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AWS Specialization Partners bring deep expertise in AWS databases, analytics, and AI/ML services, designing custom solutions that can transform your data into generative experiences.

Before diving into the world of GenAI, organizations need to get their data fundamentals right first – establishing a modern data foundation with strong data governance and high data quality processes, to generate meaningful outputs for their GenAI deployments. After all, the value of GenAI is only as strong as the data foundation that powers it.

“Migrating and modernizing applications and data on cloud is a foundational step organizations can take to create the best possible outcomes with Generative AI. This is why we are excited to welcome our cohort of Partners, who have the experience, and demonstrated success, doing this across hundreds and thousands of organizations” says Tim Finley, Worldwide Director, Data Foundations for AI at AWS.

Navigating the GenAI maturity ladder from prototype to production can be a daunting task. Wherever you are on your data journey, Cloud Kinetics harnesses best-of-breed solutions to optimize your data for enhanced performance, scalability, and cost-efficiency, driving your AI-powered business goals forward.

Together with AWS, Cloud Kinetics is poised to help more organizations build an end-to-end data foundation and operationalize GenAI at scale for transformational business impact.

• Find out more about how a solid data foundation fuels GenAI innovation here
• Read more about the AWS Data Foundation Lighthouse launch

The post Cloud Kinetics Named AWS Data Foundation Lighthouse Partner, Empowering Organizations to Accelerate GenAI Adoption appeared first on Cloud Kinetics.

This unprocessed data is collectively known as Big Data, and it is now a business’s most precious asset as it provides actionable insights that can make or break a business. But with larger volumes and more complex data being generated daily, more sophisticated analytics is needed to modernize applications and the data interpretation process for the best accuracy – which is where Big Data analytics comes in.

Big Data use cases for businesses

Businesses have many ways to collect personal, behavioural and engagement data from customers, ranging from tracking their browsing habits on their websites to more traditional surveys and feedback forms. On websites, businesses can use cookies to track a customer’s purchase journey and learn everything from how long they spend browsing to how likely they are to drop off at point of purchase. It can also tell brands what offerings are most popular, when customer traffic is at its highest or lowest and how customers are discovering the site. Social media is also one of the best ways for brands to engage with and learn about their customers. Brands can learn the demographics of their target audience based on social media profiles, evaluate the performance of a campaign, product or service based on audience feedback and reactions, and even find out where their customers are based.

Beyond the personalized recommendations and targeted ads, here are 5 innovative ways that brands can leverage big data for deeper customer understanding:

• Predict churn: Traditional churn models often lag behind customer behaviour. By leveraging real-time data like browsing patterns, cart abandonment and engagement metrics, brands can quite accurately predict which customers are likely to churn. Brands can then proactively intervene with personalized incentives or targeted outreach in an effort to control churn.
• Map customer journeys across the business ecosystem: Businesses need to analyse data from all customer touchpoints – websites, apps, physical stores, social media – to understand the complete customer journey. They can then effectively identify friction points, optimize pathways and create seamless omnichannel experiences that cater to individual customer preferences and buying stages.
• Understand the emotional footprints of customers: Businesses need to stretch their learning beyond demographics and purchase history by using text analysis tools such as sentiment analysis, to understand the emotions behind customer reviews, social media mentions, and even customer support interactions. Such an analysis can reveal unspoken frustrations, desires and brand affinities, making it a little easier for brands to tailor their messaging and experiences.
• Decode hidden needs with unstructured data: It is not enough to only focus on structured data like purchase history and demographics. To gather a deeper understanding of customer preferences, businesses need to analyse unstructured data like images, videos and voice recordings from customer interactions. Such details can reveal subconscious preferences, cultural nuances and emerging trends that surveys or focus groups might completely miss.
• Create hyper-personalized feedback loops: The importance of real-time feedback cannot be undermined. Brands can use dynamic surveys and AI-powered chatbots to collect real-time feedback from customers as they interact with the brand. Such data allows brands to instantly customize product offers and recommendations, as well as content based on individual preferences and changing needs.

Processing and analysing Big Data

The collected data is stored in a data warehouse or data lake, where it must then be organized, configured and cleaned for easier analysis. Next, analytics software is used to make sense of the data – it will sift through the data to search for patterns, trends and relationships, which can then be used to build a customer profile or predictive models that can forecast customer behaviour.

