As per a Gartner report, over 30% of GenAI projects will be dropped by 2026 due to poor data quality, high costs or unclear business value.

Your 4-step blueprint for GenAI success

1. Assess your current data capabilities and needs

Use a data & analytics maturity model to understand your organization’s maturity level – this stock taking is crucial to the success of your data strategy.

• Are you at an aspiration stage, where you will focus on building a strong data foundation and basic analytics capabilities?
• Or have you progressed to having the capability, where you can prioritize building a data science team and advanced analytics skills?
• Or have your team reached a point of competency, where you are ready to start exploring GenAI applications?

A good data and analytics strategy starts with a clear vision. Gartner

In addition to your organization’s maturity level, evaluate your team’s skills and knowledge in AI and machine learning. Identify any skill gaps that need to be addressed through training or hiring. Consider the availability of specialized AI talent within your organization or the potential to partner with external experts. By understanding your team’s capabilities, you can create a roadmap for skill development, allowing you to focus on immediate needs while planning for future expansion plans.

2. Build a robust modern data foundation

One of the first steps in harnessing GenAI is to break down data silos within your organization. Often, crucial data is scattered across multiple platforms, making it difficult to get a unified view. Collecting, integrating and processing this data into a single, cohesive platform is essential for effective AI application.

Think of a retailer with customer data locked in outdated systems. By consolidating this data, they can unlock powerful insights and deliver personalized customer experiences.

Once integrated, it’s vital to ensure the data is clean, well-governed and secure. Poor data quality can lead to flawed AI outputs, while weak governance and security can expose your organization to risks. Building a modern data foundation with these principles in mind will lay the groundwork for reliable and impactful AI applications.

3. Make the right technology and model choices for your GenAI & data needs

Choosing the right technology and AI models is crucial for project success. Consider factors like your team’s expertise, available data and cost. If your team is comfortable with open-source models like Hugging Face or GPT, they offer flexibility but require more maintenance. Managed models like Amazon Bedrock or SageMaker are easier to implement with less upkeep but offer less customization.

Balancing these factors is key to a successful AI implementation. Opt for solutions that your team can manage effectively, that leverage your available data, and that fit within your budget. A well-thought-out choice here can save you time and money in the long run while ensuring robust performance.

Remember, AI performance is not just about speed and processing power; it is about delivering these results without ballooning costs. Proprietary systems can inflate total cost of ownership (TCO) with specialized hardware, licensing fees, and other hidden costs that make scaling more expensive in the long term.

One of the common misconceptions is that all AI workloads must run on expensive GPUs, which, while powerful, are not always the most efficient or necessary option for every type of task.

When selecting a model, evaluate accuracy, cost, latency and scalability. Balancing these factors ensures the chosen solution aligns with your team’s capabilities, available resources, and long-term goals.

The AI universe is constantly evolving, and needs solutions that are not just designed for today, but for tomorrow. Proprietary systems often lock clients into specific technologies, making it costly and difficult to upgrade as AI capabilities advance. Look for open, future-proof AI systems that adapt to new technologies and workloads. This long-term flexibility gives you the confidence that their AI investments will remain viable, no matter how your needs evolve.

4. Use tools and accelerators to advance your AI & GenAI dev lifecycle

Don’t reinvent the wheel – use available cutting-edge tools and platforms to accelerate your GenAI projects. The goal is to streamline the process from data collection to actionable insights. Utilizing accelerators and pre-built tools can help you quickly move from concept to deployment, ensuring that your AI applications deliver value rapidly and efficiently.

If you are working on refining large language models (LLMs), platforms that offer distributed training and fine-tuning can significantly reduce the time from data to insights. These tools not only speed up development but also ensure that your AI models are optimized for performance and cost.

• To optimize AI pipelines, organizations can turn, for example, to the latest Intel Xeon processors with Intel Advanced Matrix Extensions (Intel AMX), a built-in AI accelerator. As part of Intel® AI Engines, Intel AMX was designed to balance inference, the most prominent use case for a CPU in AI applications, with more capabilities for training.
• To optimize Retrieval-Augmented Generation (RAG) pipelines, Langchain is a powerful tool for orchestrating workflows across different data sources. It connects language models with databases, APIs, and cloud storage, enabling seamless chaining of tasks like data retrieval and prompt transformation. This flexibility makes it easy to build scalable RAG systems that can quickly process and augment responses with relevant information.
• For managing embeddings, Amazon Bedrock Knowledgebase can offer a managed vector store solution that integrates with various embedding models. It provides scalable, efficient storage for embeddings, allowing fast retrieval of contextually relevant information from multiple data sources.
• By combining Langchain for orchestration and Bedrock for vector storage, you can build a highly efficient, adaptable RAG pipeline that accelerates insights and enhances AI applications.

By following these steps, you will be well on your way to implementing GenAI in a way that is both strategic and sustainable, driving meaningful outcomes for your organization.

The post Defining Your GenAI Data Strategy: A Roadmap for Enterprise Data Teams appeared first on Cloud Kinetics.

Recognizing the growing potential of GenAI, the company now sought to use automation and AI to enhance the speed and efficiency of its customer service. This technology would help ActiUp maintain its high level of service and offer unmatched convenience, even as the number of events and their customer base grew.

