Monthly Archives: July 2017

Anatomy of a Data Quality Failure

What does a data quality failure look like in the real world? Americans across the country found out recently when they received marketing offers for a product that was completely misaligned with their ages and genders.

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What Happened in this Data Quality Failure

We won’t name the company that made this mistake because a mistake like this could happen to any company. The problem arose from a data quality shortcoming. Any business with lots of data can easily face this type of challenge.

Suffice it to say, however, that the company is a major retailer of personal care products. In a nutshell, this is what happened:

  • The company runs a program where it delivers free products to young men on their eighteenth birthdays.
  • Recently, the company delivered several of these free products to people who were not at all its target audience. Some were middle-aged women. Others were middle-aged men. At least one was a woman who had recently turned eighteen, but had no use for a product designed for men.

While this data quality failure didn’t have huge financial consequences, it was an embarrassment for the company. It also amounted to wasted marketing and product resources.

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Data Quality’s Role

While the company has not explained exactly why it sent free materials to the totally wrong recipients, it’s a safe bet that data quality was at the root of the problem.

What likely happened is this: The company presumably uses a database of names, addresses, gender information and birth dates to find young men who are about to turn eighteen and should receive a free product. There were mistakes in that data set that led the company to misidentify a number of recipients.

The problem could have been a mismatch between names and addresses. Perhaps some birth dates or gender details were entered incorrectly.

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Or maybe there was an address overlap problem caused by eighteen-year-old men having similar street addresses as other people. For instance, a fifty-year-old woman named Jane Doe who lives at 123 Main Street in Troy, Michigan might inadvertently have received the product that was supposed to go to a John Doe who lives at 123 Main Street in Troy, New York.

When you’re trying to market to every eighteen-year-old man in America mistakes like this are easy to make. Something like 5,500 men turn eighteen each day in the United States, and keeping track of every one of them is hard.

While the exact nature of this data quality mistake is hard for people outside of the company to track down, the solution for avoiding similar problems in the future is clear. Data quality tools that can recognize data mismatches, erroneous entries and other problems within data sets can identify the types of mistakes that likely led to this company’s embarrassing marketing campaign.

To learn more about how you can prevent this kind of data quality failure and others, be sure to check out tomorrow’s follow up post, “Understanding Data Quality: How Data Quality Errors Arise.”

The data workflow is shifting from IT to the business, be sure you know The New Rules for Your Data Landscape!

 Anatomy of a Data Quality Failure

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Research Shows Positive Outlook for Wholesale Distribution

Posted by Ranga Bodla, Head of Industry Marketing

MDM Research Shows Positive Outlook for Wholesale DistributionThe 2017 Economic Benchmarks for Wholesale Distribution paint a positive picture for distributors. The annual report shows the economy and the wholesale distribution industry appear to be turning a corner this year due to positive outlooks on employment, wages and corporate investments that are expected to boost the US economic growth overall.

All 19 major wholesale distribution sectors are expected to post actual (not adjusted for inflation) revenue growth in both 2017 and 2018. Experts believe this could be the longest economic expansion in US history.

While the economy is showing signs of growth for sellers, we can’t ignore the dramatic changes in the historically conservative wholesale distribution industry.

The industry has become more competitive than ever and consumer expectations are driving major shifts in the economic landscape. Distributors are starting to understand that what led to success in the past is no longer enough to stay competitive in today’s rapidly changing environment. To remain competitive and ensure future success, distributors are looking at their entire portfolio of technology, reevaluating their ERP platforms, but also looking towards CRM and commerce to drive further business efficiency, flexibility and greater customer success.

You can read more about the 2017 wholesale distribution outlook in this white paper from Modern Distribution Management (MDM), created in partnership with Oracle NetSuite. If you are interested in additional detail, you can also tune into the one-hour webinar featuring Brian Lewandowski, associate director at the University of Colorado Boulder’s Leeds School of Business, and MDM Publisher Tom Gale where they discuss the economic drivers of last year and the industry’s outlook through 2018.

Learn more about how Oracle NetSuite is evolving the wholesale distribution industry with cloud-based software to transform, engage and modernize your business.

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The Future Is Now: Machine Learning, IoT, VR, And Microservices Are Here To Stay

When the Netflix series House of Cards premiered in 2013, it quickly became the most downloaded content in the company’s history – a statistic that came as no surprise to Netflix executives. They had previously examined a vast pool of Netflix data on subscribers’ viewing habits and determined that the show was likely to become a hit even before they purchased it.

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The wisdom behind Netflix’s sure-fire choice came from machine learning, which, loosely defined, is the ability of computers to learn on their own (without being programmed) by using algorithms that churn through large quantities of data.

Machine learning’s talents aren’t limited to picking the next TV blockbuster, either. Consider some of the more down-to-earth uses that we already take for granted today. Have you noticed how spam e-mails have almost disappeared from your inbox? That’s machine learning. Or how you can casually converse with anthropomorphic voices coming from your smartphone? Also machine learning.

But these examples pale when compared to machine learning’s potential for remaking business. Increased data-processing power, the availability of Big Data, the Internet of Things, and improvements in algorithms are converging to power a renaissance in business intelligence.

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The untapped potential of machine learning

Here are some ways that machine learning could transform the core elements of the business ecosystem– and society:

Intelligent business processes. Many of today’s business processes are governed by rigid, software based rules. This rules-based approach is limited in its ability to tackle complex processes. Further, these processes often require employees to spend time on boring, highly repetitive work, such as checking invoices and travel expenses for accuracy or going through hundreds or thousands of résumés to fill a position. If we change the rules and let self-learning algorithms loose on the data, machine learning could reveal valuable new patterns and solutions that we never knew existed. Meanwhile, employees could be reassigned to more engaging and strategic tasks.

Intelligent infrastructure. Our economy depends on infrastructure, including energy, logistics, and IT, as well as on services that support society, such as education and healthcare. But we seem to have reached an efficiency plateau in these areas. Machine learning has the potential to discover new signals in the data that could allow for continuous improvement of complex and fast-changing systems. That gives humans more time to apply their creativity (something that machines may never learn to duplicate) to new discoveries and innovation.

Digital assistants and bots. Recent advances in machine learning technology suggest a future in which robots, machines, and devices running on self-learning algorithms will operate much more independently than they do now. They may come to their own conclusions within certain parameters, adapt their behavior to different situations, and interact with humans much more closely. Our devices – already able to react to our voices – will become more interactive, continuously learning assistants to help us with our daily business routines, such as scheduling meetings, translating documents, or analyzing text and data.