Analysing such volumes of data in a short amount of time requires immense computing power and can take a heavy toll on networks, storage and servers. As such, many businesses opt to offload this task to the cloud, which is capable of handling these demands efficiently and quickly. This enables businesses to be more agile and responsive in making customer-centric decisions. Here are some examples of how cloud-based data and analytics solutions can be used to gather, process and translate business data:

• Multi-source data acquisition: Data can be gathered from diverse sources such as business websites, apps, customer interactions, social media and IoT devices.
  • Point of sales and transactional data is a starting point for many businesses.
  • Demographic data enables businesses to understand who is buying what depending on age, gender, economic condition and much more.
  • Altitudinal data  gathered through market research and social media sentiment analysis are other rich data sources too.
  • Social media profiles, reactions to promotional campaigns, products or services are all valuable sources of data.
  • Consumer trends, local preferences and acceptable prices can all be understood from such data. Businesses also get to know about the most popular brands, when consumer traffic is the highest or lowest, and customer browsing styles, among many other attributes.  Cloud-based data integration platforms can then be utilized to unify these various data streams.
• Scalable data warehousing: The massive datasets are stored in secure, flexible cloud data lakes, data warehouses, lake houses like Google BigQuery or Amazon Redshift for efficient retrieval and analysis. Such warehouse tools usually support all types of data, can work across clouds and have built-in business intelligence and machine learning. 
• Data quality management: The data is then cleaned and transformed using cloud-based data cleansing tools to ensure the data is accurate and consistent before analysis. Data management teams must ensure that the data is in alignment with global and domain rules. Ensuring that certain data quality metrics are adhered to increases the quality of the data gathered. A few common metrics include accuracy, completeness, uniqueness, validity, consistency and linkage to relevant items.
• Advanced analytics engines: Cloud-based data analytics platforms can then be employed to run large-scale statistical analyses and build predictive models for customer behaviour. Such advanced engines can work on complex datasets and derive customer behaviour details. 
• Data storytelling dashboards: This is the process of translating data analyses results into understandable terms that can be used to influence a business decision or action. Cloud-based business intelligence (BI) tools like Tableau or Power BI can be employed to visualize insights and translate them into actionable strategies for improved customer experiences.

Big Data Analytics: Building customer-brand relationships & customer engagement

With the valuable insights derived from Big Data analytics, businesses gain significant customer insight that they can then use in everything from product research and development to marketing strategies and campaigns. The goal is to resonate with the customer and build an emotional relationship that will increase customer stickiness and brand loyalty.

Some of the most famous big data analytics success stories include Spotify which uses machine learning and artificial intelligence to offer personalized “Discover Weekly” playlists that recommend songs to users based on their song history. Another is Amazon, where Big Data helps them make better product recommendations to customers and improve the delivery experience with an intelligent logistics system that chooses the nearest warehouse.

It is clear that business success and the brand-customer relationship is more tightly linked than ever, which is why businesses need to invest in their Big Data collection and analytics to reap the most benefits – especially with an increasingly saturated marketplace in the digital era.

At Cloud Kinetics, we understand the value of intelligent data analytics. Our Data Engineering team has helped many companies collect, manage, and extract valuable insights from their data, enabling them to provide an improved customer experience and enjoy better business outcomes. Connect with us today to start your journey into Big Data analytics.

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• Cloud Kinetics is amongst the earliest in the region to announce a Data Migration and Modernization service suite with Snowflake to be listed in AWS Marketplace
• The offering will enable organizations to build a strong modern data foundation and unlock the value of their data

Indonesia, 24 March 2023 – Cloud Kinetics, a leading cloud transformation and AWS Managed Service Provider, announced today that it has strengthened its partnership with Snowflake, the Data Cloud company, to drive data-led transformation for businesses across USA, ANZ and Asia. Leveraging Cloud Kinetics’ extensive hybrid and multi-cloud data expertise and Snowflake’s differentiated capabilities, enterprises will be able to build a strong modern data foundation, by way of a comprehensive Data Migration and Modernization service suite.

