Most of the companies operating today are data-driven as they have started identifying the value of data. The value of data and data marketplaces are evolving with the growth of big data and data requirements of companies.

Data Marketplace

In simple terms, these are online marketplaces where we can buy and sell data of any sort. Data marketplaces offer several kinds of data from a wide range of different data sources. These data include Business Intelligence, demographics, research, and marketing data. Data types are structured and offered to clients by data providers. Providing buyers with more choice of high-quality data generates more engagement and encourages fair pricing between the sellers. Every company has the potential to earn revenue from the information it generates. In a recent study of more than 400 organizations, only 1 in 12 were monetizing their data to its fullest extent. Modern data monetization strategies can help you open brand new revenue streams. There are 3 key steps to monetize your data and drive new revenue streams.

Understand your Data Offerings - What types of data or data services are you producing that would be valuable to other organizations?

Decide How to Price Data Products - What should be the pricing strategy followed, and how easy would it be to put into action?

Know About Data Sharing Methods - Which distribution channel should be selected for data sharing that adheres to GDPR and data governance policies?

Once you have decided on the data offerings and priced the data products, the key to successfully monetize your data is by sharing the data with the clients/ prospects. The customers and business partners would be eager to get data from you, especially if you can make the process easy and secure. Traditional data sharing technologies often use FTP, Cloud Storage, or API. These methods generally do have few disadvantages.

1. Storage costs for both vendors and buyers
2. ETL costs and effort
3. Security vulnerabilities
4. Service and support costs
5. Latency and potential errors leading to poor customer experience

How do we overcome these challenges? There are platforms that make revenue generation easier with secure data sharing methodologies. Snowflake is one of the most secure and innovative platforms.

Snowflake & Data Monetization

Snowflake is an analytic data warehouse provided as Software-as-a-Service (SaaS). It provides a data warehouse that is faster, easier to use, and far more flexible than traditional data warehouse offerings. Snowflake allows companies to easily publish a variety of data sets that become immediately available for use or purchase for clients. Snowflake Data Exchange, a modern data sharing method, reduces the time to market and significantly influences customer success. Data Exchange is your own data hub for securely collaborating around data between a selected group of members that you invite. It enables providers to publish data that can then be discovered by consumers. The benefits of Snowflake Data Exchange over Traditional Data sharing Methods are:

1. Secure Data Sharing
   • Exchange data within your organization between different business units. Collaborate with external parties such as vendors, suppliers, partners, and customers.
2. Reduce Time to Market
   • Break down data silos and reduce time to market.
   • Interchange data with third-party vendors to help augment internal datasets.
   • Break down data silos by scaling multiple data sets from different sources within your organization.
   • Find and consume data on other Data Exchanges to get business insights.
3. Speed of Processing
   • Snowflake’s multi-cluster shared data architecture is designed to process enormous quantities of data with maximum speed and efficiency.
   • All data processing horsepower within Snowflake is performed by one or more clusters of computing resources.
   • Data is cached locally within computing resources, along with the caching of query results, to improve the performance of future queries.
4. Cost Benefits
   • The costs for sharing data with Snowflake are minimal and straightforward.
   • Simply pay for the data you store, i.e., you only pay for what you use.
   • Reduce extract, transform, load (ETL), and data pipeline maintenance costs.
5. Control and Govern Access
   • Managing membership
   • Granting and revoking access to data through standard and personalized listings
   • Auditing data usage
   • Applying security controls to your data

Snowflake promises to accelerate the data exchanges by reducing the barriers to access data and time to market. It increases the intrinsic value of data by allowing to share and access of data between providers and consumers.

