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Hello Readers, fellow Analytics Enthusiasts, Data Consumers, Leaders and C-level Executives,
This series should be a beneficial piece of information, at a minimum getting the conversation started around: “What’s our Data Warehouse and Data Analytics Strategy for the Organization?”
We will break this series into three parts as below :
- Why Data Analytics is important for your organization
- The journey of the SAP Data Analytics platform within a typical organization
- Where to next and What to consider: Future Roadmap
Why Data Analytics matters
Without getting too philosophical, the fundamentals of any event occurrence or transaction are based on recording it accurately. It stretches further than this: the ability to analyze the impact of any process, event or transaction within a system depends on capability to retrieve and reporting the data. This is the beginning of Data Analytics.
We have always focused on being data-obsessed, trying to hoard copious amounts of data with a view that it will be useful and meaningful someday. This problem is exacerbated by lower cost of storage and hardware, along with the ability to push large amounts of unstructured data into the cloud.
Recently, I came across this quote, not to name any customers, from an Analytics Director during a major upgrade cycle on their SAP BW 7.5 instance. When asked what pieces of data and information the business would like to migrate, you could have probably guessed the answer: “Everything.” These very subjective requirements of data lead to chaotic data structures, which become fossilized datasets that lose their importance or essence very quickly.
Organizations are becoming more data-obsessed than data-driven. Having data captured in scattered storage will not enable you to be a data-driven organization. It is no longer a nice-to-have option but it will become paramount for the survival of any organization in this new age of Generative AI.
Crucial role of Data Analytics in the new age of decision-making
Data Analytics plays a crucial role in Big Data, Machine Learning, and Generative AI by providing the foundation for understanding, processing, and utilizing vast amounts of data. Here’s a detailed account of how Data Analytics integrates with these fields:
Big Data
1. Data Collection:
– Aggregating data from various sources such as social media, sensors, transactional systems, and more.
2. Data Cleaning and Preprocessing:
– Cleaning: Removing noise, correcting inconsistencies, and handling missing values to ensure data quality.
– Transformation: Normalizing and scaling data, extracting relevant features, and converting data into a format suitable for analysis.
4. Exploratory Data Analysis:
– Visualizing and summarizing data to uncover patterns, trends, and insights.
5. Data Integration:
– Combining data from different sources to create a unified dataset for comprehensive analysis.
Machine Learning
1. Model Training:
– Supervised Learning:
– Using labeled data to train models
– Unsupervised Learning: Finding patterns in unlabeled data (e.g., clustering, association).
– Reinforcement Learning: Training models based on rewards and penalties.
Generative AI
1. Data Preparation:
– Ensuring high-quality, diverse datasets for training generative models.
2. Model Training:
– Training a generator to produce realistic data samples and a discriminator to distinguish between real and generated samples.
– Learning a probabilistic model of the data and generating new samples by sampling from the learned distribution.
3. Quality Assessment:
– Evaluating the quality of generated data.
Conclusion
In summary, Data Analytics is integral to Big Data, Machine Learning, and Generative AI, providing the necessary tools and techniques to preprocess, analyze, and derive insights from data, ultimately driving innovation and informed decision-making in these fields.
Data Analytics should and will not be an after thought for any business transformation. Instead, any business transformation journey should begin by asking a vital Data question: – What information do we need to become an intelligent enterprise?
