Product recommendations based on personal preferences have taken customer-centricity to a whole new level. Advances in technologies such as data analytics and artificial intelligence are allowing marketers to predict customer needs better and create personalized experiences, in turn, increasing the conversion rates, revenue, average order value, and customer lifetime value.

The customer journey in today’s world is more than just their visit to physical stores or e-commerce websites. It is now focusing on staying relevant in the customer’s life at various touchpoints.

With increased competition and the availability of platforms for customers to share real-time feedback about their experiences, it…

To remain relevant and competitive in the market, a majority of CPG companies have shifted their focus on deeper analysis of customer insights. While it is hard to predict the behavior, it is imperative that companies capture the change sooner and adjust their strategies accordingly. Digital disruption and CPG marketing analytics has provided new opportunities for CPG companies to communicate and sell products, and build on new opportunities. However, they struggle with reaching out to the right target audience, resulting in high marketing spends with low conversions.

COVID-19 pandemic has further accelerated the need to target the right audience as…

CPG Companies across the globe are looking to get more insights into the latest sales trends from their retailers, to make informed decisions across the supply chain and marketing. However, the lack of availability of granular data from retailers is a huge challenge to derive meaningful insights.

COVID-19 has further fueled the need to have insights from retailer data to track the tremendous shift in consumer buying behavior, especially the CPG brands. Forbes reports that US CPG sales increased by 10.3% last year. This is 5 times the normal rate of change year on year.

While the growth of CPG

Cloud data warehouses are providing scalable systems and high-performance analytics workloads for companies to migrate to the cloud. COVID-19 pandemic has driven many companies to move to the cloud from on-prem infrastructure to realize the full potential of their advanced analytics initiatives. Sigmoid conducted a webinar to have an in-depth discussion with data science heads of major companies to understand their journey of transitioning to the cloud, best practices for efficiently operating data & analytics in the cloud, benefits of moving to the cloud, and more.

The panel consisted of:
-Nelson Chieh (Head of Data, Advanced Analytics & Martech, Natura &…

Over the past several years, organizations have progressively embraced data analytics as a solution enabler when it comes to optimizing costs, increasing revenues, enhancing competitiveness and driving innovation. As a result, the technology has constantly advanced and evolved. Data analytics methods and tools that were mainstream just a year back may very well become obsolete at any time. To capitalize on the endless opportunities that data analytics initiatives offer, organizations need to stay abreast with the ever-changing data analytics landscape and remain prepared for any transformation that the future entails.

As we move to the second quarter of 2021, experts…

DataOps (Data Operations) has assumed a critical role in the age of big data to drive definitive impact on business outcomes.

This process-oriented and agile methodology synergizes the components of DevOps and the capabilities of data engineers and data scientists to support data-focused workloads in enterprises. Here is a detailed look at DataOps.

1. What is DataOps?

In simple terms, DataOps can be defined as a methodology that offers speed and agility to data pipelines, thereby enhancing the quality of data and delivery practices. DataOps enables greater collaboration within organizations and drives data initiatives at scale. With the help of…

The number of devices connected through the Internet of Things (IoT) is increasing rapidly. Statista estimates that there will be about 50 million IoT-connected devices in use across the world by 2030. And these interconnected devices and enterprise systems will generate vast amounts of data. And, most of this data will be stored and analyzed on the cloud.

The cloud offers access to different computing services like servers, databases, data analytics, software, artificial intelligence, and others. It allows businesses to run their applications and store data on the best datacenters within reasonable costs. This helps them to simplify and accelerate…

The last decade has seen wholesome innovations in the field of artificial intelligence and machine learning (AI/ML). Today, progressive enterprises are increasingly leveraging ML models to drive operational growth through actionable decision-making. In the ML space, an important area of evolution has been MLOps — a set of practices that help companies synergize ML, DevOps, and data engineering to seamlessly deploy ML models and reliably maintain ML systems.

Even when ML has gained significant popularity in helping organizations address operational gaps, there’s still a challenge when it comes to deploying ML models to the production environment. The major obstacle that…

Hi Kimara,

Our team is working on Blog #4 at present. Hopefully it will be live soon.

If you are interested to know more about Quantum Computing implementation in IBM Q, we have a talk coming up tilted "Implementing Quantum Machine learning algorithms on IBM Q Environment" on Thursday Feb 25th, 12:30PM PST" with Stanford Quantum Computing Association.

You can know more about the talk here -

You can also register at this link -


From streamlining the flow of information, to making business intelligence available faster at scale along with safeguarding data and lowering cost of ownership, the data warehousing process has evolved massively. Data warehouse automation now plays a critical role in that pursuit. In order to automate planning, modeling, and integrating the data lifecycle, data warehouses are now using various ETL — extract, transform, and load — solutions that run on advanced design patterns and processes.

ETL has been an essential process since the dawn of big data. Today, organizations are increasingly implementing cloud ETL tools to handle large data sets. It…


Innovative Data Solutions at Scale — Open Source | Cloud | ML | AI

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