Real-life Implementation

A famous telecom organization in Europe was sitting on large silos of data that they could not monetize properly because of the complex architecture of the data warehouse operations and data security challenges involved in the data sharing process. The company has Customer Daily Records (CDR) of its subscribers that contains location data of the users. This data can be used to identify the places people visit and help with building consumer profiles. The gathered data allows advertisers to target messages to specific users while tracking whether they visited a retail store after seeing a mobile ad. This helps them plan personalized marketing strategies and business goals based on demography profiles for targeted users. However, due to the data privacy policies of the European Union like GDPR, organizations were struggling to share data with their potential clients. The GDPR policy makes it mandatory for organizations to ensure that the customer's personal information is not shared with third parties without the customer's consent and involves hefty fines and penalties for the data breach. Even the data sharing process was a source of concern as the data was often shared in text/excel files because of the different database architecture of the clients. With growing data privacy concerns and challenges in creating datasets adhering to the GDPR policies, organizations are strictly asked not to share customer data with third parties. The current system architecture forced the organizations to employ a large number of resources to extract the data from the database system and ensure that customer data is not compromised at any point. The companies were evaluating the possibilities of a potential system that would help them monetize the data they currently hold. The introduction of Snowflake into the organizational architecture solved the data monetization problem and improved the overall data culture in the organization. The unique architecture of Snowflake separates the data storage and computation layer to enhance organizational productivity. The pay as you use policy of the Snowflake and the zero maintenance of infrastructure helped the organization phase out the complex on-premise solutions required to handle the huge data volume. Easy connectivity with the existing solutions used for data analytics practice and on the fly scalability of the computation layer helped the organization increase productivity. It also paves the way for seamless integration to the organization's architecture. The Data Marketplace of Snowflake ensured secure data sharing with third parties adhering to the GDPR policies. The in-built data security policies and features minimize the role of organizations to provide data privacy as well. This enables the organizations to make only those data points visible to end-users that they seemed apt for sharing. It also ensures that the data always resides in the organizational Snowflake database rather than on third-party databases. Moreover, the organizations could reach out to thousands of potential clients through Snowflake Data Marketplace without relying on any intermediatory sources. All this ultimately brings out the scope of using the existing data to drive revenue to the organization and highlights the importance of having a complete environment like Snowflake to capture, preserve, access, and transform data. Authors: Rahul Vijayan, Firdous Maqbool

Here is a list of a few popular business intelligence tools companies use to gain insights:

1.Sisense: Leading Cloud Analytics Platform

Sisense is one of those data analytics and business intelligence tools known for its efficiency and easy-to-use quality. It enables anyone within an organisation to manage massive and intricate datasets and analyse and visualise data without any outsourcing. It also combines data from various sources, such as Adwords, Google Analytics, and Salesforce. The in-chip technology helps it to process data faster than any other tool. Gartner, G2, and Dresner have recognised Sisense as a leading cloud analytics platform.

2.SAP Business Intelligence: Price to the Upside

SAP Business Intelligence offers advanced analytics solutions such as machine learning, BI predictive analytics, and planning and analysis. This enterprise-level client/ server system application provides data visualisation and analytics applications, reporting and analysis, mobile analytics, and office integration.

The platform focuses heavily on Customer Experience (CX) and CRM, digital supply chain, ERP, etc. What's particularly appealing about this platform is the self-service, role-based dashboards, which allow users to create unique dashboards and applications. SAP is a robust software designed for all roles that provide many functionalities on a single platform. However, the product's complexity raises the price, so be equipped for it.

3.Datapine: Accessible to non-technical users

Datapine is a comprehensive business intelligence platform that makes the intricate process of data analytics accessible to non-technical users. Datapine's solution allows data analysts and business users to blend different data sources, perform advanced data analysis, build interactive business dashboards, and create actionable business insights by adopting a comprehensive self-service analytics approach.

4.Dundas BI: Access Multiple Data Sources in Real Time

Dundas BI is a browser-based business intelligence tool that enables users to access multiple data sources in real-time. It offers excellent visualisations in the form of tables, graphs, and charts that can be personalised and viewed on mobile devices and desktop computers. Users can easily create reports and extract specific performance metrics for analysis. Dundas aids all types of businesses and industries.

5.MicroStrategy: Fast Dashboarding in Action

MicroStrategy is a business intelligence tool for enterprises that provides powerful and fast dashboarding and data analytics, cloud solutions, and hyperintelligence. Users can use this solution to identify trends, and new possibilities, increase productivity, etc. It can be accessed via desktop or mobile and can be connected to one or more sources.

