Systems plus

The cloud has become an essential part of unlocking organizational growth.

Everyone is understanding the true value of disruption, and technologies like AI and IoT are seeing increased consumer demand today. This means that the demand to leverage the cloud within organizations is also on the rise.

Public cloud platforms like AWS, Microsoft Azure, Google, and Alibaba Cloud have already made a mark for themselves in this rapidly changing technological landscape, and rightly so. They offer businesses a chance at better investment value, higher delivery speeds, increased agility, better storage options, and typically cost less.

We have compiled a list of trends that we believe will help you contextualize your existing cloud capabilities and identify areas that could lead to future growth.

Trend 1: Sustainable cloud

Sustainable cloud positions companies to deliver on new commitments: carbon reduction and responsible innovation.

Public cloud migrations can reduce carbon dioxide emissions by up to 59 million tons per year. Which is the equivalent of having 22 million fewer cars on the road! This is a positive and important cloud trend that will gain momentum in years to come.

Trend 2: Cloud Security

Security is often seen as the biggest inhibitor to a cloud-first journey—but it can be its greatest accelerator.

The acquisition of cloud security access brokers (CASBs) is seen as a growing response to address these concerns. They implement a consistent system of governance by providing software that operates between cloud users and platforms to enforce centralized security measures.

Trend 3: Edge Computing

Edge computing is an alternative approach to computing and storing data in the cloud environment.

It is an emerging cloud trend that involves building localized data centers for computation and storage at or near where it’s being gathered, rather than on a central location that might be thousands of miles away.

Edge computing doesn’t just improve operational efficiency, but also enables better compliance with various data regulations by preventing data from leaving a certain area.

Trend 4:Multi-Cloud Architecture

Multi-cloud architecture is the explicit use of the same type of cloud services from multiple Infrastructure as a service (IaaS) CSPs.

The data to which frequent and quick access is needed is to be kept on public servers and more confidential data can be kept on private servers with monitored access.

We have seen significant adoption of hybrid-cloud and multi-cloud strategies across the business world and this year will witness more business leaders and organizations realize the opportunities of these models and leverage them to enjoy agility in the cloud.

Trend 5: Mobile Cloud Computing

Mobile Cloud Computing (MCC) is all about building, hosting, and operating mobile applications via Cloud Computing. It is at a nascent stage of innovation owing to the rising mobile users worldwide.

MCC combines the following to drive rich mobile computational resources to users across varied devices:

Mobile Computing

Cloud Computing

Wireless Network

The idea is to deliver rich UX. However, safety concerns especially regarding data loss are associated with MCC. Hence, enterprises must ensure necessary precautions to tackle these challenges.

Serverless computing for hassle-free service

At Systems Plus, we believe that with cloud computing, organizations get to have it all: scalability, agility, flexibility and efficiency besides saving on costs and time. Adopting the cloud is far more complex than simply using someone else’s datacenter. Cloud adoption requires organizations to fundamentally shift their model and behaviors to a consumption model rather than a Capex model.

Why choose us?

We have the proven skills, expertise and certification to help your organization successfully and strategically adopt the cloud. With experience in all layers of the platform: infrastructure, application, data, security, policy and pipeline, we navigate the people, processes and tools that script success for you.

The cloud is a means, not an end to the transformation. While large organizations successfully implement or adopt a cloud-first strategy for new systems, many struggle to extract the full value of moving the bulk of their enterprise systems to the cloud. This is because they tend to move IT systems to the cloud instead of chalking the transformational strategy needed to realize the full potential of the cloud. Just moving legacy applications to the cloud using a lift-and-shift procedure will make IT architectures complex, cumbersome and costlier instead of yielding the benefits that cloud infrastructure and systems are meant to provide.

Cloud adoption for digital transformation

The cloud acts as a force multiplier. Its actual value is best realized when you make it a part of a larger strategy to pursue digital transformation. Such a strategy is enabled by the standardization and automation of the IT environment, adopting a modern security posture, working with an automated agile operating model and leveraging new capabilities to drive innovative business solutions. Organizations that view cloud capabilities in this way can create a next-generation IT capable of enabling business growth and innovation.

Cloud adoption challenges

In an increasingly digitalized environment, whether a business requires utilizing cloud services is no longer the question. The considerations are more around which type of cloud services are required and how to execute a successful cloud migration. With the movement towards a multi or hybrid cloud environment using public cloud platforms, the typical challenges that you might face include:

Under-utilization of cloud capacity

Unclear cloud migration paths or loose adoption frameworks

Cloud security concerns

Absence of talent with the right cloud skills

Dearth of reliable cloud partners with an understanding of business processes, traditional IT and the nuances of cloud

Inappropriate pricing and licensing issues causing cost overruns

Based on the current scenarios, let’s understand what steps are essential to consider when it comes to cloud adoption for your organization.

Cloud discovery and migration

Understanding what would constitute a perfect cloud environment for your business is critical to cloud adoption. Discovering the cloud value and cloud migration paths is essential to create a sustainable cloud-based transformation. The parameters to achieve a successful migration need to be defined:

Establishing the right cloud architecture

Determining areas of your existing architecture that should be migrated or phased out

Building transition roadmaps that ensure adoption

Providing clear ROI and TCO parameters

A stronger go-to-market strategy

Cloud native development

Custom application development, monitoring and scaling in line with your unique PaaS, IaaS or SaaS needs is the way to go when it comes to cloud adoption. Determining optimal data strategies for your organization and content distribution processes is mandatory too. You should have feasible and cost-efficient solutions for your mobile, IoT, Ecommerce or machine learning depending on your priorities.

Connecting business goals to the cloud architecture

Creating effective enterprise cloud architecture requires collaboration with the business and operations teams for you to understand feasibility, usage and scale. You must first define business goals and together with a cloud reference model, figure out what traditional components from the current application architecture can be migrated to the cloud. As part of most cloud architectures, you’ll need to keep a robust security posture and leverage containerized solutions for adding value to your cloud adoption.

Extracting superior outcomes with cloud and DevOps

Using DevOps with cloud services allows you to follow a CI-CD model for pipeline automation when it comes to application, database or cloud services in general. A strong cloud presence with a DevOps culture means that your entire delivery pipeline is more responsive to change. DevOps integration targets feature development, automation testing, quality testing, product delivery and maintenance releases in order to improve reliability and security and provide faster development and deployment cycles. You can expect the following business outcomes with this combination:

Improved deployment frequency

Faster time to market

Lower failure rate of new releases

Shortened lead time between fixes

Faster mean time to recovery

Minimal or zero downtime

The cloud-DevOps combination makes your delivery pipeline agile, flexible and secure enough to adapt to changing trends and consumer preferences.

The way to the right cloud for you

You needed just one cloud vendor in the old cloud days. Today you need an all-encompassing multi-cloud strategy across multiple public clouds or a hybrid cloud strategy across your on-premise data centers and the public cloud.

Managing IT resources, ranging from servers and storage to complex applications, requires considerable time, effort and expertise. Successful companies are focusing on their core business and not managing data centers or their applications. With a fully managed cloud service, businesses can use the power of cloud computing, ranging from basic compute and storage to complex technologies such as AI and ML.

