Digital Business (DBiz)

Digital Freedom

The world has changed. Mobile devices combined with cloud apps have freed us from the traditional 9-5 commute to an office. “At work” is an activity and no longer a location or time in the day. This is explored in a recent article “Daddy are you still at work“.

And that change has happened in the last 5 years. It is a digital revolution, rather than evolution.

I am sitting under an umbrella outside a Peets Coffee shop on my super-light Macbook connected to free wifi with access to all my apps and content through a browser – on a public holiday.

I can buy almost anything online and get it delivered the next day. I can stay in touch to friends, family and work colleagues no matter where in the world they live. It is easy to connect and collaborate.

It should come as no surprise to all serious professionals that the age of Artificial Intelligence, Machine Learning, and Data Analytics is here – this time, to stay for good.

The first time around, back in the early 80’s to mid-90’s – a period of roughly two decades – AI got a lot of exciting hype, especially around the topics of Expert Systems, Natural Language Processing, and Computer Vision. The premise underlying all of AI at that time was that the human capability of learning was really a set of as yet unknown algorithms that knew how to take data of various types and reason from it. However, the common perception was that the promise of AI had not been met.

Each quarter, the American Customer Satisfaction Index issues an update of overall U.S. customer satisfaction. The national ACSI score for the period 1994 through 2015, as depicted below, reflects an aggregate of customer satisfaction with companies that comprise a large cross-section of the economy.*

National ACSI Score

It’s worthwhile noting that the ACSI score for Q$ in 2015 is well below the baseline 1994 level. This is just one indication that customers’ expectations are outpacing the ability of organizations to deliver.

Practitioners in our field have long been evangelizing on the critical link between decision management and predictive analytics. As James Taylor accurately and succinctly stated “Decision Management operationalizes predictive analytics. Traditional approaches to analytics are hard to scale and hard to use in the real-time environment required in modern enterprise architectures.”

On cue I noted with great interest several writers predicting analytics trends for 2016. These included:

As a recent research study of The Gartner Group shows , only 13% of business meets their strategic goals [1]. This means 87% of organizations prepare strategic plans and related objectives —but they don’t deliver on their strategy, at least not fully. This situation will get even more challenging with the intensifying digitalization and the adjustment of strategies to this trend. Less than 1% of companies have prepared their business processes to realize the potential of our digital world according to the same study. Hence the risk of not executing successfully on a business strategy incorporating the opportunities of digitalization becomes even higher. A just release study by BPM-D, Widener University and the Universidad de Chile demonstrates that over 55% of companies have issues finding the right opportunities to benefit from digitalization or struggle with the resistance to change and the slow decision making [2].

What stories do you tell others about the early days of computers, before their capacity to process and store information vastly expanded? Do you express amazement at the fantastic state of current digital technology? What about the down sides? Do your stories also include the inconsistencies within applications that frustrate us all? Do you speak about the enormous cost and time required by software engineering processes to develop and maintain systems?  Do you comment on the complexity needed for systems to communicate with other systems? 

Low-code apps are maturing and being adopted in more and more corporations - often taken in by the business users.  IT often sees them as threat and a risk.  But they can help IT and make them able to be more reactive and supportive to the business users' needs. But, are they too much power in the hands of citizen developers who are fragmenting the IT architecture?  However, cloud low code apps mean that IT will struggle to control their growth/usage. This article explores the pros and cons and the right reaction from the CIO.

The business is now in control
Now that every organization is dependent on technology, Line Of Business(LOB) management are more aware of the capabilities and potential for technology to drive transformational change before their business is undermined by a more nimble, technology enabled start-up.

Decisions inject Analytics smartness into Processes, making the processes smarter. 

Now that we have automated most routine processing using some programming logic and some basic rules, the next competitive frontier is making these processes adapt dynamically to changing conditions and unforeseen situations - and learning from each such situation. Processes with such sophisticated dynamic behavior are smart processes.

Smart behavior cannot be programmed into the processes without causing unmanageable complexity and catastrophic brittleness. In any case, such ‘programmed’ processes cannot ‘learn’ by themselves. Additional knowledge has to be physically programmed into the process.

So instead of trying to make the process smarter, the focus should be on making the embedded decision smarter - through automation using decision management technologies available today.

Smart Processes are really Smart Decisions

Big data and related topics like the IOT (Internet of Things) are always big topics of interest in large forums. So it was the case in CeBIT 2016, Hannover which I was fortunate enough to attend.  There were several talks on Big Data - Digital Disruption was the theme, and there was general consensus that Big data was here to stay and grow phenomenally. People were moving from basic reporting and visualization of data (Data discovery and understanding) to a phase where predictive analytics would rule. This was because of the huge strides made in the fields of Machine Learning and artificial Intelligence. One of the keynote speakers in CeBIT was Professor Nick Bostrom founding Director of the Future of Humanity Institute at Oxford. An extremely impressive speaker and futurist, I was blown away with the vision he had for the future. He spoke passionately that the time for Artificial Intelligence has finally arrived and what it means for society at large.