91���� is on a journey towards Sustainability 2.0, an agenda that reinvents sustainability under the compelling challenges of climate change and social inequity. This new agenda is driven by a remarkable combination of thought and action on 91����’s part with meaningful public-private-people partnerships. The Sustainability Report 2.0 is available .

The Need for a Sustainability Data Analytics Platform

Research by states that 86% of business leaders have invested in sustainable practices to protect their organizations from disruptions. However, reporting on sustainability initiatives and finding various data types is difficult for all relevant parties to access. In order to drive sustainability performance management, the data analytics platform is vital.

Why is Sustainability Data Analytics Platform Necessary?

• Unable to detect human errors during data collection – Leveraging AI-driven document processing methods
• Unable to trace and audit data – Ensuring end-to-end traceability by customizing system logs as user-friendly. Approvers can easily interpret and make approval decisions with sustainability data reviews
• Time-consuming while aggregating data – Introducing the power of data aggregating capabilities by customizing industry-standard data management tools
• Inconvenience while accessing sustainability reports – Enabling self-serving capability with ideal role-based access control for different stakeholders
• Difficulty in getting the approval of reported sustainability data – Leveraging integrated workflow for end-to-end automation of processes ranging from data preparation to approval
• Proactive methods needed for meeting sustainability targets – Introducing machine learning models with the help of predictive analytics tools for preventive actions to meet the defined sustainability goals
• No single source of truth for sustainability reports – Introducing a self-service platform for accessing sustainability artifacts

How Will the Sustainability Data Analytics Platform Help?

• Integrated platform with low environment code to address the above-mentioned pain points
• Single source of truth for stakeholders for complete transparency of sustainability reports
• Secured access to data across stakeholders, data providers, reviewers, sustainability officers, and external auditors
• Automated workflow for end-to-end data management and reporting
• Complete traceability of data preparation, reviews, corrections, and approvals
• Complex calculations to arrive the critical sustainability measures across environmental footprints – energy, emission, water, and waste
• Prebuilt data model to reduce the time of implementation
• Various connectors to extract data from scanned documents, PDFs, enterprise resource planning, and excel sheets

How is the 91���� Sustainability Data Analytics Platform different from other platforms?

Here are the key differentiators that make 91���� a strong player among others:

• Single Version of Truth
• Pre-built data model suitable for manufacturing and consumer product companies
• Automation of data consistency, accuracy, and completeness verification in the data collection process
• Inbuilt predictive insights for corrective actions to meet sustainability targets
• Role-Based Access to Data
• Accessible roll-out features across businesses
• Configuration options for third-party audits
• Full Stack Solution ensures data collection till sustainability reporting and analytics
• Easy and convenient to deploy across different business units and facilities
• The functional view of each of the components is depicted below:

The 91���� Sustainability Data Analytics Platform is based on cloud technology, favouring the resource-efficient use of IT resources, and enabling every company’s flexible and expandable growth. With integrated solutions, companies can introduce sustainability goals more confidently and cleanly into their daily activities and move strategically toward building more resilient, sustainable businesses.

The post Sustainability Data Analytics Platform for Implementing Sustainability 2.0 appeared first on 91����.

The question now is what is the way forward? What is the most appropriate model for your organization?

The truth is that there is no one-size-fits-all solution. Over the last decade, Data Lakes grew to be the de facto model for modernization. These days, they are being supplanted by, or in many cases have been subsumed into, Data Meshes. Both models have their votaries, and both come with their own set of challenges.

Let us examine these two models in a little more detail so that you can wrap your mind around them more easily and be better positioned to choose between them.

The Data Lake

A Data Lake is a large reservoir into which raw data can be poured and stored until needed. Thanks to its flat architecture, it stores data in its native format, as binary large objects (blobs) or files. It takes in unstructured data, such as emails, documents etc.; binary data like images, audio, and video; semi-structured data, such as CSV, logs, and XML; and structured data from relational databases. The extract-transform-load process happens within the Data Lake itself.��

The Data Lake can, therefore, efficiently manage the high Volume, high Variety, and high Velocity of Big Data. It also significantly enhances the value of Big Data by making it available as reports, dashboards, and applications, to facilitate better visualization, advanced analytics, and machine learning. All, of course, to ultimately empower organizations with the ability to take evidence-supported business decisions with more far-reaching impact than ever before.��

Being a single, integrated, and complete system, the Data Lake facilitates faster and simpler development of applications as well, which are based on one code.

