invested in their company and position. For instance, the New Zealand respondents indicated that they were more likely to have employees almost always go above and beyond to help others. Their data showed a positive rate of 35%, compared to a 30% average. This documented average clearly correlates job satisfaction and autonomy to more productive contributions to the project.

Takeaway 2: Relationship Building Pays Off

In both the Australian and New Zealand construction industry, there is a correlation between relationship building and profits. Internal trust is especially important in Australia. The feeling is that managers and executives should get to know their teams well to build a sense of relationship and trust, which leads to more productivity. These internal, personal relationships allow the crew to feel heard and respected by their managers, and each member of the team feels comfortable contributing ideas and highlighting issues. This also improves the line of communication, meaning that errors will be caught faster, and schedules are more likely to be kept.

When workers feel that they are a major part of the process, they take on responsibility naturally and are more apt to help other workers thrive. They develop relationships with each other that extend beyond the worksite, so they’re invested in each others’ success, as well as the company’s.

In New Zealand, one major factor is the close relationships that companies have with each other. These are smaller communities, and the professionals here often work with people they’ve known for the majority of their lives. This fosters a level of trust that can be hard to duplicate in other settings, and it’s simply a natural benefit to this geographic location. Workers here don’t need to spend a great deal of time learning about their partners and coworkers because they’ve known them for a long time.

This works in practice because there is such a natural camaraderie that collaboration is far more easily reached in many cases. This collaboration exists between coworkers, but it’s also exceptionally high between clients and companies, which allows for more interactive input from clients on projects. In other geographic locations, there may be limited creative input simply because the relationship and knowledge base isn’t there. 

Collaboration is the most remarked-upon benefits of this dynamic. In New Zealand, the report indicates that 30% of firms have high levels of collaboration. The average across all countries was only 24%.

Some collaboration effectiveness was brought about by the simple fact that the different people involved in projects have long-standing relationships. But many respondents also felt that the use of technology, such as cloud-based applications, helped all parties to stay informed in real-time. This is a benefit to contractors in any geographic location.

While these areas do benefit from close-knit communities and pre-existing relationships with other professionals, they still need to work to maintain these relationships to produce high levels of collaboration on every project.

Takeaway 3: Clarity and Transparency Are Critical

Clarity and transparency are also essential when fostering trust in construction. You can’t expect staff to reach autonomy if the management level isn’t clear about what they expect or transparent in the process. There is also difficulty in maintaining a solid reputation among clients without transparency and honesty.

In the report, participants indicate that it’s vital to make instructions and requests clear, as well as clearly define job roles and expectations. Australian respondents report that they clearly define expectations and empower individuals to succeed. 

In Australia, 42% of respondents strongly agree that individual roles and responsibilities are well-defined. When comparing the global average, only 32% answered the same way. Furthermore, a higher percentage of participants from New Zealand (34%) indicated that most people in their organisation were explicit about requests, compared to the global average (22%). 

Learn More About Trust in Australia and New Zealand Construction

For more information about how trust and collaboration influence the construction industry in New Zealand, Australia, as well as other countries, download our report today.

The post A Look Into Trust in the Australian & New Zealand Construction Industry   appeared first on Autodesk Construction Cloud Blog.

Source: A Look Into Trust in the Australian & New Zealand Construction Industry  

Six Do’s and Don’ts of Collaborative Data Management

Data Quality Projects are not technical projects anymore. They are becoming collaborative and team driven.

As organizations strive to succeed their digital transformation, data professionals realize they need to work as teams with business operations as they are the ones who need better data to succeed their operations. Being in the cockpit, Chief Data Officers need to master some simple but useful Do’s and Don’t’s about running their Data Quality Projects.

Let’s list a few of these.

 DO’S

 Set your expectations from the start.

Why Data Quality? What do you target? How deep will you impact your organization’s business performance? Find your Data Quality answers among business people. Make sure you know your finish line, so you can set intermediate goals and milestones on a project calendar.

