Mar 26, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Ethics  Source

[ AnalyticsWeek BYTES]

>> The Wide World of Data: Enter the Datasphere by analyticsweek

>> Business Analytics Courses Surge As Leaders Crunch Big Data by analyticsweekpick

>> Visualization’s Twisted Path by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Data Mining

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Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… more

[ FEATURED READ]

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

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In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Mast… more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:Do we always need the intercept term in a regression model?
A: * It guarantees that the residuals have a zero mean
* It guarantees the least squares slopes estimates are unbiased
* the regression line floats up and down, by adjusting the constant, to a point where the mean of the residuals is zero

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%. – Andrew Hogue, Foursquare

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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[ FACT OF THE WEEK]

30 Billion pieces of content shared on Facebook every month.

Sourced from: Analytics.CLUB #WEB Newsletter

Manage HR Data

The Challenge

The volume of data available to HR has increased exponentially over the past 30 years, overwhelming organizations with the wide variety of possible metrics to track and report.

People

Most organizations are not yet realizing value from their talent analytics investments; only 8% of senior HR leaders believe they are getting returns and just 15% of business leaders have changed a decision in the past year as a result of HR data.

While the traditional approach to improving HR’s success has been raising
the level of analytic sophistication—upgrading technology, purchasing high-end analytic techniques, hiring more skilled analysts—those efforts have not been delivering the expected results.

What the Best Companies Do

Data won’t go anywhere if it’s not applied. Leading organizations, then, focus on improving the business application of talent analytics, rather than just investing in analytic sophistication.
People

Improving business application leads to a 14% increase in Analytic Impact—the extent to which talent analytics improves decisions and provides actionable support to key stakeholders—which in turn improves talent outcomes.

Article originally published HERE

Originally Posted at: Manage HR Data by analyticsweekpick

The 3 Most-Requested Dashboard Capabilities

The process of creating an application with embedded dashboards, reporting, and analytics capabilities is complex. It doesn’t just stop with taking information and making it available to end users in dashboards and reports. Application users are demanding advanced features that allow them to examine data in different ways and make decisions that will influence business outcomes.

>> Related: 7 Questions Every Application Team Should Ask Before Choosing an Analytics Vendor <<

In today’s digital world, people have come to expect seamless analytics experiences in their applications, like those offered by Netflix or Amazon. That’s why application teams, in particular, must go further to set their solutions apart from the dozens of offerings in the marketplace.

Here are three of the most-requested analytics dashboard capabilities:

#1. Insight-to-Action Processes

A complete analytics application provides more than just a visual tier for your users to consume information. Consider what the user needs to do next – for instance, opening up another application, sending an email, or exporting data to a third-party tool. Ideally, you want to give users the means to act on the insights they’ve gained – and you don’t want them to have to leave your application to do it.

Look for an analytics development platform that uses insight-to-action processes such as database write-back and automated alerts to integrate analytics into the workflow of your application. This makes your application stickier and keeps users engaged with content for longer. Even better, add a built-in scheduler so you can trigger these processes automatically on a recurring basis.

#2. Custom Styling

When you’re embedding analytics, it’s important to consider your application from the user’s point of view. You want full control over the look and feel of the content so that users can’t tell where your application ends (native content) and the embedded analytics begins.

Logi’s analytics development platform offers multiple tools for custom styling and a seamless user experience.

  • Master Report Layout allows you to create a template you can apply to other reports across your application. You set the repeated elements once—logos, headers/footers, sidebars, menus—and decide which content will reside within that repeated framework. As the content changes, these elements will remain consistent across the application.
  • Theme Editor allows you to apply consistent design elements—color, typography, styles—to all your applications. You get a great deal of granular control in defining how these design elements appear. Once you define the theme, you can apply it however you like—from a global level down to individual pages.
  • Design Frameworks, such as Bootstrap or Material UI, are commonly used to make web application design faster and easier. Logi uses a UI repository to integrate these tools into your application for ultimate control over the look and feel.