The Data Lake can reside on the cloud, on a platform such as Microsoft Azure, or as a distributed file system such as MS SQL Server with the Hadoop Distributed File System.

However, Data Lake also has its drawbacks.��

As the volume of data increases and grows more complex, the central IT function becomes overloaded with requests and cannot keep pace. Individual project teams then try to bypass it and deploy quick fixes that are poorly integrated and create problems in the future.

What is worse, organizations keep pouring data into the Lake and eventually lose track of what it contains. Much valuable information can go unnoticed because data analysts have no knowledge vis-à-vis the data’s source domain and engage in fishing expeditions.

Many organizations have seen their Data Lakes turn into data swamps because, after a point, it entails considerable technical and organizational effort to make productive use of them.

The Data Mesh

The Data Mesh evolved in response to the many challenges that the Data Lakes posed.

Unlike the Data Lake, the Data Mesh is a composite ecosystem, not a monolith. It breaks giant, monolithic enterprise data architectures into decentralized subsystems, each owned and managed by a dedicated team.��

The Data Mesh facilitates the management, connection, and smooth flow of data from producers through to consumers, whether outside or within a Data Lake. In that sense, a Data Mesh may include Data Lakes.

Data Meshes can be said to have four pillars:

Decentralized Data Ownership

Data is owned by the entity that produces it, typically functions such as HR, Finance, Marketing, etc. Therefore, more value can be derived from it. Typically, tools such as Azure Databricks are used to process the large workloads of data.

Data as Product

Users, such as data analysts, can easily source data directly from the domain owners, who will ensure that the data is of high quality. Conflicts are eliminated by using approaches like event sourcing and CQRS.

Self-serve data infrastructure as a platform��

Domain teams can create, transform, and consume data products autonomously.��

Federated governance

Mandated universal standards to enable smooth interoperability and flow of data.

The Data Mesh brings many benefits to the table

Flexibility and Choice – Since its architecture is domain driven and distributed, you have the flexibility to choose vendors and technologies that work best for you, without getting locked onto one platform.��

Greater agility, seamless collaboration, shorter project times – Since domain teams own their data, they can operate independently, making them more agile and responsive. At the same time, since the teams are cross-functional, collaboration becomes simpler and more efficient. Development accelerates and projects go live faster!��

Superior quality – Since ownership is vested with domain experts, the quality of the data is always high. Further, by mandating universal protocols and principles, the Data Mesh promotes the delivery of data in standardized formats for easier access.��

Quick service: Data producers and data users interact based on pre-determined SLAs, which enables much faster delivery of data. All data management needs such as storage, logging, identity management, and such, which slow the process down, are handled by the Data Mesh’s inbuilt capabilities.��

Scalability: Being distributed in structure the Data Mesh is also eminently scalable with minimal disruption.

So, should your company upgrade to a Data Mesh?

A Data Mesh certainly sounds like a panacea for all data ills but, like all technology solutions, it must be opted for after due thought and diligence. Keeping the following factors in mind will help you make a better-informed decision about whether your organization needs to upgrade to a data mesh.

Duplication of data: Repurposing data to serve another domain’s needs may lead to data duplication. This can lead to higher storage requirements as well as increased data management��costs.

Quality Avoidance: The availability of multiple data products and pipelines may lead to non-compliance with governance standards. Therefore, these principles will need to be clearly articulated and compliance enforced through appropriate measures at the domain level.��

Change management efforts: Deploying data mesh architecture and decentralized data operations will entail organization-wide change management efforts. You will need to plan to allow for business disruptions and to ensure that critical operations continue.

Choosing future-proof technologies: Teams will have to think long term when selecting technologies that will be standardized across the company, to ensure easier future upgradation with minimal disruption.

Cross domain analytics: Reporting becomes decentralized as well, and a separate organization wide model may need to be defined to consolidate diverse data products into one report.

Talk to us at 91����. We’ll undertake an assessment of your existing digital landscape, identify modernization areas, build a strategic roadmap, and define the enterprise architecture you need.��

Build a Scalable, Flexible & Secure Data Ecosystem | Enable Insights-powered Decisions | Be Future Ready to leverage AI/ML | Gain Business Intelligence Faster and Cheaper | Enable Rapid Value-led Experimentation | Drive Data-as-a-Service.