Serverless computing for hassle-free service

Build and run applications without thinking about provisioning, maintaining, and administering the servers. Serverless helps reduces the complexity for building a modern scalable application, while enabling a high level of independent services with no servers to manage and a pay-per-use pricing model. You no longer need to worry about ensuring application fault tolerance and availability. This allows you to focus on product innovation while enjoying faster time-to-market.

At Systems Plus, we believe that with cloud computing, organizations get to have it all: scalability, agility, flexibility and efficiency besides saving on costs and time. Adopting the cloud is far more complex than simply using someone else’s datacenter. Cloud adoption requires organizations to fundamentally shift their model and behaviors to a consumption model rather than a Capex model.

Fast, data-informed decision-making can drive business success. Managing high customer expectations, navigating marketing challenges, and global competition – many organizations look to data analytics and business intelligence for a competitive advantage.

Using data to serve up personalized ads based on browsing history, providing contextual KPI data access for all employees and centralizing data from across the business into one digital ecosystem so processes can be more thoroughly reviewed are all examples of business intelligence.

Organizations invest in data science because it promises to bring competitive advantages.

Data is transforming into an actionable asset, and new tools are using that reality to move the needle with ML. As a result, organizations are on the brink of mobilizing data to not only predict the future but also to increase the likelihood of certain outcomes through prescriptive analytics.

Here are some case studies that show some ways BI is making a difference for companies around the world:

1) Starbucks:

With 90 million transactions a week in 25,000 stores worldwide the coffee giant is in many ways on the cutting edge of using big data and artificial intelligence to help direct marketing, sales and business decisions

Through its popular loyalty card program and mobile application, Starbucks owns individual purchase data from millions of customers. Using this information and BI tools, the company predicts purchases and sends individual offers of what customers will likely prefer via their app and email. This system draws existing customers into its stores more frequently and increases sales volumes.

The same intel that helps Starbucks suggest new products to try also helps the company send personalized offers and discounts that go far beyond a special birthday discount. Additionally, a customized email goes out to any customer who hasn’t visited a Starbucks recently with enticing offers—built from that individual’s purchase history—to re-engage them.

2) Netflix:

The online entertainment company’s 148 million subscribers give it a massive BI advantage.

Netflix has digitized its interactions with its 151 million subscribers. It collects data from each of its users and with the help of data analytics understands the behavior of subscribers and their watching patterns. It then leverages that information to recommend movies and TV shows customized as per the subscriber’s choice and preferences.

As per Netflix, around 80% of the viewer’s activity is triggered by personalized algorithmic recommendations. Where Netflix gains an edge over its peers is that by collecting different data points, it creates detailed profiles of its subscribers which helps them engage with them better.

The recommendation system of Netflix contributes to more than 80% of the content streamed by its subscribers which has helped Netflix earn a whopping one billion via customer retention. Due to this reason, Netflix doesn’t have to invest too much on advertising and marketing their shows. They precisely know an estimate of the people who would be interested in watching a show.

3) Coca-Cola:

Coca Cola is the world’s largest beverage company, with over 500 soft drink brands sold in more than 200 countries. Given the size of its operations, Coca Cola generates a substantial amount of data across its value chain – including sourcing, production, distribution, sales and customer feedback which they can leverage to drive successful business decisions.

Coca Cola has been investing extensively in research and development, especially in AI, to better leverage the mountain of data it collects from customers all around the world. This initiative has helped them better understand consumer trends in terms of price, flavors, packaging, and consumer’ preference for healthier options in certain regions.

With 35 million Twitter followers and a whopping 105 million Facebook fans, Coca-Cola benefits from its social media data. Using AI-powered image-recognition technology, they can track when photographs of its drinks are posted online. This data, paired with the power of BI, gives the company important insights into who is drinking their beverages, where they are and why they mention the brand online. The information helps serve consumers more targeted advertising, which is four times more likely than a regular ad to result in a click.

Coca Cola is increasingly betting on BI, data analytics and AI to drive its strategic business decisions. From its innovative free style fountain machine to finding new ways to engage with customers, Coca Cola is well-equipped to remain at the top of the competition in the future. In a new digital world that is increasingly dynamic, with changing customer behavior, Coca Cola is relying on Big Data to gain and maintain their competitive advantage.

4) American Express GBT

The American Express Global Business Travel company, popularly known as Amex GBT, is an American multinational travel and meetings programs management corporation which operates in over 120 countries and has over 14,000 employees.

Challenges:

Scalability – Creating a single portal for around 945 separate data files from internal and customer systems using the current BI tool would require over 6 months to complete. The earlier tool was used for internal purposes and scaling the solution to such a large population while keeping the costs optimum was a major challenge

Performance – Their existing system had limitations shifting to Cloud. The amount of time and manual

effort required was immense

Data Governance – Maintaining user data security and privacy was of utmost importance for Amex GBT

Solution:

The company was looking to protect and increase its market share by differentiating its core services and was seeking a resource to manage and drive their online travel program capabilities forward. Amex GBT decided to make a strategic investment in creating smart analytics around their booking software.

The solution equipped users to view their travel ROI by categorizing it into three categories cost, time and value. Each category has individual KPIs that are measured to evaluate the performance of a travel plan.

Results:

Reducing travel expenses by 30%

Time to Value – Initially it took a week for new users to be on-boarded onto the platform. With Premier Insights that time had now been reduced to a single day and the process had become much simpler and more effective.

Savings on Spends – The product notifies users of any available booking offers that can help them save on their expenditure. It recommends users of possible saving potential such as flight timings, date of the booking, date of travel, etc.

Adoption – Ease of use of the product, quick scale-up, real-time implementation of reports, and interactive dashboards of Premier Insights increased the global online adoption for Amex GBT

5) Airline Solutions Company: BI Accelerates Business Insights

Airline Solutions provides booking tools, revenue management, web, and mobile itinerary tools, as well as other technology, for airlines, hotels and other companies in the travel industry.

Challenge:

The travel industry is remarkably dynamic and fast paced. And the airline solution provider’s clients needed advanced tools that could provide real-time data on customer behavior and actions.

Solution:

They developed an enterprise travel data warehouse (ETDW) to hold its enormous amounts of data. The executive dashboards provide near real-time insights in user-friendly environments with a 360-degree overview of business health, reservations, operational performance and ticketing.

Results:

The scalable infrastructure, graphic user interface, data aggregation and ability to work collaboratively have led to more revenue and increased client satisfaction.

6) A specialty US Retail Provider: Leveraging prescriptive analytics

Challenge/Objective: A specialty US Retail provider wanted to modernize its data platform which could help the business make real-time decisions while also leveraging prescriptive analytics. They wanted to discover true value of data being generated from its multiple systems and understand the patterns (both known and unknown) of sales, operations, and omni-channel retail performance.

Solution:

We helped build a modern data solution that consolidated their data in a data lake and data warehouse, making it easier to extract the value in real-time. We integrated our solution with their OMS, CRM, Google Analytics, Salesforce, and inventory management system. The data was modeled in such a way that it could be fed into Machine Learning algorithms; so that we can leverage this easily in the future.

