Why DSaaS?
Alephnet’s data science as a service ecosystem empowers all companies to connect data to aspirational outcomes.
In 2022, 2.5 quintillion bytes of data are generated every day. 90% of the world’s data has been created in just the past two years. It is expected that data volume will double every two years – a staggering number.
The world’s largest companies, from Google, Amazon, and Facebook to venture capital-backed startups like Uber on their way to becoming unicorns, all harness data science to develop data-driven business models.
These companies leverage data science to enhance all their business decisions. They treat insights, not just data, as a valuable asset that provides a comprehensive view of the data. Real-time insights empower them to take immediate, context-aware actions across all business activities to achieve better results. They are dynamically positioned to delight customers, improve relationships, and gain a competitive advantage.
What is Data Science?
Data Science is a comprehensive term that encapsulates the intricate, multi-tiered processes utilized to derive value from data. This involves elements such as backend infrastructures, applied tools and technologies, and frontend data scientists. Interestingly, it’s not a lone data scientist who holds all the necessary expertise, but rather teams with a variety of complementary skills.
Data science merges the power of computation and human ingenuity to create value from extensive data sets. This amplifies our capacity to effectively utilize information, enabling us to accomplish more.
In the 21st-century business landscape, it’s crucial to harness data to your benefit. We envision a new reality where businesses of all sizes, small, medium, or large, become data proficient and intertwine insights with innovation and value creation. A reality where data insights evolve into an ecosystem.
The integration of data science is a facet of digital transformation that unfolds in several phases. A straightforward three-step method – Capture, Analyze, and Act – is employed to bolster data-driven decision-making within businesses
Future-proofing through a cycle of reinvention, and reconfiguration
Every day, the intricacy of data systems intensifies. Data and technology stacks are becoming repositories for increasing volumes of rapidly evolving data. This data, often unstructured and dispersed throughout the organization, includes valuable external sources that are frequently overlooked or not captured at all.
While many companies rely on metrics to gauge performance and guide their business direction, they often overlook a virtually untapped wealth of additional data.
This data conceals insights into your business, clients, competitors, and markets, waiting to be discovered. As long as there’s someone willing to delve in and uncover business insights that were previously unexplored. This is where the data scientist comes in.
Data scientists employ various forms of artificial intelligence to bridge the gap between data insights and valuable outcomes.
A well-planned data strategy lays the groundwork for a future driven by insights.
What is DSaaS – Data Science as a Service?
To excel in the era of data science and AI, companies must innovate and find ways to compete while addressing resource gaps. DSaaS (Data Science as a Service) serves as an equalizer, empowering all organizations to consistently leverage their own data science capabilities.
A data science platform provides secure, modular access to both front and back-end data science, similar to any other web service. This approach ensures that data science is accessible and secure.
By pooling the expertise of all internal and external members, organizations can access a wide range of relevant data science and AI disciplines. This collaborative approach enables them to meet specific capacity and skillset requirements.
Data customers hold the comprehensive expertise necessary to craft bespoke data-driven solutions, all while considering the preferences of studios and infrastructures.
So What Are Insights From
Data Science?
Machines have the capability to analyze high-dimensional data and identify patterns. Once a machine learns these patterns, it can detect anomalies, identify trends, and generate predictions based on its learning. Once these patterns are learned, predictions can be made that far surpass human capabilities.
This is the essence of machine learning, a subset of the broader field of Artificial Intelligence. In this realm, data scientists extract insights from data to tackle complex challenges. The right data enables a wide range of actionable insights across all business activities, leading to measurable impacts on both the top and bottom lines.
A crucial skill in data science is the ability to break down a data analytics problem into components that align with a known task for which tools are available.
A data strategy guides a data stack that underpins a technology stack, systems, and the overall enterprise architecture. Adopting a data and business-first approach allows for the identification of the functionality needed to support your ‘jobs-to-be-done’ across all activities involving staff, customers, and suppliers, both now and progressively into the future.
The Data Science & AI Landscape
We make a distinction between backend and frontend data science. Backend data science involves dealing with hardware, efficient computing, and data storage infrastructure, a field often referred to as data engineering. There are numerous backend tools and solutions designed to derive value from data.
On the other hand, the landscape of frontend data scientists presents a more significant challenge.
Many companies lack the expertise of data scientists or the necessary infrastructure to successfully implement AI solutions. This implies that any endeavor requiring AI can provide a long-term competitive edge.
