Service as a Software. From "Software as a Service" to "Service as a Software" Changing paradigms in analytics and decision science

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1 Service as a Software From "Software as a Service" to "Service as a Software" Changing paradigms in analytics and decision science

2 Software has been a boon to enabling and scaling analytics for decision support in large organizations. But as business complexity increases, and change accelerates, the software and Software as a Service (SaaS) models will need to evolve to a new paradigm of man and machine. We call this Service as a Software. History is written by looking backwards over large spans of time to see where we were and where we came from. Historians examine change using long lenses to see patterns of change and the learnings from the past. In some cases, the underlying rate of change is so fast that we do not even have time to reflect on the past, as the present and the immediate future intrude upon our examinations. So it is with data and decision support. Change and the VUCA world CHANGE In a. short span of a few decades, the world of data-driven decisions has gone through a significant transformation at a bewildering speed. The cause can be linked to the rate of change in the business environment. Business models change, and the rate at which new competitors disrupt existing businesses is accelerating, to the point where the list of companies at the top of the Fortune 500 changes every year as former market leaders bite the dust. Today s business world is characterized by VUCA Volatility, Uncertainty, Complexity and Ambiguity. We observe that the use of data for decision support in business has gone from traditional notions of management information systems (MIS) and decision support systems (DSS) to a vocabulary of business intelligence (BI), data warehouses, data science, big data analytics and data lakes and the emerging discipline of decision science. Underlying this change in vocabulary is a profound change in the way businesses organize themselves to use data to drive business decisions. We see an evolution of four distinct stages of how large businesses have approached problem solving using data. 2

3 Services Era Before technology, there were people. Lawyers, accountants, designers and other specialists who could help businesses understand their organizations better and make decisions to help them create new products and services, restructure through lean times, and power and sustain growth. The Software Era During the 90s, computing went through a step change from centralized mainframes to distributed computing and the client-server era. During this revolution came new solutions for decision support - specific software to address specific problems - from Swiss Army knife-like software such as spreadsheets and statistical packages to specific tools designed to address inventory management, financial systems, pricing, customer relationship management (CRM) and the like. Enterprise software became a growing business to bring packaged software for a growing range of business functions and also custom software purpose-built to reflect and support the business model and the business processes of large corporations. The data warehouse and business intelligence became real as organizations sought to harness the power of data to improve their businesses. The Software as a Service (SaaS) Era As early as the 1960s, the idea of centralized hosting of business applications saw the concept of utility computing, or time-sharing, pioneered by large companies such as IBM. This was then followed by the era of ASPs (application service providers) in the 1990s, who aggregated computing power and storage in large data centers. In the 2000s, we gave it another name - cloud computing. Models of renting business software based on use got a huge acceleration from services being hosted internally, or in the cloud, on a pay-per-use model. Players like salesforce.com were pioneers in providing CRM software to help companies improve sales force effectiveness. A marketplace of various vertical and horizontal software solutions was born. Fast forward to today, secure computing and storage are available for rent in the cloud. Google, Amazon, IBM and even Tesla are beginning to offer artificial intelligence (AI) and machine learning functionality for on-demand use of sophisticated software to tackle problems of big data. Everything, it appears, is amenable to being rented, including machine intelligence. Articles proliferated by pundits proclaiming the potential dangers of machines taking the jobs of humans. AIs replacing the knowledge worker? Not so fast! 3

4 Service as a Software A New Paradigm What we find when exploring the domain of decision science is that business problems are amenable to the use of sophisticated software, but not in splendid isolation. For one thing, decomposing business problems into units that are solvable by AIs and other software is a complicated business and requires a lot of knowledge workers to define and realize business outcomes. Add to it the complexities of the business environment and the accelerating nature of change in business models and customer preferences, and you begin to see that software will begin to struggle to keep up with the pace of this change. Problems are getting muddier as well as more granular. In an environment of complexity, problems don t follow organizational boundaries. A marketing decision is hundreds of interconnected micro-decisions each with its own complexity. Further, these interconnections span organizational silos. Marketing decisions are connected to product decisions are connected to supply chain decisions. In an environment of complexity, problems don t follow organizational boundaries. An inventory problem at a manufacturer is not necessarily a supply chain problem alone. It will have roots in the customer ordering patterns, choice of customer segments, products mix that one offers, etc. A traditional functional software product model is not geared to address this. It is not that software does not scale. One can replicate, and scale, the utility of software to multiple desktops, laptops and mobile devices. 4

