Analytics@

Your boss comes to you at the end of the day and wants you to create an analytic web application for inventory management. Your first instinct is probably to get down to business coding. First, you create a sketch board, go through the UX and UI, review all the specifications, start development, QA, develop some more, and then QA some more…you know the drill.

What if I told you that you could do all of that in less than 10 minutes instead?

At Sisense Labs, we’re driven by how people will consume data in the future. So, over the past year, we have been creating a framework for developers to create their own analytics and BI applications – packaged BI capabilities for specific needs – that can be placed anywhere. We call it Sisense BloX.

Loops = Value

The idea for Sisense BloX comes as the next step in our journey to embed analytics everywhere. The idea was inspired by this piece on The Upshot, which gave us our “Eureka! moment” to give interactive functionality to our customers wherever and however they need it. Back in November 2017, I presented the idea internally here at Sisense as “Loops = Value.”

Here’s my original slide:

The slide may be pretty bare bones, but the idea was there: data allows you to create applications, applications allow you to take concrete actions, and these actions allow you to create more data. The benefit of higher user engagement with the ease of use to support and deploy in a low-code development environment enables companies to become more data-driven by tying business action with their data. As such, they can speed the monetization of their data investments.

So what is Sisense BloX?

Sisense BloX makes it easier than ever to create custom actionable analytic applications from complex data by leveraging powerful prebuilt templates to integrate application-like functionality into dashboards.

Sisense BloX is the next evolution of our Sisense Everywhere initiative in which we unveiled integrations with products like the Amazon Echo and a smart bulb. It’s another step in Sisense Lab’s pursuit of democratizing the BI world and increasing the value of data for everyone. With Sisense BloX, we transform the world of analytics into an open platform that customizes business applications in order to be more efficient with the way that we interact with our data.

Let’s break that down step by step.

First, the Sisense BloX framework includes a robust library of templates to ensure that you can get started quickly by adding new visualization options or integration points with other applications. That tedious development cycle we mentioned earlier is a thing of the past.

Then, because we live in a world where customization is key, you can customize the code of your analytics app using both HTML and JSON. Essentially, what this means is you can take code from anywhere on the web (like, this) and simply add it to a BloX application. This helps non-developers create applications they only dreamed about before and gives developers the UX layer for their BI.

And, finally, the Sisense BloX framework includes an easy-to-use interface to expose and access many API capabilities directly in the Sisense UI using standard CSS and JSON. What we’ve done is create a low-code environment that makes these APIs accessible to a much wider range of developers and even to non-developers. You can integrate whatever action you want right into your dashboards. Anyone can create an actual BI application using this new UX layer.

Sisense BloX is currently available as a plugin in Sisense Marketplace but make no mistake, the vision is clear—soon every developer will be able to connect data with actions by using a simple coding framework and add buttons, interactivity, animation, and just about anything HTML will allow.

The Future Belongs to Action

Interacting with data is complex. With unlimited use cases and ways to use data, ensuring we provide the right analytical solution in the right scenario is critical. Sisense BloX will integrate BI with UX together in one platform, creating BI apps of all shapes and sizes.

Sisense BloX empowers the data application designers to create business applications with actions wrapped in one container, which create a narrative and have a deeper impact on the organization’s business offering. With Sisense BloX the paradigm shifts from dashboard designers to analytic applications builders and makers. Maybe you want to create a calculator, a slider, or a form that connects and writes back to Salesforce. Sisense BloX allows for this and much more.

I’m excited to introduce Sisense BloX to the world.

Source: Sisense BloX – Go Beyond Dashboards by analyticsweek

Until recently, such a subtle anomaly might have slipped by unnoticed. But with employee fraud costing the country an estimated $300 billion a year, forensic accountants are increasingly wielding mathematical weapons to catch cheats.

“The future of forensic accounting lies in data analytics,” said Timothy Hedley, a fraud expert at KPMG, the firm that did the call-center audit.

In the curious case of the call centers, several hundred operators across the country were authorized to issue refunds up to $50; anything larger required the permission of a supervisor. Each operator had processed more than 10,000 refunds over several years. With so much money going out the door, there was opportunity for theft, and KPMG decided to check the validity of the payments with a test called Benford’s Law.