Click here for Part 1 of blog: Modernize the Data Ecosystem to Lay the Foundation of an Insights-driven Digital Next Enterprise (Part 1)

Reference:

Zhamak Dehghani
Data Mesh Founder

Author:

Bhagaban Khatai
Data Transformation Leader

The post Data Modernization – What is the best route for your transformation journey? (Part 2) appeared first on 91����.

That said, technology leaders need to assess the pros and cons of a modernization exercise. Businesses must study the various avenues for modernization and choose the one that gives them the best cost-benefit balance. As with any change management initiative, it is disruptive and entails focused deployment of resources.

In this article, I will discuss three frameworks/platforms that, we at 91���� have helped our clients use to effectively leverage data for business success.

The Data Warehouse

The Data Warehouse was probably the first enterprise-level platform to use data for business decision support. It came into its own in the Nineties and at the turn of the new Millennium. As its name implies it organized data in structured and labelled fields that could be easily accessed, and it worked excellently.��

Data-driven business intelligence, as a concept, gained massive leverage thanks to the Data Warehouse. However, like its counterpart in the real world, the Data Warehouse’s key drawback is poor scalability. It works on pre-built schema and can take in only structured data. As a result, the data is siloed and not all data is captured.��

As the three Vs of data – volume, variety, and velocity – grow, as in today’s age of Big Data, the Data Warehouse becomes unwieldy and inefficient. And data’s fourth V, veracity, suffers in consequence.

This is not to say that the Data Warehouse has outlived its utility. It still works efficiently for businesses that deal with a smaller volume and variety of data and provides excellent decision support intelligence at a relatively lower investment.��

The Data Lake

The Data Warehouse’s inherent problems gave rise to the Data Lake, a platform with no hierarchical structure that is more attuned to the needs of Big Data.��

A data lake is like a reservoir into which raw data can be poured and stored until needed. It has a flat architecture and takes in data in their native formats – emails, documents, images, audio, video, semi-structured data, such as CSV, logs, and XML, as well as structured data from relational databases.��

The extract-transform-load process happens within the Lake itself and data is presented as reports, dashboards, and such, to facilitate better visualization and more accurate analytics, as well as to enable machine learning.��

The Data Lake is thus capable of managing the high Volume, high Variety, and high Velocity of Big Data.��

However, the Data Lake also has its drawbacks.��

Once data is put into the Lake, it becomes monolithic. This limits the knowledge that data analysts can gain from it and increases the risk of valuable information going unnoticed.

Its centralized control structure stretches the IT team thin. Projects get delayed, forcing teams to resort to poorly integrated ‘quick-fix’ solutions that eventually compound problems.

Consequently, it often ends up as a huge unmanageable data dump yard. Drawing any useful sense out of the Data Lake becomes a complex, expensive, and resource-intensive task.��

It is in response to these problems that the concept of a Data Mesh came into being.

The Data Mesh

Unlike the Data Lake, the Data Mesh is a composite, integrated ecosystem, and not a monolith. It is composed of decentralized subsystems or domains, each managed by a dedicated team. In a sense, you can say that the Data Mesh as a whole is greater than the sum of its parts.

It thus offers several advantages over the Data Lake.

It makes domain experts owners of their data. Thus, there is no danger of valuable nuggets of information being lost or ignored.����

It treats data as a product and enables a smooth and secure flow of data from producers to users, whether outside or within a Data Lake. In that sense, a Data Mesh may include Data Lakes.��

It encourages cross-functional teams and empowers them to operate independently, with little or no support from a central IT function. Collaboration is more efficient, the pace of development accelerates, and projects go live much sooner.

Its decentralized approach gives you the flexibility to choose vendors and technologies that work best for you, without getting locked onto one platform.��

A Data Mesh can be deployed for a broad range of needs and for diverse use cases:

• Migrating applications to the cloud
• Modernizing data lakes to make data more easily accessible
• Integrating apps, IoT, and analytics in real-time
• Streaming data pipelines within or from data lakes
• Data-in-motion analytics

So which modernization solution is best for your organization?

Talk to us at 91����. We’ll undertake an assessment of your existing digital landscape, identify modernization areas, build a strategic roadmap, and define the enterprise architecture you need.��

We are part of the Data & Analytics transformation journey over last 15 years

Build a Scalable, Flexible & Secure Data Ecosystem | Enable Insights-powered Decisions | Be Future Ready to leverage AI/ML | Gain Business Intelligence Faster and Cheaper | Enable Rapid Value-led Experimentation | Drive Data-as-a-Service

Click here for Part of 2 blog: Data Modernization – What is the best route for your transformation journey? (Part 2)

Author:

Bhagaban Khatai
Data Transformation Leader

Reference:

Zhamak Dehghani
Data Mesh Founder

The post Modernize the Data Ecosystem to Lay the Foundation of an Insights-driven Digital Next Enterprise (Part 1) appeared first on 91����.

