A SWOT for Your B2B Marketing Automation Strategy

20171115 bnr swot 351x200 A SWOT for Your B2B Marketing Automation Strategy

Deep dive: Perform a SWOT exercise for your brand

Now that you have the rough framework of a SWOT, let’s go through the exercise. Grab a piece of paper or white board and explore these concepts. (Note: I really do recommend that you get off your digital device and perform this exercise with a pen, pencil, or whiteboard marker.)

First, what are you going to SWOT? You can choose your company all-up, or a specific division or how you utilize your marketing automation platform. Choose one. We’ll call it “Your Thing.” For each category below, aim to write at least five items in each quadrant.

First, think through Your Thing’s Strengths. What does your company do really well? What evidence do you have for those proof of concepts you create? What garners you excellent buyer reviews or love letters from your customers? Write those things down. As I mentioned earlier, you don’t have to do this all on gut. Pull from your current marketing copy, as well as from industry analysts and customer testimonials.

Next up is Weaknesses. Now, as marketers, I know that we’re wired to constantly find a positive spin. But let’s get real here. I’m sure you have opinions or at least hunches about what doesn’t work so well with Your Thing. Write those down. Also go back to those customer reviews and feedback – what are the common complaints or issues? What does your customer support team receive a lot of calls about? Write those things down, too. You don’t have to engage in endless brand-flagellation, but do accurately identify your trouble spots.

Opportunities are fun. They can be aspirational – such as places you could market Your Thing, or potential customers you could reach. Also, ID brand expansion possibilities go here. For example, have your customers mentioned something they really want, or have you brainstormed great ideas but not shared them yet? Note those. Reach for the low-hanging fruit, of course – but also include your “stretch” goals ‒ those pie-in-the-sky ideas can’t become reality unless you speak them. This is your space to dream, so do so.

Finally, Threats. What real or potential things could threaten your business? This can be anything from someone stealing your idea (do you have a patent?) to an economic crash that may impact your non-elastic-good market, to a fiery tempered CEO. Some of it you may be able to see coming – other things, you can’t even begin to imagine. But try to think through a few disastrous scenarios and jot them down. And, again, be real. There’s no point in hiding the truth from yourself.

a SWOT analysis for your B2B marketing automation strategy

If you were creating a SWOT for your marketing automation platform (MAP) strategy, a strength could be that you have integrated your MAP with your customer relationship management (CRM) tool.

If you were creating a SWOT for your marketing automation strategy, a weakness is that you’re under utilizing the functions in the platform. This could be not setting up account, demographic and behavior-based segmentation for your lists. It could be not creating automated nurture programs based on those new segmented lists so that you are nurturing decision makers, influencers, tire kickers, and folks wanting to buy today all differently.

One of the biggest Threats to your marketing automation strategy is not using the product, or integrating it with your CRM so that you fail to connect your marketing efforts with sales and see the return on that investment. Another threat could be locking yourself into an all-in-one vendor technology stack that isn’t motivated to innovate, or address your specific needs.

Know what to do with your SWOT

Great work on completing your SWOT for your marketing automation setup or for whatever you choose to examine. Now go stretch your legs for a moment, grab a coffee, and return with your analyst hat on. Look at your list and see what stands out. Circle the big-ticket items. Draw lines and correlations between the quadrants. Jot notes in the margins. Brainstorm – ideas big and small.

The Opportunity in your SWOT for your marketing automation strategy is using your platform across the customer’s journey and across marketing. Are you using it for your branding efforts by nurturing industry and media influencers? Are you creating automated programs for your customers, making sure you help them successfully onboard with your product or service? As they engage more and more, whether attending a customer webinar or Tweeting your praise, you can assign them a lead score for becoming brand advocates and future referrals, as well as priming them for renewals and upsells.

It’s totally OK if you are creating a SWOT just for yourself. It can be a great tool to help you understand more about your brand or simply generate new ideas. But those SWOT results can also be invaluable to your colleagues and boss. I encourage you to share your results – to polish up your lists, remove those potentially thorny items (such as the mention of the CEO’s temper) – and turn your activity into action.

Also, as you drew up your list and made your analysis, I have no doubt your mind started wheeling with ideas. Don’t lose those – whether they be for new products or services, customer opportunities, marketing ploys, campaign slogans, or staff shufflings. The point of the SWOT is to take stock and get ideas going, so harness this energy and good work.

Add SWOT to your regular marketing exercises.

I am an avid fitness fan, and as such I’m accustomed working through many of the same exercises – pushups, sit-ups, and squats – over and over again. It’s not because I always enjoy them; but rather, because they work. Think of a SWOT in this same way. No, I’m not suggesting that you need to perform SWOTs as often as squats, but I do encourage you to try a SWOT at least once a year. You may be surprised at how each iteration garners new insights and helps you nimbly adjust your marketing strategy accordingly.

