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Jun 16 2022 | by Abhilash Poovanadka

The 8 Ds of Data Analytics on the Cloud

Data is at the root of just about everything that contributes to making an impact and bolstering an enterprise's bottom line. It's now the enabler of every meaningful action.  

But, as is sometimes assumed, analytics and data are not the same. While data is the raw material, analytics is the process of using that data to generate insights that refine actions or decisions. 

Therefore, data analytics is at the core of the competitive advantage and relevance that every enterprise seeks. It is a crucial piece of the puzzle.  

And considering the growing spending on the public cloud, data analytics has a new-found significance. Per one of the reports, public cloud services spending was marked at $332.3 billion in 2021. The projections point at $397.5 billion for 2022. 

But what does it take to move your company's analytics to the cloud? In two words — strategic adeptness. On that note, let's check out these 8 Ds of data analytics pertaining to the cloud. 

1. 'Draw' the Data from the Source 

Data is the lifeline of any organization. It is imperative to draw data systematically to manage, analyze and transform it as per the needs. The cloud provides an ideal platform for this purpose as it allows drawing data from different sources and storing them in an easily accessible way. 

2. 'Distill' the Data to Maintain the Data Quality 

Data can be divided into three basic categories: structured, semi-structured, and unstructured. Structured data is easy to understand and present, while semi-structured data requires some processing before a possible presentation.  

Unstructured data, however, is difficult to process and therefore requires some form of transformation before the presentation is possible.  

As it stands, cloud computing makes it easier for businesses to work with considerable amounts of unstructured and semi-structured data because it uses a distributed network architecture that enables the storage of large amounts of information in multiple locations simultaneously. 

3. 'Dump' the Data to the Destination, Acting as a 'Single Source of Truth' 

The third D of data analytics on the cloud is 'dump.' This refers to the process of uploading data from one location to another. In this case, one uploads data from the on-premise servers to the cloud servers. This is done using a data transfer utility (DTSU) tool. 

That said, enterprises must dump all their data at once, so there is no confusion about what kind of information is stored on each server. If they were to upload some of it and leave some behind, they would end up with two different information sources, leading to confusion later down the line. 

4. 'Describe' the Data 

This involves gathering all relevant data, including structured and unstructured data types. Data can be stored either in files or databases or reside within applications that capture it as part of their regular operation.  

Before proceeding with the rest of the analysis, one needs to determine where the data resides, how much there is, and what type it is. 

5. 'Deduce' Outcomes 

Start by asking questions about the data – what's happening right now? What will happen next? This involves making hypotheses about what one expects to see and testing those hypotheses against reality (hence 'deduce').  

Enterprises can do this manually using Excel or writing code in R or Python. They might also want to use a visualization tool like Tableau or QlikView, allowing them to visually see patterns in data without writing any code! 

6. 'Diagnose' the Data to Understand the Root Cause and Predict Future Happenings 

The most critical step in moving analytics to the cloud is 'diagnose.' Understanding the root cause of specific data is crucial for understanding what is happening and predicting future happenings. Companies must know why they have what they have and what it means for their business. 

For example, if sales have dropped by 20% over the last two months, what could be causing this drop? Is there a particular product or category that has seen a significant decrease? Are there any new competitors entering this market segment? 

7. 'Deliver' the Distilled Data and Results in a Format Consumable by the End-Users 

The goal is to deliver the analysis results in a format that makes sense to the end-user. For example, while analyzing sales data, one may want to deliver reports that show how sales are trending over time or what products have high-profit margins.  

Likewise, while analyzing customer service, one may want to ascertain how many calls go unanswered or how much time it takes to resolve issues. Whatever the case, ensure that the data analysis fits into the customers' workflow and helps them make better decisions. 

8. 'Document' and Record the Findings 

It's time to stop hiding the findings in a folder on the desktop. 

Data analytics is meant to be shared. Those not sharing their findings with others are missing out on one of the most crucial elements of data analytics: collaboration. 

Favorably, the cloud makes it easier to share insights with others and get their thoughts on what you've discovered. 

Wrapping Up 

It's estimated that by 2025, cloud storage will reach 100 zettabytes. By now, most companies have some form of cloud strategy in place—but how many are truly ready to move their analytics to the cloud? As this post makes clear, there is a lot more to analytics in the cloud than one might think at first glance. It's crucial to consider the implications of a move like that. But, at the same time, doing so will make it more likely that your company is prepared for the growing analytics space. 

For more information on cloud-first data analytics, contact our experts today

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