6.Yellowfin BI: No-co-Low-co Approach

Yellowfin BI is a business intelligence and analytics platform that combines visualisation, machine learning, and collaboration. It can quickly sort through massive amounts of data using intuitive filtering, and it is accessible from anywhere. This BI tool takes dashboards and visualisations to the next level by utilising a no-code/low code development environment.

7.Qlik Sense: Search & Conversational Analytics

A product of Qlik, QlikSense is a complete data analytics platform and business intelligence tool. QlikSense can be accessed from any device at any time. The user interface of QlikSense is optimised for touchscreen, which makes it a prevalent BI tool. It offers a one-of-a-kind associative analytics engine, sophisticated AI and a high-performance cloud platform, making it more attractive. An exciting feature of this platform is its Search & Conversational Analytics, enabling a faster and easier way to ask questions and discover new insights through natural language.

8.Zoho Analytics: Blend and Merge Data

Zoho Analytics is an excellent BI tool for detailed reporting and data analysis. It supports automatic data syncing and can be scheduled regularly. It quickly creates a connector and formulates meaningful reports by blending and merging data from various sources using the integration APIs. It helps to quickly identify the essential details by creating ersonalized reports and dashboards with an easy editor. It also includes a distinct commenting section in the sharing options, ideal for collaboration.

9.Microsoft Power BI: Identify Trends in Real Time

Microsoft Power BI is a web-based tool that is one of the best for data visualisation. It enables users to identify trends in real-time and includes brand new connectors that allow businesses to step up marketing campaigns. Microsoft Power BI is accessible virtually from any location as it is web-based. This tool is designed to integrate apps and deliver reports in real-time dashboards.

10.Looker: Ideal for SMEs

Looker, a data discovery app, is another business intelligence tool to watch for! This unique platform is now part of Google Cloud and integrates with any SQL database or warehouse and is ideal for startups, midsize businesses, and enterprise-grade businesses. This tool's advantages include its ease of use, useful visualisations, powerful collaboration features such as easy integration with apps, flexible sharing of data and reports via email or USL, and a dependable support system.

11.Clear Analytics: Just Need Essential Excel Skills to Use

Clear Analytics is an easy-to-use Excel-based software that can be utilised even by employees with just the essential Excel skills. It is a self-service Business Intelligence system with BI features like data creation, automation, analysis, and visualisation. Clear Analytics also functions with Microsoft Power BI, cleaning and modelling various datasets with Power Query and Power Pivot.

12.Tableau: Needs No Introduction

Tableau is a powerful BI tool that specialises in data discovery and visualisation. The software allows to quickly analyse, visualise, and share data without IT intervention. Tableau works with various data sources, including Microsoft Excel, Oracle, MS SQL, Google Analytics, and SalesForce. Users will have access to well-designed, user-friendly dashboards. Tableau also provides several products, such as Tableau Desktop (for anyone) and Tableau Server (analytics for organisations), both of which can be run locally, as well as Tableau Online (hosted analytics for organisations) and others.

13.Oracle BI: Proactive Intelligence Power

Oracle BI is a business intelligence technology and application portfolio for enterprises. This technology provides nearly all business intelligence capabilities, including dashboards, proactive intelligence, ad hoc reporting, etc. Oracle is a reliable choice ideal for businesses that need to analyse large amounts of data from Oracle and non-Oracle sources. Data archiving, versioning, a self-service portal, and alerts/notifications are also essential features.

14.Domo: Predictive Analysis on the Go

Domo is a fully cloud-based business intelligence platform that integrates spreadsheets, databases, and social media data. The platform provides visibility and analysis at the micro and macro levels, including predictive analysis powered by Mr Roboto, their AI engine. From cash balances and lists of your best-selling products by region to marketing ROI calculations for each channel, Domo has got you covered.

15.IBM Cognos Analytics: Hidden Patterns in Data Discovered

Cognos Analytics is a business intelligence platform powered by AI that supports the entire analytics cycle. It helps to visualise, analyse, and share actionable insights, from data discovery to data operationalisation. The data is interpreted and presented in a visually appealing report, and AI allows the discovery of hidden patterns in the data.