Bonus exercise: SWOT yourself

At one of my former jobs, part of the new-hire process included a self-SWOT. We had to assess our strengths, weaknesses, opportunities, and threats when we were hired, and then again at our 90-day review. It was a little strange at first, but the exercise proved to be a great mechanism to help me honestly assess my skills – and also to see what changed over the course of a few short months. I encourage you to try this. You never know when you may be able to use these findings, too. You can keep them in your back pocket when you’re preparing for your annual review, asking your boss to include you on a big-ticket project, or pitching for a promotion.

Back to you.

Has a SWOT ever helped you gain valuable insight? Share your experience here.

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Oracle Big Data SQL 3.2 is Now Available

Big Data SQL 3.2 has been released and is now available for download on edelivery.  This new release has many exciting new features – with a focus on simpler install and configuration, support for new data sources, enhanced security and improved performance.

Big Data SQL has expanded its data source support to now include querying data streams – specifically Kafka topics:

This enables streaming data to be joined with dimensions and facts in Oracle Database or HDFS.  It’s never been easier to combine data from streams, Hadoop and Oracle Database.

New security capabilities enable Big Data SQL to automatically leverage underlying authorization rules on source data (i.e. ACLs on HDFS data) and then augment that with Oracle’s advanced security policies.  In addition, to prevent impersonation, Oracle Database servers now authenticate against Big Data SQL Server cells. Finally, secure Big Data SQL installations have become much easier to set up; Kerberos ticket renewals are now automatically configured.

There has been significant performance improvements as well.  Oracle now provides its own optimized Parquet driver which delivers a significant performance boost – both in terms of speed and the ability to query many columns.  Support for CLOBs is also now available – which facilitates efficient processing of large JSON and XML data documents.

Finally, there has been significant enhancements to the out-of-box experience.  The installation process has been simplified, streamlined and made much more robust.

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SeriesCoefficient is broken in Mathematica 11.1, but works in 11.0

 SeriesCoefficient is broken in Mathematica 11.1, but works in 11.0

Consider the following implementation of the complex square root:

f[z_]:=Sqrt[(z - I)/(z + I)]*(z + I);

This implementation has branch points at $ \lambda=\pm i$ and a (vertical) branch cut connecting them.

Then

g[z_]:=Sinc[f[z]];

(recalling $ \mathrm{sinc}(x)=\sin(x)/x$ ) has no branch cut and it is analytic on the entire complex plane, and admits power series expansions at $ \lambda=\pm i$ . Indeed, using Mathematica 11.0.0 (Mac OS 10.10.5) gives:

Series[Sinc[rhofun[z]], {z, I, 4}]

$ 1-\frac{1}{3} i (z-i)-\frac{1}{5} (z-i)^2+\frac{11}{315} i (z-i)^3+\frac{61
(z-i)^4}{5670}+O\left((z-i)^5\right)$

and

SeriesCoefficient[Sinc[rhofun[z]], {z, I, 4}]

gives $ \frac{61}{5670}$ .

Now, using Mathematica 11.1.1 (both on Mac OS 10.12 Sierra and Linux Ubuntu 16 LTS)

Series[Sinc[rhofun[z]], {z, I, 4}]

returns

Series[Sinc[rhofun[z]], {z, I, 4}]

and

SeriesCoefficient[Sinc[rhofun[z]], {z, I, 4}]

returns

SeriesCoefficient[Sinc[rhofun[z]], {z, I, 4}].

So neither of these stock functions work in properly in Mathematica 11.1.1. Does anyone know what is going on? Will this be fixed? They worked properly even in Mathematica 9 and also in Mathematica 11.0.0

Besides any information, I’d also appreciate if anyone has a workaround for this.

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5 Hot Topics in Credit Scoring from Edinburgh

Analytics Hand 5 Hot Topics in Credit Scoring from Edinburgh

If you want to seek out the newest ideas in credit scoring — a field that advances more rapidly than many people may suspect — the best place is the annual Edinburgh Credit Scoring and Control conference (well, next to our own FICO World). At this year’s conference, I started thinking about what has changed in the world of credit scoring since my first visit to the conference almost 20 years ago.  My phone has certainly gotten smarter in that time – so what is smarter within credit scoring?

With around 70 presentations, the key questions remain the same:

  • What data is available and useful?
  • How do I best gain intelligence from the data?
  • How do I best action the intelligence?
  • How do I comply with the ever-increasing regulations?

Regarding the data, the answer is more and more! We all know that the digital age is creating vast quantities of data that is growing exponentially.  Compared to 20 years ago, there are many new and ‘alternative’ data sources that are now being used to better inform creditworthiness for certain consumer types, include psychometric data, telecoms data, social network data and transaction data.

In terms of turning this data into intelligence, artificial intelligence (AI) and specifically machine learning algorithms are being investigated and used on an increased scale.  With ever greater volume and variety of data coupled with vastly increased computer processing power, machine learning approaches to drive the value from the data are proving more and more useful.

This same processing power for the development of machine learning models is also helping with the ease of deployment of these types of models, which has been troublesome in the past in terms of both speed of deployment and speed of execution.