Plan for change

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Although machine learning has already matured to the point where it should be a vital part of organizations’ strategic planning, several factors could limit its progress if leaders don’t plan carefully. These limitations include the quality of data, the abilities of human programmers, and cultural resistance to new ways of working with machines. However, the question is when, not if, today’s data analysis methods become quaint relics of earlier times. This is why organizations must begin experimenting with machine learning now and take the necessary steps to prepare for its widespread use over the coming years.

What is driving this inexorable march toward a world that was largely constrained to cheesy sci-fi novels just a few decades ago? Advances in artificial intelligence, of which machine learning is a subset, have a lot to do with it. AI is based on the idea that even if machines can’t (yet) duplicate the actual structures and thought patterns of the human brain itself, they can at least offer a rough approximation of important functions, such as learning, reasoning, and problem solving.

AI has been around since the 1950s, but it didn’t take off until the late 1990s, when Moore’s Law’s true exponential effects on computing power were realized, and researchers reined in their impulses to build a mechanized brain, focusing instead on using algorithms and machine learning to solve specific problems. Highly publicized machine-learning triumphs by IBM, such as Watson’s drubbing of human contestants on Jeopardy, captured the imagination of the public and business leaders.

Machine learning comes in several flavors, sometimes referred to as supervised learning  (the algorithm is trained using examples where the input data and the correct answers are known), unsupervised learning (the algorithm must discover patterns in the data on its own), and reinforced learning  (the algorithm is rewarded or penalized for the actions it takes based on trial and error). In each case, the machine can learn from data – both structured (such as data in fields in a spreadsheet or database) and, increasingly, unstructured (such as e-mails or social media posts) – without explicitly being programmed to do so, absorbing new behaviors and functions over time.

Machines’ ability to learn puts them on an evolutionary path not unlike our own. They are gaining the ability to speak, listen, see, read, understand, and interact with ever-increasing sophistication. In just the last four years, the error rate in machine-learning–driven image recognition, for example, has fallen dramatically to near zero– practically to human performance levels.

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Machine learning as collaborator

As machine-learning–based skills approach those of human beings, it’s tempting to view their evolution as a zero-sum competition with humans that we are destined to lose.

However, there is another view that says that automation will lead more to collaboration rather than outright replacement. Consulting firm McKinsey & Company argues that while 49% of jobs will be subject to some degree of automation, just 5% will be fully replaced anytime soon. In most cases, says McKinsey, automation will take over specific tasks rather than entire jobs.

McKinsey’s argument is compelling, at least when it comes to knowledge work, because it mirrors the way computing has evolved within the organization. Early mainframes were programmed to perform specific tasks, such as tallying up an organization’s daily receipts. When PCs were first introduced in the 1980s, they were dismissed by businesses as expensive typewriters until packaged spreadsheet software came along, allowing organizations to automate some of their manual accounting tasks at the individual employee level. Knowledge work would never be the same.

Today, most organizations have enterprise software that uses rules-based processing to automate many tasks in departments such as finance and human resources and in warehouses. Yet while the task-based automation of enterprise software has brought tremendous productivity improvements, the software could not learn and improve with experience as humans can.

Until now.

Thanks to advances in computer processing power, memory, storage, and data tools, machine learning can be integrated into the enterprise-software systems that form the heart of most organizational IT infrastructures. This means that the software, using the mastery that it develops in individual tasks, will be able to contribute increasing levels of performance and productivity to the organization over time, rather than merely offering a one-time boost, as most software packages do today.

The strength of machine-learning integration

The improvements the software brings to organizations will not be limited to individual tasks. One of the biggest strengths of enterprise software is its integration– the ability of individual applications to share information and be part of process workflows both within individual departments and across the organization. Integration allows organizations to experiment with new combinations of ever-more intelligent and versatile machine-learning applications and, where possible, let the machines learn how to improve the ways they work with each other and with their human colleagues. Together, these applications form the intelligent enterprise.

Just as individual applications will contribute more productivity to the organization as their embedded machine-learning abilities become more sophisticated, so too will the combinations of those applications evolve to bring more intelligence and flexibility to departmental and organizational processes over time.

Here are some concrete examples of how machine learning is creating value in organizations today:

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Personalized customer service. Organizations can use machine-learning to improve customer service while lowering costs by combining natural-language processing, historical customer service data, and algorithms that continuously learn from interactions. Customers can ask the system questions and get accurate answers, lowering response times and allowing human customer service representatives to focus on higher-priority or more-complex interactions.

Financial-exception handling.
A machine-learning system can be trained to recognize payments that arrive without an order number and match them to invoices based on knowledge of customers’ order and payment histories. This lets organizations reduce the amount of work outsourced to service centers and frees up finance staff to focus on more strategic tasks.

Improved hiring.
A machine-learning system can learn to pluck the most suitable job candidates from the thousands of résumés that organizations receive. It can also spot biased language in job descriptions that might discourage qualified people from applying and rescue other top candidates who fall through the cracks because they don’t fit with traditional hiring models.


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Algorithmic security.
By building models based on historical transactions, social network information, and other external sources of data, machine-learning algorithms can use pattern recognition to automatically spot anomalies. This identification helps detect and prevent fraudulent transactions in real time, even for previously unknown types of fraud. And this type of algorithmic security is applicable to a wide range of other situations, including computer hacking and cybersecurity.

Image-based procurement. Instead of having to log into a procurement system and search manually, employees can simply use a smartphone app to snap a picture of the item they’re looking for– a particular brand and type of laptop, for example– and the system will use machine learning to hunt through its database to find a match or the nearest equivalent. It will then send a message to the employee, who can launch the ordering process with a single click.


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Brand-exposure measurement. Brands spend billions on sponsorships, often without knowing exactly what they are getting for their money. A machine-learning application can sort through thousands of hours of sports video footage or track the action in real time, for example, to tell marketers how often their logo appears on screen, how large it is, how long it appears, and where it is located on the screen. Brands can then quantify their return on investment in the moment.

Contextual concierge.
Let’s say that your flight is suddenly delayed. A travel app on your smartphone can use context-sensitive machine learning to determine how this delay will affect your other travel plans and prompt you with rescheduling options.

Visual shelf management. Employees can take photos of shelves in a store aisle, kicking off a machine-learning process that automatically senses missing or improperly displayed items and prompts the store manager and the warehouse to fill the shelves correctly.


