Data science is an umbrella term to describe the entire complex and multistep processes used to extract value from data.
Data science uses computational power and human creativity to extract value from vast amounts of data augmenting human’ ability to make sense of the information and enabling us to do more.
Data scientists use various forms of artificial intelligence to connect data insights to valuable outcomes.
It’s a Digital World
With 4.388 bn internet users, 5.112 bn mobile users & 3.484 bn active social media users worldwide.
-Global Digital Reports 2020
In a time when large amounts of information are widely accessible and circulated the capability to ingest, analyze and act on contextual insights has become instantaneous. Data is taking center stage as the lead role in a plethora of industries and innovations globally.
Leaders who embrace data science and the use of AI weaving into the organization and culture develop systems of actionable insights to out-perform their competition.
The Rise of Data Science and AI
Although AI has been around since the 1950s, it is only recently that the technology has begun to be clearly visible in real-world applications. In the last 4 years, investment in AI has dramatically risen.
The advancement and the seed that lies at the core of the rise of data science and AI is primarily due to the following three factors:
- Ready access to big data generated from e-commerce, businesses, governments, science, wearables, and social media.
- The improvement of machine learning (ML) algorithms serve as a consequence of large amounts of available data.
- Greater computing power and the rise of cloud-based services – which helps run sophisticated machine learning algorithms.
Data Science Advantaged
The largest companies globally, from Google to Amazon, and Facebook – to venture capitalized, Uber-growth startups racing to Unicorns– all drive business performance through Data-enabled business models leveraging data science.
These enterprises use data science to improve all business decisions. They treat insights, not just data, as a business asset generating a complete view of data; Real-time insight with the ability to take real-time, immediate, contextualized action across all business activities to achieve better top and bottom-line results. They are dynamically positioned to delight customers, improve relationships and sustain a competitive advantage.
Data Science Use Cases
Data analytics enable the continuous sourcing of opportunities, solving problems in real-time, deploying test cases, evaluating results, and iterating.
With Data Science and AI the Possibilities are Endless
Regardless of business function replicating and sharing a single source of trustworthy data empowers an organization with analytical capabilities to derive actionable business intelligence across all activities.
In a digital economy, the ability to generate real-time insights draw the intersection of roles and function ever closer, blurring and redefining responsibilities and accountabilities. The traditional value chain activities are evolving at speed, but so too is the competitive landscape where rivalry is shifting from defined verticals and industries to broad ecosystems.
We see this in the transition from automobiles to mobility solutions, from banking to fintech platforms, from production lines to intelligent factories. Traditional boundaries are collapsing almost everywhere you look. Competitors are coming from new directions and pursuing different goals from those of familiar rivals.
Data Science is Industry Agnostic
From retail stores updating and optimizing prices in real-time leveraging machine learning data on weather, to predict product demand, with competitors’ pricing intelligence tied to automated inventory management that helps maximize the availability of in-demand products, at the right time and price for elevated sales. Companies automate procurement management and optimize vendor spending. Banks are able to automate customer loan approval and apply non-traditional transactional data enabling better credit quality scoring, saving time, reducing loan defaults, and growing loan portfolios while improving their performance. Augmented intelligence in healthcare saves lives by improving the accuracy of identifying heart failure made by dispatchers during emergency phone calls. Data analysis from millions of prior emergency calls including both verbal and non-verbal data – like the tone of voice and breathing patterns – enable AI models to look for signs of cardiac arrest and warn dispatchers during calls.
A Data Science Framework
Companies need a new approach to strategy for managing in a world of ecosystem disruption. Leaders who embrace data science and the use of AI weaving into the organization and culture develop systems of actionable insights to out-perform their competition.
An insights ecosystem empowers an organization to drive innovation, achieve business agility, and resilience in the digital economy.
Insight-Driven Companies are more Agile and Resilient
Average growth rates of insights enabled companies
Public 27% CAGR
Private 40% CAGR
Average growth rates without insights
GDP +3.6% CAGR
* Sources: Forrester, Morningstar, PitchBook, The Economist Intelligence Unit; See also: “The Insights-Driven Business” Forrester report
Insight-driven companies are
23 X more likely to acquire customers
19 X more likely to be profitable
2.6 X more likely to exceed competitor’s ROI
* Sources: IDC, Gartner Forrester 2019 / 2020
44% of larger organizations fear they’ll lose out to startups if they’re too slow to evaluate and deploy Al in their organization
– Microsoft Digital Research
It is expected that the global artificial intelligence market will grow at a compound annual growth rate of 42.2% from 2020 to 2027 to reach USD 733.6 billion by the end of 2027
– Insights from Grandview Research
Data Science Process
Key Barriers to Entry and Challenges in Data Science
Data Science - Barriers to Entry
Barriers to entry
To compete in the digital economy companies require a data strategy to overcome a range of challenges preventing companywide data-enabled decision making.