With rapid adoption ITIL framework, the focus shifted to services and management (business outcomes). This resulted in a gradual change from IT Helpdesk to IT Service Desk, which is now equipped with multiple channels that end-users could use for requesting support services. It has also enabled and empowered the end-users to solve a certain level of issues themselves.

Technology has made quantum leaps over the past decade, but the majority of the organizations continue to operate with the dated model when fewer things went wrong with IT. The onset of the pandemic in early 2020, drove the CIOs to think digital and move towards an operation that encouraged “Shift-left” and “Remote”.

The modern Service Desk Operations or “SDOps” (as we like to call it), offers an intelligent “first line” of entry point into IT Organisation comprising of Artificial Intelligence (AI) enabled automated solutions that users can access with Zero-human touch. To reduce the involvement of human agents, it is anchored around self-service and self-healing initiatives. Some of these enabling tools/ trends are listed below:

• AI and NLP powered Chatbot – Empowering the end-user to converse with a chatbot to get their issue resolved via self-service
• Digital Experience Management (DEM) – allows organizations to develop a deep, continuous understanding of each employee’s needs across the entire digital enterprise. The DEM platform is capable of Service Desk diagnosis & remediation, Root cause analysis, Proactive Endpoint Services, and Asset Optimization
• Automation and orchestration capabilities – From the use of simple scripts to the use of RPA for simulating human-like activities for resolution of issues
• Integrated IVR based telephony – AI-based telephony option to intelligently redirect the calls to appropriate resolver groups
• Remote support and Augmented Reality – enables users to leverage both smart technologies and a remote expert at a world-class service desk to resolve their issues

Adopting a “shift-left” approach enables issue resolution close to the end-user, thereby, bringing in an enhanced end-user experience and reducing the wait time. This directly impacts the cost of dealing with the incidents and enhances the services levels. The productivity of the business end-user improves significantly, and they are motivated to resolve the issues themselves, rather than reaching out to the service desk.

What the future holds?

Post pandemic, organizations have begun to realize the power of “shift-left”, which results in minimum human interaction and resolution closer to the users, at the IT service desk. This has propelled a rise in the adoption of AI-enabled capabilities, automation, and knowledge management capabilities. AI interfaces will eventually become an integral part of SDOps in the next few years, providing the end-users with a self-learning robotic bot as an alternative to a human agent. This will lead the way in Value Demonstration, enhancing and enriching End-User Experience.

As we move ahead, the operational cost for running an IT Service desk is expected to drop as AI interactions will mature and provide round-the-clock support to end-users without the need to have human agents covering a 24×7. Moreover, AI-based interactions at Level 0 going to cost less, thus, incentivizing the organizations to drive adoption of new technologies at the end-user level. The rate of technological development in the current day and age would result in IT Service Desk becoming more of a facilitator ensuring that all the end-user services ‘just work’.

91����’s E³ framework provides a strong foundation for the ‘Digital Workplace’ to be leveraged, a differentiated framework for transforming the end-user workplace. This framework comprises of

• Experience: Identify User experience journeys through Value Stream Mapping
• Efficiency: Drive Extreme Automation for an efficient self-service experience
• Effectiveness: Accelerate adoption for benefit realization

Author:

Abhimanyu Pandey
Lead Consultant – INFRA,
91����

The post Evolution of IT Service Desk Operations (SDOps) appeared first on 91����.

With enterprises adopting Work From Anywhere reality, and with the advent of the digital age, Digital Workplace has truly evolved to be digital and is here to stay. Digital Workplace solution needs to be envisioned with end-users forming the core of the solution. It is crucial for an organization to ensure that each end-user has a seamless workplace experience regardless of time and space.

To build an effective Digital Workplace solution with the end-user being at the center, it is imperative for an organization to focus on persona-based services. To achieve this, a well-planned discovery exercise needs to be initiated. Discovery has to be an iterative process that gets refined progressively and incrementally with continuous user feedback.

Accurate Persona mapping is the base for creating truly customized and efficient digital workplace solutions for different verticals.