Build your interdisciplinary team.

Of course, it’s about having the right technical people on board: people who master Data Management Platforms. But It’s all also about finding the right people who will understand how Data Quality impacts the business and make them your local champions in their respective department. For example, Digital Marketing Experts often struggle with bad leads and low performing tactics due to the lack of good contact information. Moreover, new regulations such as GDPR made marketing professionals aware about how important personal data are. By putting such tools as Data Preparation in their hands, you will give them a way to act on their Data without losing control. They will be your allies in your Data Quality Journey.

Deliver quick wins.

While it’s key to stretch people capabilities and set ambitious objectives, it’s also necessary to prove your data quality project will have positive business value very quickly. Don’t spend too much time on heavy planning. You need to prove business impacts with immediate results. Some Talend customers achieved business results very quickly by enabling business people with apps such as Data Prep or Data Stewardship.  If you deliver better and faster time to insight, you will gain instant credibility and people will support your project. After gaining credibility and confidence, it will be easier to ask for additional means when presenting your projects to the board. At the end remember many small ones make a big one.

DON’TS

Don’t underestimate the power of bad communication

We often think technical projects need technical answers. But Data Quality is a strategic topic. It would be misleading to treat it as a technical challenge. To succeed, your project must be widely known within your organization. You will take control of your own project story instead of leaving bad communication spreading across departments. For that, you must master the perfect mix of know-how and communication skills so that your results will be known and properly communicated within your organization. Marketing suffering from bad leads, operations suffering from missing infos, strategists suffering from biased insights. People may ask you to extend your projects and solve their data quality issues, which is a good reason to ask for more budget.

Don’t overengineer your projects then making it too complex and sophisticated.

Talend provides simple and powerful platform to produce fast results so you can start small and deliver big. One example of having implemented Data Management from the start, is Carhartt who managed to clean 50,000 records in one day. You don’t necessarily need to wait a long time to see results.

Don’t Leave the clock running and leave your team without clear directions

Set and meet deadlines as often as possible. It will bolster your credibility. As time is running fast and your organization may shift to short term business priorities, track your route and stay focused on your end goals. Make sure you deliver project on time. Then celebrate success. When finishing a project milestone, make sure you take time to celebrate with your team and within the organization.

 

To learn more about Data Quality, please download our Definitive Guide to Data Quality.

 

The post Six Do’s and Don’ts of Collaborative Data Management appeared first on Talend Real-Time Open Source Data Integration Software.

Originally Posted at: Six Do’s and Don’ts of Collaborative Data Management

Sep 10, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Accuracy check  Source

[ FEATURED COURSE]

Machine Learning

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6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending … more

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … more

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q:Do you think 50 small decision trees are better than a large one? Why?
A: * Yes!
* More robust model (ensemble of weak learners that come and make a strong learner)
* Better to improve a model by taking many small steps than fewer large steps
* If one tree is erroneous, it can be auto-corrected by the following
* Less prone to overfitting

Source

[ VIDEO OF THE WEEK]

Using Topological Data Analysis on your BigData

 Using Topological Data Analysis on your BigData

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

If you can’t explain it simply, you don’t understand it well enough. – Albert Einstein

[ PODCAST OF THE WEEK]

Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

 Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

140,000 to 190,000. Too few people with deep analytical skills to fill the demand of Big Data jobs in the U.S. by 2018.

Sourced from: Analytics.CLUB #WEB Newsletter

Our reflections on the 2020 Gartner Magic Quadrant for Data Quality Solutions

 “Every organization — no matter how big or how small — needs data quality,” says Gartner in its newly published Magic Quadrant for Data Quality Solutions. However, with more and more data coming from more and more sources, it’s increasingly harder for data professionals to transform the growing data chaos into trusted and valuable data assets. Data pipelines may carry incomplete and inaccurate data, making data practitioners’ jobs difficult and preventing data-driven initiatives from delivering on their expected business outcomes.

