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Artificial Intelligence (AI), while ushering in an age of unprecedented possibilities, also presents significant ethical challenges that warrant thoughtful consideration. With astounding rapidity, AI is upending the conventional demarcation between human and machine. Acknowledging this, the responsible AI framework underscores the imperative of consciously integrating the human element within the algorithmic fabric. Central to this conversation are four key dimensions: human involvement in the AI decision-making process, user feedback optimization, ethical implications, and informed consent.

  1. The Human Touch: Involvement in Decision Making

    AI systems are designed to learn, predict, and autonomously execute decisions. However, should they be given unbridled liberty to make decisions in critical situations without human intervention?

    The ‘human-in-the-loop’ model posits that human oversight is necessary for certain AI functions—especially those with significant consequences. By keeping humans involved, we safeguard the decision-making process from autonomous AI decisions that lack ethical considerations or context understanding. For instance, in an automated traffic management system, a human in the loop could override the system in extraordinary circumstances, preventing unfavorable outcomes that a rigid algorithm may not foresee.

  2. Refining the AI Lens: Incorporating User Feedback

    The true strength of AI lies in its iterative ability to learn and improve—an approach perfected when combined with user feedback. User feedback is akin to the potter’s hands that shape the clay of AI algorithms—directing their evolution and improving their adaptability.

    Consider Netflix’s recommendation algorithm. By incorporating viewer feedback in the form of ratings, watched history, and paused or skipped content, Netflix refines its algorithm to offer increasingly personalized recommendations. Such practical incorporation of user feedback refines the AI’s functionality, aligning it closely with user preferences and needs.

  3. The Ethical Compass: Navigating AI

    While AI unleashes enormous potential, it continues to grapple with unresolved ethical quandaries. Recurrent concerns of bias, discrimination, and privacy threats refuse to be relegated to the sidelines.

    The principle of fairness necessitates that the AI system remains free from biases and provides equitable opportunities for all. However, unintended algorithmic biases can have deleterious effects. For example, an AI system used in legal sentencing showed racial bias in its predictions. Addressing these issues requires conscious efforts to ensure fairness in AI design by employing diverse, balanced datasets and utilizing debiasing techniques.

    Transparency is another ethical pillar—it demands lucid explanations of how AI systems operate, decide, and learn. Finally, the integrity of AI systems hinges on respecting privacy—using only essential, consensual data and upholding robust data protection norms.

  4. Informed Consent: Empowering Users

    Informed consent is the backbone of any user-oriented technology—a principle just as crucial in the context of AI.

    Informed consent ensures that users understand the risks, are educated about AI’s capacities and constraints, and freely decide to use AI systems. For instance, if a predictive health analytics platform uses personal health data to predict disease risk, it is vital that users understand this algorithm’s implications. They should be given explicit information regarding data usage, storage, protection measures, and the underlying algorithm’s predictive accuracy.

    Achieving informed consent involves drafting transparent user agreements, soliciting active user participation, and enabling controls for users to manage their interaction with the AI system. Such steps engender a sense of trust and empowerment among users, turning them from passive recipients to active collaborators.

Navigating the AI Age Responsibly

When implemented conscientiously, a responsible AI framework has the potent potential to harmonize AI’s relentless efficiency with the nuanced understanding of human decision makers. A judicious blend of algorithmic learning and human intuition can greatly enhance the quality and acceptability of AI decisions, translating into wider user acceptance and adoption.

Moreover, analyzing AI’s ethical implications allows us to bridle potential pitfalls, ensuring that algorithms are fair, transparent, and responsible by design. Coupled with informed consent, such ethical practices empower users, enabling them to navigate the AI landscape confidently.

In sum, the human design component—the symbiotic interplay of human oversight, user feedback, ethical standards, and user consent—resides at the heart of responsible AI systems. By consciously weaving this component into AI algorithms, we can ensure that as we stride forward in our AI journey, we don’t lose touch with the core human values that dictate progress in an equitable, understandable, and responsible manner.