Cloud Kinetics is amongst the earliest in the APAC region to launch a joint Data Migration and Modernization offering with Snowflake on Amazon Web Services (AWS) Marketplace, a curated digital catalog that makes it easy for customers to find, test, buy, and deploy the third-party software they want, with the simplified procurement and controls they need.

Cloud Kinetics is also well-poised to enter new markets ripe for digital disruption, consolidating its presence in United States and Australia while making inroads in Thailand and Malaysia in Asia. With the joint offering readily available in AWS Marketplace, enterprises looking for solutions to migrate legacy data sources or eliminate disparate data silos will have easy access to it.

Enabling enterprises to build a modern data foundation

Data silos stifle business growth and are a challenge for many enterprises today. With data growing at an exponential rate, enterprises facing siloed and fragmented data are not able to seamlessly access, share, or tap across organizational and geographical teams to generate actionable insights.

The joint offering will help enterprises break down data silos, move, distill and unify vital business data into a modern data foundation that unlocks analytics & insights, and allows for informed business decisions. Under this partnership, joint customers can migrate their existing on-premise or cloud-hosted databases to a Snowflake environment running on AWS and gain numerous performance benefits right away.

Combining Snowflake’s Data Cloud that requires near-zero maintenance with AWS’s highly scalable and secure infrastructure, the data-driven cloud solutions from Cloud Kinetics will enable enterprises to ingest, process and protect all their data reliably. With elastic engagement models, the joint offering also gives customers greater choice and flexibility to tailor the data migration and modernization process to meet their unique needs.

“Riding on our growth momentum in the APAC region, we are thrilled to deepen our ongoing partnership with Snowflake to better serve our customers. This marks a significant step for us to empower more businesses in the region to accelerate their digital transformation journey and keep pace with rapid data growth, capitalizing on Snowflake’s intelligent data capabilities running on AWS,” said Saleh M “Haji” Munshi, Group CEO of Cloud Kinetics. “We look forward to extending and evolving our robust data service offerings to bring high value to our customers, so they can truly thrive and innovate in the digital-first world.”

“Our customers’ businesses are growing at an exponential rate and data silos present one of the biggest challenges that may jeopardize this growth,” said Ash Willis, Vice President, Partners and Alliances APJ at Snowflake. “Snowflake’s expanded partnership with Cloud Kinetics will help enterprises in the region with their Data Migration and Modernization journey, making the adoption smoother and faster for the end customer.”

Cloud Kinetics’ Data Migration & Modernization service suite with Snowflake is now available on AWS Marketplace

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The utility company supplies electricity to over 5 million customers and they include domestic, commercial and industrial users so the impact of a power outage is massive. Matters came to a head when the mentioned power outage nearly cost human lives – a suburban hospital was one of the facilities that had to bear the brunt of the sudden downtime.

Challenges: Legacy ICT, siloed data, failure to scale and high storage costs

As the utility company’s business grew over time, so did complications from its legacy infrastructure and communication technology (ICT) and solutions. These included data sitting in silos, incompatible systems and solutions, high cost of storing data on-premise, duplicate and incomplete fields in vital records, and an inability to scale despite allocating higher annual capital expenditure (CAPEX).

“We needed a revolutionary solution, not an evolutionary one,” the CIO said. “Our teams required data to be available anywhere, anytime, on any device, with analytics of what’s not functioning well, where, and what action needs to be taken right away.”

To achieve this, the utility company explored the options available and settled on the Google Cloud Platform (GCP), MySQL database on cloud, Google BigQuery for analytics, and Unified Analytics (UI) dashboards for real-time actionable management. They picked Cloud Kinetics to help them manage the transformation.

“Cloud Kinetics is the top-tier MSP (managed services provider) on GCP and an enterprise integration partner on VMware, NetApp, Dhruva, Cohesity, ServiceNow,” said Ted A, Cloud Kinetics’ Chief Revenue Officer. “Cloud Kinetics has operations in Singapore, Malaysia, India, Vietnam, Indonesia, Thailand, Europe, and the US to help companies migrate to a hybrid cloud.”

Solution: Cloud migration and data modernization for real-time data analytics

The utility company embraced cloud technology and moved its workloads, its extract-transform-load (ETL) functions and data analytics to the cloud. It also changed its CAPEX model to an OPEX or pay-per-use model, streamlined its batch and real-time analytics, automated its data delivery and boosted cybersecurity.