Results:

The customer had visibility into their data from day 1, which is something they had been wanting for some time. In addition to this, they were able to build more reports, dashboards, and charts to understand and interpret the data. In some cases, they were able to get real-time visibility and analysis on instore purchases based on geography!

7) Logistics startup with an objective to become the “Uber of the Trucking Sector” with the help of data analytics

Challenge: A startup specializing in analyzing vehicle and/or driver performance by collecting data from sensors within the vehicle (a.k.a. vehicle telemetry) and Order patterns with an objective to become the “Uber of the Trucking Sector”

Solution:

We developed a customized backend of the client’s trucking platform so that they could monetize empty return trips of transporters by creating a marketplace for them. The approach used a combination of AWS Data Lake, AWS microservices, machine learning and analytics.

Results:

Reduced fuel costs

Optimized Reloads

More accurate driver / truck schedule planning

Smarter Routing

Fewer empty return trips

Deeper analysis of driver patterns, breaks, routes, etc.

8) A niche segment customer competing against market behemoths looking to become a “Niche Segment Leader”

Solution: We developed a customized analytics platform that can ingest CRM, OMS, Ecommerce, and Inventory data and produce real time and batch driven analytics and AI platform. The approach used a combination of AWS microservices, machine learning and analytics.

Results:

Reduce Customer Churn

Optimized Order Fulfillment

More accurate demand schedule planning

Improve Product Recommendation

Improved Last Mile Delivery

How can we help you harness the power of data?

The retail industry is one of the largest sectors in the world and is expected to grow as the middle classes are increasing substantially in size and in buying power. Retail purchases via ecommerce and m-commerce are growing at a high rate due to the advent of high-speed internet connections, advancements in Smartphone technology and online-related technology, improvements in the product lines of e-commerce firms, a selection of delivery options and better payment options.

The advent of big data in retail industry represents a cultural shift in the way retailers connect with consumers in a meaningful way. This bottom-line impact of big data is what makes it a business imperative and why retailers around the world are leveraging it to transform their processes, their organizations and, soon, the entire industry.

As consumer technology adoption and multi-channel shopping experiences become the norm, data becomes increasingly critical.

Retailers who use customer data to transform themselves are better positioned to thrive and create sustainable growth in the new digital retail landscape.

Below is a set of defined use cases designed to drive value through more effective application of customer data.

Use-Case 1: Personalizing the In-Store Experience with Big Data in Retail

By doubling down on the customer experience, retailers can thrive by leveraging your physical stores to deliver a powerful, personalized experience.

The key is to create a unique shopping experience where customers derive both value and pleasure from visiting physical storefronts.

It is important to collaborate with merchandising teams to understand the merchandising strategy and the key metrics for improvement. Review data, identify opportunities to drive improvement, and develop hypotheses for how to enhance the customer shopping experience.

Thanks to advances in IoT and edge-computing, we are seeing more devices being placed in stores to monitor traffic.

Companies like RetailNext are leading the charge on such solutions and helping retailers through a variety of use-cases. Operations teams can measure traffic accurately and optimize staffing to reduce costs or improve the shopper experience. Marketers can measure the impact of certain campaigns through trends in traffic and areas of the store that have been visited. Merchandising teams can understand the impact of their assortment choices and display products accordingly. Products can be placed optimally to drive conversion. Finally, retailers can change the entire store design to optimize traffic, assortment, and conversion.

Almax has created “smart mannequins” that consist of cameras for eyes and analyze shoppers’ faces to detect age, gender, ethnicity, and multiple other characteristics. A luxury goods retailer is currently piloting their technology to better assess their marketing messages, potentially discover new target groups, and tune their in-store displays.

NEC has built a similar system called NeoFace that can alert staff when a loyal customer or a big spender walks into a store.

Building an effective personalization program will help accelerate the retailer’s progress toward results: a more personalized experience, greater customer loyalty, increased wallet share, and substantially better top and bottom lines.

Use-Case 2: Customer Journey Analytics

Engaging a customer at the right place at the right time with the right offer is the holy grail for marketers.

Tracking the customer journey allows to serve the most relevant product and content recommendations based on a customer’s shopping, browsing and transaction history.

A global e-commerce platform, EMEA, wanted to expand its product recommendation capabilities across its many product verticals. The retailer spent 12‑18 months aggregating all customer data across verticals to create a single view of customers’ browsing and purchasing patterns. The retailer ran A/B tests on the customer data and developed an algorithm to drive continuous improvement in recommendation accuracy. This resulted in a 500% increase in sales conversion in some of the retailer’s product lines.

We’re primarily seeing this sort of omni-channel journey orchestration possible in customer data platforms (CDPs) and modern marketing automation tools.

Marketing automation tools like Braze and Salesforce Marketing Cloud allow marketers to create journeys based on a variety of events such as API triggers, time-based triggers, data changes and more. These journeys can then span multiple days and dynamically adapt messaging based on customer data being fed into the system during the journey lifespan. Imagine getting a thank you e-mail for a product you purchased and then getting recommendations for other similar products a few days later, only to receive a promotion after that for being a loyal customer and writing a product review on the site.

Supercharging these marketing automation tools, are CDPs which have the power to ingest unlimited amounts of data and produce personalized customer recommendations. CDPs can ingest numerous sources of customer data to create a consolidated Customer 360 view. They then enhance that view with machine learning models to figure out likelihood to churn, propensity to buy, recommended products and more. These insights can then be used in audience creation during the journey building process, and ultimately activated via marketing automation tools, advertising, Connected TV and website personalization.

Use-Case 3: Assortment Planning Analytics

Assortment planning is a lengthy and complex process that occurs before major seasons. The volume of information that needs to be collected, analyzed and acted upon becomes incredibly difficult to process manually. Legacy assortment management suites would just put a UI on top of this process, but that is no longer enough. Modern assortment management uses data to take the guesswork out of planning and drive higher sales with better product breadth and depth. They reduce excess inventory and can keep additional stock for high selling items.

Stores are no longer clustered and graded based on revenue, but on a variety of factors such as store size, location, revenue, and more. Modern systems can also help you forecast the success of new products that plan on being introduced.

Analyze customer purchasing behavior to help inform future product catalog and web placement decisions.

Below are the four steps to activate this use case after developing your data foundations.

1) Collect data:

Identify data elements created by customers on your owned and operated websites, stores. Browsing and purchasing data are two key data sets as they indicate what customers are looking at and what they respond to. Develop and document a data strategy that defines the first-party data you will collect and sets integration goals for data collection and management efforts.

2) Build Segments:

Analyze your data to reveal customer insights. Focus segmentation and targeting efforts on grouping customers based on common characteristics, such as the products they browse and buy, traffic source, or basket size. The critical element is to uncover the characteristics that will influence your product assortment, product hierarchy, and merchandising decisions.

3) Develop and run tests:

The data team and product assortment and merchandising team need to collaborate to perform A/B testing of the different levers (e.g., layouts, product categories) across the different segments. Tests should be conducted live and continuously to constantly refine your design decisions and drive incremental value.

4) Implement changes and iterate:

Use the insights gained from A/B testing to inform new decisions about product hierarchy, layout, and range, as well as how to most effectively tailor these elements for each customer. For some retailers, merchandising includes content planning to inspire customers who do not yet know what they are looking to purchase.

