Is AI a distraction???

AutomationI was recently in an exchange with a respected industry analyst where they stated that AI is not living up to its hype – they called AI ‘incremental’ and a ‘distraction’. This caught me a bit my surprise, since my view is that there are more capabilities and approaches available for AI practitioners than ever before. It may be the business and tech decision makers approach that is at fault.

It got me thinking about the differences in ‘small’ AI efforts vs. Enterprise AI efforts. Small AI are those innovative, quick efforts that can prove a point and deliver value and understanding in the near term. Big AI (and automation efforts) are those that are associated with ERP and other enterprise systems that take years to implement. These are likely the kinds of efforts that the analyst was involved with.

Many of the newer approaches enable the use of the abundance of capabilities available to mine the value out of the existing data that lies fallow in most organizations. These technologies can be tried out and applied in well defined, short sprints whose success criteria can be well-defined. If along the way, the answers were not quite what was expected, adjustments can be made, assumptions changed, and value can still be generated. The key is going into these projects with expectations but still flexible enough to change based on what is known rather than just supposition.

These approaches can be implemented across the range of business processes (e.g., budgeting, billing, support) as well as information sources (IoT, existing ERP or CRM). They can automate the mundane and free up high-value personnel to focus on generating even greater value and better service. Many times, these focused issues can be unique to an organization or industry and provide immediate return. This is not the generally not the focus of Enterprise IT solutions.

This may be the reason some senior IT leaders are disillusioned with the progress of AI in their enterprise. The smaller, high-value project’s contributions are round off error to their scope. They are looking for the big hit and by its very nature will be a compromise, if not a value to really move the ball in any definitive way – everyone who is deploying the same enterprise solution, will have access to the same tools…

My advice to those leaders disenchanted with the return from AI is to shift their focus. Get a small team out there experimenting with ‘the possible’. Give them clear problems (and expectations) but allow them the flexibility to bring in some new tools and approaches. Make them show progress but be flexible enough to understand that if their results point in a different direction, to shift expectations based on facts and results. There is the possibility of fundamentally different levels of costs and value generation.  

The keys are:

1)      Think about the large problems but act on those that can be validated and addressed quickly – invest in the small wins

2)      Have expectations that can be quantified and focus on value – Projects are not a ‘science fair’ or a strategic campaign just a part of the business

3)      Be flexible and adjust as insight is developed – just because you want the answer to be ‘yes’ doesn’t mean it will be, but any answer is valuable when compared to a guess

Sure, this approach may be ‘incremental’ (to start) but it should make up for that with momentum and results. If the approach is based on expectations, value generation and is done right, it should never be a ‘distraction’.

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Is automation forcing divergent paths of quality vs. cost?

robots-too-humanI saw an interesting post: When Robot Writers Take Over, Will Freelancers Be Obsolete? The article was focused on freelance writing, but it did make me wonder about the whole concept of freelancing, in general.

The relatively fixed and easy to automate positions in many fields are ripe for automation. Those that require creativity or unique insight should be safe for a long time to come. In fact, automation could make the freelancers life less mundane and more interesting. It reminded me of a situation earlier in my career…

Back in the early 90s, I worked in the AI space for Electronic Data Systems (EDS). We focused primarily on solving problems for GM and the US government. Somewhere around here I have a coffee cup with the moto of the group: “Make it Work, Make it Real”. Unfortunately, the folks working in the group had felt it really meant that if we could make it work, it wasn’t really AI — since someone would always say that it was just regular old programming, no matter what innovative technique or esoteric language we used.

One of the projects I led was called Knowledge-based Tool Design. We were trying to automate tooling design for clamping and welding car parts using CAD techniques, a project far ahead of its time. Programmatically determining the right type of clamp and the correct way to swing it into place was too difficult spatially, for the time. We just didn’t have the compute power and the algorithms determine orientation and approach. A good human tool designer could see the solution intuitively.

We did figure out that people are not good at pulling together the bill-of-materials to ensure that the clamp and all the hydraulic and mounting components… were defined. We shifted our attention to defining that type of detail using computers — reducing the errors and rework later in the process.

Similarly, in other industries, there are so many annoying and resource intensive, low hanging fruit to be picked that the return on investment for tackling truly intuitive problems just isn’t there. That can all change though as better algorithms and computing capabilities develop.