“The transformation was almost magical,” the CIO noted. “Our teams can now get power outage incidents on their phones and laptops as and when outages happen, with exact coordinates of where the outage occurred, how many users it impacts, and which team is the closest to rush to the site. We’re now able to schedule maintenance periods in advance and let our users know about it. All of this has doubled our return on efficiency (ROE), boosted morale in the company and will reflect on mind and market share.”

Outcomes: Immediate notifications about any power outage including location coordinates and consumers impacted

The steps undertaken had a ripple effect on all its customers. A surgeon at the affected hospital said, “We were lucky on that crucial day a year ago; the heart patient survived. While we can push our luck with surgical procedures, we don’t want luck to play any part in power supply outages.”

The post How Real-Time Data Analytics Powered Up A Utilities Provider appeared first on Cloud Kinetics.

To derive usable insights, the quantities of data analysed must be quite massive. And organizations are doing just that, they are acquiring and storing data at a tremendous pace and quantity. Managing such data is a challenge and requires organizations to have appropriate systems in place.

Cloud solutions hold the key to this challenge. An effective cloud solution can manage data at scale without any drop in performance. It is equipped with the right computing power, is scalable, reliable and transparent.

Structured and unstructured data

Data management issues are more common when handling unstructured or qualitative data. Such data is not organised systematically and cannot be stored in predefined database formats. Prominent examples of unstructured data include satellite visuals, surveillance imagery, emails, images, audio and video files, social media postings and mobile text messages, among others. Almost all communication formats involve unstructured data. In fact, Gartner estimates that over 80 percent of an organization’s digital information is unstructured.

Structured or quantitative data on the other hand, can be stored in relational databases and is understood by machines. Algorithms and data mining tools can easily access and analyse such data.

Vulnerabilities of unstructured data

Unstructured data has several concerns because of its unorganised nature.

• The huge volumes of unstructured data make data verification with standard data analytics systems difficult.
• Reliability and security of the data are frequent problems.

The smart way to manage unstructured data is to move it to the cloud and take advantage of the many benefits such as flexible storage, scalability, cost savings and security. Organisations must carefully pick the right managed cloud service providers to enjoy the benefits.

Cloud as a data management partner

Unstructured data must be converted to structured data before it can be mined for insights but there are almost no straightforward tools to convert unstructured data to structured data. The data must first be analysed and sorted to some degree before it can be converted. Cloud-based ELT (extract, load, transform) tools can come in handy to manage the conversion. The structured data is ready to be analyzed for business insights by using artificial intelligence (AI) and machine learning (ML) tools and techniques.

High processing power for faster data analysis

The raw computing power of the cloud is exemplified through virtualization technology – where previously-isolated data is now part of a single, interconnected virtual environment and is automatically managed through virtual machines (VM). Using VMs, users can simultaneously run multiple operating systems on a single hardware machine and – saving load times and enabling faster data processing.

The high computing power can also handle ML technology that would automate the conversion of unstructured data into a structured form, making the cloud enhance the data management process to optimize both speed and accuracy.

As an example, one of our clients, a leading sports analytics company, had to manually compute data inputs and rank players/teams. Managing this unstructured data was time-consuming and difficult to consolidate for further analysis. This led them to seek an automatic process for the computation and ranking of players/teams.

To address these issues, we partnered with Microsoft Azure to implement a cloud solution that had an ML feature, Azure Machine Learning. This feature would automatically rank players and/or teams – thus, computing the metric data which would then be stored within the Azure Data Lake storage system. The predictive and automated nature of our solution resulted in our client making more timely decisions while saving on manpower.

Flexible data storage with more scalability

Cloud storage allows for data to be saved in off-site locations that can be accessed either through a private network connection or the public internet network. Businesses can easily scale their storage requirements on demand without having to buy or upgrade more hardware and software. This infinite scaling makes the data stored more resilient to physical disruptions while being easier to back up.

Additionally, cloud storage costs are only derivatives of the storage service used. Maintenance of the cloud servers and other associated infrastructures become the responsibility of the cloud provider, helping to reduce costs for businesses. Cloud storage provides an overall cost-effective and elastic storage outlet for data generated by businesses.