• Personas are end-user prototypes that represent the requirements of groups of users, with users aligned with the enterprise’s business goals and therefore exhibiting a specific set of user characteristics. They act as ‘substitutes’ for real users and help guide decisions on strategy, functionality, design, and development
• Personas help in designing the solution by identifying the user motivations, expectations, and goals responsible for user behavior. They are based on knowledge of real users and imitate the end-user in the digital world
• Personas also provide a reliable and realistic understanding of how a business could expect a group of employees to embrace the new solution

The problem

The primary reason for discontentment within enterprises that deploy digital workplace solution is their one size fits all approach irrespective of the users’ requirements, their industry segment, technological maturity, and its user’s willingness to adapt to change. The ever-changing technological landscape combined with work from anywhere has made it challenging and complicated for workplace solutions to provide a consistent end-user experience.

Some of the top industry-specific persona examples include and not limited to:

Banking

Supporting on-demand customer requests and managing high customer expectations in remote work scenarios arising due to pandemic-induced lockdowns. A digital workplace solution curated for Banking Professionals enables them to connect with the banking systems remotely and securely for transactional activities in a seamless manner. The solution enables unified collaboration within the staffing ecosystem easing the processes such as inter-departmental approvals, financial transactions, and reporting.

Manufacturing

Managing and maintaining production capacity and other activities with a lower workforce and quick turnaround in case of breakdowns or IT issues. To facilitate remote resolution, the production engineer should have access to event logs, live execution system status, and augmented reality-based smart remote support. Digital Workplace solutions can enable the engineer to seamlessly resolve the breakdown through persona-driven access.

A well-researched and regularly updated set of end-user personas allows in preparing a solution that can manage the scale and complexity of the modern workplace while ensuring end-user delight and cost-saving. 91����’s E3 framework builds a strong foundation for the ‘Digital Workplace’ to be leveraged, a differentiated framework for transforming the end-user workplace. This framework comprises of

• Experience: Identify User experience journeys through Value Stream Mapping
• Efficiency: Drive Extreme Automation for an efficient self-service experience
• Effectiveness: Accelerate adoption for benefit realization

Author:

Shreet Das,
Pre-sales Lead, Digital workplace,
91����

The post Digital workplace aligned to industry-specific personas appeared first on 91����.

The number of organizations deciding to pay a ransom has risen to 32% in 2021 compared to 26% in 2020 (). Even after paying for Ransomware, only 8% of them got all their data back, nearly a third, 29%, couldn’t recover more than half the encrypted data. However, on average, only 65% of the encrypted data was restored after the ransom was paid. Approximately 37% of global organizations (more than one third) said they were the victim of some form of Ransomware attack in 2021 (IDC’s “). 92% who pay don’t get their data Back (.

We all know that Confidentiality, Integrity and Availability are the 3 pillars of security. Integrity of Data is an important dimension, which means that data has not been altered in an unauthorized manner when data is “at rest, getting processed, or in transit”. Here we will be focusing only on “at rest” Data related to Ransomware. It’s evident that, while there is a high level of efforts required to prevent “Attackers from getting in” or “escalating their privileges within system” the best bet for an organization remains to “Protect their critical data from unauthorized access and destruction”.

Ransomware attacks focus on encrypting any data to which they could get write access, including the backup system. This may also happen due to poorly implemented permissions that exposed backup data stored anywhere. This makes Ransomware attack more effective because organizations can’t recover data from backup systems.

The big step towards getting data protected is to have isolated, immutable backup of data which is not accessed in general and have very strict administrative access authentication, authorization adjustments for a set of admins. There was a time when data backup on physical tapes were kept off-site to be protected from any Data center physical damage as part of BCP/DR approach. That was one of the best ways to ensure data integrity. We should leverage Cloud offerings which are equally effective to protect data from any Ransomware attack.

Now let’s discuss about immutable backup methods which will be the key ask here. Azure has introduced Blob storage options to operate like an Immutable storage and enables users to store business-critical data in a WORM (Write Once, Read Many) state for a defined time interval. While in a WORM state, data objects can be created and read, but cannot be modified or deleted for a user-specified interval. By configuring immutability policies for blob data, customers can protect their data from overwriting and deletion. Another benefit of Azure Blob storage is having a legal hold, which stores immutable data until the legal hold is explicitly cleared. When a legal hold is set, objects can be created and read, but not modified or deleted. It’s important to understand how immutability is implemented and whether it is truly��WORM, even if OS administration accounts are compromised.