By improving their ability to deliver the right merchandise assortments to the right outlets at the right price and manage their inventories to these data-driven consumer demand signals, retail organizations are better positioned to seize market opportunities by delivering new customer-centric products at more predictable costs.

Use-Case 4: Pricing, product recommendations and promotions

We live in an era in which smart pricing strategies can catapult new brands and retailers to quick success. At the same time, failure to project the right price image can seriously undermine the prospects of businesses

Think of Dollar Shave Club, an e-commerce subscription start-up that grew rapidly on the promise of providing “a great shave for a few bucks a month.” And then, of course, there are widely admired brands such as Apple, Patagonia, American Eagle and Lululemon that have largely avoided discounting based on the merits of their product quality. As these examples demonstrate, price plays an important role in establishing the brand image and what it stands for. A brand that dependably connects with shoppers through its price message is typically a brand that enjoys long-lasting customer loyalty.

How Analytics Can Help Manage Pricing Priorities

As complex as pricing analytics may seem, managing a brand’s price position is crucial to the way it is perceived by consumers.

Industry leaders are adopting cutting-edge AI tools to stay competitive and incorporate a dynamic pricing strategy. These tools and platforms utilize price analysis data from various channels of a company’s operations (i.e., marketing channels, logistics channels, eCommerce platforms). Thus, it empowers retailers to make data-driven competitive pricing decisions while receiving a better ROI on each product.

The customization of a recommended price should be dictated by the retailer’s:

Overall price position/image (discounter vs. premium, etc.)

Strategic plan for the said product and category (traffic-driver vs. margin-driver)

Inventory capacity (long-term supply vs. quick turnover)

Pricing analytics can play a key role in bypassing the need for a MSRP and help create customized recommended prices. A good example of a retailer using intelligent application of pricing analytics is Amazon, which makes over 2.5 million price changes per day, often changing the price of a single item several times within 24 hours.

Pricing analytics allows companies to create a mechanism that acts as a catalyst for managing profitability. That term alone — managing profitability — is telling. With predictive analytics, marketers find themselves increasingly in the driver’s seat, making discreet judgments on how much margin to reserve on particular products relative to the competitive situation.

Product recommendations and promotions

Retailers need to serve the most relevant product and content recommendations for the online journey based on a customer’s browsing and transaction history. Leading retailers who excel in product recommendations exhibit the below common characteristics that enable them to succeed with this use case.

1) Culture: Ability to balance business objectives and customer insights to recommend the right product to the right customer at the right time while driving a commercial outcome.

2) Tech: Data is aggregated into a standard repository to provide a single view of the customer. Machine Learning is used to effectively analyze large volumes of data.

3) Skills: Collaboration between data and merchandising teams to constantly optimize product Skills recommendations.

4) Data: Access rich data on product performance and product relationships to feed into complementary Tech Data and substitute algorithms.

Common Challenge: Alignment with Promotional Planning Process

Retailers express a common challenge in aligning segmentation and targeting efforts with promotional teams, given promotion timelines may extend months in advance of an event and optimal targeting efforts require near-real time responses. To bridge this gap, include the data, sales, marketing, and merchandising teams in long-term planning sessions.

Use-Case 5: Fraud Prevention

With a huge growth in online sales during the pandemic, we are also seeing a big surge in fraudulent transactions. Fraud can be a pressing challenge for the retail industry, with the potential to impact finances, erode customer trust, and impact brand value. Consumer market companies tend to have several third-party touchpoints, such as vendors/ suppliers, transporters, third-party manufacturers or subcontractors, packers, distributors or other third-party service providers, which can significantly increase the risk of collusive frauds that are difficult to detect.

Having a team manually review each fraudulent transaction isn’t scalable; this eventually leads to numerous customer service complaints and an overall decline in brand recognition. Companies like Forter have built machine learning models that use past transaction history data to easily identify fraudulent transactions and prevent unnecessary loss of revenue due to chargebacks. The reduction in chargebacks automatically drives down customer service complaints and frees up time for resources to work on more important things.

Driving value from data

To compete in a consumer-empowered economy, it is increasingly clear that retailers must leverage their information assets to gain a comprehensive understanding of markets, customers, products, distribution locations, competitors, employees, and more.

Retailers will realize value by effectively managing and analyzing the rapidly increasing volume, velocity, and variety of new and existing data, and putting the right skills and tools in place to better understand their operations, customers, channels, and the marketplace as a whole.

Covid-19 has pushed businesses to embrace digital transformation to help drive innovation and thrive in the ever-changing technology landscape.

Businesses are moving faster than ever before, and the only fuel that can power this speed is data. Fortunately, the entire data industry has been innovating at a rapid pace. New architectures, new cloud platforms, and new technologies have given rise to the modern data stack, opening new opportunities for businesses brave enough to be early adopters.

We have collated the top ten trends in data management and analytics that will come to the fore in 2022.

1. Data warehouse, data lake, data lakehouse, and data mesh

A data warehouse is a unified data repository for storing large amounts of information from multiple sources within an organization. While valuable for its time, a centralized data warehouse in an on-premises world could take months to build. Highly curated data can obscure more valuable granular insights. Costs could be high.

The term data lake was coined in 2010, with the promise of speeding access to granular data and

lowering costs. Unfortunately, these became known as data swamps, too slow to be usable.

As businesses want to rely on data further, it is becoming increasingly important to analyze structured as well as unstructured data. Data Lakes became popular due to their ability to easily capture unstructured data such as log files, images, videos, and more. However, to support BI tools, this data then needed to be transformed into structured data via ETL processes. We are now seeing the rise of new Lake House approaches that apply a metadata layer on top of data lakes which allow business applications and ML applications to interact more seamlessly with structured data and unstructured data directly.

The data lakehouse combines the best of both a data warehouse and a data lake, offering converged

workloads for data science and analytics use cases.

Another significant trend that we are seeing is the rise of data mesh. A new data architecture paradigm becoming increasingly popular as forward-thinking companies embrace semi-structured and unstructured data, and seek to democratize access to it, is the data mesh. a Data mesh is a type of data platform architecture that supports distributed, domain-specific data consumers and views “data-as-a-product”, with each domain handling their own pipelines. This approach can help your data platform scale over time and is worth exploring.

The development of cloud computing technology processing has seen widespread adoption with AWS dominating the sector. A survey conducted shows that the global cloud services industry is projected to be over more than $623 billion by 2023, with a growing compound annual growth rate (CAGR) of 18%.

Navigating these new concepts is the foundation of your future digital success, and the time to embrace them is now.

2. No-code Machine Learning and Analytics

AWS recently launched Sagemaker Canvas making it incredibly easy for business users to create their machine learning models without writing a single line of code. Sagemaker Canvas works by cleaning and combining the data and creating hundreds of models. Users can import data with just one click from various data sources. This platform will enable business analysts and data analysts to work completely independently, without any reliance on engineers or data scientists.

Microsoft also has Azure Machine Learning Designer. Over 2022 we are going to see further development in no code machine learning tools. AI and ML will continue to be commoditized, making it very easy for businesses to grow using these techniques. The drag and drop methods make the interface user-friendly. Tasks like classification, regression, statistical models are supported by Azure Machine Learning. It also works well for data teams as it has the data labeling feature as the central place for teams to work.