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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.

The post Using Big Data Analytics To Know Your Customers Better appeared first on Cloud Kinetics.

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.

This insightful Lunch & Learn with Snowflake, AWS and Cloud Kinetics showed how to break free from legacy databases and data silos by building a strong modern data foundation.

Through power-packed sessions, participants gained 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.

Agenda included:

9.30 – 10.00 am Welcome & Introduction by Cloud Kinetics

10.00 – 10.20 am Presentation by Cloud Kinetics

10.20 – 10.40 am Presentation by AWS

10.45 – 11.05 am Presentation by Snowflake

11.05 – 11.25 am Customer’s sharing

11.25 – 11.45 am Q&A

11.45 – 12.30 pm Lunch & Networking

The post Unshackle Your Data With Snowflake, AWS & Cloud Kinetics appeared first on Cloud Kinetics.

Indonesia, 28 Apr 2023 – Cloud Kinetics, salah satu penyedia jasa Layanan transformasi cloud terkemuka dan AWS Managed Service Provider di Asia Tenggara, mengumumkan telah mempererat kemitraannya dengan Snowflake (Penyedia Jasa Layanan Data di Cloud) untuk mendorong transformasi bisnis Data – Led di Asia Tenggara. Dengan memanfaatkan keahlian Cloud Kinetics di bidang data hybrid dan multi cloud serta keahlian Snowflake, pelanggan dapat membangun fondasi data yang modern.

Snowflake telah mengumumkan peluncuran mereka di Jakarta, Indonesia. Sebagai salah satu ekonomi digital dengan pertumbuhan tercepat di Asia Tenggara, Indonesia merupakan pasar prioritas bagi Cloud Kinetics dan Snowflake dalam fase pertumbuhan berikutnya.

Cloud Kinetics yang berkantor pusat di Singapura adalah salah satu pionir dalam meluncurkan penawaran Migrasi Data dan Modernisasi dengan Snowflake di Amazon Web Services (AWS) Marketplace.

Cloud Kinetics juga siap untuk menjajaki pasar baru yang akan mempercepat adopsi fondasi data modern di Asia Tenggara dan sekitarnya. Dengan penawaran yang tersedia di AWS Marketplace, perusahaan bisa mendapatkan solusi untuk me-migrasi data lama dan meminimalisir adanya data silo.

Memungkinkan Perusahaan Membangun Fondasi Data Modern

Data Silo dapat menghambat pertumbuhan bisnis dan merupakan tantangan bagi banyak perusahaan saat ini. Dengan pertumbuhan data yang begitu cepat, perusahaan menghadapi data silo yang terfragmentasi, tidak mudah diakses, dibagikan, atau digunakan oleh tim lain yang membutuhkan respon yang cepat.

Penawaran ini akan membantu perusahaan mengatasi data silo, serta memindahkan, menyaring, dan menyatukan data bisnis yang vital menjadi fondasi data modern untuk memberikan analisa & sumber informasi, serta memungkinkan pengguna mengambil keputusan bisnis yang tepat. Dengan kemitraan ini, pelanggan dapat memigrasikan database on-premise atau cloud mereka ke infrastruktur Snowflake yang berjalan di AWS untuk mendapatkan manfaat secara langsung.

Dengan menggabungkan Snowflake Data Cloud dengan infrastruktur AWS yang aman dengan skalabilitas tinggi, maka solusi cloud berbasis data dari Cloud Kinetics akan memungkinkan perusahaan memproses, dan melindungi semua data pengguna dengan andal. Dengan model penawaran yang fleksibel, memberikan pengguna lebih banyak pilihan untuk menyesuaikan data migrasi dan proses modernisasi untuk memenuhi spesifikasi kebutuhan pengguna.

“Dengan momentum pertumbuhan bisnis kami di Asia, kami sangat senang dapat memperdalam kemitraan dengan Snowflake untuk melayani pasar Asia Tenggara dengan lebih baik lagi. Hal ini merupakan langkah signifikan untuk memperkuat/mempercepat perjalanan transformasi digital perusahaan dan mengimbangi pertumbuhan data yang cepat, dengan memanfaatkan kemampuan data Snowflake yang berjalan di AWS,” kata Saleh M “Haji” Munshi, Group CEO of Cloud Kinetics. “Kami berharap dapat memperluas dan mengembangkan layanan data kami yang kuat untuk menghadirkan nilai tambah bagi para pengguna, sehingga dapat benar-benar berkembang dan berinovasi di dunia digital.”

“Bisnis dari para pengguna kami di ASEAN berkembang secara cepat dan data silo merupakan salah satu tantangan terbesar saat ini,” kata Ash Willis, Vice President, Partners and Alliances APJ at Snowflake “Kemitraan Snowflake dengan Cloud Kinetics ini akan membantu perusahaan di ASEAN dalam implementasi Migrasi dan Modernisasi data lebih lancar dan lebih cepat .”

Referensi

Cloud Kinetics Data Migration & Modernization di AWS Marketplace (Klik Disini)

The post Cloud Kinetics Mempererat Kemitraan Bersama Snowflake Untuk Mendorong Transformasi Data-Led Bagi Bisnis Di Asia Tenggara appeared first on Cloud Kinetics.

• 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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