Category Archives: Allgemein

Smart Machine Marketing and the Algorithmic Economy

The reason why Smart Machines are so much more powerful than conventional computer programs are the advanced AI algorithms and the data that they can absorb. Smart Machines can sense their own state and their environment, can communicate with other Smart Machines, they are self-learning and can solve very complex problems, and they can act, sometimes autonomously. There are many technologies behind the capabilities of Smart Machines. The most important enabler is the massive amount of computing power and storage that is available today for a relatively cheap price, which makes it finally possible to apply computational heavy artificial intelligence algorithms that would have not been possible some years ago.

Many characteristics distinguish traditional software applications from Smart Machines. Computers have always been pretty good in repetitive and clearly described tasks and in applying strict logic and complex mathematics. An abundance of tasks today are solved by computers much faster, cheaper and more reliable than by humans. Yet, in many ways, computers appear oftentimes annoyingly stupid. Have you tried to have a meaningful and interesting conversation with a computer? It can be a difficult and typically very frustrating endeavour. What computers are missing is the ability to understand the meaning of what we have to say. This is because language is very ambiguous. The very same sentence can mean something completely opposite if said in another situation or by a different person. “I love this computer” could mean either did I really like my computer a lot but it could also mean that I really hate my computer because it doesn’t do what I want it to do. It is very unlikely that love refers to romantic love in this context. The idea that computers can think like a human sounds stretched, it is, however, closer than you might think.

It is changing with the up-rise of Smart Machines. It makes machines being able to handle situations with ambiguity, sparse information and uncertainty, thus, be able to solve human kind of problems. Instead calculating the optimal solution using a predefined algorithm, smart machines evaluate different options and choose the best option out of the possibilities. Problems do not need to be provided in a specified machine readable format, they can be simply formulated in natural language or even normal speech. Looking at the context of the problem makes it possible to interpret the question correctly. When I ask a smart machine “what is the best restaurant?”, it should understand that I am probably looking for a good restaurant that is not too far away from my current location. Based on the outcomes of an action, smart machines can learn and improve their problem solving. Instead of being programmed, they can read PDF documentations to understand a business process and observe how humans perform a business process to build its own knowledge base and eventually be able to handle the business process on its own.

The key components of a Smart Machine are depicted in the figure and will be explained in detail in the following. An incentive and rule system needs to be set for a Smart Machine which provides a purpose for the Smart Machine to exist (e.g. as a self-driving car) and the rules it needs to obey (e.g. ethics, law, company procedures, business goals).

Smart Machine Marketing Artwork

In order for machines to see, feel, hear, smell, and taste like human beings, all aspects of the physical world need to be translated into “digestible” data for machines to process, reason, and act. The rise of low-cost sensor technologies and the Internet of Things with its connected devices enables the collection of data from the physical world without human interaction. All senses are needed to cover an entire customer journey from inspiration to usage. The augmented senses of machines allow a broader, deeper, and more personalized customer experience. Sensed information is fed, interpreted, filtered, interlinked and used to initiate further activities.

The most important ability of Smart Machines is to process the sensed information similar to the way us humans process information (i.e. empirical learning). Smart machines are able to think and solve problems by understanding and clarifying objectives (and sometimes coming up with their own objectives), by generating and evaluating hypotheses, and by providing answers and solutions like a human would do (and unlike a search machine which gives a list of results). Smart Machines are self-learning, they can adapt their own algorithms through observing, discovery and by doing.

Finally, Smart Machines can act, by visualizing and providing the responses to a human decision maker, by informing or even commanding a human to execute certain activities, or in the extreme case, by completely autonomously executing a business process or any other actions. Based on the results of the actions, Smart Machines are able to re-calibrate their goal setting.

The impact of Smart Machines will be observable in three domains for Marketing Professisonals. First of all, customers will get a more contextualized and personalized experience. Secondly, the marketing departments will be able to do more with less people building on automation and scale of intelligent algorithms that take over some of the human labor. Thirdly, there will be advances in the customer journey possible which are of disruptive nature.

