Big Data Will Keep the Shale Boom Rolling

The number of active oil rigs in the United States continued to fall in May, as low prices pushed oil companies to temporarily shut down some of their production facilities. Since the end of May 2014, the U.S. rig count has fallen from 1,536 to 646, according to the energy analysis firm Platts—a 58 percent drop.

Low prices and plummeting rig counts have prompted a gusher of headlines claiming that the shale oil revolution, which by early this year boosted American oil production to nearly 10 million barrels a day, is grinding to a halt. The doomsayers, however, are missing a key parallel trend: lower prices are prompting unprecedented innovation in the oil fields, increasing production per well and slashing costs.
That’s the main reason that even as rig counts have fallen, total production has held steady or continued to rise. In the Eagle Ford, a major shale formation in South Texas, production in April was 22 percent higher than the same month in 2014, according to Platts.

In fact, some observers expect a second wave of technological innovation in shale oil production that will equal or surpass the first one, which was based on horizontal drilling and hydraulic fracturing, or fracking. Fueled by rapid advances in data analytics—aka big data—this new wave promises to usher in a second American oil renaissance: “Shale 2.0,” according to a May 2015 report by Mark Mills, a senior fellow at the Manhattan Institute, a free-market think tank.

Much of the new technological innovation in shale comes from a simple fact: practice makes perfect. Tapping hydrocarbons in “tight,” geologically complex formations means drilling lots and lots of wells—many more than in conventional oil fields. Drilling thousands of wells since the shale revolution began in 2006 has enabled producers—many of them relatively small and nimble—to apply lessons learned at a much higher rate than their counterparts in the conventional oil industry.

This “high iteration learning,” as Judson Jacobs, senior director for upstream analysis at energy research firm IHS, describes it, includes a shift to “walking rigs,” which can move from one location to another on a drilling pad, allowing for the simultaneous exploitation of multiple holes. Advances in drill bits, the blend of water, sand, and chemicals used to frack shale formations, and remote, real-time control of drilling and production equipment are all contributing to efficiency gains.

Photo courtesy of Ken Hodge via Flickr
Photo courtesy of Ken Hodge via Flickr

At the same time, producers have learned when to pause: more than half the cost of shale oil wells comes in the fracking phase, when it’s time to pump pressurized fluids underground to crack open the rock. This is known as well completion, and hundreds of wells in the U.S. are now completion-ready, awaiting a rise in oil prices that will make them economical to pump. Several oil company executives in recent weeks have said that once oil prices rebound to around $65 a barrel (the price was at $64.92 per barrel as of June 1), another wave of production will be unleashed.

This could help the U.S. to replace Saudi Arabia as the top swing producer—able to quickly ramp up (or down) production in response to price shifts. The real revolution on the horizon, however, is not in drilling equipment or practices: it’s in big data.

Thanks to new sensing capabilities, the volume of data produced by a modern unconventional drilling operation is immense—up to one megabyte per foot drilled, according to Mills’s “Shale 2.0” report, or between one and 15 terabytes per well, depending on the length of the underground pipes. That flood of data can be used to optimize drill bit location, enhance subterranean mapping, improve overall production and transportation efficiencies—and predict where the next promising formation lies. Many oil companies are now investing as much in information technology and data analytics as in old-school exploration and production.

At the same time, a raft of petroleum data startups, such as Ayata, FracKnowledge, and Blade Energy Partners, is offering 21st century analytics to oil companies, which have not been known for rapid, data-based innovation. Early efforts to bring modern data analytics into the oil and gas industry faltered, Jacobs says: “The oil companies tried to hire a bunch of data scientists, and teach them to be petroleum engineers. That didn’t go so well. The approach now is to take petroleum engineers and pair them up with technical experts who can supply the analytic horsepower, and try to marry these two groups.”

U.K.-based BP, for example, established a “decision analytics network” in 2012 that now employs more than 200 people “to examine ways to advance use of data and to help BP’s businesses harness these opportunities.”

