Future of Data Strategy in Cloud
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Future of Data Strategy in Cloud

Faisal Hajazi, Global Head of CloudArchitecture & Strategy, British American Tobacco [NYSE:BTI]
Faisal Hajazi, Global Head of CloudArchitecture & Strategy, British American Tobacco [NYSE:BTI]

Faisal Hajazi, Global Head of CloudArchitecture & Strategy, British American Tobacco [NYSE:BTI]

Data and Data Strategy has always been the most important asset within the organization, but in the last ten years, with the advancement in cloud computing, data is significantly changing the behaviors of how we make decisions, how we do the shopping and decide our holidays or how organizations decide strategies. We have seen tremendous reliance on the data, and decisions highly depend on data, machine learning algorithms, and artificial intelligence capabilities.

The insights revealed are precious to the organization from a strategic to operational standpoint. Still, at the same time, it presents a challenge in how we store, process and visualize the various types, formats and frequency of data and how we adhere to governance, regulatory and legal requirements.

Data Strategy Elements

Some of the core data strategy elements are Business Alignment, Data Gathering, Technology, Data Insights, Governance and People & process. These elements have not changed us in the past years, but they have evolved, and there is a considerable shift in how we design and build the data strategy as we move towards Cloud, Edge and Hybrid environments.

We need to relook and think about designing our future data strategy in Cloud and Edge. Let us look into some of the shifts.

1. Multi-Platform Architectures: Any data can be acquired and stored within a modern data architecture. Analytics, Data Lake, and ML platforms are used, and data is distributed across platforms.

2. Data Architecture is Constantly Changing: Data is coming from multiple sources, which include relational, non-relational, images, web clicks, streaming data etc. It is tough to decide at the earlier stage what target data architecture will be beneficial, with early exploration efforts to analyze data impact the shape of solutions. Raw data becomes more refined as use cases are determined. Access to data becomes progressively less restricted as curated and business data structures are defined. Sandbox or proof-of-concept solutions can become operationalized.

3. The Data Lake and the Data Warehouse are complementary: The data lake and the data warehouse are central players in the data storage area. Each is equally important, with complementary roles to play. It is exciting and vital to understand that after the Hadoop/HDFS became mainstream, we thought this was it; we had found the solution for everything Data, soon, we realized that that is not the case. Data Lake and DWH both have common but different use cases. A data scientist needs raw data, and getting the data from multiple sources is key for a successful ML model, and Data Lake is the right place to store and access that data. While for a business analyst, transformed and structured dimensional data is what he is looking for to get the business insights efficiently and accurately. The simple answer is that we cannot replace one with another.

4. Moving beyond Data Lake to Data Mesh: First of all, Data Mesh is not a technology in itself; it is a change in how we architect our technology stack. The change is how we organize our data teams within the enterprise. Mesh is a set of principles that looks at a distributed model for Data Ownership and a Product thinking way of looking at Data (Data Products). A data mesh design organizes around data domains. Each data domain owns and operates multiple data products with its data and technology stack, which may or may not be independent of others.

5. Cloud Data Platform: Born in the cloud Data Platforms: A set of new data capabilities are also emerging that necessitate a new set of tools and core systems. Many of these trends create new technology categories – and markets – from scratch, e.g. Snowflake, Looker, data bricks.

6. Cloud to Edge-Cloud: It makes it possible to decide faster, particularly in low bandwidth situations. Given the increased dependence of companies on automated, data-driven decision-making, edge analytics is a significant technology giant investment field.

Fast decision-making through cutting-edge analysis can be helpful in industries such as retail, energy, security, manufacturing, and logistics. For example, when it meets an obstacle on the road, an autonomous vehicle must make a split-second braking choice.

Edge analytics is not only for making decisions within milliseconds. There is an immediate increase in data obtained from different devices and sensors. As bandwidth is restricted between a server and the edge, data transfer speed cannot be adequate except for less time-sensitive applications.

In summary, data strategy needs to be aligned with the business strategy, and business strategy is a lot dependent on how data is shaping our current and future world. How data is generated, processed, analyzed, and visualized for all the different types of users, data scientists and data analysts is dramatically transformed by new and more advanced technologies, such as data lakehouse architectures and mesh topology. We see various sources of data batch, near-realtime, and real-time connected devices gather and transmit data and insights. More often, real-time will be an essential factor in designing the future of data strategy.

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