The evolution from predictive analytics (models that order by a single outcome of interest) to prescriptive analytics (also referred to as decision optimization, identifying the best action to take considering multiple outcome metrics or dimensions) is vastly improving the business outcomes of decisions across the credit lifecycle, from origination through to collection and recovery.  Prescriptive analytics provides the ability to make better, more informed decisions by taking account of multiple (often conflicting) objectives — for example, increasing accept rates whilst controlling losses.

Since the economic crisis there is also greater focus on modelling stressed situations, and how these stresses impact both the likely performance of individual consumers as well as total portfolios. Predictive models help lenders comply with regulations such as Basel and IFRS 9.  As compliance is gained and maintained, we are seeing these same models being used to drive business value through better insights and understanding of portfolios, acting as key inputs to both what-if scenario analysis and decision optimisation capabilities.

FICO data scientists and experts, all of whom blog here, presented no fewer than five sessions at Edinburgh this year on hot topics related to these areas I have described.

Gerald has already written a post on his talk, about new risk analytics to stress-test individual consumers – we’ll be sharing more insights on our topics here, and shining a spotlight on new trends in credit scoring.

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A Historian Walks into a Bar . . .

Introduction to Behaviours of Date and Time Fields in Dynamics 365

In Microsoft Dynamics 365, the “Date and Time” data type is used quite often. Based on the requirements, we have the options to choose the format between “Date Only” and “Date and Time”. But what matters the most is the accuracy of information being generated from the data, and this has been an issue in old versions of Dynamics CRMs.

For example, the “Date of Birth” field is used commonly in many organisations with the “Date Only” format, and some organisations use the same instance of Dynamics CRM in different time zones.  If a user enters 21-2-2017 in the “Date of Birth” field for a record from their CRM account with time zone of Auckland (GMT +12:00), then if the same record is being viewed with a different time zone such as Tokyo (GMT +9:00), it would display 20-2-2017 (one day behind).

This is because when the record was saved by the first user, its time element had the value set to 12:00 AM, and when the record is being viewed in Japanese time zone, the time is converted to 20-2-2017 8:00 PM. Therefore, the value is shown as 20-2-17.

Fortunately, in Dynamics 365 we can select the behaviour for Date Time fields. In this blog, I will walk through the differences between these behaviours.

User Local

The field values are stored in UTC time zone. However, the values being displayed to the user depends upon the time zone selected in user’s Dynamics 365 application. For example, I created a custom field called “Interview On” of “Data and Time” data type for the Contact entity. Following were the options selected in the “Type” section.

image thumb Introduction to Behaviours of Date and Time Fields in Dynamics 365

Once saved, I added the field onto the Contact main form. Then a new record was created by a user in New Zealand and a value was selected for the “Interview On” field.

image thumb 1 Introduction to Behaviours of Date and Time Fields in Dynamics 365

After the changes were made, another user from Japan opened the same record, but would see a different value than the one stored earlier.

image thumb 2 Introduction to Behaviours of Date and Time Fields in Dynamics 365

Even though the “Interview On” value in the database for this record hasn’t changed at all, the time being displayed in the CRM UI is different. This is because the value displayed to the user depends on their time zone.

Date Only

The field value doesn’t display any time, but only date. In the database, the time portion of the value stored is always 12:00 AM. The advantage of using this behaviour is that date value is displayed the same across all time zones. This behaviour is useful for fields such as “Date of Birth” because no time conversion will be done, and accurate information will be presented to the user every time.

I created a custom field called “Date of Birth” for a custom entity called “Pet”, with following options selected in “Type” section.

image thumb 3 Introduction to Behaviours of Date and Time Fields in Dynamics 365

Once saved, I added the field onto the contact main form. Then a user from New Zealand selected a date of birth for one of the Contact records.

image thumb 4 Introduction to Behaviours of Date and Time Fields in Dynamics 365

When the same record was opened by another user in Japan and date values were being displayed the same for the “Date of Birth” field.

Time-Zone Independent

The last behaviour type is “Time-Zone Independent”. When fields with format of “Date and Time” are set with behaviour “Time-Zone Independent”, the values are displayed the same across all the time zones. For example, I modified the behaviour of the field I recently created “Interview On”. I changed the behaviour to “Time-Zone Independent”. Following are the values that were set in “Type” section.

image thumb 5 Introduction to Behaviours of Date and Time Fields in Dynamics 365

Note: Once the behaviour is set to “Time-Zone Independent”, it cannot be changed.

image thumb 6 Introduction to Behaviours of Date and Time Fields in Dynamics 365

Once the changes were saved, user from New Zealand created a new Contact record and selected a value for “Interview On” field.

image thumb 7 Introduction to Behaviours of Date and Time Fields in Dynamics 365

Then the record was reopened by another user in Japan. However, the values displayed in field “Interview On” was still the same for this user too.

Conclusions

There are a few things we must keep in mind. Out of the box field’s behaviour can be set to “Date Only” from “User Local”, but this cannot be done for all out of the box fields. Such as, “Original Start Date” field for Appointment entity is a field of type “Data and Time” and has behaviour set to “User Local”, and this cannot be changed. Also for a custom field, we can change the behaviour from “User Local” to “Time-Zone Independent” or “Date Only”.