The use of advanced business intelligence reporting tools makes tasks simple and manageable. Business intelligence platforms are subject to change based on business needs and the advancement of technologies. Still, they have proven to be a great way to accomplish strategic goals effectively and efficiently.

AI in Healthcare: Building a Health-Smart Future

AI can leverage high-volume patient datasets to detect patterns, enabling medical experts to personalize treatments, optimize costs, manage medication, improve general health management, enhance healthcare research, help clinicians make informed decisions, etc. Besides automating hospital administrative tasks, AI implements technologies that improve patient care. However, medical institutions must also address challenges like data privacy and the need for human support while implementing AI in healthcare. Accurate diagnosis, early disease detection, tailored treatments, and prediction of outcomes powered by AI help build a health-smart future by delivering swift and efficient care to people around the globe. In short, AI is truly transforming patient care by enhancing outcomes and survival rates while mitigating expenses for service providers.

AI-Powered Diagnosis & Treatment Enhanced accuracy in diagnoses Faster diagnosis and treatment Tailored treatment plans	REIMAGINING HEALTHCARE MANAGEMENT WITH AI	Personalized Medication Prediction of potential side effects Optimized treatment efficacy Continuous monitoring of medication responses
Virtual Nursing Assistants 24/7 support Medication and appointment reminders Enhanced patient engagement		Real-time Analysis of Patient Data Continuous monitoring Early detection of potential health issues Improved patient outcomes
Predictive Insights Risk prediction Implementing preventive care Personalized prevention strategies		AI Chatbots Patient support Symptom assessment Appointment management

From AI chatbots to personalizing treatments to robotic surgeries, the prospect for AI to revolutionize patient care is vast and exciting.

AI-Powered Diagnosis & Treatment

Detects patterns in patient histories and laboratory results, resulting in more accurate diagnoses, enabling early disease detection, and developing tailored treatment plans.

Personalized Medication

Tailors treatment plans to an individual based on specific attributes like lifestyle elements, medical histories, and genetic profiles, helping identify possible responses to specific medications.

Virtual Nursing Assistants

Offers personalized guidance and support, monitoring remotely in real-time and improving patient engagement to ensure quality care.

Real-time Analysis of Patient Data

Analyzes high volumes of medical data in real-time, resulting in prompt insights for faster decision-making, earlier detection for timely intervention and enhanced results, and discovery of possible safety issues.

Predictive Insights

Identify high-risk patients earlier and deliver personalized recommendations by detecting patterns and anticipating future patient outcomes.

AI Chatbots

Provides services like initial symptom evaluation, medication management, and health support by analyzing the patient's responses regarding their inquiry about symptoms, resulting in effective communication and enhanced healthcare delivery.

How does AI in Healthcare Transform Patient Care?

Given the rising adoption of AI, the healthcare sector is experiencing a profound transformation. With the advancement of AI technologies in healthcare, hospitals can now redefine the boundaries of patient care, allowing healthcare providers to deliver personalized and effective treatment options. Let's look at how AI is revolutionizing the patient care experience.

Supporting Healthcare Professionals through Virtual Assistants

To align with the demand for healthcare services, healthcare experts avail the support of virtual assistants to provide tailored support based on patient queries regarding symptoms, medical advice, medication reminders, monitoring vital signs, and scheduling appointments. Besides, virtual assistants generate routine reports based on patients' health, helping improve patient outcomes and offer a better experience.

Accelerating Drug Recovery & Research

AI expedites drug discovery by analyzing genomic data, clinical trial results, and more to predict drug safety and how they interact with the body. It facilitates the faster detection of effective treatments, leading to quicker medical breakthroughs. Moreover, AI can help tailor treatment plans to patients based on their medical histories, genetic profiles, and lifestyle factors, enabling more effective targeted therapies.

Facilitating Early Disease Detection & Diagnosis

AI analyzes vast amounts of medical data, including images, lab results, and patient histories, to detect anomalies and patterns that may signify the presence of a disease. This can lead to improved accuracy, earlier intervention, and personalized medicine. It also predicts the likelihood of disease progression or response to treatment, enabling healthcare providers to take proactive measures and improve patient outcomes.