We all aspire to transform our organizations with data-driven insights, but we can’t do that if we don’t trust our data. A recent Opinion Matters survey shows that only 31% of data specialists have a high level of confidence in their organizations’ ability to deliver trusted data at speed.

Working without reliable data becomes costly, risky, and chaotic. Whether you’re unifying product and customer data in a single 360° view to transform the customer experience, or you need to comply with data privacy regulations, data quality can make the difference between the success and failure of your data-driven initiatives.

 

No data management initiative is complete without a solid data quality strategy

Data quality can dramatically impact your bottom line. Gartner stated that its “Magic Quadrant customer survey shows that organizations estimate the average cost of poor data quality at $12.9 million every year.” Another Gartner report also positions data governance and data quality as the most important initiatives for data management strategies.

As data quality is becoming a linchpin of data management, we’re proud that Talend was recognized by Gartner as a Leader for the third time in a row in the 2020 edition of Gartner’s Magic Quadrant for Data Quality Solutions. 

We believe data quality shouldn’t be managed by a standalone solution. Rather, data quality is a core discipline within data management. It should span out everywhere, and this requires integration and extensibility.  

Talend Data Fabric delivers data quality as a pervasive capability that spans across our platform and related applications, and that includes self-service data preparation, data integration, real-time integration, metadata management, and a data catalog.

We believe, being recognized as a Leader in the Magic Quadrant for Data Quality Solutions not only validates our capacity to build a vision for data quality, but also validates our ability to help organizations succeed in their digital transformation journeys.

2020 Gartner Magic Quadrant for Data Quality Solutions

Download a complimentary copy of the 2020 Magic Quadrant for Data Quality Solutions.

 

4 innovations that make the biggest impact on data quality

The research also considers the technologies and innovations in the data quality market. Let’s review those key ingredients and see how Talend addresses them.

Ubiquity: horizontal, not vertical data quality

Talend has made data quality a key component of its data management vision for a decade; we have been positioned in this Gartner Magic Quadrant since 2011. Talend has always considered data quality the key to making any data management project a success.

We embed data quality into every step of the data pipeline by making Talend Data Quality an integrated part of Talend Data Fabric instead of a standalone application, so that customers can get data they trust at every stage of the data lifecycle.

 

Simplicity: democratizing data quality with simple, efficient, collaborative data systems

Data practitioners need simple, intelligent, automated data quality tools to transform data chaos into valuable, reusable data assets.

Talend was among the first contenders to cover that need. Talend introduced self-service data preparation tools in 2016, bridging the gap between IT capabilities and business needs. The following year, Talend entered the Magic Quadrant for Data Quality Solutions as a Leader. Today, Talend Data Fabric provides a unified, collaborative platform in the cloud on which nontechnical users can profile, contribute, and improve data, removing the hassle of legacy on-premises systems.

 

Automation: data quality made intelligent

Amplifying data quality with machine learning has become a key differentiator. “By 2022,” Gartner predicts, “60% of organizations will leverage machine-learning-enabled data quality technology for suggestions to reduce manual tasks for data quality improvement.” Business users need help to accelerate preparation for better data.

To that end, Talend recently introduced more machine learning-driven features, such as Magic Fill to accelerate data preparation and let users process data quicker and better.

 

Collaboration: bring the people expertise back into the data

Still, while automation is important, it’s not the answer to everything. Data quality success often stems from the right alliance of people, technology, and processes aligning with each other to make an impact. People must remain in control, and human expertise must be captured and employed in the data chain. To capture that knowledge, another component of Talend Data Fabric, Talend Data Stewardship, helps organizations assign data validation to appointed experts across the organization and track and audit progress.

Talend’s stewardship capabilities were highlighted by our customers in the previous Magic Quadrant, and continue to provide value to customers. That’s why we made Talend Data Stewardship a key part of our Talend Data Fabric, letting organizations not only offer that functionality, but also engage users in a virtuous circle with their data.