Higher data transparency and enhanced data security

Data and applications across multiple environments are secured through the system while allowing users to see all file activity. The cloud also has built-in security measures that alert both the user and provider about security threats or malware. Comprehensive security reduces the potential for data leaks and vulnerabilities, building customer trust and avoiding legal liabilities in the process.

Cloud is a comprehensive tool for data management

To sum it all up, the cloud addresses both front-end and back-end issues of data – providing businesses with an extensive solution for data handling. Data processing and analysis can be efficiently implemented through the sheer computing power of the cloud while security and storage capabilities are maximised through the cloud’s infinite scaling and built-in monitoring features.

The post Cloud-Based Data Management: The Key To Unlocking Your Data’s Potential appeared first on Cloud Kinetics.

Why do organizations acquire so much data? Big data is the answer to AI’s demand for data in quantities that exceed what traditional IT can supply. Artificial intelligence (AI) models need to be tested with large volumes of data from a variety of sources and conditions in order to gain deeper business insights, and big data provides this test data. Big data systems acquire and process data at a scale and speed that was not possible earlier. Processing big data involves data in petabytes and this is where cloud computing steps in. The cloud is equipped with the right resources and techniques to store, process and analyse the voluminous datasets that big data involves.

Big data and data modernization

A discussion about big data must include the mention of data modernization. The latter involves the transformation of the methods used by organisations to collect, store and manage data. Data modernisation enables organisations to leverage big data and effectively meet the demands of the digital age. Adopting cloud-based solutions plays a big part in modernising an organisation’s data architecture and management.

How big data and the cloud go hand in hand

Scale-on-demand storage

The value of big data lies in its sheer volume. It enables companies to derive more accurate and holistic insights from it through detailed analysis than it would have been otherwise possible. However, these datasets require terabytes, petabytes, or more, in storage space and that poses a serious challenge to on-premise IT storage capabilities.

The cloud’s capacity to scale on demand eliminates these limitations and allows businesses to expand their storage as required, which can be as little as a few gigabytes to thousands of terabytes and beyond. It is more efficient cost-wise since businesses do not have to build or maintain any infrastructure and pay only for the storage they use at any given time.

Powerful analytics

The sheer magnitude of big data requires immense computing power, and the cloud is capable of providing that. A cloud network’s ability to simultaneously integrate sizable datasets derived from numerous sources fosters efficient real-time analysis of big data. The whole process can be smoothly performed from a singular reference point.

We, at Cloud Kinetics, have supported clients who were facing challenges not just with data volume, but with data type too. With more unstructured data being generated today than ever before, having the infrastructure to standardise it for efficient analysis is crucial, especially in the world of sports analytics.

In this case, numerous key metrics were laboriously captured, recorded and ranked manually. We successfully automated the process via our cloud-based solution with AI-enabled Azure services. This enabled an automatic input of all unstructured data generated by the client and led to more seamless data analysis, as well as enabling better predictive player rankings.

Flexible and cost-efficient budgeting

On-premise management of big data often incurs high costs due to the unavoidable capital expenditure on infrastructure, which must be constantly upgraded, maintained and expanded as they handle more data. This naturally increases operational costs too.

Outsourcing big data management to the cloud is cost-effective as it transfers most of the infrastructure maintenance and analytics costs to the cloud provider. It is the responsibility of the cloud provider to maintain and upgrade the cloud environment, covering everything from cloud storage and processing to cyber security and cloud backup. Additionally, the pay-as-you-go model, common with cloud service providers, allows for more flexibility in the business’s budget.

Ability to prepare for a data-driven future

Big data is a constantly growing phenomenon, and cloud technology can help businesses capitalise on the advantages it offers. As more and more organisations transform themselves digitally, even small to medium enterprises can expect an overwhelming amount of data to be generated.

Journal studies have described the concurrent use of cloud and big data as a ‘match made in heaven’ due to the compatibility of the immense storage and computing power of the cloud with the voluminous nature of big data. With near-infinite scalability, sheer computing power and better cost efficiency, businesses can leverage the cloud to cement their competitive positions and remain agile in a data-driven world.

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