white paper digital operations for service operators · 2019-05-06 · digital operations for...

VITRIA®

WHITE PAPER

Digital Operations for Service Operators

DIGITAL OPERATIONS FOR SERVICE OPERATORS | 2VITRIA®

DIGITAL OPERATIONS FOR SERVICE OPERATORS Retailers like Toys R’Us continued to rely on their brick and mortar operations, while Amazon made early bets that commerce would move online. Taxi services are being disrupted by juggernauts like Uber and Lyft while Netflix has reduced live TV relevancy to news and sports. Google and Facebook have demonstrated the power, possibilities and perils of this arms race towards automation and data monetization. In all aspects of our lives, Digital Transformation is happening. The rush is on to view your service centric business through the customers lens and to put all available data to work to achieve better, faster, smarter and more automated experiences and operational outcomes. One thing is certain, there will be winners and losers and the arms race will involve the mastery of multiple layers of coordinated technologies, virtual and physical assets along with analytics, AI, automation and a lot of data. In the end, the goal is to deliver a new or better business model or some differentiated service experience that somebody will pay for or at least not cancel or walk away from because someone else has figured it out first!

Not every business can be Facebook, in which the path to data monetization in hindsight appears to be straight forward; get everyone’s data and monetize it. It is common knowledge the data we collect today is valuable and vital to the future of the business. However, for most businesses, the path to actual ROI with Digital Transformation lacks some important detail. As a result, the term Digital Transformation means everything to everyone. Subsequently, in the rush to “Digitize,” there are no shortages of ideas, experiments or projects competing for executive attention, prioritization and funding. Most of them will not deliver value.

For “service centric” businesses, the drive towards the digitization of operations or simply “Digital Operations,” is proving to be the best place to begin. Providing a service while remaining dependent on silos of disconnected data across software, hardware and network is no longer an option. Winning companies are measuring service operations holistically and making use of multiple modes of technology and data to drive better experiences and outcomes. But where does a company begin, how do they begin and how do they get serious and measure success or prioritize projects to monetize and make use of their data?

In this article, we explore these topics and VIA from Vitria Technology which is a proven approach and market offering to Digitize Service Operations and monetize the value of data for service operators.

About Vitria: Vitria is a software and services company which has been in business for over 20 years and has specialized in tools, technology, applications, expertise and services in operational real-time solutions. In addition to working directly with end customers, over the years, large systems integration firms such as Accenture, PWC, Indra, Telefonica, Schneider Electric, Ericsson and many others have used the products and services from Vitria as part of the turn key solutions they provide to their end customers. With this wealth of experience, Vitria is in a unique position to offer our perspective and best practice advice for your digital operations initiatives.


CONTENTS1. What Do We Mean by a Service Operator? .............................................3

2. Digital Means Different Things ...................................................................5

3. Defining Digital Operations ..........................................................................6

3.1 Analytics Journey for Digital Operations ......................................................... 8

3.2 Digital Operations Business Case Drivers ..................................................... 11

3.3 Competing & Complimentary Initiatives ........................................................ 14

3.3.1 The Data Lake aka “Silver Bullet” .......................................................... 14

3.3.2 Robotic Process Automation ................................................................ 15

3.3.3 Application Performance Monitoring ................................................... 17

4. How-To Maturity Model for Digital Operations ................................... 18

4.1 Depth First vs. Breadth First ............................................................................... 18

4.2 Program Management & Expectations .......................................................... 19

4.3 VIA from Vitria - Architecture & Technology Considerations ..................... 19

5. Questions and Answers (Q&A) .....................................................................24


WHAT DO WE MEAN BY A SERVICE OPERATOR?Before we explore Digital Operations as a sub category of a Digital Transformation strategy, we need to first define what is meant by a Service Operator(s) and provide context about Vitria Technology.

Service operators are similar to Amazon or Netflix in that they incorporate hardware, software and data into what they do and the products or services they provide. It is important to note that under this definition of service operators, we would include any company in which a technology enabled service is core to the business. For example, Telecom and MSO (Multi-Service Operators), such as Comcast or Charter, fall under this definition. Also, in this category are energy, utilities, banking or transaction service providers, transportation providers, the new breed of IoT enabled cloud service providers and the manufacturing, service and repair operations that may exist within a larger diversified manufacturing organization. We can extend this definition to shop floor manufacturing, in which the highly-automated service is understood to mean the coordination of machines and process to reliably produce optimal output. By this definition, it encompasses multiple verticals and yet these service operators share important characteristics regardless of the vertical industry in which they reside. These four (4) common characteristics are:

1. The service IS the business

2. Operators of services are increasingly dependent on data driven automation, and this mass automation must simultaneously:

a. enhance the overall service experience for the customer

b. drive cost out of the business

3. Measuring and understanding all aspects of the service from both the customers’ perspective and across the hardware, software, people, processes and data that comprise the service is challenging

4. These service operators live and die by what we call the “Incident to Response” or “Opportunity to Offer” life cycle

1


For service operators, digitizing service operations (aka Digital Operations) should be one if not THE first priority on the road to Digital Transformation.

Vitria’s product suite, VIA offers an agile approach to digital operations through a set of AI-driven solution templates that address the common key steps of this transformation. Built on the VIA low-code analytic application development platform, VIA Solution Templates are pre-assembled artifacts allowing complex analytics-driven solutions to be released in days rather than months. Solutions include agonistic collection interfaces, pre-built data curation and schema, applied AI, intuitive graphical user interface components and workflow automation functions. The templates can easily be configured or extended by customer developers, system integrators or the Vitria field team.