Those who are on AWS platform, can use AWS Backup Vault Lock to prevent (accidental or malicious action) any user from deleting their backups or making changes to their backup lifecycle settings. AWS Backup Vault Lock (S3 Glacier) improves customer’s security postures and ensures a mechanism for restore, even in a worst-case scenario like total account compromise. Another service that’s useful for data protection is the AWS object storage S3, where you can use features such as��object versioning��to help prevent objects from being overwritten with Ransomware-encrypted files, or��Object Lock (S3), which provides a write once, read many (WORM) solutions to help prevent objects from ever being modified or overwritten.��

You can use Compliance��retention mode if you never want any user, including the root user in your AWS account, to be able to delete the objects during a pre-defined retention period. You can use Legal Hold as an infinite retention period. Once applied it is not possible to delete any object until the hold is released manually (only by users with special permissions). Every backup within the retention period is an immutable backup with point-in-time restore capabilities. Also, we have S3 MFA delete-enabled bucket option which safeguard from permanent delete of an object version or change the versioning state of the bucket.

Similarly, GCP storage containers with Bucket Lock offers write-once (WORM), immutable storage to meet your compliance standards and ensure your data’s integrity while offering instantaneous access for quick restores. As part of protection, once you lock a bucket, you cannot unlock it until all objects are out of the retention period. Retention policies prevent the deletion or modification of the bucket’s objects. Applying Bucket Lock��to a storage bucket in the Archive class can help you achieve WORM compliance for long-term data archival as well.

Apart from introducing immutable backup options which provide a secure storage for your data, we all know initial steps such as to keep multiple copies of data backup (keeping data off-site), use a standard practice of Multi-Factor Authentication (MFA) for administrative accounts, separation of administrative roles. We also need to enable encryption of the data and segment the workflow so that authorized systems and users have limited access to use the key material to decrypt the data. We know that network sharing protocols work well for general-purpose file sharing. However, minor mistakes in permissions can lead to data being exposed. In place of using them, we recommend using object storage APIs, for example Amazon S3 compatible APIs, virtual tape libraries, or keep storage as “local” to the backup server (do not access over a network sharing protocols).

Interesting part to understand here is, the technology which was initially introduced for a security compliance requirement to keep the golden copy of any data for later auditing or reconciliation, has taken a shift to be also used to safeguard from ransomware attacks to maintain integrity of data. Cloud storage is an economical solution because resources are readily available, is scalable, and multi-tiered.

Author:

Deepak Kumar,
Cloud Practice Head,
91����

The post Keeping your data protected from ransomware attack in the new era appeared first on 91����.

While intent to implement RPA is growing, there are budget cuts to contend with. Organizations have therefore turned cautious, chiefly because they cannot identify the processes, they should automate to unlock the ROI, efficiencies and the bottom-line growth promised by RPA.��

The path to successful RPA implementation is not always evident—and there is justified anxiety that the wrong decisions could produce sub-optimal returns. Processes in the functions like Finance, IT, HR and Procurement usually contribute to maximum number of use cases. Precedents could be one way to resolve the indecision.

Keep efficiency, effectiveness, and experience the focus

Organizations have a variety of stimuli that propels them towards RPA adoption: to drive efficiency, enforce compliance, create a fungible workforce, stay ahead of technology, increase the life of legacy investments, and improve employee morale. However, in the most immediate future, organizations considering RPA would do well to apply the technology to solve their most immediate business problems (see figure 1).

Figure 1

91���� has worked with several customers across industries that illustrate the business problems that make ideal use cases for RPA:

• An airline customer wanted last minute emails received for cargo bookings to be turned into orders in their cargo system. They used RPA to solve the problem
• A US-based fashion house wanted to convert spread sheets coming in from designers into a Bill of Material and thereafter into purchase orders. RPA was the solution
• A banking customer wanted real time testing to be done for 300+ mainframe scenarios for new products. RPA-based testing enabled faster time to market

For organizations considering RPA, another way to resolve the problem would be to adopt RPA if it is a customer facing process. These processes always deliver value higher than others.