According to Benford’s Law—named for a Depression-era physicist who calculated the expected frequency of digits in lists of numbers—more numbers start with one than any other digit, followed by those that begin with two, then three and so on.

“The low digits are expected to occur far more frequently than the high digits,” said Mark J. Nigrini, author of Benford’s Law: Applications for Forensic Accounting, Auditing, and Fraud Detection and an accounting professor at West Virginia University. “It’s counterintuitive.”

Most people expect digits to occur at about the same frequency. But according to Benford’s Law, ones should account for 30% of leading digits, and each successive number should represent a progressively smaller proportion, with nines coming last, at under 5%.

In their call-center probe, Mr. Hedley and his colleagues stripped off the first digits of the refunds issued by each operator, calculated the frequencies and compared them with the expected distribution.

“For certain people answering the phones, the refunds did not follow Benford’s Law,” Mr. Hedley said. “In the ‘four’ category, there was a huge spike. It led us to think they were giving out lots of refunds just below the $50 threshold.”

The accountants identified a handful of operators—fewer than a dozen—who had issued fraudulent refunds to themselves, friends and family totaling several hundred thousand dollars.

That’s a lot of $40 refunds. But before running the Benford analysis, neither the company nor its auditors had evidence of a problem.

Getting the accounting profession to adopt Benford’s Law and similar tests has been a slow process, but Mr. Nigrini has spent two decades inculcating Benford’s Lawin the accounting and auditing community, promoting it through articles, books and lectures.

“It has the potential to add some big-time value,” said Kurt Schulzke, an accounting professor at Kennesaw State University in Georgia. “There has not been much innovation in the auditing profession in a long time, partly because they have ignored mathematics.”

Now, the Association to Advance Collegiate Schools of Business emphasizes the importance of analytical capabilities. Off-the-shelf forensic-accounting software such as IDEA and ACL include Benford’s Law tests. Even the Securities and Exchange Commission is reviewing how it can use such measures in its renewed efforts to police fraud.

Recently, at the invitation of the agency, Dan Amiram, an accounting professor at Columbia University, and his co-authors Zahn Bozanic of Ohio State University andEthan Rouen, a doctoral student at Columbia, demonstrated their method for applying Benford’s Law to publicly available data in companies’ income statements, balance sheets and statements of cash flow. For example, a look at Enron’snotorious fraudulent accounting from 2000 showed a clear variation from Benford’s Law.

“We decided to take a different approach,” Mr. Amiram said. “Those are the main financial statements that companies report.”

Auditors, who are employed by companies to examine their accounts, are given free access to data that can reveal potential fraud. Investors and other individuals don’t have that luxury. But, Mr. Amiram said, they all have the same goals: “To make capital markets more efficient and make sure bad guys are not cheating anyone.”

Benford’s Law isn’t a magic bullet. It’s only one approach. It isn’t appropriate for all data sets. And when it is a good tool for the job, it simply identifies anomalies in data, which must be explained with further investigation. In many cases, there are reasonable explanations for incongruities.

And with so much attention now paid to Benford’s Law, it might occur to some hucksters to try to evade detection while still cheating. But Mr. Nigrini said it isn’t that simple.

“While you are doing your scheme, you don’t know what the data look like,” he said. “Because you don’t know what the population looks like while you are committing fraud, it’s“It’s a little tricky to beat Benford’s.”

Write to Jo Craven McGinty at Jo.McGinty@wsj.com

Originally posted via “Accountants Increasingly Use Data Analysis to Catch Fraud”

Source: Accountants Increasingly Use Data Analysis to Catch Fraud

One of the most exciting ways to use big data analytics in your corporate strategy is to target other data scientists (e.g., Cloudera). I call this using big data as a core strategy as opposed to a supporting strategy, wherein analytic strategies are incorporated into traditional products and services that target a non-analytic market (e.g., Progressive).