 

Companies rely on data quality to deliver successful data strategies

We’re witnessing these innovations and new needs firsthand and are proud to support our customers on their journey to data quality.

Talend customer: Seacoast BankTake the example of Seacoast Bank, which created a data quality index for all their financial services. Seacoast Bank relies on data to be able to provide customers the best solutions for their needs, and to develop a deeper understanding of who their customers are and how they want to work with the bank. And being heavily regulated, Seacoast Bank also understands the need for trusted data. Seacoast Bank is banking on a data quality index to measure data quality across six dimensions and track how it improves or degrades as the bank acquires other banks, and as data sources, processes, and the technical environment change.

“It’s our duty to make sure each customer’s data accurately reflects who they are in our community, and what their relationship is with our community-based bank.”

Mark Blanchette
SVP, Director of Data Management and Business Technology, Seacoast Bank

 

 

Talend is working with a renowned telco operator that serves more than 90 million mobile subscribers. Our customer was facing huge data quality challenges that led to underperforming customer communications. They used Talend Data Quality to convert bad data into a steady stream of clean and reliable source data to power advanced analytics. This happens automatically every day, allowing data analysts, the operations team, and even business users to know if the data they are using is accurate and valid. Results were impressive: The company went from a 40% to a 90%+ trust score that saw better efficiency, cost reduction, risk protection, and higher ROI of marketing campaigns.

 

Everyone should know what’s inside their data, score it, and improve it over time

Gartner predicts that “by 2022, 70% of organizations will rigorously track data quality levels via metrics, increasing data quality by 60% to significantly reduce operational risks and costs.”

Talend brought data profiling into the hands of data engineers. Now that everyone wants to use data, it’s equally important to let data workers understand the data, endorse it, score it, and improve it.

Data Trust Score by Talend Talend Trust Score does just that. The Trust Score helps anyone to answer at a glance the question “How trustworthy is my dataset?” It’s based not only on data quality indicators, but also on popularity and certification, so that reliable and authoritative datasets can be shared and populated across the organization.

 

We’re still in the early stages of the data quality journey. Data management practices are constantly evolving, and we’re seeing capabilities converging into a unified platform that can meet the needs of both business departments and IT.

We’re happy to help. We thank all the customers who have placed their trust in Talend. And to anyone who wants to bring clarity to their data chaos, we invite you to discover Talend, try our data quality stack, and become part of our growing user community.

 

 

Gartner, Magic Quadrant for Data Quality Solutions, Melody Chien, Ankush Jain, 27 July 2020
Gartner, Survey Analysis: Data Management Struggles to Balance Innovation and Control, Melody Chien, Nick Heudecker, 19 March 2020
Gartner, Build a Data Quality Operating Model to Drive Data Quality Assurance, Melody Chien, Saul Judah, Ankush Jain, 29 January 2020 
Gartner, Magic Quadrant for Data Quality Solutions, “Melody Chien, Ankush Jain”, “27 July 2020”

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and is used herein with permission. All rights reserved.
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Talend.

The post Our reflections on the 2020 Gartner Magic Quadrant for Data Quality Solutions appeared first on Talend Real-Time Open Source Data Integration Software.

Originally Posted at: Our reflections on the 2020 Gartner Magic Quadrant for Data Quality Solutions by analyticsweekpick

Sep 03, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Statistics  Source

[ AnalyticsWeek BYTES]

>> Introduction to Financial Risk Analytics by administrator

>> Landscape of Big Data by v1shal

>> 20 Best Practices in Customer Feedback Programs: Building a Customer-Centric Company by bobehayes

Wanna write? Click Here

[ FEATURED COURSE]

Probability & Statistics

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This course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and… more

[ FEATURED READ]

How to Create a Mind: The Secret of Human Thought Revealed

image

Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse… more

[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE Q&A]