Having the option to select either one of these behaviours saves us lots of additional coding time. For example, one of our clients had users from different time zones who were using same instance of CRM 2011. They used a field of format “Date Only”. Since data was being viewed in two different time zones, many records were displaying incorrect date in one of the time zones. There was no option to change the behaviour.  To come across this issue a lot of coding was required to update existing records, and automatically update future records.

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Why Data Quality Should be Part of Your Disaster Recovery Plan

When you think of disaster recovery, data quality is likely not the first thing that comes to mind. But data quality should factor prominently into your disaster recovery plan. Here’s why.

Disaster recovery is the discipline of preparing for unexpected events that can severely disrupt your IT infrastructure and services, and the business processes that depend on them.

The disasters that necessitate disaster recovery can take many forms. They could be natural disasters, like a major storm that wipes out a data center. They could be security events, wherein hackers hold your data for ransom or bring your services down using DDoS attacks. They could be an attack by a disgruntled employee who deliberately wipes out a crucial database.

blog banner Data Quality Magic Quadrant Why Data Quality Should be Part of Your Disaster Recovery Plan

What all types of disasters have in common is that it’s virtually impossible to know when they’ll occur, or exactly what form they’ll take.

Forming a Disaster Recovery Plan

That’s why it’s essential to have a disaster recovery plan in place. Your plan should:

  • Identify all data sources that need to be backed up so that they can be recovered in the event of a disaster.
  • Specify a method or methods for backing up the data.
  • Identify how frequently backups should occur.
  • Determine whether on-site data backups are sufficient for your needs, or if you should back up data to a remote site (in case your local infrastructure is destroyed during a disaster).
  • Specify who is responsible for performing backups, who will verify that backups were completed successfully and who will restore data after a disaster.

If you need help building and implementing a disaster recovery plan, you can find entire companies dedicated to the purpose. With the right planning and skills, however, there is no reason that you cannot also maintain an effective disaster recovery yourself. Regardless of whether you outsource disaster recovery or not, the most important thing is simply to have a plan in place. (See also: 5 Tips for Developing a Disaster Recovery Plan)

blog time money2 Why Data Quality Should be Part of Your Disaster Recovery Plan

Data Quality and Disaster Recovery

Now that we’ve covered the basics of disaster recovery, let’s discuss where data quality fits in.

Put simply, data quality matters in this context because whenever you are backing up or restoring data, you need to ensure data quality. Since data backups and restores are at the center of disaster recovery, data quality should be factored into every phase of your disaster recovery plan.

After all, when you’re copying data from one location to another to perform backups, data quality errors are easy to introduce for a variety of reasons. You might have formatting issues copying files from one type of operating system to another because of different encoding standards. Data could become corrupted in transit. Backups could be incomplete because you run out of space on the backup destination. The list could go on.

It’s even easier to make data quality mistakes when you’re recovering data after a disaster. Even the most prepared organization will be working under stress when it’s struggling to recover data after a disaster. The personnel performing data recoveries may not be familiar with all the data sources and formats they are restoring. In the interest of getting things up and running again quickly – a noble goal when business viability is at stake – they may take shortcuts that leave data missing, corrupted or inconsistent.

All of the above are reasons why data quality tools should be used to verify the integrity of backed-up data, as well as data that is recovered after a disaster. It’s not enough to check the quality of your original data sources, then assume that your backups and the data recovered based on those backups will also be accurate. It might not be, for all the reasons outlined above and many more.

The last thing your business needs after it has suffered through and recovered from a disaster is lasting problems with its data. To prevent a disaster from having a lasting effect on your business, you must ensure that the data you’ve recovered is as reliable as your original data.

Syncsort’s data quality software and disaster recovery solutions can help you build your disaster recovery plan. Learn about why Syncsort is a leader for the 12th consecutive year in Gartner’s Magic Quadrant for Data Quality Toolsreport.

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Big Data SQL Quick Start. Correlate real-time data with historiacal benchmarks – Part 24

In Big Data SQL 3.2 we have introduced new capability – Kafka as a data source. Some details about how it works with some simple examples, I’ve posted over here. But now I want to talk about why do you want to run queries over Kafka. Here is Oracle concept picture on Datawarehouse:

You have some stream (real-time data), data lake where you land raw information and cleaned Enterprise data. This is just a concept, which could be implemented in many different ways, one of this depict here:

Kafka is the hub for streaming events, where you accumulate data from multiple real-time producers and provide this data to many consumers (it could be real-time processing, such as Spark-Streaming or you could load data in batch mode to the next Datawarehouse tier, such as Hadoop). 

In this architecture, Kafka contains stream data and it’s able to answer the question “what is going on right now”, whereas in Database you store operational data, in Hadoop historical and those two sources are able to answer the question “how it use to be”. Big Data SQL allows you to run the SQL over those tree sources and correlate real-time events with historical.

Example of using Big Data SQL over Kafka and other sources.

So, above I’ve explained the concept why you may need to query Kafka with Big Data SQL, now let me give a concrete example. 