5 But software is not flexible, and software development is not scalable. The real challenge is that even while software might be scalable, software development does not scale well. There are inherent diseconomies of scale in the business of software development. As business problems become more and more granular, it is impossible to create software solutions for each individual problem. Further, software development for products and applications is characterized by fairly firm business requirements, followed by incremental and evolutionary updates to functionality in release cycles. This paradigm comes under increasing stress as the changes in the business environment demand faster and faster responses from software teams. The pure software model for decision support starts to fail and is not flexible enough to cope with the increasing rate of change. Software scales in volume (doing more of the same better), but not in scope (doing unanticipated different things better as requirements keep changing). Meanwhile, performance measurement and market expectations are going up (the μ), while the variability in business operations (the σ) is going up. The relentless pressure of the environment demands a faster, more flexible, approach to decision support. So how are data-rich and analytics-hungry organizations responding to this climate? We see the emergence of centralized functions, the creation of the Chief Data Officer or the Chief Analytics Officer, responsible for the service, support and propagation of data-driven decisions. We see new models of governance for analytics federated, distributed, centralized, with data scientists and business analysts working in tandem, and also the emergence of analytics service providers to bring the best of structured problem solving, sophisticated algorithms and tools to solve business problems. Problems cannot be viewed in isolation, as interactions with other business problems could void the gains of local optima. Business problems are getting muddier and fuzzier. Problems are more granular and more interconnected. And the problem space is shifting. Multiple stakeholders, with different mental models and beliefs, have different views on the nature and causes of business problems. Problems cannot be viewed in isolation, as interactions with other business problems could void the gains of local optima. In this world, the product paradigm is insufficient, and not flexible enough, to keep pace with business, with its model of release cycles to address changes. The services paradigm is not scalable because using a people-only model to address hundreds of problems in the required response times is not possible. What one needs is a new integration of the software and services model, where both the nature of the software and the nature of services need to adapt into a new operating model where both work in harmony. 5

6 Interactions between Man and Machine in a typical Analytics Process Flow Change in the Traditional Software Model The traditional software product model needs to change. Software needs to change from a finished solution approach to a Lego block approach. Lego blocks of software, which do specific things very well, need to be stitched together to attack complex business problems. The software Lego blocks need to bring in a combination of generalization and customizability. Such Lego blocks are easier to maintain and evolve rapidly to meet changing business needs. But these Lego blocks cannot be just black-box, monolithic platforms. We need open systems, open source code and functionality that can be iterated quickly with a backdrop of constant and accelerating change. At the same time, 6

7 Service as a Software A New Paradigm In Decision Support Service as a Software then Service as a Software then... Performance expectations - μ Iterations of service and software to support continuous change Service as a Software Man plus machine- with lego blocks of software Software as a Service The tools-democratized, in the cloud and mobile The Software Era The Services Era The tools that help with decision support The experts who help Business variability - σ Change in the Traditional Services Model A shift from the traditional software solution approach to the software Lego block approach also needs a shift in the services model. The traditional services model in a software environment is based on a clear distinction between tasks automated by the software and tasks done manually (configuration, etc.). However as complexity, scale and granularity of the business problems increase, the software and services need to be more and more integrated. In this case, the software needs services to orchestrate and customize, while services needs the software Lego blocks to abstract, modularize and automate. The software Lego blocks facilitate heuristic tasks, that require the intuition and judgment of human beings, while automating the algorithmic tasks. Services is not just about deployment and configuration of the software; it is as much about building and improving the software Lego blocks as the problems shift and requirements change Services is not just about deployment and configuration of the software; it is as much about building and improving the software Lego blocks as the problems shift and requirements change. The interplay between software and services is non-linear and iterative with micro-blocks of software-driven and services-driven tasks interacting across the analytical and decision support value chain. It represents a unique integration of man-machine. 7

8 Approach to Solutions in Decision Support Yesterday Low σ operations Low µ measurement Today Problem Definition Low σ operations High µ measurement Products Solution Definition Software only Services Products It is fashionable today to see talk of AIs replacing the human. This may be true of vertical and repetitive problems, but it does not expand to the world of muddy and interconnected problems. Software-as-a-Service Services Tomorrow Service-as-a-Software-as-a Service High σ operations High µ measurement µ - Level of expectations and measurement We would argue that this ecosystem of man and machine is not going to be replaced anytime soon by just machines. And the right AI in this case is not Artificial Intelligence, but Augmented Intelligence. Products Services σ - Degree of change Recommended Neutral Sub-optimal Artificial intelligence is exploitative, where the machine is deployed to match or exceed human capabilities. Augmented intelligence is exploitative and explorative, enablinghumans to experiment with the problem space and relieving them of the tedium of tasks better done by a machine. We call this new paradigm, the Service as a Software model, in clear contrast with the Software as a Service model. Businesses will need this Iron Man model decision scientists donning exoskeletons of software and tools to attack big data (and small) to find new ways of solving business problems. This model will need to operate on what Wired magazine calls Internet Time. As business problems evolve, the model will move from services to software to services to software to services to software in an iterative model of continuous engagement and improvement of how business decisions are made. ABOUT MU SIGMA For more information, visit or follow us on Mu Sigma has worked with more than 140 Fortune 500 companies, offering them our innovative philosophy and approach to solving complex business problems. We address critical organizational and problem-solving needs as a decision science company by offering sophisticated analytics services that can help companies institutionalize data-driven analytics and harness big data in a sustainable manner and help leaders stay on the right path. Mu Sigma s unique interdisciplinary approach and cross-industry learning drives advancement in solving high-impact business problems across marketing, risk and supply chain. We provide an integrated decision support ecosystem of products, services and cross-industry best practice processes transforming the way decisions are enabled. With more than 3,500 decision scientists and experience across 10 industry verticals, Mu Sigma has been consistently validated as the preferred decision sciences and analytics partner. Contact us today to find out how our unique Art of Problem Solving can help you and your business succeed in this ever-changing business environment. Mu Sigma Inc Dundee Rd. Suite 160 Northbrook, IL Tel