#3. Predictive Insights

Most applications talk about what happened in the past. That’s valuable information, but it becomes exponentially more valuable when you can use it to predict what will happen in the future. That’s the lure of our final most-requested feature: predictive analytics.

Predictive analytics works by feeding historical data into a mathematical model that considers key trends and patterns. The model is then applied to current data to predict what will happen next. Because of its big returns, this capability is increasingly being used by various industries to improve everyday business operations and achieve competitive differentiation.

Logi Predict is the only solution that makes it easy for developers to embed, scale, and maintain predictive analytics. It features a wizard that automates the complex task of creating a predictive model into a simple, intuitive process—allowing both product managers and developers to embed, scale, and maintain predictive analytics without technical expertise in statistical modeling.

Follow this step-by-step guide to enhance your embedded analytics offerings today >

 

Source: The 3 Most-Requested Dashboard Capabilities

Mar 19, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Ethics  Source

[ AnalyticsWeek BYTES]

>> IBM and Hadoop Challenge You to Use Big Data for Good by bobehayes

>> Monitoring 200 million kids to improve AI algorithms by administrator

>> Real-Time, Predictive Data Modeling by jelaniharper

Wanna write? Click Here

[ FEATURED COURSE]

Pattern Discovery in Data Mining

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Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern disc… more

[ FEATURED READ]

Thinking, Fast and Slow

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Drawing on decades of research in psychology that resulted in a Nobel Prize in Economic Sciences, Daniel Kahneman takes readers on an exploration of what influences thought example by example, sometimes with unlikely wor… more

[ TIPS & TRICKS OF THE WEEK]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:Explain likely differences between administrative datasets and datasets gathered from experimental studies. What are likely problems encountered with administrative data? How do experimental methods help alleviate these problems? What problem do they bring?
A: Advantages:
– Cost
– Large coverage of population
– Captures individuals who may not respond to surveys
– Regularly updated, allow consistent time-series to be built-up

Disadvantages:
– Restricted to data collected for administrative purposes (limited to administrative definitions. For instance: incomes of a married couple, not individuals, which can be more useful)
– Lack of researcher control over content
– Missing or erroneous entries
– Quality issues (addresses may not be updated or a postal code is provided only)
– Data privacy issues
– Underdeveloped theories and methods (sampling methods…)

Source

[ VIDEO OF THE WEEK]

#FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

 #FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

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]

Understanding #FutureOfData in #Health & #Medicine - @thedataguru / @InovaHealth #FutureOfData #Podcast

 Understanding #FutureOfData in #Health & #Medicine – @thedataguru / @InovaHealth #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

In that same survey, by a small but noticeable margin, executives at small companies (fewer than 1,000 employees) are nearly 10 percent more likely to view data as a strategic differentiator than their counterparts at large enterprises.

Sourced from: Analytics.CLUB #WEB Newsletter

5 Questions to Ask Before Implementing a Data Lake

If you work with data, you’ve probably encountered the term “data lake” – either as a general trend in data and analytics or as a solution to a particular Big Data problem you’re trying to solve. Indeed, with the astonishing growth of data, a data lake is often seen as an attractive solution for storing and analyzing large amounts of raw data. But would it be a good fit for your organization? Let’s try to answer that question, starting with a definition.

First thing’s first: what’s a data lake?

Since there’s a lot of confusion and unclarity in this area – in a 2015 survey, only 1.12% of respondents felt that the concept is well defined and consistent at a detailed level – any discussion of data lakes has to start with a definition.

The first thing to understand is that the term “data lake” would not typically be used to describe a particular product or service, but rather an approach to big data architecture that can be summarized as store now, analyze later.

In other words, unlike the traditional data warehouse approach, which entails imposing a structured, tabular format on the data when it is ‘ingested’, we would use a data lake to store unstructured or semi-structured data in its original form, in a single repository that serves multiple analytic use cases or services.

