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The Transition from Data Storage to Data Intelligence (with Salesforce Data Cloud)

  • elizabeth_keenan

The transition from traditional onsite data storage to the era of cloud computing and data intelligence has emerged as a defining feature of the digital age. 

For years, businesses relied on physical servers and data centres to store their valuable information. However, as the volume of data grew exponentially, the limitations of these traditional systems became evident. The introduction of cloud storage has revolutionised the way we access data, permitting instantaneous retrieval from anywhere in the world and offering scalable storage solutions.

With data now residing in the cloud, it becomes more accessible for advanced algorithms and artificial intelligence tools to analyse and interpret. There has also been a paradigm shift in the mindset of CIOs and senior executives, where they are now implementing these advanced tools and data platforms to generate actionable insights as opposed to simply collecting vast amounts of data.

The value of data in the modern business landscape has grown far beyond that of a mere asset, making it the bedrock upon which many enterprises now rest. No matter their size or sector, businesses now rely on data to drive strategic choices, improve daily operations, and gain a competitive edge.

In this post, we will examine the impact of Salesforce’s Data Cloud, and how the platform propels data beyond mere storage, transforming it into a dynamic resource that guides strategy, enhances customer interactions, and streamlines workflows. 

What's Wrong with the Old Category? The Pitfalls

Data storage without useful insights can result in the risky scenario of gathering enormous amounts of data and turning your storage infrastructure into a strategic liability rather than an asset. Data silos, where information is separated and lacks integration, are usually the result of data storage without a strategy. 

When different departments within an organisation store their data independently, it results in fragmented insights and a lack of understanding of how to use it enterprise-wide. Identifying relevant trends, patterns, or irregularities becomes a challenge due to the overwhelming amount of stored data. 


1. Decision-making without Holistic Data Visibility

When IT administrators realise they want extra storage, it is all too tempting to just purchase more than they require. However, this may result in waste and/or the use of incorrect storage technology in the future.

Up to 73 Percent of Company Data Goes Unused for analytics”, according to Forrester

Most of the data accrued is often cold and inactive within months of collection, yet it consumes costly storage and backup resources. Furthermore, there's no reason to overprovision now that you can acquire extra storage quickly and on-demand in the cloud and with storage-as-a-service on-premises.

2. Choosing One-Size-Fits-All Storage

Storage solutions come in a variety of shapes and sizes. When it comes to cost, performance, and security, each type of storage provides various limitations and tradeoffs. As a result, different types of storage are better suited to particular workloads. This emphasises the need for data analysis, segmentation, and comprehension. Searching across data assets for file types or metadata tags can help locate data and better inform management. 

Plus, developments such as AI, sustainability, and the increased usage of data services indicate that the typical business's storage requirements are likely to alter in the future year. To train AI models, you may want storage that can stream data more quickly than traditional systems.

3. Moving Too Quickly

There is usually a sense of urgency associated with any significant IT move or upgrade, including storage refreshes. While it's important to move as quickly as possible, it's a mistake to move so quickly that you don't adequately plan for the huge changes that come with a storage refresh. Instead, take the time to gather the data you'll need to determine which are the most problematic areas of your present storage strategy and assess which improvements to your storage solutions would provide the most economic advantages. 

The Role of Data in Modern Businesses

We need sophisticated tools and analytics to gain valuable insights from the unprecedented volume, variety, and velocity of information generated by the big data era. A company's ability to gather, analyse, and use data effectively is integral to its success. In addition to improving an organisation's ability to respond to market dynamics, the ability to harness and interpret data becomes the cornerstone for creating tailored customer experiences and niche marketing plans.

Salesforce Data Cloud: Breaking Down Silos

Salesforce Data Cloud addresses this challenge by seamlessly integrating data from various sources. 

Whether it's customer relationship management (CRM) data, external market data, or even third-party data, the platform breaks down silos and creates a holistic view of the customer journey.

Unleashing the Power of Salesforce Data Cloud

Salesforce Data Cloud stands at the forefront of this revolution, providing a robust platform that empowers organisations to make informed decisions based on real-time, relevant insights.

At its core, the platform aggregates data from various sources, breaking down silos and creating a unified view of customer information. This, in turn, enables businesses to deliver personalised experiences, anticipate customer needs, and streamline their operations.

Colm Barry, our Head of Marketing, had this to say after recently attending Salesforce’s annual Dreamforce event, “Imagine Data Cloud as your digital refinery, taking your raw data and turning it into 'digital black gold,' fuelling intelligent business decisions. Unlocking the full potential of AI on Salesforce's Einstein 1 platform hinges on the capabilities of Data Cloud.”

