From BI to AI: Data Driven Empowerment
Mavengigs
Mavengigs is a global consulting firm providing consulting services for Mergers & Integrations (M&A) and Transformations. Through our network of independent resources and partners, we serve clients in USA and Europe. Mavengigs is a division of Panvisage Inc. (a holding company with interests in consulting, education, real estate and investments).
From BI to AI: A Data Driven Empowerment
The combination of artificial intelligence (AI) and business intelligence (BI) is ready to transform the way businesses operate, especially when it comes to data-driven decision making. AI-powered analytics and decision support systems will enable organizations to leverage insights and decisions made based on data-driven recommendations. How will AI help BI draw deeper insights and make better decisions? The prevailing governance methods often lack sensitivity to the specific business context, rendering them ill-suited for agile responses to emerging opportunities.
Organizations are shifting their focus towards fostering a culture that emphasizes innovation, collaboration, and effective communication, while also embracing ongoing performance enhancement to gain a competitive advantage and enhance the experiences of both customers and employees. Currently, datasets are often examined manually to establish connections. However, with the exponential growth in data volume originating from diverse sources, there is a growing need for the adoption of a versatile unified data model that has the capability to uncover new relationships within the data. This transition will not only stimulate innovation but also empower organizations to process data in real-time.
Say, sales data is available in CRM systems, finance system and other disparate systems. These data need to be stitched in a meaningful way, cutting off the noise to make a meaning observation. The operational framework for data undergoes an agile transformation, data is redefined as a product. ‘Data as a product’ encompass a product lifecycle that offers data solutions designed for more efficient and recurring use in addressing diverse business challenges, ultimately reducing the time and expenses associated with delivering new AI-powered capabilities. Data products evolve continuously and adapt in an agile fashion to cater to consumer demands, leveraging DataOps (akin to DevOps for data) by utilizing continuous integration in delivery processes.
Organizations are ready to play an integral role in a data-driven economy that encourages the aggregation of data to generate enhanced insights through AI for all participants (internal or external). Data marketplaces facilitate the trading, sharing, and enrichment of data, thereby enabling organizations to develop distinct and proprietary data products, from which they can derive valuable insights and discovery. Using Data Marketplace, barriers will get reduced towards data collaboration of various sources making a pathway for a greater value generation.
Data as a Product is a data-serving front interface where user can find & consume the data suited for their needs, making it the primary business facing data resource. Data Marketplace enables consumers actively participate in cataloguing, curating and categorizing data sets.
Adopting a modern data architecture that incorporates a domain-focused common data model along with Data Product and Data Marketplace, as well as robust governance practices and data management, is crucial to an. effective business insights. Establishing an effective governance program becomes paramount in handling intricate cross-organizational issues, especially as platforms continue to merge in the realm of Analytics, Business Intelligence, data science, and Machine Learning. To promote use case-based insights and data narratives, metadata, governance, and data quality are poised to assume a pivotal role.
As we observe a shift in the model towards a ‘data as product’ approach through data marketplaces, data security and privacy will continue to play a critical role. Historically, organizations have developed their cybersecurity programs in response to regulatory changes, business decisions, customer needs, and evolving threats. However, modern cybersecurity will adopt a human-centric design (behavior-based risk assessment) to fortify and harness threat potential optimally. The consumerization of AI-driven fraud is ready to trigger a fundamental transformation in the landscape of enterprise security breaches. This transformation will lead to a heightened reliance on outsourcing for establishing trust within enterprises and a greater emphasis on fostering security education and awareness. Organizations that do not consistently manage their remote access architecture and processes will face an increased likelihood of security breaches.
Challenges in Adopting AI
Artificial intelligence (AI) is revolutionizing diverse industries by offering the capacity to automate tasks, refine processes, improve decision-making, and forge fresh value propositions. Nevertheless, the adoption and expansion of AI is a complex and multifaceted undertaking. It necessitates surmounting a range of technical, organizational, ethical, and social obstacles while also capitalizing on the innovative and differentiating prospects that AI presents.
Sponsorship
The key determinant for the successful, widespread adoption of an AI project aimed at addressing a business challenge is securing a highly motivated and visionary adoption champion. This champion should possess the authority required to oversee the integration of AI into the business, turning it into an application that genuinely transforms both the product and the processes it was designed to enhance. In the absence of a dedicated business leader and a committed adoption champion, the AI project will likely remain limited in scope and fail to meet the improvement expectations initially envisioned for it.
Data
High-quality, representative data serves as the lifeblood of AI, and without an ample supply of pertinent and trustworthy data, AI models struggle to deliver robust performance or adapt to novel scenarios. Consequently, it is imperative to guarantee access to a rich array of data sources, manage their storage, processing, and analysis effectively, and implement measures to safeguard against data breaches and improper usage.
Identifying the ‘First’ Problem to solve
Investing in AI is a substantial commitment for the majority of enterprises, and there is an anticipation of experiencing significant return on investment (ROI) within the initial six to eight months. To achieve this, it is crucial to select the appropriate business use case that can be enhanced and optimized effectively with AI. Leadership tends to focus on how it will drive profitability in the next earnings cycle, leading them to pursue often-unrelated use cases that offer quick and measurable financial benefit.