With little exception, the solutions from Vitria share a few common characteristics. Typically, they have been sold to or for the benefit of the operations teams in the end customers’ business. The solutions will most frequently operate on real-time data or a mix of real-time and contextual data. And finally, the solutions typically need to be flexible (agile) or “model-driven” so that they can be modified by the end customer, take advantage of 3rd party data science tools and techniques and be both portable and change resilient in the sense that the solution can be deployed, used and remain useful even if there are differences or frequent changes in client specific operational processes and technical environments.

This last point is critical. Suffice it to say that if you are considering tools, technology and a strategy for digitizing business operations, be aware that the only constant right now in Digital Transformation, AI, Cloud Technologies and Data Science is change. Vitria is in a unique position to gather insights and advise on best practices for Digital Transformation when the business driver is transformation of business or service operations.


DIGITAL MEANS DIFFERENT THINGSEvery company in every vertical industry will experience disruption and struggle to formulate a strategy around Digital Transformation. Digital Transformation is a broad topic covering a lot of things. Across all this data-driven/data-centric “goodness” we see three common initiatives being sponsored or initiated under the Digital Transformation moniker:

1. The Data Lake initiative

2. The Digital Engagement Initiative

3. The Digital Operations Initiative

While these initiatives are in many ways complimentary, we’ll focus on the payoff realized by Service Operators within a Digital Operations initiative focused on services. Digitizing a service involves putting into practical working form a series of “analytic pipelines” in which all available data is leveraged in real-time to measure, guide, change or improve some real-world outcome regardless of the channel of engagement to the end customer.

Digital Operations lives at the intersection of real-time and continuous data analytics, customer experience and operational execution. Successful Digital initiatives lead to data-driven execution. By data-driven execution we mean the correct use of all available data, to guide or automate the next best response under all operating conditions, all the time and in real-time. Digital Operations have immediate and measurable business impacts and are therefore the most practical form of a Digital Transformation initiative.

FIGURE 1: VIA IN THE CENTER OF THE TRANSFORMED CONSUMER

DigitalTransformation

Operations

CustomerExperience

DIGITALOPERATIONS

Prediction & automation are complex

Noisy alarms confuse operations

“I expect it to work, but if it doesn’t I want to fix it myself.”Self-service and personalized customer operations are expected from the new consumer

Prediction & automation are the last stages of analytical computing

“I want what I want, and I want it now.”New experiences, and availability drive new opportunities

Artificial intelligence & robotics

Machine generated, real-time big data

5G, SDN/NFV, DOCSIS 3.1Leverage ultra-wide broadband and virtualization to deliver new experiences

Predict behavior and automate processes to increase demand and lower costs

Utilize existing instrumentation and new IoT data in real-time to improve service health

Change is the constant in digital transformation

New connected assets and technologies require operations to

be adjusted

New technologies generate more data and signals than operation

teams must handle

2


DEFINING DIGITAL OPERATIONSDigital Operations is distinct from other big data or Digital Transformation projects in that it is specifically targeted towards the use of data to monitor and make better decisions during the execution or operation of a process or service. Unlike planning or historical reporting (on big or small data), with Digital Operations we are specifically attempting to use data and analytics to monitor and react more intelligently based on what is taking place in real-time in an operational environment. This entails responding optimally to situations and exceptions all the time and in real-time. This is an important distinction as reacting intelligently under circumstances that vary is a better approach than spending incremental dollars and resources to plan and replan in the face of an ever-changing operational reality. Customers and service issues are unpredictable, customers drive opportunity and are the primary source of change. In this sense, Digital Operations is the cornerstone to becoming a customer centric, data-driven enterprise.

Digital Operations in the context of the service operator refers specifically to the real-time measurement and visibility of all aspects of a service or process along with the “Digitization” of the Incident to Response or Opportunity to Offer Life-Cycle which takes place continuously during the operation of your process or service. For simplification, we mean the collection and measurement of all relevant operational data across differing technological silos associated with a Service (or Sub-service). The dependent data sources that drive a service or sub-service are brought together and aligned in real-time into a single source of truth in which we apply new (and continuously evolving) digital tools and techniques to shorten, automate and more intelligently detect operational anomalies or opportunities and the associated response during everyday execution for that particular service or sub-service.

FIGURE 2: DIGITALIZING THE RESPONSE LIFE-CYCLE

3

Today Without Digital Operations1.

2.

3.

4.

5.

6.

7.

Lack of Independent Measurement

Undetected Occurrences

Overwhelmed by Noise

Complex Human Diagnosis

Manual Repair and Workaround

Delayed Recovery Assurance

People do the Work

With Digital Operations1.

2.

3.

4.

5.

6.

Instrument Everything — Make use of Available Data Across Silos

Measure Everything in Real-Time

Software Does the Work Using Multiple Algorithms and Analytic Techniques

Management by Exception is Possible

Machine Learning and Feedback Loops are Possible

Intelligent Automation Allows for Automated for Semi-Automated Next Based Action


What we label a Service or sub-Service when we “Digitize” Operations may vary depending on the business as will the technological layers (silos) or dependencies that exist in order to provide the desired service outcome, customer experience or product. Each service or sub-service can be thought of as an “ecosystem” of the related but siloed, technologies, data, and operational processes, that must be aligned and work flawlessly to produce the business objectives (that typically manifest itself as metrics).