3. Embedded Analytics

Today, it’s not enough to have a transactional-based enterprise system that just automates processes through code. Most large enterprise software providers are embedding machine learning in every aspect of their systems. From allowing users to configure model parameters to adding analytics behind the scenes, we will see AI & ML permeate software systems. We’re not just seeing it in customer analytics, but also supply chain optimization, order management systems, PLM, and more. It uses the knowledge from the existing data to make decisions and assessments, independently. The data visualization and analytics are placed in the user interface of the application. The intuitive nature of these embedded analytics makes it popular among users.

4. Data-Driven Culture

A data-driven culture is currently amongst the hottest business intelligence trends.

While organizations have always been interested in their numbers and figures, the extent of data use is exercised at a higher level within a data-driven culture. A data-driven culture should not be only interpreted as merely following numbers. It should encourage the advancement of data interpretation skills, deriving insights from data and critical thinking, which enables businesses to base their decisions on reliable data. The idea is to build a cultural framework that helps all members of the organization to collaborate and contribute to moving data at the center of decision making.

In the new data landscape, the demand for data analysts and analytics engineers has been rapidly growing thanks to the rise of the modern data stack that brings agility, scale, and the power of low-code platforms.

A data analyst pairs their technical knowledge with an understanding of the business, a skill that has been sorely lacking in many junior data scientists.

Enterprises are going to use data in every vertical to make better decisions and improve efficiency. We will start seeing data analysts in every vertical from sales & marketing to merchandising and warehousing. Thanks to the plethora of data analytics tools available, it will become easy for these analysts to get up and running and produce results quickly. Furthermore, they will help the company make decisions faster, improving time to value drastically. Ensuring your organization can tap the capabilities a data analyst can deliver will be crucial to your success.

5. Data Management – Data Preparation

A survey of data scientists by Crowd Flower revealed, 76% of data scientists say that data preparation is the worst part of their job. But the fact remains that efficient, accurate business decisions can only be made with clean data. Data preparation helps in fixing errors quickly by helping catch errors before processing. It also helps in producing top-quality data by cleaning and reformatting datasets and eventually making timely, efficient, and better business decisions.

Additionally, as data processes move to the cloud, data preparation moves with it for even greater benefits, such as superior scalability, accelerated data usage, and enhanced collaboration.

As businesses become more data-driven, the importance of data quality will increase. Good data quality is the foundation of any data initiative, from business intelligence to machine learning. Fortunately, there are many tools and services today that can help you cleanse data quickly. Cleansing ranges from format validation (email, phone, dates, etc.) to complete address standardization. Furthermore, companies want to get accurate information on their customers, so we are seeing a wave of deduplication initiatives across companies.

Originally focused on analysis, data preparation has evolved to address a much broader set of use cases and can be used by a wider range of users.

While it improves personal productivity for anyone who uses it, it has evolved into a business tool that promotes collaboration between IT professionals, data experts, and business users.

6. Data Management – Data Governance

Data governance is a collection of policies, processes, standards, and metrics that ensure the quality and security of the data used across an organization. An elaborate data governance strategy is essential for any organization that works with big data. Data governance is critical to capturing value through analytics, digital, and other transformative opportunities. Data governance has served as a foundational component of data strategies for years.

Data governance is a complex but critical practice, and most enterprises have encountered

difficulty in mastering all its requirements. While many companies struggle to get it right, every company can succeed by shifting its mindset from thinking of data governance as frameworks and policies to embedding it strategically into the way the organization works every day.

With an ever-increasing application landscape, there is a surge in the volume of data being produced by businesses. To prevent data leaks and attacks, proper governance structures need to be put in place. Per our comments under “Data-Driven Culture”, there will be a surge in the data analyst role and as the title suggests, the need for data access by teams is only going to increase. Setting up the right access and authorization framework for data access can prevent outages due to human error.

7. Data Management – Data Discovery and Lineage

Data lineage is the process of understanding, recording, and visualizing data as it goes through all the transformations it underwent along the way. It helps understand the data life cycle and is one of the most crucial pieces of information from a metadata management perspective.

In line with data security and ensuring compliance with new data regulations, understanding the flow of data across the organization is paramount. One needs to understand what type of data lies in which systems to effectively triage issues that may arise from customer complaints to business continuity reports. Also, as enterprises begin to replace legacy systems, it will be vital that all integrations with such legacy systems are replaced with a new system without any disruption. Appropriate data discovery can mitigate downtime in such scenarios.

8. Data Activation

Finally, to take advantage of all the insights produced by various teams and systems, data needs to be activated to be effective for the business. This is done through e-mail campaigns, advertising, website personalization, and more. Businesses are moving from producing macro reports on a weekly/monthly basis to producing insights per second that are then automatically acted on by core customer communication systems in real-time to produce immediate results.

Effective data activation requires strong collaboration driven by evangelism from company leaders. It is also important to find trusted partners for both data and analytics services whose capabilities complement yours. Enlisting third-party data and analytics solution providers to help navigate change and implement new approaches often provides a cheaper and faster direct path than building those capabilities internally.

9. Hyper personalization/Real-Time Data Integration

The ability to target a user with the right offer, at the right time, in the right channel is only made possible through the rise in data and compute capabilities. Machine learning models make it possible to predict such behavior, whereas real-time integration capabilities make it easy to act on such predictions immediately. It enables rapid decision-making, breaks down data silos, and future-proofs your business. This sort of personalization leads to significantly higher customer lifetime value. Imagine getting an e-mail for an offer on new running shoes just about the time your existing ones are wearing out. Today we can make such predictions thanks to the transactional data being collected over the years.

Further, to achieve agility and speed interactions between systems, effective response to the powerful market forces and integration of data in real-time is essential.

To ensure an economical and efficient transfer of data, great customer engagement, better visibility, and insight into your business and superior customer service, real-time data integration in business is a must.

10. Rise of Data Platforms

Tying all of the above are data platforms. While each data platform is unique, they generally encompass data management, analytics, and activation in some form. At its core, a data platform is a central repository for all data, handling the collection, cleansing, transformation, and application of data to generate business insights. They are highly scalable and can perform real-time integrations with many systems; this makes them easy to insert in most product ecosystems. We are seeing Customer Data Platforms become a growing category in retail and automotive. Companies like Treasure Data and Amperity have extraordinary data capabilities that have led to enterprise success. While data platforms have been traditionally focused on B2C companies, we are seeing B2B platforms emerge as well.

A key trend that needs to be highlighted is that data-first companies like Uber, LinkedIn, and Facebook increasingly view data platforms as “products”, with dedicated engineering, product, and operational teams working to maintain and optimize them.

Become Data-driven in 2022!

Being data-driven is no longer ideal; it is an expectation in the modern business world. Organizations have accelerated their digital transformation journey; now they understand that they need to be mindful of how they integrate and manage enterprise data that is distributed, still easily accessible, trusted, and governed.

We, at Systems Plus, help businesses leverage the benefits of the cloud and harness the power of data-driven business intelligence. Speak to us to find out how we can leverage the cloud to help you unlock business value and overcome the challenges of data silos, data latency, and more!