Input for demo example:

- We have company, called MoviePlex, which sells video content all around the world

- There are two stream datasets – network data, which contains information about network errors, conditions of routing devices and so. The second data source is the fact of the movie sales. 

- Both stream data in real-time in Kafka

- Also, we have historical network data, which we store in HDFS (because of the cost of this data), historical sales data (which we store in database) and multiple dimension tables, stored in RDBMS as well.

Based on this we have a business case – monitor revenue flow, correlate current traffic with the historical benchmark (depend on Day of the Week and Hour of the Day) and try to find the reason in case of failures (network errors, for example).

Using Oracle Data Visualization Desktop, we’ve created a dashboard, which shows how real-time traffic correlate with statistical and also, shows a number of network errors based on the countries:

The blue line is a historical benchmark.

Over the time we see that some errors appear in some countries (left dashboard), but current revenue is more or less the same as it uses to be.

After a while revenue starts going down.

This trend keeps going.

A lot of network errors in France. Let’s drill down into itemized traffic:

Indeed, we caught that overall revenue goes down because of France and cause of this is some network errors.

Conclusion:

1) Kafka stores real-time data  and answers on question “what is going on right now”

2) Database and Hadoop stores historical data and answers on the question: “how it use to be”

3) Big Data SQL could query the data from Kafka, Hadoop, Database within single query (Join the datasets)

4) This fact allows us to correlate historical benchmarks with real-time data within SQL interface and use this with any SQL compatible BI tool 

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How to Learn from Travelers to Plan the Future of Airports

A few months ago, the Airports Council International (ACI) released its list of the world’s 20 busiest airport as of 2016. It’s notable that the traffic of these hubs increased by 4.7 percent in 2016, totaling over 1.4 billion passengers, and globally the number of people traveling by air grew 5.6 percent.

These figures help us understand that airports and airlines have huge opportunities sitting in front of them to offer better services and become part of the journey of each traveler and to have the possibility to monetize and increase their revenue. The daily flow of passengers are like mature cherries on a tree ready to be picked—it would be a total waste to leave them untouched.

Historically, an airport is regarded as a place where we spend time before our flight. But this attitude will change as soon as airports are considered as being part of the passenger’s journey. By 2024, most major airports will offer more than just a place to wait to catch your flight; they will offer you a gym class, invite you to attend an exhibition of masterpieces, or even let you sip a cocktail at the swimming pool while looking at the airplanes taking off.

Most of the airports have already started to offer their passengers a mostly free service: WiFi. But there is a hidden reason behind it; it allows them to track travelers, understand and learn from the walking paths, and measure how much time they spend in one area. From the arrival at the airport entrance, it is possible to track passengers’ time spent at the check-in desk, the time it takes them to go through security, how much time they spend eating at restaurants, and how long it takes them to reach the departure gate before finally taking off. This gives the airport a considerable amount of data to analyze to discover insights from passengers’ behaviors.

Tracking and optimizing the passenger dwell time is a fundamental part as recent airport studies have discovered that an extra 10 minutes at the security gate reduces the average passenger spend by a considerable 30 percent.

30 percent How to Learn from Travelers to Plan the Future of Airports

With the rise of IoT, more products and devices are connected to the internet and to each other, allowing devices to exchange informations through APIs. Just to cite one example, digital luggage tags and suitcases will include all flight details and destination information, which allows travelers or holidaymakers to track their their bags throughout their journey.

Another limitless opportunity comes from proximity marketing. Knowing in each instant where passengers are going and by combining information about their interests, it is possible to trigger marketing offers. Retailers would benefit by having more customers, and customers would benefit by getting only relevant offers.

Being able to track passengers in real time also gives the advantage to instantly visualize passenger density in different areas of the airport. For security and operational purposes, this allows airports to measure passenger throughput from one area to another in the case of exceeded thresholds to take countermeasures.

With the support of AI and machine learning algorithms, it is possible to learn and predict in real time what could happen when many passengers arrive at the airport, causing a delay, and what needs to be done to speed up operations. With dynamic check-in and security staff allocation, it is possible to optimize operations.

By gathering all this data from multiple connected devices and analyzing them, it is possible to understand traveler’s needs to plan the future of airports and offer a greater experience for a win-win situation.

Learn more about what TIBCO is doing in the travel industry.

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Connected Cars, Autonomous Vehicles, And The IoT

In the tech world in 2017, several trends emerged as signals amid the noise, signifying much larger changes to come.

As we noted in last year’s More Than Noise list, things are changing—and the changes are occurring in ways that don’t necessarily fit into the prevailing narrative.

While many of 2017’s signals have a dark tint to them, perhaps reflecting the times we live in, we have sought out some rays of light to illuminate the way forward. The following signals differ considerably, but understanding them can help guide businesses in the right direction for 2018 and beyond.

SAP Q417 DigitalDoubles Feature1 Image2 1024x572 Connected Cars, Autonomous Vehicles, And The IoT

When a team of psychologists, linguists, and software engineers created Woebot, an AI chatbot that helps people learn cognitive behavioral therapy techniques for managing mental health issues like anxiety and depression, they did something unusual, at least when it comes to chatbots: they submitted it for peer review.