For Telecom/MSO Operators example service ecosystems might include:

• Video over IP (VoIP) in which sub-services might include (Video-login, Video-browse, Video-watch).

• Key service operations dependencies might include software applications, network infrastructure, storage & hardware.

• Key service metrics at the macro level such as a Net Promotor Score (NPS) which is a measure of loyalty to your service which is influenced by the service experience and correlated to login failures, dropped connections, truck rolls, trouble tickets or call center calls.

For an Energy Utility, example service ecosystems might include:

• Power transmission, distribution or smart metering at the points of consumption.

• Key service operations dependencies might include the network topology, transmission and distribution infrastructure, software applications, smart metering devices and sensors.

• Key service metrics might include service uptime, outage incidents, meter and alarm status, faults, voltages, service and repair tickets or customer calls.

For a Manufacturing environment, example service ecosystems might include:

• A product line in which sub-services might include (Part#-stamping, Part#-welding, etc.).

• Key service operations dependencies might include robotic machines and tooling, software applications, network infrastructure & hardware.

• Key service metrics might be Overall Equipment Effectiveness (OEE) which is a best practices metric that identifies the percentage of planned production time that is truly productive. Where the OEE is computed across combinations of manufacturing process and equipment and correlated to equipment downtime, maintenance schedules, dwell times, error/rework rates, defect rates etc.

For Financial Services example service ecosystems might include:

• A customer facing transaction like a trade, money transfer or account action and sub-services might be a critical step in that transaction or process (login, credit check, account update etc.).

• Key service operations dependencies might be the SOA application interfaces, hardware, software, network and storage technologies that underpin these applications etc.

• Key service metrics might be throughput baselines, machine learned volume-based metrics on different layers of the technology etc. fraud, error and straight through processing metrics etc.

The objective then becomes the classification of services in your company, line of business or processes and the justification or payback by service/sub-service for its “Digitization”.


3.1 Analytics Journey for Digital OperationsWe have already discussed what we mean by Digital Operations in the context of the Service Operator. In order to “Digitize” a service or sub-service in any vertical industry, there is a requirement to establish a repeatable, technology neutral approach to accomplish the following eight (8) steps in real-time and at scale:

1. Ingest and align data (across technology silos).

2. Enrich this data with reference data and business context.

3. Assign metrics to the data (measurements) and cross correlate the metrics across service layers.

4. Apply an ever growing/changing (hopefully improving) set of analytic algorithms and models to these cross correlated data streams to detect opportunities and anomalies within the layers (note that the analytic algorithms and models may be unique to each layer --- that’s fine/expected).

5. When opportunities (or anomalies) are detected by this portfolio of analytic models --- trigger incidents that group the cross correlated data sets.

6. Process these cross correlated data sets (incidents) with another portfolio of analytic algorithms, rules and techniques to match them to a root cause or intelligent real-time action.

7. Take the action or update the other enterprise systems with the latest and greatest data driven awareness.

8. Perform the above as rapidly as possible in real-time. This is important because shortening this analytic pipeline allows for faster customer response or at a minimum more compute time to make an even smarter, situationally appropriate action within the execution window.

We call this pipeline the “Analytics Journey” or the Analytics Value Chain (AVC). In the Digital Operations solutions, we recommend to our clients, that this pipeline represent the logical steps required for every service or sub-service.

FIGURE 3: ANALYTICS VALUE CHAIN AND PIPLEINE STAGES

Service Layer Matric Assignment

Business Layer

Service Layer

Application Layer

Network Layer

Compute/Store Layer

Ingest/Enrich Real-time & Reference Data

Multi-Layer Detection

Cross Layer IncidentClassification

RCA/Action Analytics

Guided Automation

Analytics Value Chain & Pipeline Stages

Digital Operations involves real-time understanding across all layers in context. The Analytic rules and models are ever changing, ever improving and vary by stake

holder and stage of the pipeline


This framework decomposes Digital Operations into a set of stages for every service or sub-service. This allows for parallel efforts to take place not only in terms of onboarding multiple services and sub-services but also in terms of the incremental improvement of intelligence applied within the pipelines themselves. By this last point, we mean that at each stage of the analytics journey there should be the opportunity to apply and grow a never-ending portfolio of analytic models and rules. This portfolio of rules and analytic models becomes the core IP and differentiator of your business or service going forward.

When architected and solutioned properly, the following characteristics should be true for both the approach and solution to service digitization:

1. The Analytic algorithms, tools and technologies applied in the various stages can grow, change and vary without disruption to upstream or downstream processing.

2. Ideally the data models to “Digitize” a service using such a pipeline will already exist in standards-based technologies.

3. The pipelines themselves can be executed and scaled on commodity hardware/compute infrastructure with open core commercially supportable technologies neutral to cloud, on-premise or hybrid deployment architectures.

4. We should be able to “onboard” a new service or sub-service in minutes/days/hours (not months).

5. End users in different areas of the business should be able to make their own changes to the library of analytic models and rules. Agility is a must.

6. The analytic pipeline should be applicable across verticals and services. Both customers and 3rd parties should be able to add their own models and analytic IP to the solution offering.

Regardless of the tools and technologies involved in digitizing a service or sub-service there are core use case scenarios that need to be supported for every digitized service or sub-service in the enterprise.


FIGURE 4: STAGES OF THE DIGITAL OPERATIONS FRAMEWORK

The act of “Digitizing” any service or sub-service involves establishing analytic pipelines that can be grouped into these five (5) categories:

Real-time Visibility --- The ingest of data at every layer of the service necessary to measure the current state and historical state of performance for this service. Usually this Digitization effort takes the form of reducing a set of data feeds into time series data then establishing metrics to the time series data across various time windows.