Artificial Intelligence (AI), while ushering in an age of unprecedented possibilities, also presents significant ethical challenges that warrant thoughtful consideration. With astounding rapidity, AI is upending the conventional demarcation between human and machine. Acknowledging this, the responsible AI framework underscores the imperative of consciously integrating the human element within the algorithmic fabric. Central to this conversation are four key dimensions: human involvement in the AI decision-making process, user feedback optimization, ethical implications, and informed consent.

To explore the other parts in this series, click here.

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Welcome to our blog series dedicated to Responsible AI!

Artificial intelligence (AI) has revolutionized the way we live and work. However, with great power comes great responsibility, and the need for responsible AI has never been more apparent. Responsible AI refers to the ethical and conscientious development, deployment, and management of artificial intelligence systems to ensure that they not only deliver benefits but also mitigate potential harms.

In this blog series, we explore the six essential facets of Responsible AI, dedicating each part to a distinct element within this transformative discipline. We transcend theory and digital lectures, offering practical examples and guidance to navigate the ever-evolving AI landscape with integrity and foresight.

Harnessing Human Ingenuity Responsible AI

Part 1: One of the vital aspects of responsible AI is ‘Human Design’. It’s not about emulating human intelligence but tailoring AI to comprehend, adapt, and cater effectively to the diverse and nuanced needs of Homo sapiens. The first article in the series will elucidate the tenets of human-centric AI design, bridging the perspective gap between artificial intelligence and its organic counterparts.Read More

Harnessing Human Ingenuity Responsible AI

Part 2: Next on our journey will be a comprehensive exploration of ‘Fairness’ within AI systems. The imperative is to guarantee AI as a benign tool, preventing it from perpetuating societal biases or disadvantaging certain demographics inadvertently. Drawing from real-world predicaments and their remediations, the second article will guide you towards unbiased AI design.Read More

Harnessing Human Ingenuity Responsible AI

Part 3: Explicability or ‘Explainability’ of AI decisions is the third pillar we shall explore. A superior performing AI model that cannot rationalize its decisions is akin to an eloquent scholar communicating in an unknown dialect – remarkable yet inaccessible. Throughout the third article, we’ll simplify the complexities of making AI transparent and understandable.Read More

Harnessing Human Ingenuity Responsible AI

Part 4: ‘Security’, the fourth pillar, is the fortress that safeguards AI from malicious intents or accidental breaches. The prominence of security measures is paramount in today’s cybersecurity panorama. The fourth post promises to decode the woven intricacies of AI security while providing feasible safeguards.Read More

Harnessing Human Ingenuity Responsible AI

Part 5: ‘Reliability’ refers to the consistent, accurate functioning of AI systems under diverse scenarios. A perfect webcam that fails during an important Zoom call is no better than a defunct one. Similarly, an efficient AI faltering under distinct situations is unreliable. Fifth article intends to deepen your understanding of enhancing AI’s reliability.Read More

Harnessing Human Ingenuity Responsible AI

Part 6: The sixth and final aspect we explore is ‘Compliance’ with legal, ethical, and societal norms prevalent in the AI ecosystem. Regulatory compliance ensures legal congruity; ethical compliance ensures righteousness, and societal compliance ensures alignment to prevalent norms. Our conclusive post will dissect and present these aspects in an interdisciplinary light.Read More

As we navigate the intricate web of algorithms and data, we must prioritize ethics, accountability, and transparency. In doing so, we ensure that AI evolves into a force for good in our society. By harmonizing AI systems with our collective values, we pave the way for a technologically harmonious future.

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Last week, I was immersed in excitement and fervor, standing among ardent New York Giants (NYG) supporters in Austin. Together, we watched the NFL season opener, a clash between the New York Giants and the local favorites, the Dallas Cowboys. The evening was a rollercoaster of emotions with thrilling action, thunderous cheers, passionate chants, and moments of disappointment as the game unfolded.

As I watched the match, I couldn’t help but draw parallels between the high-octane world of American football and the domain of data and analytics. When the game unfolded, one thing that struck me was the unwavering passion of the NYG supporters, even in the face of adversity. They rallied behind their team with unrelenting enthusiasm, reminding me of the dedication required to excel in the ever-evolving field of data analytics.