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Manufacturing quality control. By examining video of an assembly line, a machine-learning system can spot defects that a human might miss and automatically reroute the damaged parts or assemblies before products leave the factory.

Drone- and satellite-based inspection. A machine-learning system can sift through thousands of aerial images
of a pipeline, for example, and automatically spot areas that need maintenance or replacement.


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Machine learning needs a platform

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To be sure, organizations will gain tremendous benefits from individual machine-learning applications, even if they are never integrated into a larger whole. However, the benefits become much greater when these applications are on an integrated platform.

The business press has been discussing the power of platforms a lot lately, with iTunes being a well-known example. By creating a set of common software development tools that are available free to anyone who wants them, Apple has enabled developers to create thousands of applications for the iTunes App Store. Developers win because they can easily reach vast numbers of Apple device owners through iTunes. Apple wins because it takes a cut of the revenues for each app it makes available in the App Store.

Platforms are equally important to enterprises, not necessarily because of the profit motive (though some organizations are launching their own public, for-profit platforms similar to iTunes), but because having a platform gives them a base for quickly and cost-effectively combining different applications together, whether they are from different software vendors or are built in house.

No software vendor will ever be able to claim that it offers every machine-learning–enabled application that an organization needs out of the box. But vendors do offer platforms that organizations can use as bases for building out their entire machine-learning infrastructure.

The core of these machine-learning–enabled platforms is application programming interfaces (APIs). APIs are a kind of software version of those universal electric plug adapters that business travelers lug around with them so they can charge their electronic devices wherever they may be in the world. APIs allow software developers to plug into another software vendor’s applications without having to know anything about the complex code at the heart of those applications.

Another benefit of having a unified software platform is that organizations can use it to create a single point of access to data from across the organization. Data is the sole nutrient in a machine-learning diet. Algorithms need to binge on it constantly to lead a healthy and successful life. The larger and richer the data set, the more accurate the results. Having a single platform helps break down the data silos that exist across the organization so that organizations can make the most of machine-learning intelligence.

Organizations don’t need to go it alone

Inevitably, organizations will want to develop machine-learning–based applications that are not available in the marketplace. However, this does not mean that they need to create large internal machine-learning centers of expertise (although having some internal experts is recommended). Service providers can bring the expertise and perspective from within and across industries to help organizations focus on a small set of highly strategic processes that will benefit from machine learning.

The first step toward developing such applications is to determine where to apply machine learning. Organizations need to ensure that it erects barriers to entry against competitors or provides new ways of capturing and retaining customers by improving repurchase cycles or achieving new levels of win rates.

That means focusing investments on the machine-learning problems that will matter most to the industry’s basic competitive economics. Developing those engines will take considerable effort and time, so focusing the enterprise on those one or two projects that will really make a difference matters.

Here are five criteria to determine how to apply machine learning in a way that will create lasting differentiation.

1. The focus area as an appropriate candidate.

Not every facet of business will benefit from machine learning. The greatest potential is in automating high-volume tasks that have complex rules and large amounts of unstructured data.

Is your focus area big and complex enough for machine learning?

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2. A clearly formulated issue. Machine learning works best on specific, well-defined tasks where the desired output and relevant inputs can be clearly stated: given X, predict Y. While it isn’t a magic bullet that will automatically help organizations learn from all the data in their enterprise, machine learning can be valuable in discovering correlations in large amounts of data that humans could never have deduced for themselves.

3. A sufficient quantity of examples to learn from. Machine learning requires a lot of data to be accurate. There must be enough examples for the machine to learn meaningful approximations of the decisions you want to make. This is discovered through experimentation.

4. Meaningful differences within the dataset. If the data you are trying to learn from does not contain meaningful differences, then the algorithm will fail at its mission. Let’s say that you are trying to identify different types of buyers. If the training data does not contain significant differences in buyer characteristics, the algorithm cannot give you useful results.

5. A clear definition of success. Machine learning is always evaluated by measures of performance on a specific task. Typically, the computer will try to optimize whatever performance measure is defined. Clear evaluation criteria for the algorithm are therefore critical. You also need to be certain that the evaluation criteria are actually helpful for solving your business problem.

Key evaluation criteria for machine learning

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The human factor

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Ultimately, the technical barriers to machine-learning adoption will be easier to solve than the human ones. Predictions of steep job losses due to automation are stoking fear and uncertainty about how these self-learning systems will impact our roles and our livelihoods.

These fears must be addressed, and significant investment must be made in change management as business processes and models are reworked to integrate self-learning systems into collaborative human-machine environments.

Indeed, self-learning machines have the potential to become valuable collaborators with humans, augmenting their skills and helping employees become more productive in their current jobs while freeing them from boring, repetitive tasks.

Experts also predict that machine learning will create new roles inside the organization. There is already a shortage of data analysts and those capable of developing the intricate algorithms that machine learning requires. Other new roles will become evident as machine learning integrates deeper into the organization – and not all roles will require a degree in computer science or math. For example, creative thinking, strategy development, quality management, and people development and coaching will be crucial skills in an AI-driven organization, according to a survey by consulting firm Accenture2.

What’s next

When machine learning matures to the point that it can handle unstructured data (still an issue today), when organizations openly share data, and when algorithms begin to interact with each other more freely, machine learning will be embedded in all systems, devices, machines, and software. That will enable highly context-sensitive insight at both the organizational and individual levels. We can only guess at the level of automation that will result, but the impact on business – and society – will be significant.

Already, commercial machine-learning applications based on these technologies are available, and more are being created all the time. That is why business leaders should engage now with trusted providers that can help them evaluate data structures and availability, free up information from siloed systems, and identify the richest areas for machine-fueled insight and improvement. Together, they can address the cultural and change management challenges to take advantage of this new wave of business intelligence.

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Download the white paper Why Machine Learning and Why Now?


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Daniel Wellers is Digital Futures Lead, Thought Leadership Marketing, at SAP.

Jeff Woods is Vice President, Marketing Strategy and Head of Thought Leadership Marketing at SAP.

Dirk Jendroska is Head of Machine Learning Strategy and Operations, SAP Innovation Center Network, at SAP.

Christopher Koch is Director, Thought Leadership Marketing, at SAP.

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Introducing TIBCO Nimbus 10.1!

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We at TIBCO are pleased to announce the release of TIBCO Nimbus™ 10.1. Continuing from the success of the TIBCO Nimbus™ 10 release last year, 10.1 builds on the principles of an improved browser-based experience for all users of the application.