Offering Mental Health Support

AI supports mental health by offering personalized and accessible patient care through digital applications. Research has proven the efficacy of AI-powered cognitive-behavioral therapy (CBT) in diagnosing and managing conditions like depression. Such tools offer 24/7 support to track patient progress and improve treatment adherence to ensure effective and empathetic treatment.

Improving Patient Education

AI has become an effective tool for patient education, with AI-powered chatbots being used for diet recommendations, smoking cessation, and cognitive-behavioral therapy. Educating patients about their diagnoses, treatments, and preventive care improves adherence and health outcomes. AI can personalize and simplify medical information, empowering patients to take control of their health. The future of AI in healthcare holds enormous prospects for augmenting diagnostics, treatment, and patient management. The capabilities of advanced AI tools extend to predicting disease progression and improving surgical assistance through remote collaboration. Though challenges exist in integrating AI into clinical practice, the power of AI to enhance outcomes, efficiency, and patient experiences is undeniable. Beinex collaborated with Sultan Qaboos University Hospital (SQUH), University Medical City, and Novartis to combat Atherosclerotic Cardiovascular Disease (ASCVD) using AI and analytics, reimagining cardiovascular health. Beinex adopted a data-driven methodology to manage ASCVD risks, facilitating proactive disease identification and intervention. By employing NLP, BI, Data Engineering, and Data Science and leveraging innovative data analytics and real-time insights, we helped revolutionize cardiovascular disease risk management, improve prevention strategies, and offer personalized patient care in the GCC region. Explore the success story and get in-depth insights into how we redefined healthcare and established new standards with life-saving intelligence. Download the case study titled 'Revolutionizing Healthcare in 2025: How AI is Transforming Hospitals of the Future' to discover how AI disrupts healthcare and builds a health-smart future. Click here: https://beinex.com/artificial-intelligence-in-healthcare-case-study-download

[sc name="quote" quote="“The functions, even as they may look simple, are pretty useful for any Tableau Consultant especially if they are working on finance-related projects,”" author="Joseph John, Senior Consultant, Analytics, Beinex"][/sc]

What is Tableau Sum and Running Sum?

Sum

SUM is one of the commonly performed functions in Tableau. The Tableau Sum function seeks out the Sum of records under consideration. It is the total of the values present in a field. The screenshot provided below exhibits the total sum of sales for each of the three categories as given in Sheet 1.

In the sample dataset shown below, the sum of sales is shown corresponding to each of the corresponding values in the dimension "Category". For example, "Furniture" has a total sale of 754,748, which could be comprising of furniture related products such as Tables, Chairs etc.

Running Sum

A RUNNING SUM is a cumulative total in a row or column from the first value to the final value in the respective row or column. For instance, in the example given below, the cumulative values of Furniture and Office Supplies stand at 1,486,641 and that value when added to Technology’s value of 839,893 gives 2,326,534.

You can summarise or modify the granularity of your data using aggregate functions. An aggregate part combines the values of multiple lines to provide a single value. Examples of aggregate functions apart from sum are measurements based on Count, Count Distinct, Fixed Calculations, and other standard integration functions.

Every time you include a measure in your view, an aggregate is automatically applied to that measure. Depending on the context of the view, different aggregation techniques are used. Analysts can well utilise these features to simplify the whole complex process of data analysis, and organisations can harness them for insightful decisions.

AWS: Four Types of Cloud Business Value

In the era of digital transformation and infrastructure modernization, the journey to the cloud has been both exhilarating and challenging. Many businesses have successfully migrated their "easy" applications to the cloud. However, a prevailing concern lingers among business leaders—an uncertainty about the returns on investment in terms of time and effort. Questions arise about productivity gains, data security, and the impact on critical agility metrics, such as time to market. Here, we delve into the distinct results observed by AWS users across four pivotal domains of cloud business worth: cost efficiency, workforce efficiency, resilience, and adaptability.