However, the true highlight of the week was not the game itself but the insights I gained from Forrester’s Data Strategy & Insights summit that followed. In this article, I share some key takeaways from the summit.

The Generative AI Wilderness

Forrester’s opening remarks centered on Generative AI—a technology that empowers individuals to converse with huge piles of data, enabling them to craft entirely new and original creations, as defined by Forrester. During his welcome keynote, George Colony, Chairman and CEO at Forrester Research, shed light on a diverse array of real-world applications for Generative AI, from amplifying productivity and personalizing interactions to generating content and streamlining operational processes, among others.

One concept stood out among these ideas: creating personal Large Language Models (LLMs). Imagine a digital entity trained on everything a person has ever written during their lifetime. When that individual passes away, their descendants could converse with this digital representation at various stages of their ancestor’s life. This raises an intriguing question: Can technology offer a form of immortality for humans?

As Generative AI gains momentum, Forrester anticipates a significant departure from the traditional web, making way for the rise of Generative AI capabilities that will gradually replace it. Simultaneously, the era of search ads may be waning in response to the evolving web landscape. Forrester underscored the critical importance of reinvigorating customer trust in the era of Generative AI. This renewed trust-building effort should revolve around reassuring customers that there remains a human element behind the prompts they encounter.

Forrester Analyst Rowan Curran aptly drew an analogy between the vast wilderness of Alaska and the uncharted terrain of Generative AI. Just as explorers venture into the unknown, organizations are navigating the unpredictable landscape of Generative AI, seeking to harness its potential while avoiding potential pitfalls.

From “Decision-Driven Data Making” to “Data-Driven Decision Making”

Numerous studies have shown that technology is rarely the impediment to achieving insights-driven maturity. Yet, organizations often struggle to translate insights into action, with gut-based decision-making prevailing. Frequently, decisions are made first, and then data is sought to validate them. The question arises: How can organizations pivot from “decision-driven data making” to “data-driven decision making”? To achieve true insights-driven success, four critical elements come into play:

  • Strategy: Support at the highest levels, from the CEO downwards, to foster a data-driven culture.
  • People:  Data teams should not operate in isolation. All decision-makers should be empowered with access to data and equipped to work with it in their decision-making processes.
  • Process: Learning and improvement must be continuous. The iterative approach is crucial, starting with the desired business outcomes and working backward. It’s essential to be business-led rather than tech-led, with data and analytics serving as a means to an end.
  • Technology: Technology should facilitate easy data consumption and embed decision-making into daily operations at scale. The ultimate goal is to integrate data and intelligence seamlessly into daily workflows, moving beyond dashboards, presentations, and prompts.

With these key elements in place, organizations can bridge the data-to-insights-to-action-to-business outcome gap.

What’s Trending in Data Management?

Cutting-edge technologies & solutions are rapidly gaining traction in the realm of data management, driving significant advancements. Notable among these are:

  • Generative AI-Led Transformation is driving the demand for more integrated data management systems and natural language interfaces for accessing enterprise data. It also redefines how organizations create semantics and manage Data Quality (DQM) with unprecedented efficiency and accuracy.
  • Real-Time and Connected Data adoption is experiencing a remarkable surge, with most applications now requiring sub-second response times. This shift is especially prominent in use cases like customer 360 and fraud detection, where timely and synchronized data is paramount for informed decision-making and effective fraud prevention.
  • Global Data Fabrics  offer the ability to seamlessly connect and manage data across geographically dispersed locations, providing organizations with the agility and scalability needed to harness the power of data on a global scale.
  • Integrated Data Meshes  provide organizations a comprehensive framework to seamlessly access, analyze, and share data across their network. This approach fosters collaboration and facilitates data-driven decision-making, revolutionizing data utilization.
  • Vector Databases  have exceptional capacity to handle diverse data structures and deliver lightning-fast query performance. Their true value shines in scenarios where rapid processing and precision are paramount, such as high-frequency trading or real-time analytics.