A core strategy is exciting for your data science team because they get to build products and services for people just like them — other data scientists. This is a very sound idea that I fervently advocate.

Like attracts like
People have a natural affinity for others like them, and data scientists are no exception.

Although data science is a multi-disciplinary skill that has its tentacles in a wide range of areas, it’s the narrow intersection that defines the field. As such, the population of true data science enthusiasts is quite small, which makes their social bonds very tight.

Two data scientists meeting for the first time can carry on a conversation for hours on subjects the vast majority of the population won’t understand, much less care about. So when the people creating your offering (your in-house team) also have the same passion and knowledge as the people consuming your offering (your customers), you have an amazing opportunity to accelerate customer loyalty.

Be intentional about setting up these meetings
These relationships are going to form no matter what, so it’s best to be intentional about how these meetings happen. Like any other group of professionals, there are several associations available for data scientists, and with the recent explosion of corporate interest in data scientists, it seems like a new one pops up every other day. Add to this trade shows, online forums, and other community events, and you have a great potential for your staff to at least casually bump into your customers, if not meet with them on a regular basis.

Wouldn’t you want to control these interactions instead of leaving these relationships to organically grow on their own? It makes sense to me.

Suggestions to point you in the right direction
There are several possibilities for controlling the interactions between your data scientists and your customers, and the one you choose depends on your resources and the value you place on strategic loyalty. I’m an advocate of infusing loyalty into your strategy, so I’ll always recommend that you show no reticence in pouring funds in this direction. That said, this approach isn’t for everyone, and I respect that.

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Sponsored events

For those who would rather reserve the bulk of their strategic stockpile for other pursuits, I recommend at least a moderate investment in bringing your staff and your customers together with regularly sponsored events. It doesn’t take much to sponsor a regular (and fun) event where your data scientists can network with existing and potential customers. It’s also a great opportunity for you to strengthen your brand within a very vertical market.

Don’t let the informal structure of sponsored events detract you from coaching your data scientists on the necessary do’s and don’ts. It’s good to talk freely with other professionals; however, there’s a line of confidentiality that must be maintained. It’s important that you explain this to your data scientists, as you probably won’t be asking your guests to sign a non-disclosure agreement before they start eating their salad.

Strategic, legal partnerships

On the other end of the spectrum is a strategic, legal partnership; this makes sense if you have a very short list of high-value customers and/or you face fierce competition in the marketplace. Bringing your customers on as partners binds their allegiance and widens the communication channels without worrying of a confidentiality breach.

You must be willing to commit a serious amount of time and resources to make this work. It defeats the purpose of structuring formal arrangements like this only to have one annual get-together each year where very little information is exchanged.

Special projects

Another idea is special projects, which is somewhere between sponsored events and legal partnerships. Similar to a consulting arrangement, a special project has a beginning and an end and serves a specific objective. The idea is to put your data scientists and customers together as a team to accomplish a goal. The project sponsor could be you, your customer, or a third party. Confidentiality agreements are in place to promote an open exchange of ideas, but the relationship isn’t evergreen like a legal partnership. In this way, you can network and brand with a larger audience without the anxiety of trade secrets leaving your fortress.

Summary
I’ve given you three ideas for putting your data science staff and your customers together, and there are many more worth exploring. Take some time today to figure out which idea makes the most sense for your organization, and put a plan in place to make it happen.

Birds of a feather flock together; it’s your job to manage their migration path.
Originally posted at: http://www.techrepublic.com/article/three-ways-to-help-your-data-science-team-network-with-other-big-data-pros/

Source: Three ways to help your data science team network with other big data pros

There seems to be a consensus among customer feedback professionals that business growth depends on improving customer loyalty. It appears, however, that there is little agreement in how they define and measure customer loyalty. In this and subsequent blog posts, I examine the concept of customer loyalty, presenting different definitions of this construct. I attempt to summarize their similarities and differences and present a definition of customer loyalty that is based on theory and practical measurement considerations.