New capabilities and benefits

Manage compliance and internal frameworks in the browser

Statement sets are used to capture industry standards, compliance frameworks, or internal control and risk registers that can all be defined in a hierarchical list. Combined with data tables, the metadata and information behind each of these statements or clauses can be extended and tailored to provide a central repository of all compliance information. These can be attached and linked through to processes in order to demonstrate regulatory compliance.

— Create new statement sets in the browser
— Edit existing statement sets in the browser
— Define roles and responsibilities for statement sets in the browser
— Extend the statement sets using additional fields defined as data tables
— Create and manage individual statements, clauses, or references within the statement sets
— Define access rights and controls for statement sets

Navigation of process hierarchies

The diagram explorer is used for displaying and navigating the process hierarchy. This has been rolled out to the modern mode of process diagrams and the changes have been replicated over the full screen view of process diagrams.

Managed structured forms and tables in the browser

Where structured data capture forms or fields are required to be attached to process activities, data tables provide a way of defining a template of fields where records can be added to processes. This capability has been revamped for the browser interface to provide a way of managing these tables in a much better user experience.

— Create new data tables in the browser
— Edit existing data tables in the browser
— Define and select field types to be added to data tables in the browser
— Manage and control the access rights for each data table instance

TIBCO Nimbus 10.1 ensures that more users can utilize the browser to capture, analyze, and improve their processes using the redesigned user interface that was introduced in Nimbus 10. This allows process and operational compliance to be managed in a way in which business users can understand.

Learn more about TIBCO Nimbus, request a Nimbus product demo, and download the Release Notes for more details.

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Talent Management – Interview Talking MarTech Careers with Michelle Huff, CMO at Act-On Software

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Ginger Conlon: 

Hi, I’m Ginger Conlon, Contributing Editor to MarTech Advisor and welcome to our MarTech Advisor video series speaking with CMO’s, and joining me today, I’m excited to announce, is Michelle Huff who is CMO of Act-On software. Welcome Michelle.

Michelle Huff: 

Thanks! Nice talking with you again.

Ginger Conlon: 

Yes, thank you for joining us. So, we are going to talk about the marketing skills that you need to have today. Michelle and I talked about all the things going on in marketing automation and so, check out that video because these things are linked, they are so important to have the skills to take advantage of all these great things going on in marketing automation today. So, let’s start with a little bit about you, tell us briefly about your career journey to becoming CMO of Act-On.

Michelle Huff: 

Yes. So, my journey started off years ago in high tech and I first started off at a small, 30 person company doing web marketing in the late ‘90’s and then over time took on a product marketing role and it continued to be a fast growing business, through that I ended up taking on a product line and then we were acquired by Oracle and so I moved to Oracle. Eventually on the way ended up running, what they call, their outbound product management team for an entire product portfolio that had four different lines. It was interesting just to kind of see, you know, the old company I was at was about 600 people and Oracle at the time was about 80 something thousand and grew to a 120,000 when I was there in five years.

But then I ended up hopping over to Salesforce and so I was at Salesforce for about four years running for one of their divisions first product marketing, then all of their marketing departments and then ended up doing product management, was the General Manager of the division before I jumped over to Act-On. So, it’s been a fun ride, being able to see different companies and how they grow, how you scale, I mean just different sizes of companies and it’s fun to be back into a kind of a mid-sized business where we’re all in it together, really focused on making marketers successful.

Ginger Conlon:

Excellent, that’s a great journey.

Michelle Huff: 

Yeah.

Ginger Conlon:

So, with all the change today, there are skills and traits that have always been important, will continue to be important and then there are some new and evolving in importance traits and skills. So, what are you seeing as one or two of the most important traits and skills that a marketing leader specifically needs today?

Michelle Huff: 

Yeah, I mean, it’s interesting because it’s something I’ve always talked about with my team where, it’s funny, marketing, where, as you’re kind of growing in the ranks, some of the skills that require you to grow, to become a, specialist, senior specialist, manager, senior manager, are really different when you start taking on broader leadership roles and I think one really important one is around aligning and communication because at the end of the day, we’ve talked about before in our past interview about how marketers, leaders of marketing focus on the whole customer lifecycle, right, partnering not just with sales but also with the customer success team, normally we partner with whoever the product or service that you’re representing that team as well as the President and people officer. So, how you’re going about communicating your vision, what you’re trying to do, how you’re aligning expectations and goals across all of them determine kind of the success of your career.

So, I think those in particular is so important and then I also feel like, you know, marketing has become such a broad role, right, talk about all those different components and you think about digital, you think about, all the different things with marketing automation but then you also think about brand and customer marketing and it requires so many different skillsets and so, you’ll never be able to be an expert or have done, you know, the work in every single part of marketing and so I think the second part is really how do you, you know, build the skills that you can build a team of experts around you and be able to rely on them to help, you know, your whole entire department become more successful. Then I really just think, you know, being a little more tech savvy and understanding kind of that business acumen part, kind of that both, is just becoming more and more critical for marketers today.

Ginger Conlon: 

Great. So, you would think that as marketers we’re great communicators but maybe, you know, communicating with all these different constituents in your company isn’t what you’re used to do doing and trying to have this kind of broad approach to what you’re learning can be challenging, any advice for senior marketers to get better in these complicated areas?

Michelle Huff: 

It’s interesting because as you say, marketers are, it’s something we’re used to doing and I think it’s so true and I think sometimes people forget internally that you have to think about who your target audience is, right, and what’s important to them and then be able to make sure that you are communicating advantages, the benefits, you know, of what you’re trying to do and so, I think, that’s why the business acumen is really important because it’s – you have to understand that department, what’s driving them, what’s motivating them, what makes them successful, so that when you are trying to build an alliance, when you’re trying to get things done, you know, you’re really selling it, you’re always, selling your vision, getting people to adopt and be excited about what you’re trying to do and so it is using the same skillsets for marketers but I think sometimes people just forget that you need to kind of apply that internally no matter how big or small the company you’re in.

Ginger Conlon: 

Right, it’s great. It’s like you have to be customer centric internally and externally.

Michelle Huff: 

Exactly, right.

Ginger Conlon: 

Definitely. So, let’s talk about marketing teams now. So, there’s all this data, there’s all this technology, it’s changing the marketing landscape and that means that, as we talked about, you know, the skills, the marketers roles are changing, what they need to do is evolving and broadening. So, where are you seeing the greatest need in terms of marketing teams for new skills and what are some of the new skills that you’re seeing a need for today?