These technologies & solutions collectively signify a shift towards more efficient and agile data management practices, enabling organizations to leverage data as a strategic asset.

In Conclusion…

As I reflect on the football match and the enlightening discussions from Forrester’s Data Strategy & Insights summit, I am reminded that in both sports and business, success hinges on strategy, resilience, and adaptability. In the ever-evolving world of data and technology, embracing innovation and staying passionate are the keys to staying ahead of the game.

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In today’s fast-paced business landscape, data analytics has emerged as a cornerstone for informed decision-making and driving growth. However, several challenges can impede the scaling of data analytics initiatives within an enterprise. From grappling with legacy systems that lack compatibility to establishing robust data governance frameworks, and from facing cultural resistance to ensuring data quality and measuring the return on investment (ROI) of data analytics projects, organizations often encounter roadblocks on their path to success.

In this article, I identify top obstacles and offer practical and effective solutions to help enterprises overcome them. By taking a proactive approach to confront these challenges, you can unlock the true potential of your data and analytics programs, enabling smarter, data-driven decision-making, and propelling your business towards unprecedented growth and success.

Here are the five most significant challenges when it comes to scaling data analytics with an Enterprise:

  1. Outdated Systems – Scaling data and analytics in numerous enterprises is hindered by obsolete legacy systems. These systems present inflexibility, high maintenance costs, and an inability to support modern analytics tools. Consequently, data engineers encounter significant challenges when attempting to derive insights from the data.

    The remedy lies in modernizing the legacy systems. Enterprises should consider migrating their data to cloud-based systems or embracing agile applications that seamlessly integrate with modern analytics tools. This approach streamlines data extraction, enhances scalability, and empowers data engineers to extract insights from the data with greater efficiency.

  2. Ineffective Data Governance – In any enterprise, data governance plays a vital role in scaling data and analytics. It encompasses the establishment of policies, procedures, and standards to ensure data integrity, availability, and security. Proper implementation of data governance is paramount, as it safeguards against storing and utilizing incorrect data, which could result in flawed analyses.

    To achieve an effective data governance framework, clear communication of governance policies and procedures to all stakeholders is essential. Additionally, these policies and procedures should be customized to suit the unique needs of the enterprise, while defining the roles and responsibilities of various departments.

  3. Cultural Resistance – Enterprises may encounter employee resistance while attempting to scale data and analytics, as some employees view it as a threat to their job security. Additionally, resistance to change can emerge due to a lack of buy-in from senior management.

    To foster employee buy-in, it is important to educate them about the advantages of embracing data and analytics solutions within the enterprise. Providing training and education on the latest technology and techniques can alleviate concerns and reinforce the benefits of the initiative. Furthermore, demonstrating leadership through a top-down approach, where senior management leads by example and showcases effective data and analytics utilization, can inspire confidence and acceptance among the employees.

  4. ROI Measurement Challenges – Measuring the return on investment (ROI) for expanding data and analytics initiatives poses a considerable hurdle, particularly when the initial investment is perceived as a fixed cost. This perception can make it challenging to obtain funding or allocate resources for future scaling efforts.

    To gauge ROI efficiently, businesses must prioritize the assessment of how data and analytics initiatives directly influence their overall business outcomes. Measuring metrics like operational efficiencies, revenue growth, and cost savings can demonstrate the ROI of data and analytics initiatives. Furthermore, by conducting regular assessments, organizations can pinpoint areas requiring improvement and use this insight to guide their future investments in D&A initiatives.

  5. Poor Data Quality – Ensuring data quality is essential for the success of data and analytics initiatives. Insufficient data quality can result in misleading analyses, inaccurate insights, and potentially lead to legal or financial consequences for the enterprise. These data quality issues may arise due to inconsistent data, inaccuracies, or other data quality control challenges.

    To guarantee data quality, the enterprise must implement data quality control procedures and conduct regular checks to ensure that the data meets rigorous standards. Additionally, investing in data quality management technologies can enhance the efficiency of data quality control procedures, further bolstering the overall data quality.