Advanced Detection --- This is the application of a variety of analytic rules, models and algorithms that look for unusual boundary conditions, patterns of interaction or anomalies to the metrics and measurements for each layer separately and cross correlated across layers. When used standalone, real-time analytic measurement (visibility) and advanced detection allow for management by exception of operational processes and services.

Incident Management --- When opportunities or anomalies are detected by a portfolio of analytic rules and models the Digitized service requires that we make available all related information and data correlated in time with its associated reference data for further evaluation. We call this stage incident management and it involves the application of another portfolio of analytic models and rules to associate the incident to its root cause or next best action. This stage allows for the automaton of the right response and eliminates the alarm noise and waste associated with monitoring independent silos of business events with their application and technology layers.

Change Management --- Dynamic change management is a separate category of opportunity or anomaly detection and incident management in which some form of “change” can be correlated with an operational anomaly. It represents the capability to automatically detect changes to entities related to a Digitized service (for example changes to the attributes of a subscriber to my service or to a physical asset such as a server or router configuration in one of my technological enablement layers). The ability to dynamically discover if these populations or groups of entities that have experienced a changed is meaningful in comparison to some business metric (for example truck rolls, service calls, login or transaction failures) and to identify and respond to this change intelligently.

Dynamic Prediction --- Involves the use of the resulting history from the above processing stages to learn and detect in advance, sequences of operational states and activities, that are the early predictors of anomalies and incidents and proactively initiate corrective action or intelligent alerting.

Real-TimeOperationalVisibility

AdvancedAnomalyDetection

DynamicPopulation

IncidentLife-CycleAutomation

DynamicFailure

Prediction

Automated orSemi-Automated

Action

1

2

3

4

5


FIGURE 5: DIGITAL OPERATIONS AND THE ANALYTICS JOURNEY

Collect, Visualize & Measure Across Silos

Can’t Manage What We Can’t Measure

Let Technology Highlight What is Important & Find

Problems or Opportunities Sooner

Management by Exception

Ensure Changes Have the Desired

Outcome

Digital Operations Feedback Loop

Solve the Right Problem or Make the Right Decision Faster

Enable Intelligent Response &Automation

Anticipate Operational Events

Digital OperationsMaturityOBJECTIVE

STAGES

Real-Time Operational

Visibility

1Advanced Anomaly Detection

2Dynamic

Population

3Incident

Life-Cycle Automation

4Dynamic Failure

Prediction

5

3.2 Digital Operations Business Case DriversFor Service Operators, the Digitization of a service and its associated incident to response (or opportunity to offer) lifecycle can be pegged back to a business case with quantifiable ROI in many ways.

FIGURE 6: SELECT A SERVICE TO DIGITALIZE

Incident Life-Cycle Process

Order Life-Cycle

54

6

7

81

2

3

Ordering

Scheduling

Shipping

Bill Presentation Architecture

Autoinvoice toReceivables

1.

2.

3.

4.

Transfer Accountingto General Ledger

Cash Management

Receipts

Invoicing

8.

7.

6.

5.

1.

2.

3.

4.

5.

6.

7.

Lack of Independent Measurement

Undetected Occurrences

Overwhelmed by Noise

Complex Human Diagnosis

Manual Repair and Workaround

Delayed Recovery Assurance

People do the Work

1.

2.

3.

4.

5.

6.

Instrument Everything — Make use of Available Data Across Silos

Measure Everything in Real-Time

Software Does the Work Using Multiple Algorithms and Analytic Techniques

Management by Exception is Possible

Machine Learning and Feedback Loops are Possible

Intelligent Automation Allows for Automated for Semi-Automated Next Based Action

Example — Order lifecycle or some customer facing service you provide or operate.

Collecting

Ordering Accountin

g


Typically, there are four (4) major buckets of benefit:

1. Increased Uptime or Throughput When anomalies or opportunities are detected late or not at all the risk of down time increases. Early detection translates into more units in a manufacturing process, more orders booked per unit of time, more transactions for a transaction processing service or more billable hours of enjoyment and less service credits for subscription offerings.

2. Improved Customer Experience Uptime and rapid response clearly improves customer loyalty and satisfaction but there is also incremental value in smarter, faster and more situationally appropriate responses. Letting a client know ahead of time that there will be a delay and not to worry or avoiding that call when a Digitized service understands that certain clients will not be impacted has real value.

3. Reduced Opex/Waste Without the benefit of Digital Operations, a company’s resources are often wasted. Late detection of opportunity or an anomaly leaves less time for diagnosis, repair/response and recovery/action. Within each of these activities, humans typically do the work and even attempts to automate the steps involved in this work (for example Robotic Process Automation “RPA” initiatives) cost money and frequently involve the automation of steps that are not necessary if the incident to response life cycle has been digitized. Some estimates indicate 20% or more of opex resources goes to chasing the symptoms. This takes away from the time available to repair/recover and has a knock-on effect in terms of uptime/throughput and customer experience.

4. Optimization Benefits Digitizing a service allows real-time analytics to optimize situationally appropriate responses that improve the services experience and improve margin. A better, timelier or more appropriate offer is made, or a higher margin product or service is sold. Additionally, the shift to real-time evaluation of service exceptions or opportunities is beneficial even in situations where the execution window is measured in hours or days vs. seconds. In these situations, all available cycle time can be put to work improving the intelligence of the response.