1. Connect any data from anywhere - what does that mean?

Data Cloud is seamlessly integrated into the Einstein 1 Platform to make it easy to connect your data across any system- whether that is a Salesforce application, an external data lake, or any legacy system.” - Salesforce

Imagine a situation in which silos don't restrict your customer data and instead it flows naturally to produce a comprehensive view. The strength of this platform lies in its capacity to integrate many data sources, regardless of whether they are found in on-premises databases, legacy systems, or developing cloud infrastructures. It turns data connectivity from a challenging jigsaw puzzle into a beautiful symphony in which every piece, regardless of where it came from, contributes to a thorough and compelling story.

2. Harmonise 

The process of transforming (or modelling) several data sources into one is referred to as harmonisation. Data may be utilised for insights, segmentation, and activation when it has been consolidated (or harmonised) into a structured data model.

a. Prepare & Transform

The mapping of the source data is the initial stage, which is what is referred to as data harmonisation. If you want to minimise this risk, it is advised that a data dictionary be created for each data source prior to applying data mapping. 

Establishing a dictionary for each data source essentially allows for the determination of a data mapping specification, but it also highlights common properties across data sources and how (or whether or not) they connect to one another.

b. Map to a unified metadata model - simply put, this implies the standardisation of metadata from various sources into a single, consistent framework, facilitating better data integration, understanding, and governance. 

c. Resolve customer identification - Data Cloud helps to accurately match and recognise individual customers across various data sources to ensure a singular, unified view of each customer's interactions and history.

Introducing actionable Data Intelligence with Salesforce's data capabilities, Einstein AI and Co-Pilot

Salesforce's robust set of tools significantly improves data management by incorporating data intelligence into routine business procedures. The convergence of Salesforce's data capabilities, Einstein AI, and Co-Pilot ushers in a new era of actionable data.  

With Trust at it’s core, Salesforce's Einstein AI, examines large datasets to find hidden patterns and forecast future trends. On the other hand, Co-Pilot functions as a virtual assistant, giving users suggestions and contextual insights to increase productivity. 


 AI is only as good as your data

How it empowers AI? What is the relevance of AI in Data? 

For AI to function, a solid foundation must be laid, and Salesforce Data Cloud provides just that. Imagine this as the AI's sandbox, a place where it can build a living picture from a wide range of detailed, real-world data, rather than just crunching numbers. By leveraging Data Cloud's vast datasets, AI algorithms can learn to recognise nuances, patterns, and context, leading to better decisions. 

What is the importance of Data?

1. Applications and Analytics with Salesforce

Businesses are increasingly turning to Salesforce's cloud-based AI to harness insights from vast data, informing smarter decisions. Salesforce excels in processing, understanding, and organising data. This scalability and centralisation enhances data accessibility and quality. Using Salesforce for advanced analytics reveals trends, assesses risks, and segments customers in sectors like finance and healthcare. This data-centric approach fosters improved strategy formulation, resource distribution, and customer engagement, boosting operational efficiency.

2. Cost Efficiency with Salesforce

With Salesforce, scalability also means cost-effectiveness. Resources adapt automatically, ensuring businesses pay only for what they use, eliminating the need for maintaining redundant infrastructure. Unlike traditional setups demanding hefty initial investments, Salesforce offers a subscription model, reducing financial risks and liberating capital for other strategic endeavours.

3. Salesforce Enhancing Security through AI

At the core of each Salesforce AI offering is the Einstein Trust Layer—a stronghold of security measures crafted to guard your data as if it were in a safe. It not only conceals confidential details and fetches them securely when required but also ensures complete data erasure due to its no-storage commitment. Salesforce refrains from using your data to develop any AI models, providing unmatched data protection, confidentiality, and ethical assurance.

4. Tailored Customer Experiences with Salesforce 

Salesforce Data Cloud empowers companies to leverage data for crafting individualised experiences. Businesses can better meet the needs of their customers by learning about their preferences and how they interact with them across channels. This not only strengthens relationships with existing customers, but also helps drive new business through targeted advertising and sales efforts.

5. Instantaneous Decision-Making with Salesforce

Instantaneous decision-making sets companies apart. Salesforce Data Cloud equips businesses with real-time data insights, enabling them to promptly react to market shifts, customer needs, and budding trends. Such nimbleness is vital for outpacing rivals and navigating the ever-evolving commercial terrain.

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