The marketing profession will be impacted fast and significantly by Smart Machines and the Algorithmic Economy. Personalizing and contextualizing the customer experience is the aim of everyone. But, creating meaningful continuous 1:1 interactions can only be feasible on a large scale with thousands or millions of customers if Smart Machines take over a lot of the work. This means that smart machines take over work reserved for humans in the past, as, for example, generating new content, and supervising staff in retail stores to ensure high customer engagement. It also means that those companies that still struggle with data-driven marketing will be in deep trouble. Those who embrace Smart Machines will be able to drive productivity beyond the imaginable for marketing and sales within the next decades.

Like all things in life, Smart Machines are all a matter of perspective. For marketing divisions in traditional companies, they might be seen as the biggest threat in history. The way most marketing departments work today is very reliant on human labor and decision making. Shifting the work to Smart Machines will make a lot of the abilities needed for traditional marketing personnel redundant and will require new capabilities that the workforce does not necessary have. For others, Silicon Valley startups and companies, Smart Machines generate an once in a lifetime opportunity. Smart Machines enable them to scale their limited resources and, thus, be able to challenge even the largest established players in their own strongholds, irrespective if it is retail, consumer goods, banking, insurance, manufacturing, entertainment or any other type of industry which requires Smart Machine Marketing.

Is it Time for a 2-Speed Business?

Shortly before my summer break – a lovely holiday in Northern France – I gave a keynote at a data science event that highlighted the importance of a bimodal IT for digital innovation.

The key idea behind bimodal IT is that IT needs to offer a second mode in addition to traditional IT that is more risk taking, agile and customer-centric in order to drive digital & analytics innovation more effectively.

Mode 1 is characterized by Gartner as the traditional mode of IT, which has a focus on reliability, is plan and approval-driven, uses large enterprise IT suppliers and typically follows a waterfall approach for implementations.

Mode 2 emphasizes agility and, hence, uses agile implementation approaches, it utilizes often small, new innovative vendors and works closely with the business to drive fast and frequent customer-centric business innovations.

There are many organizations that have started to establish a second, more agile, mode of IT (e.g. in form of a data science lab, a digital factory, or an agile development and DevOps department) and they usually run into two major challenges which impede them to reap the expected benefits:

(1) The two modes of IT are not synchronized well enough

(2) Business is not able to engage effectively with agile IT

I will explain these issues in more depth in the following and some lessons learned how to resolve them.

(1) The two modes of IT are not synchronized well enough

What many organizations get wrong is that they focus to much on creating the new agile Mode 2 of IT.  However, this is only one component of implementing a bimodal IT. The real challenge is how to synchronize both modes so they can play as a team. Having them in silos will not only create conflicts, but also will limit the success of any projects that need both Mode 1 and Mode 2 resources to succeed – which is rather the usual. So, what organizations need to establish is a bridge between the two modes.

Practically speaking, it all starts with mutual understanding and respect between the two modes. If Mode 1 resources have the feeling the they are a second class of IT, they will stop supporting Mode 2 and hinder them wherever possible. Leadership needs to communicate that no mode is better than the other, and both modes of IT are equally needed for success. Mode 2 resources need to understand that Mode 1 is crucial to renovate the core of IT, which enables innovative digital apps to be built on top of a healthy infrastructure efficiently and securely.

Moreover, there are touchpoints between Mode 1 and Mode 2 that require bimodal synchronization through explicit governance:

~ When a new application is planned to be developed, selection criteria have to be defined that outline which implementation should be done in which mode of IT.

~ When a new Mode 2 implementation project is starting, it has to be examined if interfaces to Mode 1 applications are needed and/or if other Mode 1 resources are required.

~ In particular, when the Mode 2 product is supposed to be released in a Mode 1 production environment, traditional release management needs to be involved already in the beginning of an agile project.

~ Finally, when a Mode 2 product is released, there might be a decision to further manage it in Mode 1 in the future.

(2) Business is not able to engage effectively with agile IT

Today´s businesses are not ready yet to engage with Mode 2 IT in a productive manner. This has two main reasons.