Make data-driven business decisions with BI and analytics

Is your data trying to talk to you? Is it trying to give you insights that you can leverage? How do you interpret your data in a mobile and cloud computing world where the volume and velocity of data are growing exponentially by the minute? Besides, what do you do to manage the big data platform? Business Intelligence (BI) and analytics have all the answers.

BI helps you make data-driven business decisions. It has transformed the erstwhile reactive approach to data to a more proactive one. While BI reveals the happenings of the past and present to portray business trends and patterns, analytics refers to data analysis techniques that are predictive and prescriptive by nature. They inform about future incidents and the measures to be taken to prevent disasters.

Applying a custom BI layer to data architecture makes data volume, variety and velocity easier to manage. Analytics utilizes technologies such as scalable databases and streaming technologies, state-of-the-art data warehouses, dashboards and visualizations to help better your decision-making based on facts, trends and predictive analytics.

Let’s take a look at the key advantages of using BI and analytics.

Better data-driven sales and marketing strategies

Data analytics software can analyze campaign outcomes. Companies can use it to decide how to prioritize campaigns, tailor promotions and make strategic decisions that will fine-tune their marketing strategies, reduce overheads and garner a better ROI on ad spend.

Industry-specific business intelligence enables companies to discover detailed sales trends based on their customers’ preferences, reactions to promotions, online/retail shopping experiences, purchasing habits, and patterns and trends which affect sales.

Having a clear picture of sales trends also makes way for better collaboration in marketing and management decision-making.

Greater revenue with predictive analytics

Companies that embrace data and analytics initiatives experience significant financial returns. According to various global reports, organizations that invest in big data yield a six percent average increase in profits, which goes to nine percent for investments spanning five years. Businesses that can quantify their gains from analyzing data, report an average eight percent increase in revenues and a 10 percent reduction in costs.

Improved operational efficiency with futuristic insights

Many firms are now using BI and predictive analytics to anticipate maintenance and operational issues before they become larger problems. For mobile network operators, it leverages data to foresee outages days before they occur and prevent them by timing maintenance, which saves operational costs and ensures keeping assets at optimal performance levels.

BI is critical in helping access, analyze and share information across the business and allows for more intelligent responses to trends in production, material usage, supplier information and more.

Insights and smart decision-making

Competition moves quickly. It’s vital for companies to make decisions as fast as possible. BI and analytics provide clearer insights through past, present and future data visualization. Comprehensive charts and graphs ensure that decision-making is accurate. Through visual representations of extracted data, organizations get the advantage of taking strategic decisions faster and leveraging businesses more efficiently.

Increased customer satisfaction with predictive analytics

BI software can help companies understand customer behaviors and patterns. Taking customer feedback in real-time helps retain customers and reach new ones better. Modern consumers are easily swayed by better alternatives. Predictive analytics gives organizations insights in to how target markets think and act. It also makes them act dynamically and adapt to the needs of changing consumer mindsets.

Building efficiency with big data analytics

Efficiency for businesses has been improving since the advent of BI. With the ability to collect a large amount of data at a faster rate and present it in visually appealing formats, companies can formulate decisions to help achieve specified goals. Analytics encourages a company culture of efficiency and teamwork where employees can express their insights and share in the decision-making process.

Real-time performance measurement with live insights

BI tools continually monitor large amounts of data generated by an organization and analyze it in real-time for several performance metrics such as efficiency, sales figures, marketing costs and more. This keeps top management informed about the status and performance of various critical components within the organization and the collaboration between business units. It also helps detect market opportunities and take advantage of them.

The advantages of BI and analytics can now be better illustrated through a case study where the challenge was to build a data platform for advanced analytics for our customers.

Inheriting a legacy is highly beneficial in life, but not in IT! Legacy systems or bimodal environments will inevitably slow you down. For today’s fast-evolving technology, speed and agility is the need of the hour!

How do you increase efficiency systematically, speed up exponentially and grow dynamically? DevOps best practices have all the answers. Supported by software and technology, DevOps automates the entire operation to create a seamless culture of collaboration. It helps your software teams better leverage their time and skills to build, test and deploy software faster, while bolstering communication and data transparency.

Through planned digital transformation with DevOps, greater quantum of releases is managed at an unimaginable velocity. Technologies such as microservices architecture, containers and, of course, cloud adoption capabilities make it happen.

Powering DevOps with Microservices

The beauty of microservices architecture is that it can build a single application as a set of small services. It decouples large and complex systems into simple and independent projects to make applications more flexible and enable quicker innovation.

The use of microservices leads to increased release frequency which makes way for a larger number of deployments. Various DevOps methods, including continuous integration, continuous testing, continuous feedback and continuous delivery, manage them efficiently so that you deliver rapidly in a safe and reliable manner. Testing, packaging and deployment tasks are automated for each service in an independent DevOps pipeline to prevent affecting other services.

Combining DevOps and microservices brings greater agility to your operations. While DevOps improves the development and IT operations culture, microservices helps you build, release and maintain applications faster.

Improving collaboration with DevOps and Containers

Containers help to fortify DevOps workflows and make collaboration easier in your customized DevOps model. Different parts of an application are distributed into multiple microservices and hosted in different containers to update them easily.

The entire runtime environment which includes the application, its dependencies, libraries and configuration files are packed in a container. It lends consistency and speed in development, testing and production environments throughout the delivery chain.

The DevOps chain consists of various tools and processes involved at each link. Containerization cuts down this bulk. It provides lighter and faster applications with a powerful capacity for versioning. Containers build agility to switch speedily between various frameworks or deployment platforms while working on DevOps. This achieves efficiency, saves time and controls costs too.

Turbocharging DevOps with Cloud Computing

Cloud computing increases the pace of DevOps implementation throughout the development lifecycle. The cloud enables collaboration, while saving the downtime of sending files back and forth to team members. It builds on speed through automated testing in simulated environments.

With secure cloud gateways for DevOps, users can safely access enterprise resources anytime, anywhere. Constant access allows for continuous collaboration and increases execution velocity.

DevOps should be embraced for taking advantage of its continuous integration and continuous development (CI/CD) tools. These enable teams to frequently validate and deliver applications. The tight integration provides centralized governance and control through the cloud for a faster DevOps process.

As Cloud adoption increases, the threat of determined and meticulous hackers is also increasing. Across industries, businesses have realised that just perimeter security is an insufficient defence mechanism and puts their business at risk for financial and reputational damage. On the opposite end of the spectrum a complex environment of regulation, frameworks, security tools and services has created a situation where companies are confused about the security direction they should take.

Managing IT security is all about trade-offs. A lot of companies are either missing key capabilities or don’t execute processes effectively, which results in a security incident. Today, let’s talk about an innovative approach to IT security that will not only make it more robust, cheaper and simpler, it will also turn security into an advantage for your organisation.