In Summary…

Scaling data and analytics within the enterprise presents its share of obstacles, but with focused efforts, it is attainable. Enterprises must make strategic investments in modern technologies, create well-defined governance policies, offer comprehensive training and education, accurately measure ROI, and implement effective data quality control procedures to achieve successful scaling. Addressing the needs of all stakeholders and departments is crucial throughout this process. By following these steps and ensuring alignment with various stakeholders, data and analytics can become a powerful tool that facilitates business growth.

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The representation of women in leadership roles within the field of Data & AI is alarmingly low. According to a recent report by the World Economic Forum, only 26% of women are represented across all levels in technology, and a mere 15% hold top leadership positions. Furthermore, research indicates that diversity of perspectives is critical for effective AI. According to a recent Deloitte survey, 71% believe that adding women to Data & AI teams brings unique perspectives necessary for building effective AI systems. 63% agreed that AI models will continue to produce biased results until the field becomes more gender diverse. The study emphasizes that diverse teams excel in challenging assumptions and problem-solving in AI.

The glaring gender gap and the need for women in data & AI suggest a pressing problem that requires immediate attention. In this article, I share five tips based on my own experience to help women data & AI professionals reach leadership positions.

  1. Be Open and Flexible:

    Many college grads and young professionals are attracted to the idea of becoming data scientists, often equating it to coding models and considering them cool. However, in the age of Auto ML, individuals no longer add value solely through model building. Instead, you must possess a comprehensive understanding of the business problem, connect it with data, handle complex data preparation tasks, identify relevant models, derive insights, and effectively communicate with business teams to inspire trust and adoption. Developing well-rounded expertise will make you valuable in leadership roles. Additionally, consider exploring different roles within the field to gain diverse experiences, such as business analysis, project management, solutions, and advisory.

  2. Focus on Your Soft Skills:

    In addition to technical proficiency, soft skills play a significant role in leadership. It is said that, when a message is presented in the form of a story, people are 20 times more likely to retain and take action upon it. So, develop your storytelling abilities to effectively convey your message and make a lasting impact. Problem-solving is another critical aspect of data analysis – focusing on asking the right questions that address the root of the problem. Utilize frameworks like the “5 Whys” to develop your problem-solving skills. Remember, the ability to connect data insights to business and industry problems is essential.

  3. Make Sure You Are Heard:

    Women often face credibility challenges in the workplace. Use every opportunity to showcase your capabilities, generate powerful ideas, and prove their effectiveness. Advocate for yourself, be confident in your abilities, and don’t shy away from negotiating salary raises and promotions. Remember that your worth is not determined solely by others but by your own belief in yourself. Claim your seat at the table and assert your value.

  4. Build Your Personal Brand:

    Creating a strong personal brand sets you apart from the competition and helps build trust with prospective clients and employers. Start by developing your presence on professional networking platforms like LinkedIn. Consistently craft and curate your digital presence, showcasing your expertise and sharing valuable insights. Be authentic, transparent, and honest in your interactions. Cultivating your personal brand requires consistent effort and will pay off in the long run.

  5. Seek Help and Mentorship:

    Recognize that as a woman, you may have multiple responsibilities in life. Instead of trying to do everything on your own, seek and accept support. Begin by involving your family and ensuring they understand the importance of your career. Prioritize what matters most to you and delegate or seek assistance for other tasks. Look for mentors both within your organization and in external women-leadership communities. Role models within your family and professional circles can also provide inspiration and guidance. Remember that mentorship can come in various forms, and actively seek individuals with whom you can relate.

Closing the gender gap and increasing women’s representation in leadership roles in Data & AI requires concerted efforts not just from organizations but also from individuals. By being open and flexible, focusing on soft skills, asserting your value, building your personal brand, and seeking help and mentorship, you can empower yourself to break barriers and thrive in leadership positions.