Michelle Huff: 

There’s a lot of them. We talk a lot more of marketing being more data driven, so having the skillsets around being more analytical, is definitely a key component. Content marketing is huge, so having creative writers as part of the team, there’s a lot of companies where it’s much more consultative selling, so, having kind of this expertise either in the market or the business or that, a better understanding of the broader solution is really important.

I just feel there are so many different skill-sets that in some sense, we talked about before, where it’s really that team because you don’t always want the person who’s going to be running your brand campaigns to always be the most data driven person, it’s great if they understand it but it’s nice to pair someone on that team who is, it can really help all the different parts of the business think about their world and how they want to be able to articulate the benefits and the value of what they’re trying to do. So, I think a lot of times it’s thinking about you and what skillsets you need to kind of balance with yourself but then also all the people on the team, so that collectively, you know, you’ve got kind of the best combination.

Ginger Conlon: 

Right. So, no matter what level you’re at, you need to be a great communicator, is kind of how it works now.

Michelle Huff: 

Well, yeah and thinking about complimenting, you know, your strengths and weaknesses with the rest of your team.

Ginger Conlon: 

Right. So, for you personally, when you’re hiring, what are some of the things that you look for in a marketer?

Michelle Huff: 

Well, it’s interesting because I’m obviously looking for expertise, right. So, when I’m filling a particular role you’re looking have you done it before, like, have you been there long enough to not just create something but see how it turns out, were you there long enough to see how well it did, any of the challenges, did you learn from any of it. I think communication is important, so much of it is, you know, because it’s how do you communicate internally but also marketers oftentimes are the ones who are speaking, they’re the ones who are presenting, so having great presentation and communication skills are really important.

Then I also just feel like, part of it is cultural fit because one person might work really well in one company but not necessarily kind of fit within that team, so, I think a part of it is testing that out for you and for your team because sometimes the brightest people, if they’re the wrong cultural fit, it just, it’s hard for them to be successful. Then for me, I like people to – a part of it is fun, like you travel a lot, would you go out for beers with someone, you spend so much time at work, is it someone that you think would just be a fun person to be around, I think it just makes work more enjoyable.

Ginger Conlon: 

Yes, absolutely. So, like you said, different skills for different roles. Is there any skill though that’s especially relevant today as part of a marketing team. I mean, you’ve mentioned a lot of things like the ability to collaborate, anything else?

Michelle Huff: 

Yeah, I would come back to the analysts. I think what’s been interesting is that, you know, I think sales a long time ago had their sales ops departments and people to help, you know, with kind of sales strategy and I think marketing also should really have a marketing ops person if they can, I know it depends on the size of the team but really having someone who can help you better track, measure, score, what you’re doing and then tie back because at the end of the day, you know, the impact that marketing is having, the way that you can justify more budget, the way that you can better help and understand are you being effective is how you measure things and so having someone who has not just the ability to be data driven and analytical but be thinking about how it applies to everyone’s role and can work with everyone jointly, just I think elevates the whole team and makes them that much better.

Ginger Conlon: 

Definitely. So, all right, there’s lots of change but some things don’t change, what’s the one trait or skill that’s always been important, that is just always going to be important as a marketer?

Michelle Huff: 

Yeah, I think it’s the communication thing, I don’t know, I keep coming back to it because I feel like as a marketer it’s how you do it internally but, sometimes when I see some of the best marketing campaigns out there, it’s not always the most creative, it’s really how are they communicating the value of what they offer and have connected with that persons need and it’s how they’re communicating, communicating visually, sometimes kind of emotionally or just kind of in the written form and that’s so important. At the end of the day, people are buying things because they’re seeing value and if you can communicate that it will take you a long way.

Ginger Conlon: 

Absolutely. Well, Michelle, thank you again for joining us. Michelle Huff, CMO of Act-On software great conversation, loved speaking with you, thanks everyone for joining.

Michelle Huff: 

Thanks for having me.

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Four Big Reasons Your Email Campaigns Are Being Ignored

When the Netflix series House of Cards premiered in 2013, it quickly became the most downloaded content in the company’s history – a statistic that came as no surprise to Netflix executives. They had previously examined a vast pool of Netflix data on subscribers’ viewing habits and determined that the show was likely to become a hit even before they purchased it.

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The wisdom behind Netflix’s sure-fire choice came from machine learning, which, loosely defined, is the ability of computers to learn on their own (without being programmed) by using algorithms that churn through large quantities of data.

Machine learning’s talents aren’t limited to picking the next TV blockbuster, either. Consider some of the more down-to-earth uses that we already take for granted today. Have you noticed how spam e-mails have almost disappeared from your inbox? That’s machine learning. Or how you can casually converse with anthropomorphic voices coming from your smartphone? Also machine learning.

But these examples pale when compared to machine learning’s potential for remaking business. Increased data-processing power, the availability of Big Data, the Internet of Things, and improvements in algorithms are converging to power a renaissance in business intelligence.

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The untapped potential of machine learning

Here are some ways that machine learning could transform the core elements of the business ecosystem– and society:

Intelligent business processes. Many of today’s business processes are governed by rigid, software based rules. This rules-based approach is limited in its ability to tackle complex processes. Further, these processes often require employees to spend time on boring, highly repetitive work, such as checking invoices and travel expenses for accuracy or going through hundreds or thousands of résumés to fill a position. If we change the rules and let self-learning algorithms loose on the data, machine learning could reveal valuable new patterns and solutions that we never knew existed. Meanwhile, employees could be reassigned to more engaging and strategic tasks.

Intelligent infrastructure. Our economy depends on infrastructure, including energy, logistics, and IT, as well as on services that support society, such as education and healthcare. But we seem to have reached an efficiency plateau in these areas. Machine learning has the potential to discover new signals in the data that could allow for continuous improvement of complex and fast-changing systems. That gives humans more time to apply their creativity (something that machines may never learn to duplicate) to new discoveries and innovation.

Digital assistants and bots. Recent advances in machine learning technology suggest a future in which robots, machines, and devices running on self-learning algorithms will operate much more independently than they do now. They may come to their own conclusions within certain parameters, adapt their behavior to different situations, and interact with humans much more closely. Our devices – already able to react to our voices – will become more interactive, continuously learning assistants to help us with our daily business routines, such as scheduling meetings, translating documents, or analyzing text and data.

Plan for change

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Although machine learning has already matured to the point where it should be a vital part of organizations’ strategic planning, several factors could limit its progress if leaders don’t plan carefully. These limitations include the quality of data, the abilities of human programmers, and cultural resistance to new ways of working with machines. However, the question is when, not if, today’s data analysis methods become quaint relics of earlier times. This is why organizations must begin experimenting with machine learning now and take the necessary steps to prepare for its widespread use over the coming years.