1. Cost Savings: More Than Meets the Eye

Migrating infrastructure to Amazon Web Services (AWS) yields significant cost savings, averaging around 20% in total infrastructure expenses. Top performers witness a staggering 47% reduction, highlighting the potential for substantial savings. Yet, the nuances lie in application-specific cost reductions that vary based on factors like application complexity and cloud tenure.

Studies reveal a significant financial shift for organizations venturing into AWS migration. On average, businesses witness a remarkable 20% reduction in total infrastructure costs—a noteworthy achievement translating to a staggering $69 million slashed from an average pre-migration annual IT spend of $345 million. However, the journey to AWS doesn’t merely stop at these impressive numbers. For top performers, the stakes are higher, yielding an exceptional 47% savings in total infrastructure costs. This equates to a colossal $164 million reduction from an average annual IT spend of $349 million. (Refer to Fig. 1 for a graphical representation.) Fig.1 Study participants also provided data for up to three specific applications transferred to AWS, allowing the calculation of cost savings specific to these applications. On average, infrastructure costs related to these applications witnessed a consistent decline of around 20% annually over a span of six years post-deployment. Following migration, the extent of cost reductions varies among different application types. Multiple factors influence this variability, including the complexity of applications, their level of modernization and optimization before and after migration, and their duration within the cloud. Notably, patience yields rewards in this scenario. Analysis of application costs based on their tenure in AWS demonstrates an incremental improvement in savings rates with each passing year. (Refer to Fig. 2 for a graphical representation.) Fig.2 Comparative analysis between cloud and on-premises deployments highlights a positive correlation between increased cloud adoption and various efficiency metrics.

Nearly 3X lower total cost for procurement functions compared to equivalent on-premises solutions.

A $0.24 decrease in HR costs for every dollar invested in cloud.

Labor cost reductions of 18% to 20% in both HR and finance.

A 4% increase in HR productivity (output) for every dollar invested in cloud.

A productivity increase of 7% to 12% in procurement.

Factors impacting potential cloud cost savings include baseline infrastructure costs, application types, duration in the cloud, and post-migration innovations. Organizations with high infrastructure spending can see a 41% reduction after migration compared to the 20% average. Additionally, AWS migration leads to a 23% cut in overprovisioned application capacity, optimizing efficiency and reducing unnecessary costs.

2. Workforce Efficiency

Following the migration of applications to AWS, there's a noticeable enhancement in IT infrastructure staff productivity among respondents. They report a remarkable 66% rise in the number of retained VMs managed per administrator. Such post-migration efficiencies enable a strategic shift, allowing 17% of staff positions to transition from routine infrastructure maintenance to pivotal roles focusing on infrastructure planning, architecture, orchestration, and innovation (Refer to Fig. 3 for visual representation)

Fig.3 Moreover, a substantial productivity boost unfolds within development teams post-AWS migration. There's a notable 29% increase in developer time dedicated to crafting new features and functionalities, shifting from minor updates handled by cloud providers. Leveraging AWS developer tools streamlines coding processes, further amplifying developer productivity. These enhancements result in a 32% average reduction in the time interval between production releases for migrated applications. This surge in productivity not only streamlines operations but also empowers organizations to overcome the barrier to innovation identified by respondents—the challenge of dedicating ample time to innovation amidst other pressing concerns.
A commercial finance organization exemplifies the tangible impact of AWS migration on innovation. Their technology division experienced a significant leap in developer focus post-migration. Initially allocating 20% of developer time to crafting new features, their shift to AWS elevated these efforts drastically. For customer relationship management and business intelligence, the focus surged to an impressive 60%, witnessing a threefold increase. However, the transformation was even more substantial for their migrated financial planning and analysis system, skyrocketing from a mere 20% pre-migration to a staggering 80% post-migration—showcasing an unparalleled fourfold increase.