Stanford University researchers recruited a sample group of 70 college-age participants on social media to take part in a randomized control study of Woebot. The researchers found that their creation was useful for improving anxiety and depression symptoms. A study of the user interaction with the bot was submitted for peer review and published in the Journal of Medical Internet Research Mental Health in June 2017.

While Woebot may not revolutionize the field of psychology, it could change the way we view AI development. Well-known figures such as Elon Musk and Bill Gates have expressed concerns that artificial intelligence is essentially ungovernable. Peer review, such as with the Stanford study, is one way to approach this challenge and figure out how to properly evaluate and find a place for these software programs.

The healthcare community could be onto something. We’ve already seen instances where AI chatbots have spun out of control, such as when internet trolls trained Microsoft’s Tay to become a hate-spewing misanthrope. Bots are only as good as their design; making sure they stay on message and don’t act in unexpected ways is crucial.

SAP Q417 DigitalDoubles Feature1 Image3 Connected Cars, Autonomous Vehicles, And The IoTThis is especially true in healthcare. When chatbots are offering therapeutic services, they must be properly designed, vetted, and tested to maintain patient safety.

It may be prudent to apply the same level of caution to a business setting. By treating chatbots as if they’re akin to medicine or drugs, we have a model for thorough vetting that, while not perfect, is generally effective and time tested.

It may seem like overkill to think of chatbots that manage pizza orders or help resolve parking tickets as potential health threats. But it’s already clear that AI can have unintended side effects that could extend far beyond Tay’s loathsome behavior.

For example, in July, Facebook shut down an experiment where it challenged two AIs to negotiate with each other over a trade. When the experiment began, the two chatbots quickly went rogue, developing linguistic shortcuts to reduce negotiating time and leaving their creators unable to understand what they were saying.

The implications are chilling. Do we want AIs interacting in a secret language because designers didn’t fully understand what they were designing?

In this context, the healthcare community’s conservative approach doesn’t seem so farfetched. Woebot could ultimately become an example of the kind of oversight that’s needed for all AIs.

Meanwhile, it’s clear that chatbots have great potential in healthcare—not just for treating mental health issues but for helping patients understand symptoms, build treatment regimens, and more. They could also help unclog barriers to healthcare, which is plagued worldwide by high prices, long wait times, and other challenges. While they are not a substitute for actual humans, chatbots can be used by anyone with a computer or smartphone, 24 hours a day, seven days a week, regardless of financial status.

Finding the right governance for AI development won’t happen overnight. But peer review, extensive internal quality analysis, and other processes will go a long way to ensuring bots function as expected. Otherwise, companies and their customers could pay a big price.

SAP Q417 DigitalDoubles Feature1 Image4 1024x572 Connected Cars, Autonomous Vehicles, And The IoT

Elon Musk is an expert at dominating the news cycle with his sci-fi premonitions about space travel and high-speed hyperloops. However, he captured media attention in Australia in April 2017 for something much more down to earth: how to deal with blackouts and power outages.

In 2016, a massive blackout hit the state of South Australia following a storm. Although power was restored quickly in Adelaide, the capital, people in the wide stretches of arid desert that surround it spent days waiting for the power to return. That hit South Australia’s wine and livestock industries especially hard.

South Australia’s electrical grid currently gets more than half of its energy from wind and solar, with coal and gas plants acting as backups for when the sun hides or the wind doesn’t blow, according to ABC News Australia. But this network is vulnerable to sudden loss of generation—which is exactly what happened in the storm that caused the 2016 blackout, when tornadoes ripped through some key transmission lines. Getting the system back on stable footing has been an issue ever since.

Displaying his usual talent for showmanship, Musk stepped in and promised to build the world’s largest battery to store backup energy for the network—and he pledged to complete it within 100 days of signing the contract or the battery would be free. Pen met paper with South Australia and French utility Neoen in September. As of press time in November, construction was underway.

For South Australia, the Tesla deal offers an easy and secure way to store renewable energy. Tesla’s 129 MWh battery will be the most powerful battery system in the world by 60% once completed, according to Gizmodo. The battery, which is stationed at a wind farm, will cover temporary drops in wind power and kick in to help conventional gas and coal plants balance generation with demand across the network. South Australian citizens and politicians largely support the project, which Tesla claims will be able to power 30,000 homes.

Until Musk made his bold promise, batteries did not figure much in renewable energy networks, mostly because they just aren’t that good. They have limited charges, are difficult to build, and are difficult to manage. Utilities also worry about relying on the same lithium-ion battery technology as cellphone makers like Samsung, whose Galaxy Note 7 had to be recalled in 2016 after some defective batteries burst into flames, according to CNET.

SAP Q417 DigitalDoubles Feature1 Image5 Connected Cars, Autonomous Vehicles, And The IoTHowever, when made right, the batteries are safe. It’s just that they’ve traditionally been too expensive for large-scale uses such as renewable power storage. But battery innovations such as Tesla’s could radically change how we power the economy. According to a study that appeared this year in Nature, the continued drop in the cost of battery storage has made renewable energy price-competitive with traditional fossil fuels.