Talent
While many organizations have already initiated the integration of AI into their operations, a shortage of talent possessing the requisite skill sets remains one of the most formidable obstacles to successful AI adoption. This challenge encompasses the establishment of in-house AI capabilities, which necessitates having internal staff with the appropriate skills.
Social Perception
The impact of AI can elicit a wide spectrum of emotions and responses from customers, partners, competitors, and the public, spanning from curiosity and enthusiasm to apprehension and reluctance. It is essential to have a deep comprehension of the requirements, expectations, and apprehensions of your intended audiences, and to communicate and interact with them in an effective manner. Furthermore, establishing trust and credibility for your AI solutions is vital, and it is crucial to showcase their advantages and value.
Ethics
The adoption of AI introduces a range of ethical considerations and associated risks. The impact of AI on society can be either beneficial or detrimental, contingent upon the design, application, and regulation of AI systems. It is essential to ensure that AI solutions are in harmony with established values, principles, and standards, and that they uphold the rights, dignity, and interests of stakeholders. Transparency, explainability, fairness, accountability, and trustworthiness are vital qualities to be integrated into AI solutions, and they should also adhere to applicable laws and regulations.
Infrastructure Technology
Graphics processing units (GPUs) have evolved into the cornerstone of artificial intelligence, revolutionizing the landscape of machine learning. Shortage of GPU is a high risk for companies implementing AI. High GPU is required for neural networks, image processing, video processing etc. Shortage of GPUs could lead to inaccurate, inadequate, and slow processing of models and results.
Other barriers to AI Adoptions are:
- Lack of clear strategy for AI
- Functional silos constrain end-to-end AI solutions.
- Uncertain or low expectations for return on AI investments.
- Lack of changes to frontline processes or no change management incorporated after AI’s adoption.
- Limited usefulness of data.
- Personal judgment overrides AI-based decision making.
- Limited relevance of insights from AI
Way Forward
Companies should adopt a more systematic approach, concentrating on their organization’s broader scope, which serves as a long-term indicator of their ability to contribute value. This approach necessitates the consensus among company leaders that the purpose of AI is to fundamentally reshape how the business conducts its daily operations. In practical terms, this involves using AI throughout the entire process to capture each customer, process, or machine-generated event or data point (such as clicks, transactions, milestones, indicators, or sensors). The goal is to ensure that subsequent actions, decisions, and interactions become more precise and effective. This forms the foundation for an ongoing cycle of learning and enhanced performance, which we will elaborate on in the following section
Define and Align Strategy
Senior leadership needs to establish a definitive objective of incorporating analytics not only into specific business units and functions but also across the entirety of the organization’s operations. This level of dedication begins at the highest echelons of leadership but should also permeate deeply within the organizational structure. Translate your business and digital strategy into your vision and strategy for data and AI, emphasizing the most promising areas for optimization in your current business processes, as well as for pioneering innovative ventures that leverage AI and data.
Define a clear Data Strategy and Data Governance
- Establish a well-defined data ontology that encompasses both existing and future use cases. Identify the specific business processes, such as product development, production, sales and marketing, supply chain management, pricing, human resources, finance, and more, in which you intend to leverage data and AI.
- Create a coherent master data model that spans essential domains, including customer, product, location, and employee data. Ensure that there is clear business ownership assigned to these domains to govern how they are managed and utilized.
- Develop governance plans that explicitly define the individuals or roles responsible for maintaining the quality of each data set. Categorize the data sets hierarchically to distinguish their importance and treatment. This includes identifying mission-critical data stored in high-quality and easily accessible systems. The next tier comprises meticulously curated data sets for analytics, which may not require the same level of stringent governance. For everything else, opt for the most cost-effective storage solutions to minimize overall expenses.
- Gain comprehensive insights into the existing state of your data and AI capabilities. Develop a thorough understanding and strategy for the technical prerequisites of data environments. For instance, consider scenarios where use cases demand a dynamic environment that automatically and consistently updates data. Ensure you have systems in place capable of transitioning data from one classification to another as their significance evolves over time.
- Envision the desired future state of your business processes after implementing data and AI capabilities. Clearly articulate new data-driven business and product concepts. Establish the execution roadmap, outlining the necessary investments. Initiate the first data and AI use cases by creating an AI playbook, with a focus on achieving production readiness. Implement automation and expand operations to scale effectively.
Methodologies
AI and analytics programs should embrace model development methodologies that encompass ongoing maintenance and enhancement of models through a refined model management process. Continuously assess and improve the quality and performance of analytical models using a rigorous challenge and testing approach that compares existing data sources and algorithms against new and potentially superior alternatives.
Skilled Talent Acquisition
Assemble a team with profound functional proficiency in data science, data engineering, data architecture, and analytics transformation (analytics translation). Consider implementing a dual career track system, differentiating between technical and managerial roles, and explore the potential of rotational programs that enable analytics talent to transition between both business and technical positions.
Empowerment
To realize substantial benefits from analytics, it’s essential to prioritize and map the decisions that promise the greatest value. The careful selection of the right use cases and business processes is crucial. Prioritizing and integrating analytics into critical decision-making processes can enhance performance and, simultaneously, garner support and build confidence among all stakeholders. The leadership team should be held responsible for ensuring that team members have access to the necessary tools and possess the knowledge or literacy required to comprehend analytics. Employees should be empowered to act upon the insights derived from the data.
AI Lifecycle
AI Governance
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