The Different Faces of Customer Loyalty

There are many different definitions of customer loyalty. I did a search on Google using “customer loyalty definition” and found the following:

• Esteban Kolsky proposes two models of loyalty:  emotional and intellectual. In this approach, Kolsky posits that emotional loyalty is about how the customer feels about doing business with you and your products, “loves” what you do and could not even think of doing business with anybody else. Intellectual loyalty, on the other hand, is more transactionally-based where customers must justify doing business with you rather than someone else.
• Don Peppers talks about customer loyalty from two perspectives: attitudinal and behavioral. From Peppers’ perspective, attitudinal loyalty is no more than customer preference; behavioral loyalty, however, is concerned about actual behaviors regardless of the customers’ attitude or preference behind that behavior.
• Bruce Temkin proposed that customer loyalty equates to willingness to consider, trust and forgive.
• Customer Loyalty Institute states that customer loyalty is “all about attracting the right customer, getting them to buy, buy often, buy in higher quantities and bring you even more customers.”
• Beyond Philosophy states that customer loyalty is “the result of consistently positive emotional experience, physical attribute-based satisfaction and perceived value of an experience, which includes the product or services.” From this definition, it is unclear to me if they view customer loyalty as some “thing” or rather a process.
• Jim Novo defines customer loyalty in behavioral terms. Specifically, he states that customer loyalty, “describes the tendency of a customer to choose one business or product over another for a particular need.”

These definitions illustrate the ambiguity of the term, “customer loyalty.” Some people take an emotional/attitudinal approach to defining customer loyalty while others emphasize the behavioral aspect of customer loyalty. Still others define customer loyalty in process terms.

Emotional Loyalty

Customers can experience positive feelings about your company/brand. Kolsky uses the word, “love,” to describe this feeling of emotional loyalty. I think that Kolksy’s two models of customer loyalty (emotional and intellectual) are not really different types of loyalty. They simply reflect two ends of the same continuum. The feeling of “love” for the brand is one end of this continuum and the feeling of “indifference” is on the other end of this continuum.

Temkin’s model of customer loyalty is clearly emotional; he measures customer loyalty using questions about willingness to consider, trust and forgive, each representing positive feelings when someone “loves” a company.

Behavioral Loyalty

Customers can engage in positive behaviors toward the company/brand. Peppers believes what is important to companies is customer behavior, what customers do. That is, what matters to business is whether or not customers exhibit positive behaviors toward the company. Also, Novo’s definition is behavioral in nature as he emphasizes the word, “choose.” While loyalty behaviors can take different forms, they each benefit the company and brand in different ways.

Customer Loyalty as an Attribute about the Customers

To me (due perhaps to my training as a psychologist), customer loyalty is best conceptualized as an attribute about the customer. Customer loyalty is a quality, characteristic or thing about the customer that can be measured. Customers can either possess high levels of loyalty or they can posses low levels of loyalty, whether it be an attitude or behavior. While the process of managing customer relationships is important in understanding how to increase customer loyalty (Customer Loyalty Institute, Beyond Philosophy), it is different from customer loyalty.

Definition of Customer Loyalty

Considering the different conceptualizations of customer loyalty, I offer a definition of customer loyalty that incorporates prior definitions of customer loyalty:

Customer loyalty is the degree to which customers experience positive feelings for and exhibit positive behaviors toward a company/brand.

This definition reflects an attribute or characteristic about the customer that supports both attitudinal and behavioral components of loyalty. This definition of customer loyalty is left generally vague to reflect the different positive emotions (e.g., love, willingness to forgive, trust) and behaviors (e.g., buy, buy more often, stay) that customers can experience.

In an upcoming post, I will present research on the measurement of customer loyalty that will help clarify this definition. This research helps shed light on the meaning of customer loyalty and how businesses can benefit by taking a more rigorous approach to measuring customer loyalty.

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The cloud is not “just someone else’s computer”, even though that meme has been spreading so fast on the internet. The cloud consists of extremely scalable data centers with highly optimized and automated processes. This makes a huge difference if you are talking about the level of application software.

So what is “cloud-native” really?

“Cloud-native” is more than just a marketing slogan. And a “cloud-native application” is not simply a conventionally developed application which is running on “someone else’s computer”. It is designed especially for the cloud, for scalable data centers with automated processes.