Get around the uncertainty��

It is natural to be anxious about making investments in RPA. The technology is transformative but unfamiliar. Our experience shows that organizations are most concerned about selecting the right platform, they are unclear if their processes are ready for automation, if their business cases can withstand the test of time, and how quickly they will see ROI.��

• Rule of Five: Our advice to organizations dipping their toes in RPA for the first time is relatively simple. Identify five processes that run across five application used by jobs that follow business rules and have little movement. We call it the Rule of Five. It provides a safe and quick test ground to understand what works for your organization and what doesn’t
• Process characteristics: The other approach to identifying the right use cases is to look at RPA to address tasks with a high volume, that are sensitive to speed, that are generally error prone and have irregular labor demands
• Part of digital transformation: Many organizations are gravitating towards the thought that RPA is not just a way to mitigate costs or improve operational efficiency but as a key component of their digital transformation agenda (added to the list of strategies that include data, analytics, social, and mobile)

Going wrong is not failure – it is learning

A CPG customer got 91���� to help identify process that could be automated. After process mining and value modelling, we identified 10 processes ripe for RPA. The customer wanted six automated. However, we soon realized that not one of the six could be automated because the process templates were not standardized. The program had to be put on hold, the processes standardized, and the program re-started. But failure is not always a reason to bring an RPA program to a halt. It is often an indicator of a need for course correction.��

Another CPG customer opted for a “fail fast approach” and started RPA implementation using a low scale product that did not touch their ERP. As adoption increased, SAP users in the organization also wanted to implement RPA. The customer realized the need for a product with larger capability and chose Automation Anywhere.

Anxiety around RPA vaporizes as adoption increases. Typically, organizations with less than 10 bots have challenges around process selection and business case creation. Organizations with 10 to 50 bots have challenges around governance, establishing operating models and modifying processes for automation. Organizations with over 50 bots cement an automation first cultural and nurture an RPA talent pool that ultimately sees the emergence of citizen developers.

Learning from experience

Our experience of working together provides insights that are worth sharing. When we decided to provide every one of our 9,000 employees with a bot – called Digital Buddy – we got Automation Anywhere as the expert services partner to assist in developing the bots and in managing change. We knew that our young and agile workforce wanted IT to help them move at speed. Our employees were eager to get their hands-on new technology and learn how to subsequently apply it to solve customer problems. Internally, we also realized there was more work than available skills. All the pieces necessary for RPA to find acceptance and deliver value fell in place.��

Today, we have the expertise to deliver RPA projects to customers using an extensive bot library, through our automation-as-a-service offering and a catalogue of automatable processes based on experience.��

Tomorrow’s economy will be led by digital workers that leverage RPA, with humans required for more cognitive work. Learning to tread the pathway to successful RPA implementation is therefore an urgent need today.

Author:

Sumeet Pathak,
Senior Director, Digital Innovation and Solutions,
Automation Anywhere

Mayank Jain
VP, Business Consulting Group,
91����

The post Pathway to successful RPA Implementation appeared first on 91����.

Robotic Process Automation (RPA) has caught the attention of CXOs who are grappling with the�� business impact induced by the COVID pandemic. �� It is no longer a luxury but a business imperative to bring in automation in order to save time & costs, gain efficiency and provide more work-life balance to employees by allowing them to focus on higher value tasks while the bots take care of the mundane, repetitive tasks.

This frenzy has led many organizations to take the plunge into automation with most of them being at some stage of either piloting RPA or having deployed a few bots in production. It all seems very lucrative and thus very high expectations are set from the onset about the success the bots will bring for the company. But then, reality strikes, and most companies see that the outcomes aren’t really what they had expected. There are issues in scaling the bots beyond the initial few, issues with maintenance and frequent modifications required for handling different scenarios and paths for the same process, and updates required when the IT systems undergo a change. This results in a long and steep learning path for the companies that brings with it the frustration and a misdirected agony towards the usefulness of RPA.

But is it really the bots that are to be blamed for the problems being faced by such companies? Why is it that RPA is working like a charm for some companies while others are struggling to crack the code?��

It has to do with the way the successful companies look at (read as: treat) bots.

By humanizing bots and incorporating the following practices, companies can�� derive maximum value from the investments made in RPA.

Treat them as�� digital employees and name them

The premise of ‘Virtual Workforce’ is based on treating bots as digital employees. It is only natural to think so about bots. After all, the bots work 24×7, doing tasks that are �� repetitive and mundane for humans. But treating them as employees and naming them gives them more mind space and top of the mind recall during change management. When employees name their digital assistants, they think of bots more like junior team members who report to them and help them with their daily tasks.