This is a massive shift. Or, as David Roberts of news site Vox puts it, “Batteries are soon going to disrupt power markets at all scales.” Furthermore, if the cost of batteries continues to drop, supply chains could experience radical energy cost savings. This could disrupt energy utilities, manufacturing, transportation, and construction, to name just a few, and create many opportunities while changing established business models. (For more on how renewable energy will affect business, read the feature “Tick Tock” in this issue.)

Battery research and development has become big business. Thanks to electric cars and powerful smartphones, there has been incredible pressure to make more powerful batteries that last longer between charges.

The proof of this is in the R&D funding pudding. A Brookings Institution report notes that both the Chinese and U.S. governments offer generous subsidies for lithium-ion battery advancement. Automakers such as Daimler and BMW have established divisions marketing residential and commercial energy storage products. Boeing, Airbus, Rolls-Royce, and General Electric are all experimenting with various electric propulsion systems for aircraft—which means that hybrid airplanes are also a possibility.

Meanwhile, governments around the world are accelerating battery research investment by banning internal combustion vehicles. Britain, France, India, and Norway are seeking to go all electric as early as 2025 and by 2040 at the latest.

In the meantime, expect huge investment and new battery innovation from interested parties across industries that all share a stake in the outcome. This past September, for example, Volkswagen announced a €50 billion research investment in batteries to help bring 300 electric vehicle models to market by 2030.

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At first, it sounds like a narrative device from a science fiction novel or a particularly bad urban legend.

Powerful cameras in several Chinese cities capture photographs of jaywalkers as they cross the street and, several minutes later, display their photograph, name, and home address on a large screen posted at the intersection. Several days later, a summons appears in the offender’s mailbox demanding payment of a fine or fulfillment of community service.

As Orwellian as it seems, this technology is very real for residents of Jinan and several other Chinese cities. According to a Xinhua interview with Li Yong of the Jinan traffic police, “Since the new technology has been adopted, the cases of jaywalking have been reduced from 200 to 20 each day at the major intersection of Jingshi and Shungeng roads.”

The sophisticated cameras and facial recognition systems already used in China—and their near–real-time public shaming—are an example of how machine learning, mobile phone surveillance, and internet activity tracking are being used to censor and control populations. Most worryingly, the prospect of real-time surveillance makes running surveillance states such as the former East Germany and current North Korea much more financially efficient.

According to a 2015 discussion paper by the Institute for the Study of Labor, a German research center, by the 1980s almost 0.5% of the East German population was directly employed by the Stasi, the country’s state security service and secret police—1 for every 166 citizens. An additional 1.1% of the population (1 for every 66 citizens) were working as unofficial informers, which represented a massive economic drain. Automated, real-time, algorithm-driven monitoring could potentially drive the cost of controlling the population down substantially in police states—and elsewhere.

We could see a radical new era of censorship that is much more manipulative than anything that has come before. Previously, dissidents were identified when investigators manually combed through photos, read writings, or listened in on phone calls. Real-time algorithmic monitoring means that acts of perceived defiance can be identified and deleted in the moment and their perpetrators marked for swift judgment before they can make an impression on others.

SAP Q417 DigitalDoubles Feature1 Image7 Connected Cars, Autonomous Vehicles, And The IoTBusinesses need to be aware of the wider trend toward real-time, automated censorship and how it might be used in both commercial and governmental settings. These tools can easily be used in countries with unstable political dynamics and could become a real concern for businesses that operate across borders. Businesses must learn to educate and protect employees when technology can censor and punish in real time.

Indeed, the technologies used for this kind of repression could be easily adapted from those that have already been developed for businesses. For instance, both Facebook and Google use near–real-time facial identification algorithms that automatically identify people in images uploaded by users—which helps the companies build out their social graphs and target users with profitable advertisements. Automated algorithms also flag Facebook posts that potentially violate the company’s terms of service.

China is already using these technologies to control its own people in ways that are largely hidden to outsiders.

According to a report by the University of Toronto’s Citizen Lab, the popular Chinese social network WeChat operates under a policy its authors call “One App, Two Systems.” Users with Chinese phone numbers are subjected to dynamic keyword censorship that changes depending on current events and whether a user is in a private chat or in a group. Depending on the political winds, users are blocked from accessing a range of websites that report critically on China through WeChat’s internal browser. Non-Chinese users, however, are not subject to any of these restrictions.

The censorship is also designed to be invisible. Messages are blocked without any user notification, and China has intermittently blocked WhatsApp and other foreign social networks. As a result, Chinese users are steered toward national social networks, which are more compliant with government pressure.

China’s policies play into a larger global trend: the nationalization of the internet. China, Russia, the European Union, and the United States have all adopted different approaches to censorship, user privacy, and surveillance. Although there are social networks such as WeChat or Russia’s VKontakte that are popular in primarily one country, nationalizing the internet challenges users of multinational services such as Facebook and YouTube. These different approaches, which impact everything from data safe harbor laws to legal consequences for posting inflammatory material, have implications for businesses working in multiple countries, as well.