We’re talking about a Hybrid SOC (Security Operations Center) built through our Virtual Captive to give you 24×7 coverage at a fraction of the cost of traditional outsourcing. This approach, which includes an expert team of security analysts and engineers utilizes threat detection tools and up-to-the-minute intelligence to help you upgrade your organizations’ defences and mitigate security risks. The Hybrid SOC functions as the nerve centre of your business’s defence and covers the entire spectrum of security tasks ranging from threat investigation to recommending new policies. With our Hybrid SOC you get:

Complete threat investigation and analysis to deliver recommended

remediation steps to the impacted IT Infrastructure

Threat assessment reports to identify risks to applications,network, and

computing infrastructure, based on threat intelligence mined

from a variety of internal and external sources

Driving new security content used to protect network and IT Infrastructure

by reverse engineering malware and suspect applications

to obtain valuable information

Performing PCI and vulnerability scans to determine at risk

systems for either compliance violations or known exploits

Many organisations believe that setting up the right security tools will provide them with the most effective defence against attacks and breaches. This is because the security industry is always trying to sell them tools and services, instead of telling businesses that execution is much more important!

An open secret in IT security is that adequate tools and great execution beats great tools and poor execution. But the IT security industry always tries to sell tools and services, while underplaying the importance of execution. With solid execution being the most important success factor of an IT security program, it is the Hybrid SOC that can provide businesses with the most effective and comprehensive defence. The ultimate benefit though is the low cost. A Hybrid SOC approach not only works better, it costs less. Often half or even 1/4th the cost of the multi-vendor approach.

Over the years, we have helped many businesses across the world leverage cloud technologies to maximize value for their digital transformation journeys. By migrating and modernizing in the cloud, we have enabled companies to transform into more productive and agile entities that are better equipped to handle the increasing demands of the digital age. While every organization’s journey is unique, the first step towards modernization is always the same – defining the value that a cloud data warehouse is expected to add to the business. Whether it’s greater agility, advanced real-time analytics, or cost savings, the organization’s goal will help determine its unique migration path.

As per the latest IDG survey, a large number of companies are planning to increase their investments in the cloud data warehouses and the data lakes in 2021. The survey also found that over the next six to twelve months, 77% of IT decision-makers intend to migrate to a cloud data warehouse, or expand an existing cloud data warehouse. Additionally, 57% of the companies surveyed, said that a hybrid option would be the data management strategy of choice for their enterprise. To be honest, it comes as no surprise to me that organizations of all sizes are pivoting to a cloud data warehouse. Given its multitude of benefits, it is the next frontier for companies as they embark on their digital transformation journeys. The adoption of the Cloud Data Warehouses has also been further fuelled by the uncertainty brought on by the pandemic, causing organizations to seek alternatives to legacy systems in a bid to achieve sustained and long-term business growth.

By strategically migrating their data and processes to the cloud, companies can effectively mitigate against failure while modernizing their IT capabilities. Establishing a scalable, secure and cost-efficient data warehouse has helped many businesses become more agile and maximize value from their digital transformation journey. Apart from the capital and operational cost savings, one of the key benefits of the cloud data warehouses is that it enables businesses to store large amounts of data sustainably and securely, while also providing access to real-time, actionable business insights. The Cloud Data Warehouse as it exists today, is indeed at the heart of the data analytics architecture that fuels enterprise growth.

Here I’d like to share a case about how we helped a specialty US retailer modernize their data platform, which helped the business make real-time decisions while also leveraging prescriptive analytics. In this case, the retailer wanted to discover the true value of data being generated from its multiple systems and understand the patterns, both known and unknown, of sales, operations and omni-channel retail performance. We helped them by building a modern data solution which consolidated their data in a data lake and data warehouse, making it easier to extract value in real time. We integrated our solution with their OMS, CRM, Google Analytics, Salesforce and their inventory managements system. We also had the data Machine Learning-ready, so that it could be easily leveraged in the future. The result was absolutely amazing! Our client now has visibility into their data from day one, something they had been wanting for a while. In addition to this, they were also able to build better reports, dashboards and charts to understand and interpret the data – true actionable insights. The icing on the cake was the ability to get real-time visibility and analysis on instore purchases based on multiple slice and dice options! We helped our client consolidate the data from disparate systems and get visibility of all the data from a single source of truth – which ultimately helped the business influence its top line in a meaningful way!

When it comes to choosing the optimum offshore delivery model for your company, there is a lot to consider. Yet we find that most companies opt for the high-turnover, “black box” offshore model that has been proliferated by legacy outsourcing companies. This doesn’t have to be the case. It’s time to break out of the ‘black box’ and adopt a proven model of IT outsourcing that gives you all the strategic benefits of a Global In-House Center (GIC), without the risk or hassle of having to own the platform. We call this a Managed GIC or ‘Virtual Captive’ model. It is the opposite of a black-box model and has been used by many businesses to meet their long-term strategic objectives.

Let’s take a look at some of the distinct advantages a Virtual Captive model offers over a traditional IT outsourcing model. While cost is a great advantage, allow me to keep it for the last as I consider it as table-stakes.

Transparent pricing

The offshore center conventionally operates as an ‘opaque’ cost center. Whereas the Virtual Captive model is based on complete transparency over salary, operational expenses, and profit margins.

Total control over operations and outcomes

The Virtual Captive model is a collaborative model where the vendor acts as a proxy for the client, but the client gets to maintain full operational control. What this means is that client calls the shots in terms of deliverables, personnel decisions, and execution methodology.

Access to a high-skill talent pool

Legacy offshoring firms often struggle to provide experienced resources that have the skills to add long-term value. The Virtual Captive model solves this problem by taking a strategic approach to talent management and builds client teams of experienced resources instead of the typical fresh out of college graduates. Virtual Captives work as the client’s offshore arm that delivers superior outcomes.

Faster time to market

A Virtual Captive is quick to set-up and offers the flexibility to scale as per the businesses’ evolving needs. An experienced service provider typically brings along existing infrastructure and a facilities management team which makes it easier for the business to get started quickly without worrying about infrastructure, talent, or any other needs.

Now, the cost arbitrage or quick realization of ROI

In today’s bimodal IT environment, clients not only want more cost savings but are also looking to minimize their cost of entry. This is where the Virtual Captive model fits perfectly. It has a much lower cost of entry thanks to the already established infrastructure and facilities the service provider brings to the table. Plus, in the Virtual Captive model, the right team size and structure are designed to specifically meet the clients’ needs. This astute talent and budget management help create at least 30%+ cost savings as compared to a legacy offshoring setup.

A virtual captive also know as a Managed GIC works better than traditional outsourcing because it is designed differently.

We believe that companies achieve peak performance by hiring and nurturing talented people. This is also true for IT, and it is especially true for offshore talent.

Over the years large, legacy outsourcing firms have introduced the idea that IT talent is less important than IT documentation and processes. This has led to a proliferation of what we call “black box” outsourcing models where clients don’t know how the gets done because the “how” is hidden behind the process.

We believe that talented people do great things by creating great processes and we have designed our Virtual Captive outsourcing model to be a “people first” model so we can apply the best talent to generate the very best results for our clients.

Virtual Captive teams have more experienced resources that bring the very best talent to the client’s business processes. Furthermore, the model has lower turnover which offers better knowledge retention and intellectual property protection than legacy providers.

Finally, the mGIC model operates with a speed and agility that can adapt to a client’s changing business needs. The model also costs less, often 40% less than traditional legacy outsourcing. Our clients are operating their Virtual Captive teams at less than $20 per hour.