- Organizational data culture maturity
- Data access, quality, activation, analytics application
- Access to frontend and backend resources
- Growing regulations
Organizational Data Culture Maturity
An organizations’ analytical prowess is largely dependent on a cultural appetite for data.
Most organizations want to be more data-driven in their decision-making, but the ones that succeed are those in which leaders commit to making the investments they need to realize their goals. These commitments range from talent, processes, and the broad technologies available to help adopt and deploy data and analytics at scale.
- A set of transactional systems that generate data to be gathered and analyzed for actionable insights
- A willingness of leaders, owners, and the C-Suite to invest in and deploy a data strategy
Companies prioritizing data culture empower data literate talent to take advantage of opportunities, drive growth, innovation and differentiate the organization from competitors.
Data Access, Quality, and Activation Challenges
Companies have a growing complexity of systems.
Many companies have specific software for each function from Payroll, Accounting, HR, ERP, Billing, CRM, analytics, and industry-unique systems. Data and technology stacks house growing amounts of fast-changing data, scattered across the organization, often with no standard structure and external sources of great value, not considered or captured at all.
Up to 95% of all data is unstructured – social media content, call transcripts, video, audio, and much more.
Unstructured data is often where the most insight is mined, but it’s also largely not used. As a result, most organizations use a mere 1% of available data to help improve decisions. Source: Hopkins et al. Forrester.
- Cumbersome access consumes lots of time.
- Up to 30% of an employee’s workday can be spent searching and gathering data – 2 ½ hours per day
- Missing information stymies business application
- Opportunity loss due to inability to find relevant data
- Productivity loss
- data duplication
- shifts concern from missing data to bad data.
Companies attempt to empower their workforce, make them citizen data scientists, only to fail partly because the approach has been technology-focused and the skillsets necessary to manage a data product lifecycle are broad and uncommon.
Using data science and AI to support business-critical decisions makes it vital to understand what AI is doing and why. Is it providing valuable contextual insight or making accurate, bias-aware decisions? Does it violate privacy regulations? Can you govern and monitor the models to ensure compliance with progressive regulations?
AI development by someone without the expertise and proper training, or operating without appropriate controls can create something dangerous; not being able to explain the model’s results, problems may become evident after system implementation, leaving companies with severe liability. Even highly qualified engineers’ most sophisticated AI systems can cause explainability reinforcement risks, unintended consequences, and other issues. AI should be designed, tested, and maintained by people with relevant expertise.
When organizations consider how to implement and maximize data science, they must be aware of the demand and difficulty of attracting data scientists that impact not only the targeted nature and quality of outcomes but also the ability to manage and mitigate growing risks
Data science and the use of AI bring unlimited potential, but with great potential comes significant risks. Expertise trained in AI is needed to develop unique solutions that competitively position a company but also mitigate growing regulatory risk.
Data-Science as a Service platform elegantly empowers all companies to solve these growing, critical pain points and drive a return on data.
Access to Frontend and Backend Resources
There are many backend tools and solutions designed to extract value from data. The backend is the part that deals with hardware, efficient computing, and data storage infrastructure, or what is often referred to as data engineering.
The frontend data scientist(s) landscape is more challenging.
The Data Science & AI Landscape – Technology and Data Scientist – Spectrum provides a deeper dive into the evolving landscape and battle for resources.
Data science and the use of AI is not a choice anymore it’s necessary for long-term success. Of course, AI requires data, and data without context is really just noise. To succeed companies need to utilize technology, knowledge, computational power, and access to data. Hence, we need a data strategy.
Data studio as a service provides the framework and roadmap for companies to accelerate the adoption and benefits of data science.
The service encompasses three modular solutions to meet each client’s specific needs that bring progressive value and benefit in each part.
They are part of the broader DSaaS ecosystem – leveling the playing field – making accessible on-demand front and backend resources to all companies to deliver company-wide insights fueling data-enabled business models for a competitive advantage securing a company’s future viability.