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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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Data analytics plays a crucial role in the energy & utilities sector, revolutionizing the way utilities operate, manage resources, and serve their customers. Companies in the sector are no longer only providers of energy, but also receivers and processors of energy and consumption data. Energy data analytics leverages big data, statistical software and machine learning (ML) techniques to analyse energy production and consumption. By harnessing the power of data, energy & utility companies can gain valuable insights, optimize their operations, and make informed decisions that lead to higher efficiency, reliability, and customer satisfaction while reducing wasteful production and costs. Future market behaviour and customer consumption patterns become easier to understand and predict too.

The growing demand for data analytics in the energy sector is largely a result of the role big data analytics plays in improving energy efficiency and reducing energy consumption. In fact, a market intelligence and advisory report states that the big data analytics market in the energy sector is expected to grow significantly — at a CAGR of 11.28 percent — from USD 8.37 billion in 2023 to USD 14.28 billion in 2028. While the largest market is the North American market, the fastest growing market is in the Asia Pacific region.

8 Business use cases: Data analytics in the energy & utilities sector

• 1. Grid optimization

Data analytics enables utilities to analyse vast amounts of data from numerous sources, such as smart metres, sensors, and SCADA systems, and optimize the performance of the power grid. By collecting and analysing data, utilities can identify patterns, detect anomalies, and predict potential failures. This information helps them proactively address grid issues, minimize downtime, and enhance grid stability and reliability.

• 2. Demand response and load management

Customer energy consumption patterns and opportunities for demand response and load management programmes can be better identified. Historical data and real-time information can help identify peak demand periods and effectively manage the load on the grid. Strain on the grid during peak periods is reduced, power generation and distribution is optimized and lower operational costs are lowered.

• 3. Asset management

Data analytics plays a vital role in optimizing asset management for utilities. By collecting and analyzing data from equipment sensors, utilities can monitor the health and performance of their assets in real-time.

• 4. Proactive maintenance

Predictive analytics can help identify maintenance needs and potential equipment failures before they occur, enabling utilities to schedule proactive maintenance and minimize costly downtime.

• 5. Energy theft detection

Employ advanced analytics to detect unusual consumption patterns that may indicate energy theft or meter tampering. This helps utilities identify and address these issues promptly, preserving revenue and ensuring fair billing.

• 6. Renewable energy integration

Data analytics is instrumental in facilitating the integration of renewable energy sources into the grid. By analysing weather patterns, historical data, and real-time information from renewable energy generators, utilities can optimize the integration and management of these intermittent energy sources.

Analytics can assist in forecasting renewable energy generation, optimizing power dispatch, and managing the grid’s stability and reliability while maximizing the utilization of renewable resources.

• 7. Carbon footprint reduction

Use data analytics to monitor and analyze energy consumption patterns, identify areas for improvement, and implement strategies to reduce carbon emissions. This supports the transition to more sustainable and environmentally friendly practices.

• 8. Regulatory compliance and risk management

By analyzing data related to compliance regulations, utilities can ensure they meet reporting obligations, monitor emissions, and adhere to environmental standards. Furthermore, analytics can identify potential risks and vulnerabilities in the grid, enabling utilities to implement proactive measures to mitigate those risks and ensure system reliability and security.

Data analytics is certainly transforming the energy & utilities sector. By adopting data analytics, energy & utility companies are better equipped to meet the evolving demands of the modern energy landscape and deliver efficient, reliable, and sustainable energy services to their customers.

The post Shaping The Future Of Energy & Utilities With Data Analytics: 8 Business Use Cases appeared first on Cloud Kinetics.

With the exponential growth of data, legacy databases are falling short on scalability and flexibility. Research shows that data silos pose one of the major roadblocks to informed decision-making across enterprises.

Join us on 22 June for an insightful Workshop with Snowflake, AWS and Cloud Kinetics as we show you how to break free from legacy databases and data silos by building a strong modern data foundation.

Why attend?

Through power-packed sessions, you’ll gain expert insights on how to overcome data silos with a single source of truth, democratize data access and strike the best balance between data sharing and security.