What is driving this inexorable march toward a world that was largely constrained to cheesy sci-fi novels just a few decades ago? Advances in artificial intelligence, of which machine learning is a subset, have a lot to do with it. AI is based on the idea that even if machines can’t (yet) duplicate the actual structures and thought patterns of the human brain itself, they can at least offer a rough approximation of important functions, such as learning, reasoning, and problem solving.

AI has been around since the 1950s, but it didn’t take off until the late 1990s, when Moore’s Law’s true exponential effects on computing power were realized, and researchers reined in their impulses to build a mechanized brain, focusing instead on using algorithms and machine learning to solve specific problems. Highly publicized machine-learning triumphs by IBM, such as Watson’s drubbing of human contestants on Jeopardy, captured the imagination of the public and business leaders.

Machine learning comes in several flavors, sometimes referred to as supervised learning  (the algorithm is trained using examples where the input data and the correct answers are known), unsupervised learning (the algorithm must discover patterns in the data on its own), and reinforced learning  (the algorithm is rewarded or penalized for the actions it takes based on trial and error). In each case, the machine can learn from data – both structured (such as data in fields in a spreadsheet or database) and, increasingly, unstructured (such as e-mails or social media posts) – without explicitly being programmed to do so, absorbing new behaviors and functions over time.

Machines’ ability to learn puts them on an evolutionary path not unlike our own. They are gaining the ability to speak, listen, see, read, understand, and interact with ever-increasing sophistication. In just the last four years, the error rate in machine-learning–driven image recognition, for example, has fallen dramatically to near zero– practically to human performance levels.

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Machine learning as collaborator

As machine-learning–based skills approach those of human beings, it’s tempting to view their evolution as a zero-sum competition with humans that we are destined to lose.

However, there is another view that says that automation will lead more to collaboration rather than outright replacement. Consulting firm McKinsey & Company argues that while 49% of jobs will be subject to some degree of automation, just 5% will be fully replaced anytime soon. In most cases, says McKinsey, automation will take over specific tasks rather than entire jobs.

McKinsey’s argument is compelling, at least when it comes to knowledge work, because it mirrors the way computing has evolved within the organization. Early mainframes were programmed to perform specific tasks, such as tallying up an organization’s daily receipts. When PCs were first introduced in the 1980s, they were dismissed by businesses as expensive typewriters until packaged spreadsheet software came along, allowing organizations to automate some of their manual accounting tasks at the individual employee level. Knowledge work would never be the same.

Today, most organizations have enterprise software that uses rules-based processing to automate many tasks in departments such as finance and human resources and in warehouses. Yet while the task-based automation of enterprise software has brought tremendous productivity improvements, the software could not learn and improve with experience as humans can.

Until now.

Thanks to advances in computer processing power, memory, storage, and data tools, machine learning can be integrated into the enterprise-software systems that form the heart of most organizational IT infrastructures. This means that the software, using the mastery that it develops in individual tasks, will be able to contribute increasing levels of performance and productivity to the organization over time, rather than merely offering a one-time boost, as most software packages do today.

The strength of machine-learning integration

The improvements the software brings to organizations will not be limited to individual tasks. One of the biggest strengths of enterprise software is its integration– the ability of individual applications to share information and be part of process workflows both within individual departments and across the organization. Integration allows organizations to experiment with new combinations of ever-more intelligent and versatile machine-learning applications and, where possible, let the machines learn how to improve the ways they work with each other and with their human colleagues. Together, these applications form the intelligent enterprise.

Just as individual applications will contribute more productivity to the organization as their embedded machine-learning abilities become more sophisticated, so too will the combinations of those applications evolve to bring more intelligence and flexibility to departmental and organizational processes over time.

Here are some concrete examples of how machine learning is creating value in organizations today:

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Personalized customer service. Organizations can use machine-learning to improve customer service while lowering costs by combining natural-language processing, historical customer service data, and algorithms that continuously learn from interactions. Customers can ask the system questions and get accurate answers, lowering response times and allowing human customer service representatives to focus on higher-priority or more-complex interactions.

Financial-exception handling.
A machine-learning system can be trained to recognize payments that arrive without an order number and match them to invoices based on knowledge of customers’ order and payment histories. This lets organizations reduce the amount of work outsourced to service centers and frees up finance staff to focus on more strategic tasks.

Improved hiring.
A machine-learning system can learn to pluck the most suitable job candidates from the thousands of résumés that organizations receive. It can also spot biased language in job descriptions that might discourage qualified people from applying and rescue other top candidates who fall through the cracks because they don’t fit with traditional hiring models.


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Algorithmic security.
By building models based on historical transactions, social network information, and other external sources of data, machine-learning algorithms can use pattern recognition to automatically spot anomalies. This identification helps detect and prevent fraudulent transactions in real time, even for previously unknown types of fraud. And this type of algorithmic security is applicable to a wide range of other situations, including computer hacking and cybersecurity.

Image-based procurement. Instead of having to log into a procurement system and search manually, employees can simply use a smartphone app to snap a picture of the item they’re looking for– a particular brand and type of laptop, for example– and the system will use machine learning to hunt through its database to find a match or the nearest equivalent. It will then send a message to the employee, who can launch the ordering process with a single click.


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Brand-exposure measurement. Brands spend billions on sponsorships, often without knowing exactly what they are getting for their money. A machine-learning application can sort through thousands of hours of sports video footage or track the action in real time, for example, to tell marketers how often their logo appears on screen, how large it is, how long it appears, and where it is located on the screen. Brands can then quantify their return on investment in the moment.

Contextual concierge.
Let’s say that your flight is suddenly delayed. A travel app on your smartphone can use context-sensitive machine learning to determine how this delay will affect your other travel plans and prompt you with rescheduling options.

Visual shelf management. Employees can take photos of shelves in a store aisle, kicking off a machine-learning process that automatically senses missing or improperly displayed items and prompts the store manager and the warehouse to fill the shelves correctly.


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Manufacturing quality control. By examining video of an assembly line, a machine-learning system can spot defects that a human might miss and automatically reroute the damaged parts or assemblies before products leave the factory.

Drone- and satellite-based inspection. A machine-learning system can sift through thousands of aerial images
of a pipeline, for example, and automatically spot areas that need maintenance or replacement.