3. Resilience

Cloud technology has redefined resilience by elevating system availability, reducing latency, and fortifying security measures. The disruptive impact of the pandemic accentuated the criticality of resilience, surpassing the effectiveness of traditional disaster recovery approaches and redundant data centers. Cloud-based infrastructure emerged as a crucial linchpin, ensuring uninterrupted operations during tumultuous times.
The scalability inherent in cloud services played a pivotal role during the pandemic, exceeding initial capacity plans by an astounding 90%. This scalability was instrumental in swiftly connecting and supporting remote workers, effectively mitigating disruptions. Chief Information Officers (CIOs) emphasized that their previous migration to AWS significantly facilitated the accommodation of sudden surges in demand, underscoring the importance of cloud infrastructure in maintaining seamless operations during critical periods.
A U.S.-based healthcare nonprofit's strategic migration of its last on-premises ERP system to the cloud in 2019 fortuitously positioned it for the subsequent shutdowns and challenges brought on by the pandemic. The seamless integration of cloud technology enabled the organization to navigate and maintain operational continuity amidst adversity, highlighting the indispensable role of cloud solutions in supporting mission-critical functions and adapting to hybrid office-remote work models.
In today's digital commerce landscape, system downtime carries profound consequences far beyond inconvenience. It significantly impacts businesses, directing customers towards competitors' platforms. The financial implications of downtime are staggering, averaging 79 minutes and costing approximately $84,650 per hour in 2020. Furthermore, the aftermath of data breaches and ransomware attacks extends beyond financial costs, causing public embarrassment and substantial recovery expenses, averaging $4.6 million for cyberattacks and $4.2 million for data breaches.
The migration to AWS represents a transformative milestone, significantly bolstering both availability and security. Many key resilience Key Performance Indicators (KPIs) exceeded the 50% mark, as illustrated in Fig. 4. This substantial enhancement underscores the transformative impact of AWS migration in fortifying operational resilience and proactively mitigating potential threats.

Fig.4

4. Adaptability

The most strategic of all cloud migration benefits, agility equips organizations for success in a digital business environment. Agility refers to the ability to respond quickly and effectively to changes in business conditions or disruptive business events. Broadly speaking, the more technology infrastructure an organization has in the cloud, the more agile it can be. A study by The Hackett Group on agility in business services functions (i.e., finance, HR, IT, and procurement), found that the most agile organizations have 41% of their infrastructure in the cloud, compared to just 29% for the average respondent.
Winning in a digitally competitive industry or market depends heavily on agility in product development. A major aspect is the speed at which new technology-enabled products and features are brought to market. For applications in the cloud, technology organizations are better able to use agile/DevOps methodologies thanks to services that simplify and accelerate provisioning infrastructure, deploying application code, automating software release processes, and monitoring application and infrastructure performance. These changes, combined with the development-team productivity gains discussed earlier, contribute to a 43% decrease in time to market and a faster pace for production releases (Fig. 5).

Fig.5

Recommendations

The Cloud Services Study underscores how migrating to AWS drives substantial business value across cost savings, staff productivity, resiliency, and agility. It dispels the notion that the cloud is solely a cost-saving venture, highlighting its strategic superiority over traditional on-premises infrastructure. The agility alone—enabling quicker time to market, enhanced insights, and frequent production releases—positions organizations competitively in the digital sphere. This transformative shift from infrastructure management to innovative development is a pivotal game-changer.
In forthcoming reports, we'll delve deeper into specific migration strategies and cloud financial management practices that contribute to superior outcomes. Meanwhile, here are key insights from this study:

Migration's financial benefits transcend infrastructure cost reductions. Anticipate increased efficiencies in application provisioning, reduced security incidents, minimized downtime, and amplified agility.

Optimize cloud costs post-migration. Implement strategies like consumption-based pricing and best practices in cloud financial management, leveraging tools like AWS Cost Explorer for uncovering cost-saving opportunities.

Cloud hosting should form a fundamental aspect of any digital transformation strategy. Prioritize migrating core applications and data to AWS to enable faster data access, insight generation, and expedited time to market, essential for competing in digitally advanced sectors.

Cloud serves as a crucial pillar for future workforce enablement. The accessibility and scalability of AWS-hosted data and applications support hybrid or digital workforces, characterized by constant mobility, adaptable headcounts, and seamless real-time access to highly secure systems.

The pandemic experience validated the efficacy of cloud-based infrastructure as a robust defense against catastrophic disruptions. Accelerated cloud migration should feature prominently in every business continuity investment strategy and resiliency improvement roadmap.