Any one of these ROI buckets is likely enough to justify a program to Digitize your services.


Let’s apply these concepts in the context of a service. For example, let’s assume the service is a streaming video service. We’ll use this example, but the logic holds true for any service offered by a service provider. If we think about the incident to response life-cycle for this service, let’s assume the service is Digitized and we have some basic anomaly detection and incident management analytics operating in real-time. In this basic example, we can detect unusual call volume counts and unusual failed login attempts to use the service, we can then cross correlate the call volumes with the failed login attempts down to the service layer. By pinpointing what is happening in the network and which users/services are affected, there is real value. This scenario for Digital Operations is not difficult to deploy and it does not involve incredibly advanced predictive data science models. VIA from Vitria regularly and easily achieves these results and a business case can be made on the back of an envelope.

If a truck roll costs $100 each and dispatchers cannot correlate the causation of trouble tickets for large segments of subscribers who may be experiencing issues that cannot be resolved within the home, the business case is simple.

Example:

$100 X # of avoidable truck rolls

X # of incidents per unit of time

= $$$ a large number for the typical MSO(Multi-service operator like

your cable company)

By applying real-time baselining techniques and cross correlation techniques, eliminating the noise from unrelated or downstream issues and pinpointing the problem pays huge returns. This leads to a virtuous cycle in which the same analytics pipeline introduces ever improving, more sophisticated analytics and rules to all services at every stage; the company is on the path to payback for their Digital Operations initiatives.

Moreover, the prioritization of Digital Operations projects can take place by organizing project “sprints” by service across the dimensions of business value vs. complexity and time to implement. In this way a self-funding roadmap can be put in place that both achieves benefits quickly and provides incremental benefits over sustained periods of time.


3.3 Competing & Complimentary Initiatives3.3.1 THE DATA LAKE AKA “SILVER BULLET”

The data lake is a popular initiative that is often funded by marketing or IT based on the understanding that data is likely to be valuable even if the immediate payback or insight to be derived from the data has yet to be identified. In some ways, the generic data lake project might be considered a “breadth first” approach to Digital Transformation in the sense that it can sometimes be the catch all project justified by the notation that data is good, it must have value, so let’s just start collecting some.

IT teams like the data lake initiative because, like the Digital Operations initiative, it involves new technologies and with advances in large scale data storage and processing technologies like Hadoop (HDFS, Spark, etc.), there is a business case to be had by looking at the cost to store data vs. traditional Enterprise Data Warehouse (EDW) technologies. EDW vendors have enjoyed high margins which have come under attack recently as data volumes have increased and the cost per terabyte (TB) to store data has been driven down through open core/open source alternatives. Additionally, real-time sources of truth that underpin Digital Operations solutions can pick away at the value of static or look back reporting for operational performance or status.

The data lake initiative is often sponsored as a sand box for experimentation and discovery of interesting insights allowing companies to form an understanding of the value of their data and become conversant in the core competencies critical as the world goes digital.

Data lake initiatives are complimentary to Digital Operations initiatives since they likely share core technologies, resource skill sets and architectural similarities. They differ in one important aspect, however. Digital Operations initiatives look to make use of the company’s data to achieve faster, smarter more automated operational outcomes. The data lake can provide reference data, and analytic models and insight but it is only part of the solution not the silver bullet answer to digitizing operations.

Sometimes, in the case of the data lake project, the initiative takes on a life of its own and becomes an initiative in search of a problem to solve. Or the data lake fosters the collection of data for data’s sake without the forcing function of a specific ROI or business case to sharpen direction and scope.

To maximize ROI from a data lake initiative, putting the data to work and taking better action (hopefully automated action) than otherwise possible are key. The Digital Operations initiative brings it all together and produces quantifiable value in the context of business operations.

For these reasons with service operators we recommend a depth first vs. breadth first approach to achieving value from data lake initiatives associated with Digital Operations.


3.3.2 ROBOTIC PROCESS AUTOMATION

Digital Operations initiatives are sometimes sponsored within the same organization as other initiatives that deal with automation and operational efficiency. Robotic Process Automation (RPA) is an example of one such initiative and technology that has sparked interest for service operators wanting to cut costs in service delivery.

RPA refers to preconfigured software that uses business rules and predefined activity choreography to complete the autonomous execution of a combination of processes, activities, transactions, and tasks in one or more unrelated software systems to deliver a result or service with human exception management.

Investment in RPA technology typically attempts to address the automation of repetitive steps that might need to take place within the incident to response life-cycle associated with a service being operated by the service operator.

For example, if a service is not functioning properly (assume this has been detected somehow --- e.g. a Digital Operations detection model) perhaps there are a number of steps operators typically go through to diagnose before they log an incident for further investigation. The operator may physically log into a console and type some commands to see if the network is operational. They may “ping” one or more application to see if the application is alive and they may log into the call center application and check the queue manually looking for some threshold to be exceeded.

FIGURE 7: RPA WITHOUT DIGITAL OPERATIONS

EXAMPLE: BOTS Using machine learning to automate repetitivediagnostic tasks of logging into multiple systems trying todetermine if the alarms are real or just symptoms. INVESTIGATIONS

DIAGNOSTICCHATTER

Alarm Noisesurfacing from manylayers and silos

Repetitive work being done with lessheadcount but with increased complexity.BOTS managing BOTS, IT teams managingRPA infrastructure. SME oversight of RPAactivities, etc.