First, the second mode of IT is all about experimentation. Trying out new features, new approaches to analyze data and new ways to interact with customers, and taking into account that many of the experiments will not turn into viable products after all. Today, most traditional organizations have not developed a mindset for experimentation yet.

Second, using agile IT methods requires a much more intense participation of business during IT projects. Business is used to “throw business requirements over the fence” and IT would take them, take a few months or even years to implement them, and would come back eventually for testing. In the meanwhile, business does not need to spend much time for the  IT project. This is not the case for agile projects. In each sprint, the business needs to closely work with the developers and defines the business requirements on the run during the project.

These two points highlight some of the obstacles that come up, when there is a two speed organization on the IT side, but only a one speed organization on the business side. The solution is simple, but substantial: Many large organizations that I work with have recognized the need to establish also a second mode of business, which is more experimental, fast paced and enables real digital innovation.

The consequences are visible: There are more and more business labs and business innovation centers of large enterprises popping up around the world in addition to data labs that have the role to work with agile IT to come up and test new innovative ideas in a fast mode. They aim to imitate a startup environment  where creativity, experimentation  and disruptive innovation is in the focus. The results are impressive so far. Mode 2 IT can be much better utilized and the collaboration between business and a bimodal IT becomes significantly better when a two speed business has been established.

This is only the beginning, but one new imperative clearly emerges: It is time for a two speed business for any organization. The pace of change will become faster and volatility will increase in the future. So, let´s get business ready for it.

 

Dr. Alexander Borek advises Forbes 500 companies in multiple industries with regards to their digital transformation, data governance and Big Data Analytics innovation strategy.

All opinions in this blog are written in private capacity and do not express or reflect the opinions of his employer.

Industrializing Data Science and Analytics

Gartner´s “Hype Cycle for Advanced Analytics and Data Science 2015″ has just been published. The trends indicated in the hype cycle show a rising maturity of this young organizational discipline.  It is interesting to see that the buzzword “Big Data” has finally disappeared from the hype cycle, while machine learning (a discipline that has been there for decades, at least in academia) has reached the peak of inflated expectations.  This underpins a tendency to  move from big data (the bigger the better) to smart data (the smarter the better). Simply said: “No matter if it is big or small data, it is still data and we aim to get more value out of it.”

A trend that is also visible at a second glance is the emerging industrialization of data science, which is underpinned by a number of developments. Vendors increasingly support the management of analytical models built by data scientist over their entire life cycle, when they are scaled from prototype to company-wide adoption. So far, the management of analytical models has been rather disorganized in most companies.  Data scientists would create new models on an use case by use case basis. Some of the models have been actually doing what they promised to do and would be deployed in operations.

An end to end management of the models and a reuse of solution patterns for analytical models across the enterprise has not been actively enforced or governed. In a new project, they would often start nearly from scratch although a similar model might have been already developed in a different business unit. From an organizational point of view, it makes sense to have a centralized data science unit that can support data scientists in decentralized business units. A central data science unit can ensure that learnings are incorporated and fed back to the organization and that analytical models are consistently governed even after they are handed over to IT.

Very connected to this is the concept of the model factory. The idea is to bring automation and scalability to the process of building and deploying predictive models.  To find the best models, a huge number of models are built and tested using software tools that provide a high  degree of automation during devleopment. At the end of the process, only the best few models are deployed.

Finally, a thrilling concept comes from Gartner´s Alexander Linden, which is the concept of the analytics market place. Some companies such as Microsoft, Rapidminer and FICO have created marketplaces, where data science services and additional functionality are provided by third parties which can be purchased by users of the analytics platforms. This can become a true game changer. Similar to the third party apps and services provided at Salesforce.com, analytics marketplaces could become a source of millions of very domain specific analytics micro-applications that drive innovation.

Today, we stand only at the beginning. I am convinced that in a few years time, data science and advanced analytics will be as industrialized as traditional IT. What has changed with the uprise of data science is the speed with which new applications are developed and deployed, the increased willingness to experiment and the direct way data innovates business models and business operations. Now, we only need to scale it to the rest of the enterprise to reap the full benefits.