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One of the biggest challenges facing tech organizations today is not wealth creation, but building a culture that will enable wealth creation for all its stakeholders, including the primary stakeholders, its employees. In my experience, a workplace where employees feel valued, are free to innovate, can achieve work-life balance, and have opportunities to learn and grow their careers will automatically see the company grow exponentially. Hence, a positive organizational culture is increasingly the focus of organizations today.

Traditionally, HR was seen as the custodian of organizational culture. But increasingly, we are seeing an awareness that managers and business leaders need to share this responsibility. They have a direct influence on what an employee experiences at the workplace. HR and business hence need to partner to create a positive organizational culture.

This is easier said than done. In the current era of rapid change and uncertainty, short-term business priorities will always take precedence over what is a long-term investment in building a culture. As managers, however, there are certain basic steps that we can all follow so that we keep moving forward on this journey. Sharing some

  1. Listen and communicate honestly

    Too often, as leaders, we are so caught up in strategies that we forget to listen to our teams, who will actually execute those strategies, and being the closest to the customers, will have the best insights that can guide us. I recently observed that in a staff meeting, I ended up speaking for 20 minutes out of the 30 minutes planned. It is important that we give our teams the opportunities to speak and spend more time actively listening to them. While the team is closer to the ground, managers have a better view of the organizational imperatives. Hence, these interactions have to be two-way with managers responding basis the larger view at the organizational level.

  2. Keep it light and simple

    I feel that we tend to take our work too seriously. I am sure that all of us have experienced meetings that are held with the utmost gravity. I believe in keeping things light, as a small smile can help break the ice, and a dash of humor improves the communication and creativity of our teams. A little bit of humor also goes a long way in boosting morale and making people feel good about their work and their colleagues. But like all things in life, humor should also be in the right balance. It should be used to enhance interactions and not distract.

  3. Encourage inclusivity where everyone’s opinions are heard

    A feeling of being included leads to diverse viewpoints coming out leading to better outcomes, promoting a sense of ownership, fostering respect and trust, and encouraging innovation. As managers, it is important that we create an inclusive and safe environment where people feel safe to voice their opinions.

  4. Lead by example

    Something we all know but very difficult to implement. I have a simple rule that I follow. In any interaction, I behave with others the way I would want them to treat me if I was in that role. This guides my behaviours and actions.

  5. Promote a healthy work-life balance

    I recently read a report that workplace burnout is on the rise. Burnout is slowly becoming an increasing cause of attrition. Employee well-being should be our top priority as managers, and we need to ensure that we keep a lookout on how stretched our team members are and balance accordingly.

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Amidst the fiercely competitive business environment, organizations constantly need to attract and retain skilled professionals who possess both technical expertise and a deep understanding of business principles. This unique amalgamation of expertise enables companies to connect technical solutions with strategic decision-making. In this article, I explore effective strategies that companies can adopt to successfully identify, attract, and retain individuals who possess this highly desirable blend of technical and business skills.

The Value of Technical and Business Acumen

Before delving into the strategies for finding and retaining talent with both technical and business acumen, it is important to understand why such individuals are highly valued in today’s business world. Technical acumen refers to deep knowledge and expertise in a specific technical domain, such as data science or software engineering. Business acumen, on the other hand, encompasses the understanding of how businesses operate, including financial analysis, strategic planning, and market dynamics.

Employees with technical and business acumen play a crucial role in driving innovation and achieving strategic goals. Their ability to not only grasp technical complexities but also align them with overall business objectives is invaluable. These individuals can effectively communicate with cross-functional teams, translate technical concepts into business terms, and propose solutions that address both technical and strategic challenges. They serve as a bridge between technical teams and executive leadership, enabling seamless collaboration and informed decision-making.

Identifying Talent

Craft Clear Job Descriptions – The first step in finding talent with technical and business acumen is to craft clear and comprehensive job descriptions that explicitly state the desired skills and qualifications. This clarity will help attract individuals who possess the right blend of technical and business acumen.