For instance, Twitter is legally obligated to hide Nazi and neo-fascist imagery and some tweets in Germany and France—but not elsewhere. YouTube was officially banned in Turkey for two years because of videos a Turkish court deemed “insulting to the memory of Mustafa Kemal Atatürk,” father of modern Turkey. In Russia, Google must keep Russian users’ personal data on servers located inside Russia to comply with government policy.

While China is a pioneer in the field of instant censorship, tech companies in the United States are matching China’s progress, which could potentially have a chilling effect on democracy. In 2016, Apple applied for a patent on technology that censors audio streams in real time—automating the previously manual process of censoring curse words in streaming audio.

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In March, after U.S. President Donald Trump told Fox News, “I think maybe I wouldn’t be [president] if it wasn’t for Twitter,” Twitter founder Evan “Ev” Williams did something highly unusual for the creator of a massive social network.

He apologized.

Speaking with David Streitfeld of The New York Times, Williams said, “It’s a very bad thing, Twitter’s role in that. If it’s true that he wouldn’t be president if it weren’t for Twitter, then yeah, I’m sorry.”

Entrepreneurs tend to be very proud of their innovations. Williams, however, offers a far more ambivalent response to his creation’s success. Much of the 2016 presidential election’s rancor was fueled by Twitter, and the instant gratification of Twitter attracts trolls, bullies, and bigots just as easily as it attracts politicians, celebrities, comedians, and sports fans.

Services such as Twitter, Facebook, YouTube, and Instagram are designed through a mix of look and feel, algorithmic wizardry, and psychological techniques to hang on to users for as long as possible—which helps the services sell more advertisements and make more money. Toxic political discourse and online harassment are unintended side effects of the economic-driven urge to keep users engaged no matter what.

Keeping users’ eyeballs on their screens requires endless hours of multivariate testing, user research, and algorithm refinement. For instance, Casey Newton of tech publication The Verge notes that Google Brain, Google’s AI division, plays a key part in generating YouTube’s video recommendations.

According to Jim McFadden, the technical lead for YouTube recommendations, “Before, if I watch this video from a comedian, our recommendations were pretty good at saying, here’s another one just like it,” he told Newton. “But the Google Brain model figures out other comedians who are similar but not exactly the same—even more adjacent relationships. It’s able to see patterns that are less obvious.”

SAP Q417 DigitalDoubles Feature1 Image9 Connected Cars, Autonomous Vehicles, And The IoTA never-ending flow of content that is interesting without being repetitive is harder to resist. With users glued to online services, addiction and other behavioral problems occur to an unhealthy degree. According to a 2016 poll by nonprofit research company Common Sense Media, 50% of American teenagers believe they are addicted to their smartphones.

This pattern is extending into the workplace. Seventy-five percent of companies told research company Harris Poll in 2016 that two or more hours a day are lost in productivity because employees are distracted. The number one reason? Cellphones and texting, according to 55% of those companies surveyed. Another 41% pointed to the internet.

Tristan Harris, a former design ethicist at Google, argues that many product designers for online services try to exploit psychological vulnerabilities in a bid to keep users engaged for longer periods. Harris refers to an iPhone as “a slot machine in my pocket” and argues that user interface (UI) and user experience (UX) designers need to adopt something akin to a Hippocratic Oath to stop exploiting users’ psychological vulnerabilities.

In fact, there is an entire school of study devoted to “dark UX”—small design tweaks to increase profits. These can be as innocuous as a “Buy Now” button in a visually pleasing color or as controversial as when Facebook tweaked its algorithm in 2012 to show a randomly selected group of almost 700,000 users (who had not given their permission) newsfeeds that skewed more positive to some users and more negative to others to gauge the impact on their respective emotional states, according to an article in Wired.

As computers, smartphones, and televisions come ever closer to convergence, these issues matter increasingly to businesses. Some of the universal side effects of addiction are lost productivity at work and poor health. Businesses should offer training and help for employees who can’t stop checking their smartphones.

Mindfulness-centered mobile apps such as Headspace, Calm, and Forest offer one way to break the habit. Users can also choose to break internet addiction by going for a walk, turning their computers off, or using tools like StayFocusd or Freedom to block addictive websites or apps.

Most importantly, companies in the business of creating tech products need to design software and hardware that discourages addictive behavior. This means avoiding bad designs that emphasize engagement metrics over human health. A world of advertising preroll showing up on smart refrigerator touchscreens at 2 a.m. benefits no one.

According to a 2014 study in Cyberpsychology, Behavior and Social Networking, approximately 6% of the world’s population suffers from internet addiction to one degree or another. As more users in emerging economies gain access to cheap data, smartphones, and laptops, that percentage will only increase. For businesses, getting a head start on stopping internet addiction will make employees happier and more productive. D!


About the Authors

Maurizio Cattaneo is Director, Delivery Execution, Energy, and Natural Resources, at SAP.

David Delaney is Global Vice President and Chief Medical Officer, SAP Health.

Volker Hildebrand is Global Vice President for SAP Hybris solutions.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.


Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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