Highlights

• Why data is a double-edged sword
• Value of data modernization & obstacles organizations face while migrating legacy data
• Key considerations for a successful legacy data migration
• The power of Snowflake Data Cloud
• End-to-end data warehousing in Snowflake
• Centralization of data warehouses and lakes into a single system

The post Unshackle Your Data: Draw Rich Insights & Make Smart Business Decisions With Your Data 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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Truth be told, forecasting has been a part of business planning for quite some time. Back in the day, this was done using an estimating technique in which the last period’s values were used as a base to arrive at future forecasts. This method typically relied on patterns and recognizable sequences of events to forecast future events. This type of traditional forecasting is understandably basic and has its limitations. For one, it does not adjust the values or make any attempt to establish causal factors. It is also humanly impossible to take disparate sets of data factors over a large period and analyse them accurately.

On the other hand, Machine Learning based forecasting is based on a systematic statistical examination of data representing past observed behaviour of the system to be forecast, including observations of useful predictors outside the system. These innovations have been possible as many algorithms have been developed over the years, ranging from commonly used statistical algorithms like Autoregressive Integrated Moving Average (ARIMA), to complex neural network algorithms like CNN-QR and DeepAR+.

Introducing Amazon Forecast

Despite the rapid developments of AI/ML based forecasting, many companies are still not utilizing its power. Organisations usually park such initiatives, either due to lack of appreciation of its utility in business or due to lack of the required resources. Recognizing these limitations, Amazon launched ‘Forecast’ – a fully managed service by AWS that uses machine learning algorithms to deliver highly accurate forecasts. Now with Cloud computing and PaaS services like Amazon Forecast, these traditional limitations are being challenged in a big way. Companies can now easily embark on ML projects using the power of such PaaS services on the cloud, which almost gives access unlimited compute.

Working with Premier AWS Partners like Cloud Kinetics, can further accelerate the adoption by bringing in key elements of automation and visualisation as a value-add on top of the Forecast service, thus adding deep business relevance to such initiatives.

However, companies find that their needs are unique and specific. Therefore, where PaaS tools like Amazon Forecast are a great starting point, they usually need additional solutioning catering to the unique needs of each business case.

Driving an effective onboarding of a workload into Amazon Forecast.
As with any solution, successfully onboarding a workload into Amazon Forecast starts with a clear business problem definition. For example –

I want to forecast sales of products A, B and C for the next quarter by region in order to better optimize my quarterly sales and marketing spends.

Having a good problem statement makes all the difference when it comes to designing the solution.

Secondly, quality and quantity of input data and regular input data updates are crucial for Forecast to be effective. When we consider a machine learning solution, the accuracy of the results achieved is mostly directly proportional to the quality and quantity of input data provided. Therefore, it is advisable to provide maximum amount of time-series data (say 2 to 3 years) on the input side and support the data with related time-series data as well, in order to get the best results. In addition, regular updates of the input data helps the predictor models produce even more accurate results over time and this helps optimize the results achieved further.

Finally, it helps to have a collaborative solution development approach with an experienced partner. For maximizing the value that the Forecast service can bring, a mere usage of the Forecast service in isolation may not always be the most prudent approach. The ability of the team to align the entire solution footprint alongside the Forecast service surely yields measurable business value and results. As an Amazon Forecast Partner, Cloud Kinetics has already worked with many clients to provide an effective Forecast solution for their unique business requirements.

How can Cloud Kinetics Help?

CK has integrated Amazon Forecast into its Cloud Management Platform – Arcus. Through Amazon Forecast, our clients using Arcus platform can perform a detailed Cloud cost analytics and get a spend forecast for the next three months. If you are interested to structure and deploy a forecasting project with Amazon Forecast or want to leverage any other AWS Artificial Intelligence and Machine Learning service, get in touch with us at contactus@cloud-kinetics.com

The post The Evolution Of Forecasting – From Estimation To AI appeared first on Cloud Kinetics.