If these initiatives succeed, big data could not only prolong the shale boom in the U.S., but also launch similar revolutions overseas. Applying the lessons from North America to low-producing oil fields elsewhere could unlock 141 billion barrels of oil in countries like China, Iran, Russia, and Mexico, IHS forecast in a report released last month.

To read the original article on the MIT Technology Review, click here.


Can You Use a Single Item to Predict SUS Scores?


sus-predictThe System Usability Scale (SUS) has been around for decades and is used by hundreds of organizations globally.

The 10-item SUS questionnaire is a measure of a user’s perception of the usability of a “system,” which can be anything from software, hardware, websites, apps, or voice interfaces.

The items are:

  1. I think that I would like to use this system frequently.
  2. I found the system unnecessarily complex.
  3. I thought the system was easy to use.
  4. I think that I would need the support of a technical person to be able to use this system.
  5. I found the various functions in this system were well integrated.
  6. I thought there was too much inconsistency in this system.
  7. I would imagine that most people would learn to use this system very quickly.
  8. I found the system very cumbersome/awkward to use.
  9. I felt very confident using the system.
  10. I needed to learn a lot of things before I could get going with this system.

Why 10 items?

The SUS was built by John Brooke using an approach inspired by what’s come to be known as Classical Test Theory in psychometrics. He started with 50 items that he thought would address the construct of what people think of when they think of the ease of use of systems.

The final set of 10 were the ones that best differentiated between a software application that was known to be easy and one that was difficult. The original study used a relatively small sample size (n =20) and reliability figures weren’t reported. Later research with more data has shown these final 10 items correlate with each other (some very highly, r >.7), with the total SUS score (all r >.62), and have high internal consistency reliability (Cronbach Alpha > .85).

The items are somewhat redundant (hence the high intercorrelations) but some redundancy is by design. To achieve high reliability in Classical Test Theory, you essentially ask the same question about a single construct in many different ways.

At the time the SUS was put forth, it was believed that these 10 items measured only one construct (perceived use). With only 20 participants, it was difficult to test whether the SUS was unidimensional. With more items, the greater the chance of measuring more than one construct.

About 10 years ago, Jim Lewis and I had enough data collected to test the dimensionality of the SUS using a factor analysis. We originally found two dimensions, which we labeled “usability” and “learnability” based on the items that loaded on each factor. This finding was even replicated by other researchers. However, with more data sets we found that the two dimensions were actually an artifact of the positively worded and negatively worded items  [pdf] and not two meaningful dimensions about perceived ease.

In other words, the SUS is after all unidimensional—measuring only the construct of perceived usability. And if it’s measuring only a single construct, do we still need all 10 items?

Too Redundant?

While the SUS is relatively short, are all 10 items necessary? How much is lost by reducing the number of items? How accurate would the SUS be if we used only two items (the bare minimum to assess reliability or load on a factor) or even a single item, which we’ve found sufficient for other simple constructs, including individual SUS items.

To find out, we analyzed SUS scores from 16,010 respondents from 148 products and websites with between 5 and 1,969 responses per product experience—one of the largest sets of SUS scores ever analyzed.

SUS scores for an interface are created by averaging together all the individual scores. Predicting individual scores is more difficult because of the higher variability at the participant level and a topic for a future article. Because our goal is to estimate the SUS score by product, we computed SUS scores for the 148 products, then created average scores for each of the 10 items. We tested all combinations of items to predict the SUS scores.

We found three items to have the highest correlations to the total SUS score: item 2 (r=.96), item 3 (r =.95), and item 8 (r=.95) weren’t statistically different from each other. Each of these items alone can explain at least a whopping 90% of the variation in SUS scores.

The best two-item combination are items 3 and 8 and then items 7 and 8, which together account for 96% of the variability in SUS scores by product. It’s interesting that item 8 is one of the best predictors because this is also the item that’s caused some trouble for respondents—cumbersome is usually changed to awkward because some participants have trouble understanding what cumbersome means. We suspect this item best predicts SUS because it’s related to the negatively worded tone of half the items in the SUS score. It could also be that these negative items add error to the measurement—people respond differently to negative items rather than the experience—and is a good topic for our future research.