Data lakes are typically used to store data that is generated from high-velocity, high-volume sources in a constant stream – such as IoT, product logs or web interactions – and when the organization needs a high-level of flexibility in terms of how the data will be used.

data lakes

With this definition in mind, let’s go on to ask the 5 questions that you need to answer before deciding whether this is the way to go:

1. What type of data are you working with?

As we’ve described in the previous section, data lakes are best used to store streaming data, which has several unique characteristics:

  • Unstructured or semi-structured
  • Constantly being generated, in small bursts (e.g., every time a user sees an ad generates a new record with several dozen fields)
  • Often accumulates quickly – tens of billions of records ‘weighing’ a total of hundreds of terabytes is a common workload for streaming data

If you’re working with this type of data, you should definitely consider a data lake – since the costs of structuring and storing it in a relational database will quickly become very prohibitive.

However, if you’re mostly working with traditional, tabular information – e.g., data generated by financial, CRM or HR systems – you might want to stick to a data warehouse.

Either way, the two are not mutually exclusive, and you can definitely consider keeping some data in your RDBMS, and use a data lake for sensor or SaaS data that you would like to analyze separately. However, if you don’t have anything that even remotely resembles big or streaming data, a data lake might be overkill.

2. Do you know exactly what you’ll want to do with the data?

One of the great things about data lakes is the flexibility they provide when it comes to how the data will eventually be used. In a data warehouse, we would store the data in a certain structure that would best be suited for a specific use case, such as operational reporting; however, the need to structure the data in advance has costs, and could also limit your ability to repurpose the same data for new use cases in the future.

This brings us back to the core tenet of data lakes: store now, analyze later. If you’re still unsure whether you’ll be launching a machine learning project, or want to provide a higher level of flexibility for your future BI analyses, a data lake could be a good fit. However, if you’re only looking to generate a few predefined reports, a data warehouse would probably get you there faster.

3. How complex is your data acquisition process?

Adding new sources to your data warehouse can often be a resource-intensive process. If you’re constantly acquiring new data, particularly from unstructured or semi-structured sources, you might quickly find yourself dealing with serious ETL overhead in order to “cram” this data into a format that your data warehouse can work with.

If the costs of ingesting data into your data warehouse are becoming prohibitive, especially if this is leading you to consider giving up on some sources altogether, you should consider a data lake – which will allow you to store all the data with minimal overhead, and then extract and transform the data when you want to actually do something with it.

4. What type of tools and skills exist in your organization?

Building and maintaining a data lake is not the same as working with databases. If the latter requires some level of DBA / IT to maintain the infrastructure, with the rest being handled by business users (analysts or executives), a data lake would typically require more significant investment in engineering – and specifically in big data engineers, which are in high-demand and difficult to find.

If you don’t have these skills in your organization, transitioning to a data lake approach might prove difficult. In this case, you should consider sticking to your data warehouse until you manage to hire the prerequisite engineering talent; or use a Data Lake Platform such as Upsolver (where, for full disclosure, I am the CEO and co-founder) to streamline the process of building and managing your cloud data lake, and to eliminate the need to devote extensive engineering resources to the matter.

5. What is your strategy for data management and governance?

Both data lakes and data warehouses pose challenges when it comes to governance. In the data warehouse, this challenge would be the need to constantly maintain and manage all the data that’s coming in, and to make sure it is added according to a consistent business logic and data model; whereas data lakes are often criticized as chaotic and impossible to effectively govern. Whichever approach you choose, make sure you have a good way to address these challenges.

Are you ready for the data lake?

It’s cliched but true that there is no “one size fits all” when it comes to data. Each organization and even each project is unique and needs to be approached with an open mind and a good understanding of the ever-evolving tech landscape.