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Machine learning needs a platform

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To be sure, organizations will gain tremendous benefits from individual machine-learning applications, even if they are never integrated into a larger whole. However, the benefits become much greater when these applications are on an integrated platform.

The business press has been discussing the power of platforms a lot lately, with iTunes being a well-known example. By creating a set of common software development tools that are available free to anyone who wants them, Apple has enabled developers to create thousands of applications for the iTunes App Store. Developers win because they can easily reach vast numbers of Apple device owners through iTunes. Apple wins because it takes a cut of the revenues for each app it makes available in the App Store.

Platforms are equally important to enterprises, not necessarily because of the profit motive (though some organizations are launching their own public, for-profit platforms similar to iTunes), but because having a platform gives them a base for quickly and cost-effectively combining different applications together, whether they are from different software vendors or are built in house.

No software vendor will ever be able to claim that it offers every machine-learning–enabled application that an organization needs out of the box. But vendors do offer platforms that organizations can use as bases for building out their entire machine-learning infrastructure.

The core of these machine-learning–enabled platforms is application programming interfaces (APIs). APIs are a kind of software version of those universal electric plug adapters that business travelers lug around with them so they can charge their electronic devices wherever they may be in the world. APIs allow software developers to plug into another software vendor’s applications without having to know anything about the complex code at the heart of those applications.

Another benefit of having a unified software platform is that organizations can use it to create a single point of access to data from across the organization. Data is the sole nutrient in a machine-learning diet. Algorithms need to binge on it constantly to lead a healthy and successful life. The larger and richer the data set, the more accurate the results. Having a single platform helps break down the data silos that exist across the organization so that organizations can make the most of machine-learning intelligence.

Organizations don’t need to go it alone

Inevitably, organizations will want to develop machine-learning–based applications that are not available in the marketplace. However, this does not mean that they need to create large internal machine-learning centers of expertise (although having some internal experts is recommended). Service providers can bring the expertise and perspective from within and across industries to help organizations focus on a small set of highly strategic processes that will benefit from machine learning.

The first step toward developing such applications is to determine where to apply machine learning. Organizations need to ensure that it erects barriers to entry against competitors or provides new ways of capturing and retaining customers by improving repurchase cycles or achieving new levels of win rates.

That means focusing investments on the machine-learning problems that will matter most to the industry’s basic competitive economics. Developing those engines will take considerable effort and time, so focusing the enterprise on those one or two projects that will really make a difference matters.

Here are five criteria to determine how to apply machine learning in a way that will create lasting differentiation.

1. The focus area as an appropriate candidate.

Not every facet of business will benefit from machine learning. The greatest potential is in automating high-volume tasks that have complex rules and large amounts of unstructured data.

Is your focus area big and complex enough for machine learning?

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2. A clearly formulated issue. Machine learning works best on specific, well-defined tasks where the desired output and relevant inputs can be clearly stated: given X, predict Y. While it isn’t a magic bullet that will automatically help organizations learn from all the data in their enterprise, machine learning can be valuable in discovering correlations in large amounts of data that humans could never have deduced for themselves.

3. A sufficient quantity of examples to learn from. Machine learning requires a lot of data to be accurate. There must be enough examples for the machine to learn meaningful approximations of the decisions you want to make. This is discovered through experimentation.

4. Meaningful differences within the dataset. If the data you are trying to learn from does not contain meaningful differences, then the algorithm will fail at its mission. Let’s say that you are trying to identify different types of buyers. If the training data does not contain significant differences in buyer characteristics, the algorithm cannot give you useful results.

5. A clear definition of success. Machine learning is always evaluated by measures of performance on a specific task. Typically, the computer will try to optimize whatever performance measure is defined. Clear evaluation criteria for the algorithm are therefore critical. You also need to be certain that the evaluation criteria are actually helpful for solving your business problem.

Key evaluation criteria for machine learning

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The human factor

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Ultimately, the technical barriers to machine-learning adoption will be easier to solve than the human ones. Predictions of steep job losses due to automation are stoking fear and uncertainty about how these self-learning systems will impact our roles and our livelihoods.

These fears must be addressed, and significant investment must be made in change management as business processes and models are reworked to integrate self-learning systems into collaborative human-machine environments.

Indeed, self-learning machines have the potential to become valuable collaborators with humans, augmenting their skills and helping employees become more productive in their current jobs while freeing them from boring, repetitive tasks.

Experts also predict that machine learning will create new roles inside the organization. There is already a shortage of data analysts and those capable of developing the intricate algorithms that machine learning requires. Other new roles will become evident as machine learning integrates deeper into the organization – and not all roles will require a degree in computer science or math. For example, creative thinking, strategy development, quality management, and people development and coaching will be crucial skills in an AI-driven organization, according to a survey by consulting firm Accenture2.

What’s next

When machine learning matures to the point that it can handle unstructured data (still an issue today), when organizations openly share data, and when algorithms begin to interact with each other more freely, machine learning will be embedded in all systems, devices, machines, and software. That will enable highly context-sensitive insight at both the organizational and individual levels. We can only guess at the level of automation that will result, but the impact on business – and society – will be significant.

Already, commercial machine-learning applications based on these technologies are available, and more are being created all the time. That is why business leaders should engage now with trusted providers that can help them evaluate data structures and availability, free up information from siloed systems, and identify the richest areas for machine-fueled insight and improvement. Together, they can address the cultural and change management challenges to take advantage of this new wave of business intelligence.

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Download the white paper Why Machine Learning and Why Now?


Machine Learning Screenshot Four Big Reasons Your Email Campaigns Are Being Ignored


Daniel Wellers is Digital Futures Lead, Thought Leadership Marketing, at SAP.

Jeff Woods is Vice President, Marketing Strategy and Head of Thought Leadership Marketing at SAP.

Dirk Jendroska is Head of Machine Learning Strategy and Operations, SAP Innovation Center Network, at SAP.

Christopher Koch is Director, Thought Leadership Marketing, at SAP.

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Baby Elephant Runs To Mother

 Baby Elephant Runs To Mother

When the going gets tough, the tough run to mom.

“Baby elephant chases guinea fowl, falls down & goes Boom, runs to mother for solace.”
Image courtesy of http://imgur.com/gallery/a38zi7x.

When blockchain meets big data, the payoff will be huge

 When blockchain meets big data, the payoff will be huge

If there is a “sweet spot” for blockchain, it will likely be the ability to turn insights and questions into assets. Blockchains will give you greater confidence in the integrity of the data you see. Immutable entries, consensus-driven timestamping, audit trails, and certainty about the origin of data (e.g. a sensor or a kiosk) are all areas where you will see improvement as blockchain technology becomes more mainstream.