There are a couple of ways this could go:

  • The intuitive functions will likely become more of a freelance function, since companies will not need (or be willing to pay) for those expert roles all the time and the work will be interesting.
  • The focus shifts to less high-quality designs that can be automated.

In any case, employment as we know it will be changing.

Elastic Map Reduce on AWS

derived dataLast week, I put out a post about Redshift on AWS as an effective tool to quickly and dynamically put your toe in a large data warehouse environment.

Another tool from AWS that I experimented with was Amazon’s Elastic Map Reduce (EMR). This is an open source Hadoop installation that supports MapReduce as well as a number of other highly parallel computing approaches. EMR also supports a large number of tools to help with implementation (keeping the environment fresh) such as:  PigApache HiveHBase, Spark, Presto… It also interacts with data from a range of AWS data stores like: Amazon S3 and DynamoDB.

EMR supports a strong security model, enabling encryption at rest as well as on the move and is available in GovCloud, handling a range of big data use cases, including log analysis, web indexing, data transformations (ETL), machine learning, financial analysis, scientific simulation, and bioinformatics.

For many organizations, a Hadoop cluster has been a bridge to far for a range of reasons including support and infrastructure costs and skills. EMR seems to have effectively addressed those concerns allowing you to set up or tear down the cluster in minutes, without having to worry much about the details of node provisioning, cluster setup, Hadoop configuration, or cluster tuning.

For my proof of concept efforts, the Amazon EMR pricing appeared to be simple and predictable allowing you to pay a per-second rate for the clusters installation and use — with a one-minute minimum charge (it used to be an hour!). You can launch a 10-node Hadoop cluster for less than a dollar an hour (naturally, data transport charges are handled separately). There are ways to keep your EMR costs down though.

The EMR approach appears to be focused on flexibility, allowing complete control over your cluster. You have root access to every instance and can install additional applications and customize the cluster with bootstrap actions (which can be important since it takes a few minutes to get a cluster up and running), taking time and personnel out of repetitive tasks.

There is a wide range of tutorials and training available as well as tools to help estimate billing.

Overall, I’d say that if an organization is interested in experimenting with Hadoop, this is a great way to dive in without getting soaked.

Six thoughts on mobility trends for 2018

mobility walkLet’s face it, some aspects of mobility are getting long in the tooth. The demand for more capabilities is insatiable. Here are a few areas where I think 2018 will see some exciting capabilities develop. Many of these are not new, but their interactions and intersection should provide some interesting results and thoughts to include during your planning.

1. Further blurring and integration of IoT and mobile

We’re likely to see more situations where mobile recognizes the IoT devices around them to enhance contextual understanding for the user. We’ve seen some use of NFC and Bluetooth to share information, but approaches to embrace the environment and act upon the information available is still in its infancy. This year should provide some significant use cases and maturity.

2. Cloud Integration

By now most businesses have done much more than just stick their toe in the cloud Everything as a Service (XaaS) pool. As the number of potential devices in the mobility and IoT space expand, the flexibility and time to action that cloud solutions facilitate needs to be understood and put into practice. It is also time to take all the data coming in from these and transform that flow into true contextual understanding and action, also requiring a dynamic computing environment.

3. Augmented reality

With augmented reality predicted to expend to a market somewhere between $120 and $221 billion in revenues by 2021, we’re likely to see quite a bit of innovation in this space. The wide range of potential demonstrates the lack of a real understanding. 2018 should be a year where AR gets real.

4. Security

All discussions of mobility need to include security. Heck, the first month of 2018 has should have nailed the importance of security into the minds of anyone in the IT space. There were more patches (and patches of patches) on a greater range of systems than many would have believed possible just a short time ago. Recently, every mobile store (Apple, Android…) was found to have nefarious software that had to be exercised. Mobile developers need to be ever more vigilant, not just about the code they write but the libraries they use.

5. Predictive Analytics

Context is king and the use of analytics to increase the understanding of the situation and possible responses is going to continue to expand. As capabilities advance, only our imagination will hold this area back from increasing where and when mobile devices become useful. Unfortunately, the same can be said about the security issues that are based on using predictive analytics.

6. Changing business models

Peer to peer solutions continue to be the rage but with the capabilities listed above, whole new approaches to value generation are possible. There will always be early adopters who are willing to play with these and with the deeper understanding possibilities today new approaches to crossing the chasm will be demonstrated.