Q:What is better: good data or good models? And how do you define ‘good”? Is there a universal good model? Are there any models that are definitely not so good?
A: * Good data is definitely more important than good models
* If quality of the data wasn’t of importance, organizations wouldn’t spend so much time cleaning and preprocessing it!
* Even for scientific purpose: good data (reflected by the design of experiments) is very important

How do you define good?
– good data: data relevant regarding the project/task to be handled
– good model: model relevant regarding the project/task
– good model: a model that generalizes on external data sets

Is there a universal good model?
– No, otherwise there wouldn’t be the overfitting problem!
– Algorithm can be universal but not the model
– Model built on a specific data set in a specific organization could be ineffective in other data set of the same organization
– Models have to be updated on a somewhat regular basis

Are there any models that are definitely not so good?
– ‘all models are wrong but some are useful” George E.P. Box
– It depends on what you want: predictive models or explanatory power
– If both are bad: bad model

Source

[ VIDEO OF THE WEEK]

George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast

 George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

@AngelaZutavern & @JoshDSullivan @BoozAllen discussed Mathematical Corporation #FutureOfData

 @AngelaZutavern & @JoshDSullivan @BoozAllen discussed Mathematical Corporation #FutureOfData

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iTunes  GooglePlay

[ FACT OF THE WEEK]

29 percent report that their marketing departments have ‘too little or no customer/consumer data.’ When data is collected by marketers, it is often not appropriate to real-time decision making.

Sourced from: Analytics.CLUB #WEB Newsletter

The Potential to Enhance the Citizen Experience With Automation

The citizen experience is the government equivalent of the private sector’s customer experience (CX). It is a person’s perceptions of the quality and value of all their interactions with a government agency. CX happens not just at the level of a service or program, but with each agency and across government itself. Improving CX is about meeting public needs with minimal frustration and maximum efficiency.

GovLoop’s survey reflects the importance of creating a better CX. The good news is that 89% percent of respondents said improving CX is on their agency’s priority list (Figure 5).

Although most agree that improving CX is needed, we wanted to understand whether they see a role for automation technology in that endeavor. According to the survey results, 48% of respondents believe the technology can help human agents deliver better CX (Figure 6).

At state agencies, the confidence in these tools is considerably higher, with 67% saying they see automation’s potential, compared to 44% at the local level and 40% at the federal level.

The survey insight about state agencies being especially favorable toward automation aligns with what others have observed. The National Association of State Chief Information Officers’ (NASCIO) 2019 State CIO Survey found that 65% of CIOs view artificial intelligence (AI)/robotic process automation (RPA) as the most impactful emerging technology in the next three to five years. It was the top choice by a wide margin.

Tim Friebel, Innovation Sales Lead for Service Automation at Genesys, had advice for those trying to advocate for automation at their agency. “Most decisions are driven by resources and where to allocate them,” he said. “You have to find areas where automation can free up time and money while improving citizen experience. That business case is the first lever to pull.”

At the federal level, a mandate for automation exists. The 2018 President’s Management Agenda set a cross-agency goal to “shift time, effort, and funding currently spent performing repetitive administrative tasks … toward accomplishing mission outcomes,” and specifically pointed to automation technologies as one way to achieve that goal. The White House also issued an executive order in 2019 to “promote and protect national AI technology and innovation,” including in government services.

State and local governments often don’t have a single overarching standard or mandate to adopt automation and AI solutions. However, momentum for them is growing. Some have launched initiatives to evaluate and make recommendations about agencies’ use of these technologies and their regulation. These include:

There is also a Massachusetts bill that would establish a state commission to review the government’s use of automation.

It’s clear many agencies see the potential for automation to positively affect CX and those that don’t may soon be compelled by policy to pilot these technologies.

This article is an excerpt from GovLoop’s recent report, “Team Up With Automation for a Better Citizen Experience.” Download the full report here.

Illustration credit: GovLoop


The post The Potential to Enhance the Citizen Experience With Automation appeared first on GovLoop.

Originally Posted at: The Potential to Enhance the Citizen Experience With Automation