Software that is really born in the cloud (i.e. cloud-native) automatically leads to a change in thinking and a paradigm shift on many levels. From the outset, cloud-native developed applications are designed with scalability in mind and are optimized with regard to maintainability and agility.

They are based on the “continuous delivery” approach and thus lead to continuously improving applications. The time from development to deployment is reduced considerably and often only takes a few hours or even minutes. This can only be achieved with test-driven developments and highly automated processes.

Rather than some sort of monolithic structure, applications are usually designed as a loosely connected system of comparatively simple components such as microservices. Agile methods are practically always deployed, and the DevOps approach is more or less essential. This, in turn, means that the demands made on developers increase, specifically requiring them to have well-founded “operations” knowledge.

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Cloud-native = IT agility

With a “cloud-native” approach, organizations expect to have more agility and especially to have more flexibility and speed. Applications can be delivered faster and continuously at high levels of quality, they are also better aligned to real needs and their time to market is much faster as well. In these times of “software is eating the world”, where software is an essential factor of survival for almost all organizations, the significance of these advantages should not be underestimated.

In this context: the cloud certainly is not “just someone else’s computer”. And the “Talend Cloud” is more than just an installation from Talend that runs in the cloud. The Talend Cloud is cloud-native.

In order to achieve the highest levels of agility, in the end, it is just not possible to avoid changing over to the cloud. Potentially there could be a complete change in thinking in the direction of “serverless”, with the prospect of optimizing cost efficiency as well as agility.  As in all things enterprise technology, time will tell. But to be sure, cloud-native is an enabler on the rise.

About the author Dr. Gero Presser

Dr. Gero Presser is a co-founder and managing partner of Quinscape GmbH in Dortmund. Quinscape has positioned itself on the German market as a leading system integrator for the Talend, Jaspersoft/Spotfire, Kony and Intrexx platforms and, with their 100 members of staff, they take care of renowned customers including SMEs, large corporations and the public sector. 

Gero Presser did his doctorate in decision-making theory in the field of artificial intelligence and at Quinscape he is responsible for setting up the business field of Business Intelligence with a focus on analytics and integration.

The post Why Cloud-native is more than software just running on someone else’s computer appeared first on Talend Real-Time Open Source Data Integration Software.

Source: Why Cloud-native is more than software just running on someone else’s computer

Sabre Airline Solutions (Sabre) supplies applications to airlines that enable them to manage a variety of planning tasks and strategic operations, including crew schedules, flight paths, and weight and balance for aircraft.

The challenge for Sabre was that many airlines had not implemented the proper upgrades. That meant some large customers were as many as five versions behind. And moving them to the new suite would have been a time-consuming, expensive, version-by-version process. Customers, understandably, were nervous about tackling that process.

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Reducing upgrade time and costs for customers

Talend was given a deadline of two weeks to complete the migration for an important customer and surpassed the company’s expectations. This enabled Sabre to complete the needed migrations in just a matter of hours.

“To help our airline customers succeed in a very competitive industry, we need a way to migrate data more efficiently. Talend is the solution for data mobility ” – Dave Gebhart, Software Development Principal

Replicating a process to save time and money

As a result of the shorter, more-cost efficient process, Sabre can now easily replicate it. The new process reduced the cost of doing migrations by 80 percent, and it enabled Sabre to do as many as 25 upgrades in a year, whereas previously they could manage only about 10. That means Sabre more than doubled the upgrade slots they are able to serve because of all the benefits of using Talend.

What’s next? Sabre is currently working on a project that uses Talend for a more complex task. “We’re integrating three legacy applications, and we’re using Talend to extract data and transform it into objects that can be converted into XML service requests, which are then processed so the data can be loaded via web services into a new system,” he says. “Talend is the engine we’re using to drive this multi-step process.” 

[youtube https://www.youtube.com/watch?v=8jF3sgRpFhk]

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The post Sabre Airline Solutions Gives Airline Data a Critical Upgrade appeared first on Talend Real-Time Open Source Data Integration Software.

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