You can use the five questions we posed above as a general guideline for deciding whether your company or organization should be thinking seriously about building a data lake. If you want to read an example of a company that did it successfully, check out this case study.

About the Author


Ori Rafael is the CEO of Upsolver, which provides a leading Data Lake Platform for AWS S3. Ori has a passion for making technology useful for people and organizations, and has previously held roles as the Head of Data Integration Platforms for the IDF’s elite technology unit, as well as senior management positions in the private sector.

What BI architecture is right for you? Find out now with our Ultimate Data Warehousing Selection Sheet

Source by analyticsweek

Successfully Implementing Self-Service, Mobile Big Data Analytics

The deployment of mobile technologies complicates big data analytics in two fundamental ways. The widespread aggregation of sources, types, large sizes and high speeds of big data becomes contextualized by the two pivotal distinctions of contemporary mobile technology: small screen sizes and, subsequently, the greater propensity for accidental clicks.

The latter is particularly eminent when using touchscreen devices, which represent the future of internet interactions for everything from personal computing to the Internet of Things.

“When you think about it, everything is touchscreen,” SoMo Audience CEO Robert Manoff observed. “We have our phones, our tablets…computers more and more have touchscreens. Now all of a sudden you’re having home devices with touchscreens.”

Monetizing big data from mobile technologies—whether for marketing, ecommerce basics, research and development of new products or services, or any other purpose—requires distinguishing intended clicks from accidental ones. That distinction provides the foundation for data quality, which means the difference between optimizing costs or potentially squandering funds on inaccurate data.

The capability to actually verify mobile interactions down to individual clicks solves a multilayered problem which takes on greater proportions as mobile devices become more and more entrenched in society. “The accidental click issue is a front-facing issue for the consumer,” Manoff explained. “But utilizing gesture analytics and data provides for the other [enterprise] areas. These include fraud, campaign optimization, and cost-efficiency for a marketer that’s either spending or wasting a lot of their budget on ad placements that just aren’t doing them any good.”

Gesture Analytics and Swiping
Both of these issues are ameliorated by swiping techniques with which consumers are able to confirm intended clicks by making a swiping motion on their touchscreens. “By changing the gesture that triggers an ad to click through, from tapping to trigger that click-through URL to the gesture of a swipe on an advertisement or even content, you’re therefore really verifying the engagement of the consumer and eliminating the opportunity for accidental taps,” Manoff indicated. Organizations investing in platforms that offer swiping receive an additional verification layer whereby consumers receive a message asking them to authorize an intended click with a swiping gesture.

Competitive solutions in this space are able to increase this utility with a rich array of mobile, touchscreen-based analytics that optimize customer interactions. Gesture analytics pinpoint (via sophisticated heat maps) where on a site a customer made his or her initial touch to trigger the swiping mechanism. Furthermore, this information is delivered according to an assortment of key mobile categories such as type of device, operating system, browser, website, and more. Stratifying the ensuing data according to these facets provides a roadmap for increasing viewability—which conventionally required substantial investments in expensive third-party verification platforms. “We’re actually able to use that data and understanding for creative optimization, and more importantly, campaign optimization,” Manoff said. “In this world of programmatic advertising and running your ads across thousands, if not tens of thousands of sites, understanding what site your ad is really getting touched is a pretty important piece of information.”

In Time
According to Manoff, the granular nature of this analytic detail is readily found on advertisements on large sites such as Facebook and Snapchat, yet much less so when placing ads on the rest of the internet. Moreover, the competitive nature of self-service solutions in this space is that practically any business with an internet connection can quickly register online and implement a campaign with gesture analytics. There is also an immediacy of such solutions that plays a crucial role in the optimization of ongoing internet interactions between organizations and their consumers. “Every 15 minutes or on an hourly basis we’re able to understand what’s happening with a certain site or app you’re running on, and make automatic, dynamic optimizations for the next hour,” Manoff acknowledged. Thus, organizations can quickly gain insight into relevant factors for their marketing needs and adjust specific features of their advertisements based on them. Such on-the-fly adjustments to online marketing can considerably impact opportunities for ecommerce and investments in data-centered approaches in general. This fact is underpinned by the reality that the core tenet of this approach is to disambiguate intentional clicks from inadvertent ones while provisioning gesture analytics. “We’re getting rid of all the accidents, all the unwanted interactions, and we’re providing only true, desired data,” Manoff said.