Without Digital Operations, these are all necessary steps. However, many of these steps will be completely eliminated or become unnecessary under a Digital Operations initiative. Make no mistake the automation of manual steps can be valuable, but it should not be the priority when it involves the automation of steps that in fact are unnecessary to begin with.

In many cases, RPA is most useful when the process or steps being automated have been directed or triggered through Digital Operations. In this way we expend dollars and effort on the automation of correct and necessary actions vs. the use of RPA as a Band-Aid to do the busy work of unnecessary tasks currently being performed by humans on a service that has not been Digitized.

Additionally, RPA technologies frequently deal with the automation of human tasks associated with interacting with business applications and human interfaces. In Digital Operations initiatives it’s frequently the case that analytic models and rules can be used to directly trigger an automated response system to system. For example, instead of an RPA automation to log into the work order system and update it, the Digital operations initiative may update the work order system directly, machine to machine.

FIGURE 8: DIGITAL OPERATIONS ALONG WITH RPA

Service Layer

Application Layer

Storage & Compute Layer

Network Layer

Your Service

SYSTEMS FORACTIONS

BOTS that learn tomanage repetitive

INTELLIGENT ACTIONS

USEFULAUTOMATIONS

Alarm Noise surfacing from many layers and silos

Digital Operations – All the best data including transactional data, customer data and cross correlated service layer data goes through a series of AI/ML pipelines.

Alarm noise, RCA and intelligent action are processed in real-time.

Automations and updates take place system to system. RPA can be used for repetitive automations that are not practical for direct system process automation

Your CustomersExperience

USEFULAUTOMATIONS


In conclusion, RPA initiatives can be valuable and complimentary as a sub-initiative to Digital Operations for specific kinds of automation but we need to be careful we are not exerting effort and dollars to cover up the lack of visibility, analytic insight and data driven automation at the heart of Digital Transformation.

3.3.3 APPLICATION PERFORMANCE MONITORING

APM or Application Performance Monitoring is another interesting and complimentary set of initiatives and technologies. Service Operators by definition are dependent on software, applications and technology working in concert in order to provide the goods and services they sell. There are software vendors who focus on instrumenting (typically through agent software or probes) these applications and help trace performance or detect anomalies in the application layer.

In some sense the Digital Operations initiative is a super set of these initiatives that brings all available data sources together (not just the performance data of one or more applications or application interfaces) and puts this information in the context of the service, business transactional data and layers of technologies involved in providing the process or service.

Digital Operations initiatives should be agnostic to the technologies and instrumentation used to surface data and digitize the service. The APM tools and vendors can provide useful data feeds and alarms that help in the effort to digitize the service. In the case of Digital Operation, we work backwards from the customer experience or expected results of the service and we look to use all available data to drive a better, smarter, faster business outcome across the technological layers involved in providing the service.

Put another way, if APM initiatives are valuable, the Digital Operations initiative applies these same concepts to the entire service stack and service offering and not just the application layer.


HOW-TO MATURITY MODEL FOR DIGITAL OPERATIONSVitria has experience and best practice to advise in the approach to digitizing service operations. The VIA solution suite from Vitria provides a complete and easy to adopt set of templates to guide adoption and achieve success. The solution and approach allow for rapid realization of value while taking into consideration the sometimes-conflicting requirements for agility, self-service, open standard inclusiveness and the need for insulation from change during this period or rapid innovation.

4.1 Depth First vs. Breadth FirstWe recommend a systematic approach to Digital Operations that organizes the program by service and sub-service and implements the end-to-end stages of the analytics journey discussed herein. This approach may be considered a “depth first” approach to digitizing a service.

By depth first, we mean taking the digitization of a small group of service all the way from measurement to initiating action in a way that ROI and value can been realized quickly. Establish this type of Digital operations pipeline before onboarding the rest of your services and sub-services. Minimal depth-first coverage should include real-time metrics, visibility, detection, incident analytic and some automated or semi-automated actions being triggered. Change management & dynamic prediction pipelines may lag and each stage may mature at a different pace, but we recommend implementation of the full pipeline across stages by service for a group of services and then moving on in parallel across all services following the formula.

FIGURE 9: STAGES OF THE DIGITAL OPERATIONS FRAMEWORK

4

Real-TimeOperationalVisibility

AdvancedAnomalyDetection

DynamicPopulation

IncidentLife-CycleAutomation

DynamicFailure

Prediction

Automated orSemi-Automated

Action


The contrast would be organizing projects that leave off at operational visibility and detection and begin a path of applying that across multiple services before following through on incident analytics and automated/semi-automated action. We have found that failure to establish the full path to value is a mistake that leads to failed initiatives. The reason being that real-time metrics and real-time anomaly detection generates noise. The last thing we need in a high-pressure, service-centric operating environment is yet another noise maker and yet another screen to look at.

A new or special monitoring tool or data science model to detect a specific anomaly within a layer or across layers is good of course. But operationalizing the full pipeline to include the stages post detection in which business context is used to determine relevancy, root cause or next best action results in the real benefit. We encourage taking the automated or semi-automated action and we encourage developing maturity in the types of analytic models and techniques required in these other stages of the pipeline. Our recommendation is to use this as an opportunity to gain expertise all the way to the end goal and then iterate on improving the capabilities within each stage over time. We expect iteration on the models and rules and a growing portfolio of models and rules in each stage of the pipeline. Hence, going too deep on any one stage or rolling out monitoring without a proven approach to dispatch incidents or enable the desired automation is not desirable.