Beyond data integrity (which is a huge component), the shared data layer that blockchains will introduce creates an entirely new set of possibilities for AI capabilities and insights. Trent McConaghy, CTO of BigChainDB does a great job in explaining the benefits of decentralized/
shared control, particularly as a foundation for AI. In this world, he says, you get:
• More data, thus improved modelling capabilities
• Qualitatively new data leading to entirely new models.

The inherent immutability leads to more confidence in training and testing data and the models they produce.

We are also likely to see blockchain-based technology make an impact in the cost of storing data and in the amount (and quality) of data available. Cost savings in data storage will come from the disintermediation of centralized storage providers, thus reducing the “trust tax” you pay them currently. This should also create downward pricing pressure on SaaS suppliers as they move to decentralized storage providers.

You can expect to see decentralized solutions like Storj, Sia, MaidSafe, and FileCoin start to gain some initial traction in the enterprise storage space. [Disclosure: Storj was previously a client of mine.] One enterprise pilot phase rollout indicates this decentralized approach could reduce the costs of storing data by 90 percent compared to AWS.

As for blockchain-driven AI, you can expect to see a three phase roll-out. First, within the existing enterprise. Then, within the ecosystem. Finally, totally open systems. The entire industry might be termed blockchains for big data (McConaghy’s words).

Longer term, we will see an expansion of the concept of big data, as we move from proprietary data silos to blockchain-enabled shared data layers. In the first epoch of big data, power resided with those who owned the data. In the blockchain epoch of big data, power will reside with those who can access the most data (where public blockchains will ultimately defeat private blockchains) and who can gain the most insights most rapidly.

There are two significant implications here:
• Customer data will not belong to organizations, locked away in corporate databases. It will belong to each individual, represented as tokens or coins on an identity blockchain. The customer of the future will grant access to others as necessary.
• Transaction data will be viewable by anyone. Anyone can access the data about the transactions that occur on a given blockchain. (For example, here are the latest Bitcoin transactions.)

When data moves out of proprietary systems onto open blockchains, having the data itself is no longer a competitive advantage. Interpreting the data becomes the advantage. In a blockchain world, all competitors are looking at the same ledger (imagine you and your competitors all have the Google Sheet or Excel file).

Anyone can provide an interface to that ledger. That’s relatively easy. That’s what you see here for Ethereum or zCash.

And many companies will provide applications that enable a customer to interact with a protocol. This is what Jaxx or BitPay do, for Bitcoin.

Yet, there are very few companies that provide a set of analytic capabilities that suck up all of this data and explain what it all means or what should be done about it. Fewer still have figured out the scalable process for doing this. This is the opportunity. Some have called it the “Data Industrialization Opportunity.”

Simply put, it is the question of who can put the best AI/machine learning solution on top of open, shared, blockchain-based data layers. Whoever does that gains some degree of competitive advantage. If a Bitcoin wallet, for example, instead of being “dumb” (as it is now) is actually “smart” — in the sense that it can advise or help customers make sense of the world (based on all the data available on the blockchain) — that one will be market leader.

The world’s top 50 physical mining companies are worth about $ 700 billion. You can expect to see blockchain-based data mining companies that will easily take us into trillions of dollars of market capitalization, although, granted, this may be many years off.

This article is adapted from an excerpt of the author’s new book, The CMO Primer for the Blockchain World.

Jeremy Epstein is CEO of Never Stop Marketing and author of The CMO Primer for the Blockchain World. He currently works with startups in the blockchain and decentralization space. He advises F2000 organizations on the implications of blockchain technology. Previously, he was VP of marketing at Sprinklr from Series A to “unicorn” status.

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SQL Nexus 6.0 is released to github

With codeplex shutting down, we have moved SQL Nexus to github with a new release (6.0).   Now both Pssdiag/SQLDiag manager and SQL Nexus are on github.

As you navigate to SQL Nexus, you can download code and released binary files.

If you choose to download binary files, you can go to releases and download the zip file.

image thumb384 SQL Nexus 6.0 is released to github

image thumb385 SQL Nexus 6.0 is released to github

Complete release notes is at https://github.com/Microsoft/SqlNexus/wiki/SQL-Nexus-6.0.0.8-Release-notes

Major rules added

  1. warn if change table is used because it can cause high cpu
  2. warn if “access check” configuration is not set correctly because it can cause high CPU
  3. warn if attention occurs and involves in blocking
  4. warn if there are major gaps detected in perf stats script run
  5. warn presence of TF 1222

Major fixes

  1. not all trace flags apply to aall versions. This fix will raise warning based on version
  2. removed limit report times out at 60 seconds
  3. change nexus DB recovery to simple
  4. provides warning if you have both .xel and .trc files captured (import will fail)

You can also report issues and do pull requests directly from the website.  We will monitor issues.  If you do a pull request, we will review and decide to merge

image thumb386 SQL Nexus 6.0 is released to github

Jack Li |Senior Escalation Engineer | Microsoft SQL Server

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PowerApps and Power BI, together at last

The wait is over. PowerApps and Power BI can play together, regardless of who’s hosting!  We already showed you how you can embed your app inside a Power BI dashboard. Now with the new Power BI tile control, you can show your Power BI tiles inside your app.

Why is this big news?

Power BI is a powerful analysis and visualization tool. PowerApps is great at enabling people to take action on the web and mobile. Now you can build apps that give users great insight and let them act right away.  Same place, same time.

Showing Power BI tiles inside your app

  1. To show a Power BI tile, first add the new Power BI tile control to your app.

    0f63c811 843a 4cbd b674 331493e75256 PowerApps and Power BI, together at last

  2. Choose the tile you want to show by setting its Workspace, Dashboard and Tile properties in the Data tab of the options panel.

    ee43a08e 5fa4 482e 9b87 188d3e4c6385 PowerApps and Power BI, together at last

The Power BI visual should appear on your design surface.

That’s it, you’re done.

Sharing and security

Once shared, the PowerApps app will be accessible by all users who have permissions to access the app. However in order to make the Power BI content visible to those users, the dashboard where the tile comes from needs to be shared with the user on Power BI. This ensures that Power BI sharing permissions are respected when Power BI content is accessed in an app.

Looking ahead

Most users will want the ability to click on the Power BI tile to be taken to its Power BI dashboard, in case they need to dig deeper into the data.  This is already in the works and will be coming out in our next release in August.

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