It should be an interesting year…

Groundhog Day, IoT and Security Risks

groundhogs dayLately I’ve been hearing a great deal of discussion about IoT and its application in business. I get a Groundhog day feeling, since in some sectors this is nothing new.

Back in the late 70s and early 80s, I spent all my time on data collection off factory equipment and developing analytics programs on the data collected. The semiconductor manufacturing space had most of its tooling and inventory information collected and tracked. Since this manufacturing segment is all about yield management — analytic analysis was a business imperative. Back then though you had to write your own, analytics and graphics programs.

The biggest difference today though is the security concerns. The ease of data movement and connectivity has allowed the industries lust for convenience to open our devices and networks to a much wider aperture of possible intruders. Though there are many risks in IoT, here are a few to keep in mind.

1) Complexity vs. Simplicity and application portfolio expansion

Businesses have had industrial control system for decades. Now that smart thermostats and water meters and door bells are becoming commonplace, approaches to managing this range of devices in the home has required user interfaces to be developed for the public and not experts. Those same techniques are being applied back into businesses and can start a battle of complexity vs. simplicity.

The investment in the IoT space by the public dwarfs the investment by most industries. These new more automated and ergonomic tools still need to tackle an environment that is just as complex for the business as its always been – in fact if anything there will be more devices brought into the business environment every day.

Understanding the complexity of vulnerabilities is a huge and ever-growing challenge. Projects relying on IoT devices must be defined with security in mind and yet interface effectively into the business. These devices will pull in new software into the business and increase the application portfolio. Understand the capabilities and vulnerabilities of these additions.

2) Vulnerability management

Keeping these IoT devices up-to-date is a never-ending problem. One of the issues of a rapidly changing market segment like this is devices will have a short lifespan. Business need to understand that they will still need to have their computing capabilities maintained. Will then vendor stand behind their product? How critical to the business is the device? As an example of the difficulties, look at the patch level of the printers in most businesses.

3) Business continuity

Cyber-attacks were unknown when I started working in IoT. Today, denial of services and infections make the news continuously. It is not about ‘if’ but ‘when’ and ‘what you’re going to do about it. These devices are not as redundant as IT organizations are used to. When they can share the data they collect or control the machines as they should, what will the business do? IoT can add a whole other dimension to business continuity planning that will need to be thought through.

4) Information leakage

Many of the IoT devices call home (back to the businesses that made them). Are these transferred encrypted? What data do they carry? One possible unintended conscience is that information can be derived (or leaked) from these devices.  Just like your electric meter’s information can be used to derive if you’re home, a business’s IoT devices can share information about production volume and types of work being performed. The business will need to develop a deeper comprehension of the analysis and data sharing risks that has happened elsewhere, regardless of the business or industry and adjust accordingly.

The Internet of Things has the potential to bring together a deeper understanding of the business. Accordingly, security at both the device and network levels needs to develop as strongly. The same analytics enabling devices to perform their tasks can also be used nefariously or to make the environment stronger.

Abundance and the value potential of IT — things have changed…

Since I have moved to a new blog site I decided to update a post on my foundational beliefs about IT, the future and what it should mean to business.

A number of years back, I posted that the real value for business is understanding unique and separating what was abundant from what was scarce and plan to take business advantage of that knowledge.

I came up with this model to look at how things have changed:

abundanceToday, there is an abundance of data coming in from numerous sources. A range of connection options can move the data around to an abundance of computing alternatives. Even the applications available to run on the data continues to grow almost beyond understanding. Various service providers and options even exist to quickly pull these together into custom (-ish) solutions.

Yet there are elements of the business that remain scarce or at least severely limited by comparison. The attention span of personnel, the security and privacy of our environment and even actions based on the contextual understanding of what’s happening persist in being scarce. Part of every organizations strategic planning (and enterprise architecture effort) needs to address how to use the abundance to maximize the value from the scarce elements and resources – since each business may have its own set of abundant and scare components.

For IT organizations one thing to keep in mind is: almost every system in production today was built from a scarcity model of never having enough compute, data… Those perspectives must be reassessed and the implications of value for the business that may be generated reevaluated, since that once solid foundation is no longer stable. The business that understands this shift and adjusts is going to have a significant advantage and greater flexibility.