Fraud and Viewability
The nature of that data facilitates a viewability which surpasses that of traditional internet advertising. According to Manoff, online viewability is traditionally established as “one or two seconds” of screen time. Self-service, mobile big data analytic platforms extend that definition to include whether or not a client interacted with an advertisement, where, with what type of device, and other optimization factors that reinforce viewability. Gesture analytics are equally meritorious for their fraud detection applications. Because they illustrate exactly what part of the screen or advertisement a user touched, they can distinguish fraudulent clicks not preceded by a touch from genuine ones that were. Similarly, their analytics can indicate which device type should match which types of clicks—and delineate when they don’t. Additionally, the swiping technique makes such clicks more difficult to duplicate by bots and those attempting to generate fraudulent data. “We can determine if somebody’s able to get a click through without that swipe action first,” Manoff commented. “It’s a combination of gesture data and analytics that enables us to understand whether or not that could have been a real person.”

Mobile Developments
Data-driven applications are becoming increasingly linked to mobile technologies. The type of big data accessed through these technologies is in turn shaped by them, which significantly factors into the type of analytics required to translate that data into insight. The trend towards mobile technologies will continue with the surging adoption of the IoT, which could greatly affect both big data and tactile measures for analyzing it. “Refrigerators of the future will have touchscreens on them to reorder things and make things with,” Manoff reflected. “I do foresee [gesture analytics] happening in the Internet of Things in the home. I definitely see it going that way because anytime you’re tapping using your hand, the opportunity for an accidental interaction is high,” Manoff said.

Originally Posted at: Successfully Implementing Self-Service, Mobile Big Data Analytics by jelaniharper

Mar 12, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Convincing  Source

[ AnalyticsWeek BYTES]

>> Deriving Value from Data Lakes with AI by analyticsweek

>> New Survey Uncovers 4 Reasons to Embed Analytics by analyticsweek

>> 7 Key Areas to Cover in Your RFP Template for BI (& Why They Matter!) by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

CS109 Data Science

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Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data managem… more

[ FEATURED READ]

Big Data: A Revolution That Will Transform How We Live, Work, and Think

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“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… 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:Is it beneficial to perform dimensionality reduction before fitting an SVM? Why or why not?
A: * When the number of features is large comparing to the number of observations (e.g. document-term matrix)
* SVM will perform better in this reduced space

Source

[ VIDEO OF THE WEEK]

@EdwardBoudrot / @Optum on #DesignThinking & #DataDriven Products #FutureOfData #Podcast

 @EdwardBoudrot / @Optum on #DesignThinking & #DataDriven Products #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. – Atul Butte, Stanford

[ PODCAST OF THE WEEK]

@EdwardBoudrot / @Optum on #DesignThinking & #DataDriven Products #FutureOfData #Podcast

 @EdwardBoudrot / @Optum on #DesignThinking & #DataDriven Products #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

Facebook users send on average 31.25 million messages and view 2.77 million videos every minute.

Sourced from: Analytics.CLUB #WEB Newsletter

Periscope Data Is now Sisense for Cloud Data Teams

Blog

Sisense News is your home for corporate announcements, new Sisense features, product innovation, and everything we roll out to empower our users to get the most out of their data.

Uniting to empower data analytic teams

Last May, we made a decision to join forces with Periscope Data to build a true end-to-end analytics platform. Our vision was a single data platform that combined features custom-built for data, product, and BI teams for every step in the analysis process. Both companies shared the common vision that the most successful companies are transforming into data companies. The end product that we envisioned would allow these customers to extract deeper insights from their data and make those insights more actionable across every line of business.