Interestingly, there is a significant diminishing return after adding two more items to predict SUS scores. The best combination of three items only adds 1.8% more explanatory power than two items. And adding four items only adds 0.8% more explanatory power. Table 1 shows how much explanatory power each best combination of variables adds to predicting the overall SUS score compared to one fewer item.

# of Items R-Sq(adj) Improved Prediction
1 91.7
2 96.1 4.4
3 97.9 1.8
4 98.7 0.8
5 99.2 0.5
6 99.4 0.2
7 99.6 0.2
8 99.8 0.2
9 99.9 0.1
10 100 0.1

Table 1: Improved prediction in going from 1 to 2, 3… 10 items.

Using Item 3: Easy To Use

With the similar high correlations between the best single item, we selected item 3 (I thought the system was easy to use), which we felt has the best content validity and is used in other questionnaires (SUPR-Q,  UMUX Lite). You can see the relationship between the mean of item 3 and the total SUS score in the plot in Figure 1.

Figure 1 Item 3


Figure 1: Item 3 (“I thought the system was easy to use”) accounts for 90% of the variation in total SUS scores at the product level.

To predict the SUS score from item 3, we simply use the regression equation:

SUS (estimated) = -2.279 + 19.2048 (Mean of Item 3)

The following calculator will do the math for you. Enter the mean of Item 3 to get a predicted SUS score.


Item 3 Mean:


To predict a SUS score using items 3 and 8, use the regression equation:

SUS (estimated) = -6.33 + 9.85 (SUS03) + 10.2(reverse coded item SUS08*)

*Note: The negatively worded items have been reverse-coded in our regression equation and scaled from 1 to 5 so higher values all indicate better scores.

For example, if a product receives a mean score of 3.43 on item 3, it has a predicted SUS score of about 64:

SUS (estimated) = -2.279 + 19.2048 (3.43)

SUS (estimated) = 63.59

If a product receives a mean score of 3.81 on item 3, and 3.48 on item 8,* then the predicted SUS score is about 67:

SUS (estimated) = -6.33 + 9.85 (3.81) + 10.2(3.48)

SUS (estimated) = 66.68

How much is lost?

By reducing the number of items, we, of course, lose information. We introduce errors: the fewer the items, the less accurate the prediction of the 10-item SUS score. Some estimates will overestimate the actual SUS while others will underestimate it. We can assess how much is lost when using fewer items by using the regression equations, generating a predicted SUS score, and then comparing the predicted scores to the actual scores from this data set.

For example, one of the products in the dataset had a SUS score of 76.8. (See Table 2.) Using the mean from item 3 (4.20), the predicted SUS score is 78.4 (from the regression equation). This represents an error of 1.6 points or about 2%. Using the means from items 3 and 8 in the regression equation, the predicted SUS score is 77, an error of only 0.3%.

Item Mean
1 4.03
2 4.07
3 4.20
4 4.03
5 4.03
6 4.10
7 3.98
8 4.12
9 4.23
10 3.93
Actual SUS Score 76.8
Item 3 Predicted SUS 78.4
Error(%) 1.6(2%)
Items 3&8 Predicted SUS 77.0
Raw Error 0.3(0.3%)

Table 2: Means for each of the 10 SUS items for a software product in the database.

Across all 148 products, the median absolute error is 3.5% when using item 3 alone and 2.1% when using both items 3 and 8. However, in some cases, the score for item 3 was off the mark (predictions are rarely perfect). Eight products had a predicted value that deviated by at least 6 points (the highest deviation was 13.8 points). It’s unclear whether some of these deviations can be explained by improper coding of the SUS or other scoring aberrations that may be examined in future research.

Figure 2 shows the predicted SUS score from using just item 3. For example, if the mean score is 4 on item 3 from a group of participants, the estimated SUS score is 75. Anything below 3 isn’t good (below a predicted SUS score of 55) and anything above 4.5 is very good (above a predicted SUS of 84).