Give time and team to maintain the bots and look after them��

Just like humans, bots can also fall sick and have breakdowns. What’s important to note is that just like humans, there is a reason behind their falling sick. In most cases, the reason tends to be associated with change management aspects. When there is a process change in the traditional world (where people are managing the processes), documentation upgrades and training are part of the change management plan. This helps the operations teams to manage the changed processes effectively. But this is often not the case with automated processes. By thinking of bots like people, the team thinks about them during change management and caters to their needs just like the needs of people being impacted by the change. This makes sure that they are looked after, and the team keeps getting the benefits they originally got by keeping the bots working.

Build resilience into the bots

Processes can break for several reasons, whether being done by humans or bots. It could be due to bad data, unavailable source/target application, network connectivity, or any number of other reasons. Just like humans take alternate routes or steps to resolve the issue and move forward, so should the bots be able to. But bots won’t do this until they are trained�� on it. Many a times, bot designers and developers consider only the ‘happy path’ while building the bots. When creating or training the bots, the ‘unhappy paths’ or ‘exception scenarios’ should also be considered, and the bot should be trained on what to do if such a scenario occurs. This not only keeps the bots running more efficiently, but also reduces maintenance and support efforts when they hit the ‘unhappy path’ because there are proactive actions built into the bots to handle such situations.

Manage the performance of bots like it’s done for people

People performance management is one of the most important aspect that is taken up in every organization. We pay a lot of attention and effort to make sure our people have the right toolset and mindset to perform at their best possible levels. Seldom does it ever happen for the bots deployed in the organization. By making sure that there are people assigned to monitor and manage the performance of the bots, we can make sure that the bots are performing at their best possible levels. With the new age automation platform, this is quite easy as they come with built-in performance dashboards and bots that monitor the performance of process bots and run diagnostics to make sure the overall performance is at expected levels. In addition, staff in the different business units with live bots can also access real-time dashboards that display the performance of the bots deployed in their unit.

Measure the success by number of automated process

Another common practice in organizations implementing automation is to measure the success of the program by the number of bots deployed. We often come across news flash about how an organization successfully deployed 100 RPA bots in 6 months’ time. Ideally, this should not be the measure of success. The true measure of success should be the number of processes automated by the bots. Don’t think about how many bots are deployed. Think instead, about how many processes can be run with a bot. When humanizing it, think of how many tasks or jobs a person can do in a day, then expand that across the bots running 24×7 and think of how many tasks can the bot do.

Conclusion

Initially it might seem a bit far fetched to think of a bot as a human and to take into consideration of the bot when thinking of process optimization. The business units need to work in collaboration with IT to make sure the bots are set up in a way that maximizes return on their investment.��

While looking at optimizing a process, planning for it with the bot in mind is different from planning for it with a human in mind. When optimizing a process with a human in mind, we would look to reduce the number of steps. When optimizing a process with a bot in mind, the more important factor should be the efficiency the bot brings, even if it means having a greater number of steps.��

Remember, the bot doesn’t mind doing more steps in a process since it’s executing them at a much faster pace than human. If the overall efficiency of the process improves, then that is a�� trade-off that we should be in favor of.��

In short, giving bots names gives them a profile that has top of the mind recall and makes sure they are considered during the change management process. Also, when designing the bots, building fail safe mechanism by considering exception scenarios goes a long way to keep them running. Performance management for bots, just like for humans makes sure the company is getting its targeted�� ROI and like humans, focus on how many processes the bot can handle rather than how many bots the company has.

Making the above mentioned psychological / behavioral changes to our outlook towards RPA bots can go a long way in improving the efficiency the bots bring while making sure the company is getting back the�� ROI on the RPA initiative.

At 91����, we take this psychological approach and have embedded it into the DNA of the company’s approach to RPA. That is the reason why we have embarked on a journey to provide every employee (~9500) with a personalized digital buddy�� that will make their life easier. And we have been able to successfully deploy close to 20 digital personas for various functional roles to help people in their daily work. Talk to us if you’d like to know how 91���� can help make digital workforce a reality for your company.

References:

1. Everest Group – RPA Annual Report 2018 –��
2. The Future Digital Work Force: Robotic Process Automation (RPA) –
3. Gartner’s 2019 Predictions for RPA Offerings –
4. Optus brings bot needs into IT change planning –

Author:

Tanmay Prakash
Senior Principal Consultant,��
Business Consulting Group, 91����

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Author:

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Cloud Practice Head ��

Abhishek Asija
Cloud Architect

Jay Prakash Shukla
Sr. Cloud Architect ��

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