AI Presents Endless possibilities across all Industries and Organizations.

Applications and use case possibilities are limitless. They depend on the growth stage, status quo existing data stack, priorities and budget. Industry, organization size, and personas can segment AI Use Cases. Determining the relevant data universe and identifying models to solve a defined challenge can be alike regardless of industry.

Persona

A CFO may focus on efficiencies and OpEx reduction to eliminate manual data entry, automate accounts payable, or integrate a business algorithm to optimize vendor spending, automate procurement management or cash flow forecasting.

A CRO will focus on revenue-generating functions within Rev-Ops. (Rev-Ops) is an example of the convergence of interrelated roles made possible by the speed and escalating volume of data production and the ability to ingest and drive insightful business intelligence.

Revenue operations are the strategic integration of revenue-related roles, sales, marketing, and service departments. The main goal of Rev-Ops is to connect data from these departments to provide a better 360º customer view before, during, and after the sale. As digital customer experience evolves, the need for various departments to share information has grown. Rev-Ops is the strategy to bring them all together to precisely manage and measure the return on investment (ROI). It takes responsibility for the software, systems, processes, and data for all the revenue-generating teams inside a company.

Industry

AI in the healthcare industry already saves lives by improving the accuracy of identifying heart failure made by dispatchers during emergency phone calls. Speech recognition, natural language processing, & other forms of AI translate verbal and non-verbal data such as the tone of voice & breathing patterns. Data analyzed from millions of initial emergency calls enable ML models to look for signs of cardiac arrest. During calls, alerts of warning signs, questions, & recommendations are sent to dispatchers. Contextual insight supports and elevates dispatcher decision-making, leading to improved accuracy of diagnoses and lifesaving remediation by an additional 20%+.

Retail stores update and optimize prices in real-time, leveraging machine learning with weather data to predict product demand.  Tied to automated inventory management and competitor pricing, companies maximize the availability of in-demand products at the right time and price to help elevate sales.

Banks automate customer loan approval. Digitized non-traditional transactional data ingested by predictive AI algorithms enable better credit quality scoring. Workforce and time are saved, defaults are reduced while loan portfolios grow, performance improves with 10x+ ROI’s.

Data Science and AI Advantaged

It is undeniable that many modern businesses are swimming in data. Last year, McKinsey estimated that significant data initiatives in the US healthcare system alone “could account for $300 billion to $450 billion (about $1,400 per person in the US) in reduced healthcare spending or 12 to 17 percent of the $2.6 trillion (about $8,000 per person in the US) baselines in US healthcare costs”. To balance that, lousy data is estimated to be costing the US approximately $3.1 trillion (about $9,500 per person in the US) a year.

“Firms that have scaled AI are almost seven times more likely to be the fastest-growing businesses in their industry compared to firms that have not scaled AI.” Forrester

Data Science and AI Adoption

Research indicates that approximately 10 -15% of mid-size firms – annual revenue of $50 + million – have adopted data science and the use of AI in their business. This compares to 30% > in larger organizations with $1 billion> in annual revenue. Or a meager 5% or less of SMEs with $50 million< annualized revenue.

“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

Data Science Levels the Playing Field

Data science and AI are great equalizers, where smaller organizations can create a significant presence and big companies can make familiar connections.

Let’s look through the lens of the organization’s book and records.

All company’s financials reflect a degree the health of the business. The 3-staple financial statements, income, balance sheet, and cash flow represent backward-looking historical data. Past outcomes scribed in a ledger are helpful if verified and meet GAAP standards, but understanding what is behind the financials and what actions to take to impact and steer them towards predictable outcomes is of more value.

In a digital economy, data enables an organization to see what is behind the financials and take a closer look at activity metrics, not just the KPIs or OKRs, but also at a window into the future. Machine-driven intelligence, functionality efficiency, and insight accelerate value creation, digitization, scaling quality, and volume to outperform competitors while future-proofing the business.

AI-enabled business solutions help organizations connect data to real-world outcomes and solve complex operational challenges. This happens in the context of ‘jobs to be done’ through actionable intelligence that fulfills unmet needs and enables talent to do more and better. Identifying complex relationships and hidden patterns in data reduces the reliance on human expertise and judgment while keeping intentionality in the hands of human actors. This is the essence of augmented intelligence, offering a meaningful balance between artificial intelligence and human agency.

Analytical Capabilities bring Organizations a Myriad Competitive Advantage

Descriptive analytics enable them to answer what happened in the past that produced current financial results.

Predictive analytics elevate performance by answering what might happen in the future.

Prescriptive analytics answer the question of what to do to make it happen.

Data science enables small companies to punch above their weight. It changes the playing field and the game, the activities necessary to win, the players, dynamics, and how organizations operate.

Data Science is a simple 3-step method

Gather, Analyze, and Act.

Establishing a comprehensive 360º trustworthy view of data is foundational to organizational analytics.

Turning data into actionable insights, greater efficiencies, cost savings, elevating productivity, workforce engagement, revenue, and overall organizational value through a simple 3-step method.