Widen Recruitment Channels – To increase the chances of finding individuals with technical and business acumen, companies should consider casting a wider net in their recruitment efforts. This can involve exploring alternative recruitment channels, such as industry-specific conferences, online communities, and professional networking platforms. By leveraging a diverse range of recruitment channels, companies can tap into talent pools that may not be reached through traditional recruitment methods.

Conduct Skill-based Assessments – Traditional interviews and resumes are often limited in their ability to assess a candidate’s technical and business acumen. To overcome this limitation, companies can incorporate skill-based assessments into their hiring process. These assessments could include technical problem-solving exercises, case studies, and business simulations. By evaluating candidates’ skills in real-world scenarios, companies can gain a better understanding of their ability to apply both technical and business acumen.

Attracting & Retaining Talent

Competitive Compensation Packages – To attract and retain talent with technical and business acumen, companies must offer competitive compensation packages. This includes a combination of salary, benefits, and potential for growth. Offering salaries commensurate with the market and providing opportunities for professional development and advancement are key factors in enticing individuals to join and stay with a company.

Professional Development Opportunities – Employees with technical and business acumen thrive in environments that foster continuous learning and growth. Offering opportunities for professional development, such as training programs, mentoring relationships, and access to industry conferences, not only helps individuals enhance their skills but also signals to potential candidates that the company values and invests in their development.

Engaging Work Culture – A positive and engaging work culture is crucial in attracting and retaining talent with technical and business acumen. Companies should foster an environment that promotes collaboration, creativity, and autonomy. Encouraging cross-functional collaboration and employee input in decision-making processes can create a sense of ownership and empowerment, making employees more likely to stay with the company.

Clear Career Pathways – Providing clear career pathways is essential for retaining talent with technical and business acumen. Employees want to know that their contributions are recognized and that they have opportunities for growth and advancement within the organization. By regularly communicating career progression opportunities and actively supporting employees in achieving their career goals, companies can significantly increase their chances of retaining top talent.

Bridging the Business Value Gap

In today’s data-driven business landscape, companies that secure talent with technical and business acumen gain a distinct advantage. These individuals bridge the gap between technical solutions and strategic decision-making, driving innovation and growth.

Remember, effectively identifying, attracting, and retaining top talent requires a deliberate approach: clear job descriptions, diverse recruitment channels, skill-based assessments, competitive compensation, professional development, engaging work culture, and clear career pathways. With these strategies in place, companies can build high-performing teams that deliver technical excellence and strategic value.

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I had an opportunity to participate in the 9th Annual Big Data & Analytics Summit in Toronto, Canada – that brings together both the public & private sectors to have a dialogue on the latest trends, business successes, innovation, and related capability requirements. This was my first exposure in the Canadian market as we continue to expand our reach to service clients in North America with business-centric data & AI solutions. Here are my takeaways from the summit:

  1. Good decisions made on bad data, are just bad decisions you don’t know about yet

    SCOTT TAYLOR – The Data Whisperer has a unique perspective on how to master the language of business data which is essential to ensure your leaders are getting the results from your D&A investments. He advocates pursuing data “truth before meaning” – in other words – you must first do the hard work around data quality, master data, & related disciplines before you move to the more glamorous ones around machine learning & AI.

    And if your business leader or CFO is unable to fund the “truth”, then you need to get inspiration from Scott on how to make your case, so they get it! He provides linguistic tools & guidance to connect any company’s strategy with their data management strategy. You may want to explore his book for the nuggets of wisdom pulled from his experience working with organizations such as Nielsen, D&B, & WPP/Kantar. Or better yet, have him speak at your company!

  2. If your CEO is unhappy with the results from the CDAO role, you may have your operating model wrong

    Despite widespread adoption of the CDO/CDAO role in various industries, a 2023 Data & Analytics Leadership Executive Survey finds that only 35.5% of organizations report success in the CDAO role and only 23.8% report they are doing enough to ensure responsible & ethical use of data within their organization & industry.

    So, it was refreshing for me to hear a success story from Sandeep Kumar, MMA, CFA, CDAO of the Retail & Wealth business at Scotiabank. Close alignment between data & analytics, technology & business teams; focus on practical analytics; and collaboration with finance were key to their success. Dealing with legacy technology, being proactive with data & ethics, and assimilating talent were their main challenges.