Today, I’m proud to announce that we’ve hit another important milestone on our merger journey. We no longer have any reason to talk about Sisense and Periscope Data as separate entities. I couldn’t be more excited to say that effective today, Periscope Data will be renamed Sisense for Cloud Data Teams. 

A Whole New World for Data Teams with Sisense

Building the future for advanced data teams

Periscope Data has officially been a part of Sisense for quite some time now. The effect can be seen on the combined company culture, product roadmap, and especially our current data offerings. The announcement of Sisense for Cloud Data Teams illustrates the progress we’ve made as part of the merger.

The platform will still provide the same powerful in-app experience that our thousands of customers have come to love, now as an official part of the Sisense product family. The name will be changing, but the offering will remain the same: world-class technology for advanced data teams to discover powerful insights in data. We’re 100% committed to executing on our roadmap and continuing to innovate Sisense for Cloud Data Teams going forward.

Supercharging advanced data teams

Part of the new name is an appropriate recognition of the community of builders that has partnered with us to evolve the combined Sisense-Periscope offering to the world-class solution it is today: cloud data teams. These data professionals are driving the data revolution that is changing the way companies win with data. They’re asking new questions and continually pushing the boundaries of what can be done with our products. 

In addition to the broad range of capabilities already offered as part of the Periscope Data product, such as data pipelines and advanced ad-hoc analysis, the Sisense for Cloud Data Teams package will enjoy numerous benefits from being a part of the core Sisense platform. Over time users will be empowered with self-service dashboard creation and a more interactive exploratory environment.

These builders are the reason Sisense invests so heavily in delivering new technology to elevate the effectiveness and enhance the impact of data and insights. The announcement of Sisense for Cloud Data Teams is an acknowledgment of the value these experts provide and a promise to execute on an aggressive roadmap for them. 

Building a common Sisense vision

From the beginning, the merger of Sisense and Periscope Data was always about finding a way to create a single data platform that would deliver the most value to every user, regardless of their data needs or technical ability. However, Sisense for Cloud Data Teams is a huge step for advanced data teams. This data analytics platform gives those teams a more powerful way to make their insights operational for other leaders within their organizations. 

The co-founder of Periscope and now one of my partners on this journey, our CMO Harry Glaser, has brought his own perspective and energy to Sisense’s vision of a true end-to-end data platform and has expressed his thoughts here.

Sisense for Cloud Data Teams is the operational realization of the vision we shared last year at the outset of the merger. It’s the result of years of work to build a platform that enables advanced data teams to accelerate organizations all over the world. We couldn’t be more excited about the journey to deliver this functionality to modern data builders.

While delivering Sisense for Cloud Data Teams is an important milestone, it’s only the beginning of our goal to deliver the best data analytics platform. I’d like to thank the customers who have come this far with us and encourage builders everywhere (including product and BI teams) to continue innovating with Sisense. Power to the builders; insights for everyone!

A Whole New World for Data Teams with Sisense

Amir Orad is the CEO of Sisense, a successful entrepreneur and a Big Data, cybersecurity, and financial technology thought leader, with proven success in leading and scaling businesses.

Source

Understand Your Company Ecosystem to Improve Company Performance

Figure 1. Service Delivery Model Highlights the Impact of Employees and Partners on Customer Loyalty and Business Growth

We have much evidence that effective customer experience management (CEM) needs to include an understanding of your employees’ experience as well as your business partners’ experience with your company. These constituencies are your face to the customers and have an impact on how the customer perceives you. For example, I found evidence that employee metrics, including job satisfaction and number of training hours, were related to customer satisfaction. Specifically, account managers who were more satisfied with their jobs had customers who were more satisfied with the account manager’s performance. Also, technical account managers who took more hours of employee training received higher customer ratings compared to their counterparts who took fewer hours of employee training. Siebel Systems reported that customers were more satisfied with their applications when the third party system integrator (e.g., partner who helped implement the application) was happy.

