Increasing it, that is. Make decisions faster.
Chris Taylor says that there is simply too much data out there for humans to use effectively. So bring in the machines. Machine Learning is where raw computing power is let loose on stacks of data in order to find useful patterns there.
Fair point. The machines are coming.
Opher Etzion points out the issues with relying on Machine Learning alone – cannot be done in real-time; cannot scale since every use case needs its own ‘learning’; and, finally it needs to be supplemented by humans who can provide a future vision that machines that learn from past data cannot.
Sound assessment of the current situation. No doubt the technology to support real time machine learning will improve – using Event Processing infrastructure as pointed out by Opher. Better tools will emerge that allow generalized learning across multiple use cases – promoting reusability and thereby providing scalability.
The open question is how to get humans to participate in machine learning.
This is where Decision Management Technology comes in. It includes Advanced Analytics like Machine Learning as well as Business Rules Management Systems (BRMS). The latter is where Human Expertise can be explicitly stated and managed. So, ideally we need to create a Decision Service black box that holds the Machine Learning model surrounded by Human Expertise expressed as business rules. And viola, this Decision Service can now automate most operational decisions – and you have increased Decision Velocity. Building Decision Management systems does require explicit decision modeling and the need to start there first.
Let us tame the Machine by giving it business rule Prime Directives.
One of the abiding tenets of ‘Social Business’ has been the use of employees to spread the company’s marketing message. This is the problem of employee advocacy where the first order thinking is the following.
…. if your company prospers [the employees] will also, so it’s to their benefit to support your brand. The result is that if employees retweet or share content published by their marketing department, they could save their company thousands, or hundreds of thousands of dollars in advertising costs.
From a pure business perspective this is perfectly rational thinking – optimizing scarce resources in support of business goals. The missing element is the inherently different mechanics of social interaction – in person or via digital media. First, this is not a one-way broadcast and a reasonable opportunity for a dialog is assumed. Most Dilbertesque organizations have strict controls over who can engage on behalf of the company and who cannot. Second, a public retweet or ‘like’ is a personal endorsement. Employees have the inside view on how things are put together within an organization – and they are likely to assume that their product is not good enough. Trusting their own organization comes first. Public endorsements, next.
Getting employees to engage in social business is more than making it easier for them to ‘share’ approved marketing content. Successful social businesses will have to rethink organization culture and organization. It is a tightrope walk between too much flexibility and too little. Still, Enterprise 2.0 does not add window dressing on the periphery of industrial-age assembly lines but organizes itself as a knowledge network that it really is.
Dilbert needs open, social interaction within his company before he retweets some of it.
Where did Social Media come from? And where did Big Data come from?
First, technology started advancing faster than human capacity to absorb and exploit it. Next, consumer and personal technology has been able to make an impact at a much faster rate than enterprise technology leading to the social digitization revolution. All our social interactions are now digital. Third, technology led automation of devices has led to a plethora of data being generated leading in turn to the age of Big Data – again defined fuzzily as any data too big to be managed by current infrastructure. And finally, Fourth, Enterprises are stuck today trying to cope with the changes mentioned above.
In this situation of technology driven environmental changes, what are the main enterprise initiatives that matter. What is the enterprise response? Not surprisingly, enterprise responses are driven by the buzzwords that rise and fall in the media and trade publications. We have had our share of revolutions – social, mobile, cloud and big data. Each and every enterprise strategy has to hook into one or more of these buzzwords – whether directly or in a contrived fashion.
Reacting to these individual trends with point solutions has been the most common strategy so far; but there is a clear realization that the forces of change are pushing deeper into the traditional enterprise. Well established processes and systems at the core of the enterprise are not able to integrate well with the rapid transformation at the periphery.
The cause-effect chain that can be established is the following. Rapid Technology Changes> Social Digitization > Consumer Technology> Big Data> Enterprise Technology Transformation.
Enterprise IT in its current state is the last domino to fall.
SAP Imagineering has recently released Screen Personas for creating better user experience (UX) for SAP users.
Personalization without Programming. SAP Screen Personas provides a simple, drag and drop approach to modify many common SAP GUI screens to make them more usable as well as more visually appealing.
While it may be tempting to focus on the Screen and make it ‘better’ by eliminating steps, this solution offers a great opportunity for rethinking users’ goals and the most effective means of achieving them. Even though users appear to be clicking buttons and entering data on the Screen, they have a Goal and they are actually making operational decisions. Picking values from a drop down or routing the transaction to colleagues are all decisions that the user makes. Decisions are based on experience and expertise – or based on applicable business policies and rules – or based on insights from a report or analytics.
The most effective SAP Screen Persona will place all elements of decision-making in context with process-steps on the Screen.
This design approach would require an explicit focus on Decisions first. Decision Models will need to be built that map user Goals to Information Sources – SAP Transactions, Screens, Fields and Business Objects required; and to Knowledge Sources – HTML components that provide contextual business rules, policies and analytic insights to help make the decision. Decision models have business and systems contexts that take into account organization structure, functional authorizations, business processes, events and other environmental elements.
A decision-centric design is the most comprehensive way to benefit from SAP Screen Personas technology.
Do you step through a process as you try to achieve your goals?
Or do you make a series of inter-related decisions in getting to the goal?
Your answer will determine your approach to optimizing your activities as you navigate a complex, dynamic environment on your way to your goals. If you are thinking of your activities in terms of a process, you are likely to be more set in your approach. After all, processes are a set of pre-defined activities that you expect to stay consistent for some period of time. Yes, processes will evolve and improve over time – but not right away. What are the chances that you are following an outdated process?
A decision-centric approach recognizes that there are choices to be made in every activity – and that each choice has to be informed by insight. The insight could be subject matter expertise, a business policy or an algorithm mined via advanced analytics. This is where there is the most scope for improvement – both in terms of sophistication and experimentation, and in terms of the speed of change.
So, you still need to act – possibly in a pre-defined series of activities bundled into a ‘process’ – but you need to shift your focus to the series of decisions that show up at every turn. That is how you stay agile and become smarter, embedding insights from your experts and from your data into your business operations.