A sole source of reliable data empowers an organization with analytical capabilities to derive actionable business intelligence across all activities for intelligent outcomes.

  • Automation; improved efficiency, lower costs, elevated productivity
  • Anomaly & Trend Detection; detect threats and risks earlier
  • Auto Discovery: gain insights to empower the workforce
  • Catalyst Detection; benchmarking, R&D advanced deeper learning
  • Enrichment Pipeline; drive new opportunities, services, and products
  • Intelligent Recommendations; augmented intelligence and system of insights

Does size matter in Data Science?

Many companies are of sufficient scale and complexity to derive substantial value from data science and AI but cannot advance.

Mid-sized and smaller companies, many of which are family-controlled, cannot keep up. Prior research has documented how these firms have already struggled in a polarizing digital economy. Data science is intensifying that struggle. Even well-resourced organizations face a myriad of frontend resourcing challenges and legacy systems with siloed data that impede progress.

Today, digital systems power every aspect of a business, making them critical to its success.

Counter-intuitively, selecting the optimum combination of digital systems for your organization requires focusing on data and business, not technology. A data and business-first approach enables identification of the functionality needed to support your jobs to be done across all activities staff, customers, and suppliers, now and progressively into the future.

This requires a data strategy. The right talent that can develop a data strategy and skill to execute. A plan without execution is a hallucination. This can manifest in broad resourcing challenges How to attract and retain data science talent in a highly competitive marketplace in a time when there’s a fierce battle for expert resources.

Data Scientist Supply is Low

The field of data science is still relatively new, even in 2021. Twenty years ago, it was impossible to learn data science because of slow internet connection and low computational primitive programming languages. Traditional education was not ready to meet the needs of those who wanted to know, so the world is playing catchup.

Demand for Data Scientists is High

Demand is incredibly high and in no sight of slowing down as more companies recognize the need to adopt data science and the use of AI.

According to LinkedIn, there has been a 650% increase in data science jobs since 2012. The demand is expected to continue. The U.S. Bureau of Labor Statistics sees strong data science growth and predicts that the number of jobs will increase by about 28% through 2026. That is approximately 11.5 million new jobs in the field.

Why wouldn’t an organization buy Off-The-Shelf Vendor AI Solutions?

The choice of buying off-the-shelf vendor technologies incorporating AI can work for smaller firms or where applications require minor customization. But as business complexities increase, the application of AI becomes progressively targeted and strategically important. Companies that rely solely on plug-and-play AI solutions jeopardize long-term value creation.

All companies can benefit tremendously from developing data models from the ground up, creating their own AI intellectual property, and the advantages of interoperability, being reusable and extensible: Tackling similar problems in the future and extending to novel problems as circumstances change.

Whether you buy or build or combine data science and AI with supporting business-critical decisions, it is vital to understand what AI is doing and why. Expertise trained in AI is needed to develop unique solutions and mitigate evolving challenges and regulations.

So, where does this leave small mid-market firms and SMEs?

Even in small organizations, the complexity of data systems grows every day. Many 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 mined with the most insight, but it is also broadly not used. This means only a tiny percentage of all the data available to an organization is fully utilized. Incomplete data prevents many businesses from dynamically improving across all business functions.

In the past, if you wanted to analyze supply chain, human resources, financial, or any business activity’s data, you would first need to find the relevant data scattered across siloed systems and direct the right analytics tool to it. This required extensive knowledge of what data was the correct data, trustworthy, and pertinent to analysis and where to find it. Furthermore, analysts may have lacked the understanding of the role and functional context necessary to connect to worthwhile outcomes.

Augmenting human-powered business operations and management with machine-driven intelligence involves interdisciplinary teams, including data scientists with non-overlapping complementary skills. To make use of analytics in a way that drives a sustainable competitive advantage, companies require access to the “right” team ability who can extract maximum value from a world of data regardless of source, format, use cases, and challenges.

Democratizing Data Science – A choice for all organizations

Regardless of where an organization is on a data science journey, in the early stages or more advance, all should be asking the question “how can data help us improve across all business activities?”

Today, digital systems power every aspect of a business, making them critical to success. Data Science and AI expertise is necessary to ensure digital systems are optimized for a business purpose to support the job to be done with appropriate evolving technology.

Data science and the application of AI have fundamentally changed the way companies can operate and compete. Automating data analytics changes the field of play, the makeup of team members that play, and the dynamics. This means companies have to bridge the gap and solve resources challenges to compete

Whether you are a C-suite leader business owner, Founder, Co-Founder, CEO, CTO CRO, or otherwise, perhaps the most critical initiative an organization can take in 2022 is to ensure they have a compelling data strategy and the right talent to execute.

Conclusion & Next best actions

If companies want to thrive in the data science and AI era, they must find new ways to compete and address the resourcing gaps.

Data science is attainable for nearly all organizations. Data scientists in multidisciplinary teams and the capabilities of high-tech solutions are made fractionally accessible through cloud-based services just like any other web service. Organizations can reliably advance digital transformation and realize the benefits of data science and AI with measurable ROI.

Data will not put companies out of business, but failure to use it will.

What is your data strategy?

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