These findings suggest that improving the customer experience and loyalty depends on effectively managing not only customers themselves – although this is critical – but also effectively managing employees and partners who can directly impact, positively or negatively, the customer relationship.

Figure 2. General life cycle model

Understanding the Ecosystem

Effectively managing relationships with all three of these constituencies (e.g., customer, employee and partner) is the key to optimizing customer loyalty, improving your competitive advantage and ensuring long-term financial stability. In Figure 1, we see how these three constituencies ultimately impact company performance (as measured by revenue, profits, market share). Mismanagement of any one of the key constituencies would necessarily lead to poor company performance. To help you effectively managing each constituency throughout their respective life cycle, you need to understand the quality of each of the three relationships.

Life Cycles

We created a generic life cycle model (see Figure 2) to help us understand the major phases or touch points for each of the constituencies. Generally speaking, each life cycle contains three phases that are common to each: 1) Attract, 2) Acquire, 3) Service. Attraction is about how the constituency discovers you and your offerings. Acquire is about how the constituency comes on board and makes the relationship with you formal. Service is about how the constituency is serviced/managed by you. For the customer, the three life cycle phases would be Market, Sales and Service. For the employee, the three phases would be Recruit, Hire and Manage. For the partner, the three phases would be Market, Enlist and Manage.

Loyalty-Based Approach

We employ a loyalty-based approach to help companies understand where to make improvements to improve the relationship and optimize loyalty of the three key constituencies. From survey design to analysis, we consider loyalty as our ultimate criterion and have developed metrics and analytic methods to support our loyalty-based approach.

Each relationship survey adopts best practices in survey research. Each relationship survey contains four general sections: 1) Loyalty questions, 2) Experience questions, 3) Relative Performance questions and 4) Company-specific questions.

Our analytic approach to the survey data (see Simplifying Loyalty Driver Analysis) helps extract deep insight about each constituency. Using predictive modeling (e.g., correlation, regression analysis), we use the loyalty metrics to identify which business areas (e.g., product, tech support, sales, service) are responsible for driving each type of loyalty. By identifying how much each business area impacts loyalty, you are better able to determine the ROI for different experience improvement initiatives.

Segmentation helps us gain additional insight about the reasons why constituencies are dis/loyal. Examining open-ended comments for specific segments will reveal issues that are common among them, helping you target improvement programs that will address broad constituency concerns.

Integrated Survey Program

Companies use satisfaction surveys – customer, employee and partner – to effectively manage the relationship with each constituency. We developed an integrated survey program to help companies create and maintain high levels of customer loyalty, employee retention and partner allegiance. These key satisfaction surveys enable you to measure and improve relationships and increase loyalty throughout each constituency’s life cycles.  These surveys and their key indices appear below.

Survey Type Key Indices Benefits Frequency / Audience
Customer (Customer Relationship Diagnostic)
  • Customer Loyalty Indices:
    • Retention: likelihood of leaving / churning
    • Advocacy: likelihood of referring others
    • Purchasing: likelihood of buying more/different

  • Customer Experience
  • Relative Performance
  • Attract and retain customers
  • Improve sales and marketing efforts by highlighting perceived value
  • Improve resource allocation for better ROI
  • Increase revenue and profits
  • Improve lifetime value of customers
  • Semi-annually
  • Sent to Program Executive, Sponsor and Program Management team
Employee (Employee Relationship Diagnostic)
  • Employee Retention Index: Measures the employees’ likelihood of remaining on the job and expressing positive behaviors toward the company.
  • Employee Experience
  • Relative Performance
  • Attract and retain exceptional employees
  • Deliver superior service to customers
  • Create work climate that supports great service delivery
  • Achieve higher customer satisfaction and loyalty
  • Implemented once per year
  • Sent to all internal employees
Partner
(Partner Relationship Diagnostic)
  • Partner Allegiance Index: Measures the partner’s likelihood  of continuing and investing in the relationship
  • Partner Experience
  • Relative Performance
  • Attract and retain exceptional partners
  • Deliver superior service to joint customers
  • Ensure partners are offered necessary support for delivering superior service
  • Achieve higher customer satisfaction and loyalty
  • Implemented once per year
  • Sent to all partners