The first step toward a sound and secure RPA infrastructure begins with an understanding of the core Architectural components of the solution, their operations, and the requirements for secure RPA platform.

Securing RPA Platform

Security measures to be considered while designing secure RPA platform. Below mentioned are few steps Automation Anywhere takes for ensuring all of the data is protected.

Multi-layer identification and authentication

A security level let you determine how strong the identity of user is or if he is really the user he claims to be. It is very clear that a user logged from company network is more trustworthy then someone logged in from the internet.

It is necessary for fixing security in order that humans and bots must be authenticated before accessing or performing actions within the RPA platform

Automation anywhere offers flexibility to have single or multi-factor authentications and application credentials in the Control Room, which manages and monitors all processes of the infrastructure. This involves integration with Microsoft Active Directory using LDAP, Active Directory using Kerberos and native authentication using the embedded Credential Vault for identity and access management

You can also use an external third-party-privileged access system (Burp suite, Nessus Scanner, Open Web Application Security (OWASP), Black Duck) or support for SAML 2.0 based single sign-on (SSO)

Multi-Level Authentication allows you to define, assign levels to your users and protect your services based on below strategies:

Contextual-based

Credential-based

Authenticator-based Encryption

Encryption

Encryption can ensure a basic level of security for all sensitive data so that it remains hidden from unauthorized users. Encryption is vital because it allows you to securely protect data that you simply don’t want others to possess access.

Many RPA tools allow for the configuration and customization of encryption methods, from securing specific types of data to safeguarding against the interposing of network communication

Encryption is majorly effective when implemented with a comprehensive information security plan

RPA provides file and text encryption

Access control

Though RPA replaces humans with bots, people still need to work with bots to schedule, run, and view and edit their processes. To successfully and securely do that, security admins must be ready to specify who does what — access control for humans and bots alike is critical. Access control for humans and bots is critical.

Audit logs

RPA platforms, provides comprehensive audit logging, monitoring, and reporting capabilities.

Extensive audit logging is performed for 185+ activities on Automation Anywhere platform. Comprehensive and continuous audit logging capabilities within the Enterprise room enable you to spot and alert abnormal activities like bot performance errors, misuse by employees, malicious code so on

It ensures enterprise-level security and audit compliance

Full audit trails enable you to make quicker and cleaner audit reports, and make sure that you’ll retrace the steps that led to a selected problem, be it a mistake within the robot’s performance, malicious code or other misuse by an employee

Bot-specific security

It is important that the bot code be secured from piracy. Because bots mimic users, they interact with applications using keyboard and mouse peripheral inputs.

How can I secure my RPA environment?

For leveraging the cyber security your organization approach should provide the following:

Integrity

Traceability

Confidentiality

Control

How can RPA improve your security organization?

Decrease time to detect and respond to incidents, helping minimize risk exposure to an attack

Minimize employee turnover due to lack of challenge or career progression by permitting employees to focus on higher value tasks

Make intelligent decisions quickly, leading to high-quality and consistent outcomes

Resiliency doesn’t mean having a good DR strategy during these uncertain times. In fact, its much broader than that. It’s about an organization’s ability to withstand a major shock and come out of it with core capabilities intact. Typically the ability to continue operations, maintain baseline and maintain talent are the keys for organizations to emerge from any major disruption. Furthermore, when a shock occurs, financial survival instincts take over and all 3 points discussed above become afterthoughts, especially talent. People and therefore salaries for most organizations are usually the highest cost thus easiest to part with and hence naturally during shocks you usually see massive layoffs. But with that, what companies often forget is that headcount reductions often lead to loss of key capabilities. Furthermore, due to leaner teams trade-offs are made to SLA’s, security, response times etc. As people walk out the door, so does knowledge and during shocks, doing proper KT/ building good documentation is never even considered. All of this eventually leads to chaos and often companies are never able to recover and get back to the glory days. But its not all doom and gloom. Since talent, or in such a situation, the loss of talent is usually the main reason organizations are not able to recover, the managed GIC or the Virtual Captive as its often called comes to mind to mitigate a lot of this risk.

A Virtual Captive model is a “people first” outsourcing model which applies the best talent to generate the very best results for its users. Virtual Captive teams have more experienced resources and is a lower turnover model that offers better knowledge retention and intellectual property protection than with legacy providers. Finally, Virtual Captives are built and sourced from scratch and belong exclusively to a client therefore the risk of dependency on the legacy provider is greatly reduced.

The Virtual Captive model operates with a speed and agility that can adapt to an organization’s changing business needs and typically costs 70% less than onshore talent and often 40% less than traditional legacy outsourcing. It has been observed that even highly specialized and experienced teams can be built within the Virtual Captive model at rates as low as $20 per hour.

But how does a Virtual Captive aid in absorbing shocks? Well its quite simple really. Even as some onshore talent may need to be reduced, given that the offshore talent is already supporting a major part of the organization at a very low-cost structure, the disruptions due to onshore exits are negligible. Key knowledge is not lost, neither are capabilities. While Virtual Captive teams can easily support new initiatives/ digital transformations, usually these teams support the core services. When you reduce disruptions to an organization’s core services, it reduces disruptions to ongoing operations and baseline support, thus making it more resilient.

Talent is the largest component of most IT budgets and you need to be able to find the best balance of cost and capabilities with your pool of talent BEFORE times get tough.

This is where Strategic Talent Management comes in. Strategic Talent Management simply means separating your talent into three layers:

A low cost layer in a Virtual Captive or an Managed GIC which typically costs between $15-20/hour and is the best place for all baseline support costs.

The second layer is your internal team, which is the next most expensive source of talent.

The third layer is the flex consultant and contractor layer. This is usually the most expensive source of talent.

The point is to use a Virtual Captive for as much as you can. Don’t get caught in a situation where you have all your talent in the most expensive layers (which is internal talent, and consulting talent) because when times get tough, you are going to have to cut staff and reduce capabilities, experience and knowledge.

The fact is a Virtual Captive is the lowest total cost of external talent available anywhere. And that matters because talent is such a big part of IT costs.

A Virtual Captive also known as a Managed GIC can help you improve your digital capabilities and Agility. It’s the only approach that can help a company achieve Modern Agility in a few important ways.

The first is that a Virtual Captive is the best way to create cost effective technology centers of excellence. These COEs are focused on Iterative delivery of solutions, quality assurance and test execution, Data Analytics, Security Operations, and Robotic Process Automation. Establishing a COE gives you the capacity to focus resources on important activities, retains knowledge, and becomes more effective over time.

Next, a Virtual Captive is the best place to put baseline support activities. Baseline support activities are part of every IT organization. These are the on-going tasks required to keep the lights on and they are never going away. Many companies have chosen to use tradition outsourcing to handle baseline support tasks, and have had mixed results. A mGIC provides the talent, agility, knowledge retention and cost structure needed to create valuable centers of excellence

A Virtual Captive is also a cost-effective source of great technology talent. Talent is the largest cost in most IT organizations. A Virtual Captive is the best way to address that. Our clients operate their Virtual Captives at less than $20 per hour for top talent, and often much less.