4.2 Program Management & ExpectationsDigital Operations initiatives don’t have to be difficult. Using a solution like VIA from Vitria we can organize a small team into a delivery sprint or train the end customer or a systems integration partner to onboard a service to some pre-existing templates that match the solution requirements described herein.

These templates are designed to make it easy to digitize across service layers and come complete with the starting analytic models to realize value quickly. They also include the tools and open architecture necessary to make configuration or customization an easy exercise.

If you are interested in example skill sets, staffing and durations of these sprints, please contact your local Vitria contact or ask a Vitria solution provider. Typically, services can be onboarded in days vs. weeks or months.

4.3 VIA from Vitria - Architecture & Technology ConsiderationsFor Digital Operations we recommend a proven “open core” architecture that supports operation on premise, in the cloud or in hybrid configurations. To remain somewhat neutral to the shifting sands of cloud service offerings we recommend sticking with commodity hardware and operating system environments.


FIGURE 10: LEVERAGING PROVEN TECHNOLOGY, SCIENCE & PROCESS

PARSERS SCHEMAS ALGORITHMS DASHBOARDS ACTIONS

REAL-TIME

VIA SOLUTION TEMPLATES

VIA PLATFORM COMPONENTS

BIG DATA MESSAGE BUS ANALYTICS APP BUILDERSADF™

Dashboard Builder™Visual Flow™

Visual Explorer™BusinessWare™

Look for supported solutions that bring together combinations of these technologies and open standards into solution offerings that are comprehensive, well tested, agile to change and support the full requirements of service digitization and the aforementioned analytics pipeline.

Solutions like VIA from Vitria promote a pluggable architecture with a suite of VIA Solution Templates that cover the common stages and requirements for your Digital Operations Initiative. These Templates are complete and yet configurable by the end customer or 3rd party solution providers.


FIGURE 11: VIA DIGITAL OPERATIONS

Streaming or batch, Kafka or a CSV, VIA has ADF building

blocks to automate onboarding of a new data set.

INGEST

VIA LOW CODE ANALYICS PLATFORM

VIA DIGITAL OPERATIONS SUITE

VIA Analytics Data Flow (ADF)

CURATE

Data is messy. VIA’s model-driven approach allows for

easy enrichment and modeling of disparate data.

ANALYZEVIA removes the complexity of deploying AI/ML with drag and drop libraries like Spark ML & Tensor Flow, and native R &

Python support.

VISUALIZEFrom real-time dashboards to

custom reports, VIA’s Dashboard Builder brings

insights to the screen with ease.

AUTOMATE

Insights are just data if not operationalized. Leverage VIA

ADF blocks to automate process or drive work.

Digital Operations Journey

Know what’s happening right now.

REAL-TIMEOPERATIONAL

VISIBILITY

BES

POK

E U

SE C

ASE

S

ADVANCEDANOMALY

DETECTION

Find problems faster & reduce the noise.

DYNAMICPOPULATION

MANAGEMENT

Ensure the changes you make are having a positive impact.

INCIDENTLIFE-CYCLE

AUTOMATION

Solve the right problems faster and automate fixes.

DYNAMICFAILURE

PREDICTION

Prevent problems from happening in the first place.

OPE

N S

DK

The logical architecture and capabilities of the VIA Digital Operations Suite have been packaged to support the stages of the Digital Operations Journey.

FIGURE 12: VIA DIGITAL OPERATIONS SUITE OVERVIEW

Digital Operations UI Digital OpsManagement

Analytic - Services

Digital Operations Real-Time Pipeline(s)

Tested Model Libraries

Time Series Data Management

Via ADF Low Code Tools - Platform Layer

“Executive”Overview

IncidentAnalysis

IncidentViewer

TrendAnalysis

DimensionAnalysis

AnomalyAnalysis

AnomalyAnalysis

BaselineGeneration

IncidentAnalysis

DimensionAnalysis

TrendAnalysis

Ingestion & Parsing

Template

Anomaly“Detection”

Template

Incident Identification

Coalesce& Classify

Incident Correlation

GroupAnomalies

Incident Scoring

Severity& Impact

Incident Causation

Root CauseAnalysis

Incident Prediction

Incident Prescription

RecommendBest Action

Incident Resolution

Management

System Configuration UI

System Model & Meta-data

DO Monitoring (as a “service”)

Metric HistoryMetrics Incident

#1...n

INCIDENT LIFECYCLE


Underpinning the solution offering for Digital Operations we include the low code/no code VIA Platform which allows our customers and solution partners to modify or enhance all aspects of the Digital Operations Suite. The VIA platform allows for drag and drop configuration and rewiring of analytic models, rules and the user experience along with the way the solution interfaces intelligently with operations systems in real-time.

These tools allow the customer or solution partner to make use of 3rd party or custom developed analytic models and libraries over open core architectures in a scalable supportable solution.

FIGURE 13: VIA’S LOW CODE PLATFORM & TOOLS FOR DIGITAL OPERATIONS

CONNECTIVITY & IOT

BIG DATA

STREAMING ANALYTICS

ARTIFICIAL INTELLIGENCE

AUTOMATION

FULLY AWARE

LEANER

RELIABLE

MONETIZATION

Real-Time Collect & Compute Open Core Data Lake & In-Memory Technologies

Pre-built Templates for Digital Operations

Visual Low-Code DevelopmentOver Open Core

Real-timeAnalytics

Advanced Analytics Engine

HistoricalAnalytics

PredictiveAnalytics

PrescriptiveAnalytics

Automation

Solution Templates

Digital Operations Solutions

StreamingIngestion

IoT Communications& Protocols

OpenData Lake

Reusable analytic models and rules can be exposed for configuration by less skilled resources. Your own libraries of analytic algorithms can be made available to be composed into new and supportable use cases in support of your evolving Digital Operations needs and requirements. This provides a standardized way for your data science resources and 3rd party consultants to make the value of their work deployable, maintainable and reusable.