Figure 2: Predicted SUS score from the mean of item 3.

Grade Change

SUS scores can be interpreted by associating letter grades based on their percentile rank. The best performing products above the 90th percentile get an A (raw scores of 80.8), average products around the 50th percentile get a C (raw SUS scores around 68), and anything below the 14th percentile gets a failing grade of F (raw scores of 52).

Another way to interpret the accuracy of the prediction is to see how well the predicted SUS scores predict the associated SUS grades. A bit more than half (57%) product grades (84) differed between the predicted and actual SUS score. While this seems like a lot of deviation, of these 84, 57 (68%) only changed by half a letter grade. Figure 3 shows the differences between predicted grades and actual grades from the full 10 items.

Figure 3 Grade differences

Figure 3: Grade differences between item 3 only and the full 10 items.

For example, 7 products were predicted to be a B- using only item 3 but ended up being Bs for the full 10 items. Or put another way, 82% of all grades stayed the same or differed by half a letter grade (121 out of the 148 products). An example of predicted scores that changed more than half a letter grade were 6 products predicted to be a B, but ended up being an A- or A using the full 10 items (see Figure 3 row that starts with “B”).

We can continue with the grading metaphor by assigning letter grades numbers, as is done to compute a Grade Point Average or GPA (having high school flashbacks now?). The College Board method assigns numbers to grades (A = 4, B = 3, C = 2, D = 1, F=0, and the “+” and “-” get a 0.3 adjustment from the base letter value in the indicated direction.

Letter Grade 4.0 Scale
A+ 4.0
A 4.0
A- 3.7
B+ 3.3
B 3.0
B- 2.7
C+ 2.3
C 2.0
C- 1.7
D+ 1.3
D 1.0
F 0.0

Table 3: Number assigned to letters using the College Board designations.

The average GPA of the 148 products is 2.46 (about a C+) and the average GPA using the predicted grade is also 2.46! In fact, the difference is only 0.0014 points (not statistically different; p=.97). This is in large part because a lot of the half grade differences washed out and 7 products had predicted A scores but were actually A+ (both A and A+ have the same GPA number of 4).

Summary and Conclusions

An analysis of over 16,000 individual SUS responses across 148 products found that you can use a single item to predict a SUS score with high accuracy. Doing this has a cost though, as some information is lost (as is the case with most predictions).

Item 3 predicts SUS scores with 90% accuracy. If researchers ask only the single “easy to use” item 3, they can still predict SUS scores with 90% accuracy and expect the full SUS score to differ on average by 3.5% from the prediction.

Two items predict with 96% accuracy. Using only two items (item 3 and 8) can predict the SUS with 96% accuracy and expect the full SUS score to differ on average by 2.1% from the prediction. Future research will examine whether there are better ways to predict the SUS using different items (e.g. the UMUX-Lite).

After Three items Not Much Is Gained: There is a major diminishing return in adding additional items to improve the SUS score prediction. After three items, each additional item adds less than 1% to the accuracy of the SUS score.

Grades differ only slightly. By using only a single item to generate a grade, 82% of all grades were the same or differed by less than half a grade (e.g. predicted A-, actual B+) compared to the full 10-item grades. Using the GPA method, the average GPA was essentially identical, suggesting differences are minor.

Thanks to Jim Lewis and Lawton Pybus for contributing to this analysis.


Source by analyticsweek

Sisense BloX – Go Beyond Dashboards

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

Accountants Increasingly Use Data Analysis to Catch Fraud

When a team of forensic accountants began sifting through refunds issued by a national call center, something didn’t add up: There were too many fours in the data. And it was up to the accountants to figure out why.

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

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

Source: Accountants Increasingly Use Data Analysis to Catch Fraud

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

Business people working in a conference room.
Business people working in a conference room.

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.

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:

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

What is Customer Loyalty? Part 1

True Test of Loyalty
Article on RAPID Loyalty Approach – click to download article

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.


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

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.


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