 

Each company will have its own survey administration timeline, based on their particular needs. We present one in Figure 3 below. For this timeline, survey administration is staggered throughout the year to help facilitate survey management and provide for quarterly reporting for one constituency. The graphic represents a complete annual cycle and the interactions of the various surveys over time.

Figure 3. Timeline for Different Surveys

Summary

Your company is part of a larger ecosystem. Your customers’ experience with your company is impacted by both employees who serve them and partners who help sell/service your solutions to your joint customers.  In addition to helping you understand your customers, an effective CEM strategy needs to help you understand your employees and business partners. I presented a loyalty-based approach to help companies manage these three key relationships that will impact the company’s sustainability. Create a great customer experience and improve customer loyalty by harnessing the power of your entire ecosystem.

Originally Posted at: Understand Your Company Ecosystem to Improve Company Performance

Mar 05, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Statistics  Source

[ AnalyticsWeek BYTES]

>> #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise by v1shal

>> What is Customer Loyalty? Part 1 by bobehayes

>> The Pitfalls of Using Predictive Models by bobehayes

Wanna write? Click Here

[ FEATURED COURSE]

Baseball Data Wrangling with Vagrant, R, and Retrosheet

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Analytics with the Chadwick tools, dplyr, and ggplot…. more

[ FEATURED READ]

The Future of the Professions: How Technology Will Transform the Work of Human Experts

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This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … 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:How do you know if one algorithm is better than other?
A: * In terms of performance on a given data set?
* In terms of performance on several data sets?
* In terms of efficiency?
In terms of performance on several data sets:

– ‘Does learning algorithm A have a higher chance of producing a better predictor than learning algorithm B in the given context?”
– ‘Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets”, A. Lacoste and F. Laviolette
– ‘Statistical Comparisons of Classifiers over Multiple Data Sets”, Janez Demsar

In terms of performance on a given data set:
– One wants to choose between two learning algorithms
– Need to compare their performances and assess the statistical significance

One approach (Not preferred in the literature):
– Multiple k-fold cross validation: run CV multiple times and take the mean and sd
– You have: algorithm A (mean and sd) and algorithm B (mean and sd)
– Is the difference meaningful? (Paired t-test)

Sign-test (classification context):
Simply counts the number of times A has a better metrics than B and assumes this comes from a binomial distribution. Then we can obtain a p-value of the HoHo test: A and B are equal in terms of performance.

Wilcoxon signed rank test (classification context):
Like the sign-test, but the wins (A is better than B) are weighted and assumed coming from a symmetric distribution around a common median. Then, we obtain a p-value of the HoHo test.

Other (without hypothesis testing):
– AUC
– F-Score

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[ VIDEO OF THE WEEK]

Making sense of unstructured data by turning strings into things

 Making sense of unstructured data by turning strings into things

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[ QUOTE OF THE WEEK]

What we have is a data glut. – Vernon Vinge

[ PODCAST OF THE WEEK]

@Schmarzo @DellEMC on Ingredients of healthy #DataScience practice #FutureOfData #Podcast

 @Schmarzo @DellEMC on Ingredients of healthy #DataScience practice #FutureOfData #Podcast

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[ FACT OF THE WEEK]

More than 5 billion people are calling, texting, tweeting and browsing on mobile phones worldwide.

Sourced from: Analytics.CLUB #WEB Newsletter