FIGURE 14: REUSABLE ANALYTIC MODELS WITH ANALYTIC DATA FLOW; MODEL-DRIVEN DEVELOPMENT FOR CREATING ANALYTIC PIPELINES

Model-Driven Analytic “DataFlows”Streaming & Batch Applications

Interaction Development & Debugging

Deploy to Spark Server or Elastic Cluster

Cloud Ready

•

•

•

•

Common Building BlocksSource and Target Resources (e.g., HDFS, Feeds, Sockets, Kafka, connectors)

Parse — Schema on Read

Aggregate (Time-Series Enabled)

Filter, Correlate and Join

In-Memory Tables, SQL Execution

Predictive Analytics (PMML, Spark R, MLlib)

Geospatial Correlation and Location Analytics

Time-Series Analytics with Late Event Support

Create “Libraries” of Re-usable analytic building blocks for

composite applications and your own solution offerings

•

•

•

•

•

•

•

•

•




Cloud Ready

•

•

•

•











•

•

•

•

•

•

•

•

•




Cloud Ready

•

•

•

•











•

•

•

•

•

•

•

•

•


QUESTIONS AND ANSWERS (Q&A)What value do VIA Digital Operations Templates have vs. just developing on open core technologies?

1. Faster Time to Value --- With the VIA solution template you can digitize a service and generate value in hours/days vs months/years. The solution template is ready to use or sell from day one.

2. Extendable System Model --- The data model and system models for Digitizing services (across multiple technology layers) already exists and is transparent allowing for easy extension or modification with open standards.

3. Proven to Work --- The solutions are tested, supported and scale in real-world deployments.

4. Higher ROI --- The analytics pipelines in the VIA digital Operations templates offer a structured starting point to insert analytic logic/value specific to your business so you work on only what is core/unique to your services.

5. Easy to Maintain/Modify --- The solution templates have been well designed into reusable components. They come complete with low code/no code drag and drop VIA tools to configure, extend or modify the solution. This allows less skilled resources to make changes to solutions for client specific requirements while preserving the ability to add your own analytic intellectual property.

6. Reusable Across Verticals - For Systems Integrators and Partners these templates can form the basis of your resellable market offering.

5


Is the value of the VIA Digital Operations Template the long list of pre-tested analytic algorithms that you provide out of the box?

1. The VIA solution offering does include a complete set of pre-tested Digital Operations algorithms for AI/ML monitoring of your service offerings. Furthermore, the open core architecture provides your company everything necessary to modify, grow and maintain your own libraries of analytic models and rules essential to your data driven Digital Operations roadmap.

2. The solution templates contain a set of all the common reusable analytic models for common use in digital operations such as baseline and scoring algorithms that have been abstracted and parameterized for re-use (differing widow sizes and intervals for example).

3. These reusable models and building blocks have been composited into the common processing and pipeline stages described earlier in the “analytics journey”. You have working pipelines in well-formed stages in which to compartmentalize change and to add and grow the sophistication of your own libraries of analytic models, rules and algorithms.

4. In addition, the VIA Templates have the service layer silos and topologies modeled and exposed for independent analytic processing as well as for cross silo/layer correlation and processing so they can be used out of the box and then enhanced by the client or partner.

5. Furthermore, the VIA Digital Operations Templates come with the low code/no code configuration and development environments to allow the packaging of your own analytic IP into the templates for use directly in the template solution AND for the re-use/configuration by others without having to.

ABOUT VITRIA

Vitria optimizes the Digital Operations Journey. VIA Digital Operations Solutions offer an agile, fast and simplified approach to digital transformation. From first breaking down data and organizational silos for real-time visibility, through the detection of nuanced incidents within a sea of anomalies and alerts, to dynamically predicting potential failures, VIA’s Digital Operation Solution Templates and low-code platform delivers business value 10X faster than alternatives. Discover more at http://www.vitria.com

VITRIA®

What is the value of the platform layer (VIA ADF for example) vs. open source and 3rd party alternatives?

1. VIA Platform tools have been specifically designed to offer a complete solution to creating and maintaining operational real-time analytic applications.

2. Operational real-time analytic applications have similar requirements to other analytic application with the following additional capabilities and requirements:

a. The tools and applications must work with streaming real-time data;

b. The tools and applications need to handle common operational issues such as out of order events, missing events, exception conditions on windows of events and timeouts etc.; and,

c. The tools and applications must operate all the time and be suitable for driving real-time automated action into external systems and applications.

3. VIA Platform tools allow for the low code/no code configuration of analytic applications and real-time analytic pipelines. You compose analytic applications vs. writing code and yet the runtime makes use of open standards and open core architectures. Additionally, the same tools used to create analytic applications can be provided to the end user for the configuration, extension or modification of the analytic solutions.

4. The VIA Platform and tools have specific capabilities designed to allow the developer to package analytic logic for re-use, re-sale or configuration by your customers or for the benefit of your own resources that might be less skilled or specialized than the original author(s).

5. The entire product line is commercially supported to take the guess work out providing reliable real-time analytic solutions.

http://www.vitria.com

white paper digital operations for service operators · 2019-05-06 · digital operations for...

Documents