    A key observation was that the CDAO role reports to the Chief Risk Officer in the bank with a clear delineation from the CTO. Clearly banks are in the business of managing risks, so it made sense. But an even more compelling model would be for the CDAO to report into Enterprise Strategy/COO, or directly to the CEO. Unless you are in the technology business, keep the CDAO reporting separate from the CIO/CTO to bring data, adoption, & value closer to business.

  3. Initiate Responsible AI in your organization to prepare for the inevitable regulations

    The rapid advance of generative AI has drawn the attention of regulators across the world. Stanford university’s 2023 AI Index shows 37 AI-related bills were passed into law globally in 2022. More regulations such as EU AI Act & Canada’s Bill C-27 are coming up. These regulations seek to broadly define AI, protect individual rights, classify risks, and require governance measures including internal processes, impact assessments, and penalties.

    Several speakers – from government to private sector to technology companies – emphasized the need for business leaders to adopt Responsible AI in their organization. Responsible AI helps companies design & operate systems that align with their values & widely accepted standards of right and wrong. It means embracing principles of fairness, privacy & security, ethics, accountability & transparency throughout an AI project lifecycle as shown by Scotiabank. It appeared that Financial & Insurance sectors in Canada were the early adopters whereas public sector may be in the late majority.

    Recommendations include creating & instilling data ethics framework; explicitly reviewing decisions where humans are on the loop, in the loop or out of the loop to balance accountability, safety, and speed; and taking advantage of various consortia. For example, IBM has an explainability toolkit & more recently Singapore Monetary Authority released Veritas Toolkit 2.0 for financial institutions. If done correctly Responsible AI can help companies scale their AI efforts, identify & fix problems early, and facilitate innovation for business.

  4. Design explainability into AI models to simplify conversations with business & gain acceptance

    It was very interesting to listen to Eric Lanoix, FRM, an applied mathematician & problem solver, on the topic of increasing customer, business, and government trust in the use of AI in high impact & regulated applications like credit underwriting. If you approve or disapprove someone’s credit application using an AI recommendation, you better be able to explain why to auditors & regulators – and for that your model needs to be fully interpretable.

    High performant models such as Neural Networks are low in interpretability & will not pass muster with emerging regulations. He shared an example formulation of a fully interpretable basic ExNN model resembling logistic regression using three factors – credit score, asset price, income – affecting credit decisions. I continue to realize the importance of business translation skills in ensuring stakeholders understand AI. Eric’s Quantitative Risk Analytics team offers innovative quantitative finance solutions to both internal & external partners including an innovative model-as-a-service. Worth exploring further.

  5. Innovation has different forms – you can innovate with approach, design, tool, or technology

    Innovation to me means finding valuable ways to solve new & existing problems. I’ve often heard that innovation requires new technology, more funds, or a lab – while it’s true that some innovations require these resources, it is often more important to be resourceful & persistent. Here are four useful ideas that I gathered from the event that may spark innovation:

    • Olga Tsubiks, Director, Strategic Analytics & Data Science, RBC shared her thoughts on driving strategic workforce planning using big data. One of her main ideas was to look at the state of your business (rapid growth, steady growth, decline, reinvent) & then use relevant data (market leads, client opportunities, workforce data, and acquisition data) to approach planning insights. Infocepts Employee360 supports such analysis.
    • Paul Moxon, Chief Evangelist, Denodo, shared his thoughts on adopting a logical data architecture design due to fast-growing data & rapid technology evolution. Organizations should accept that the future is distributed & diverse & separate business view of data from physical data storage. This can be done through design of data fabrics in organizations that connect to various systems to get real-time data.
    • Almost everyone has data quality challenges. Vicky Andonova, Anomalo, shared their thoughts on why legacy data quality approaches no longer work when scaling data quality. I saw a demo of the tool & could appreciate why rules- & metrics-based approaches are no longer sufficient. You must take advantage of automated machine learning models & Anomalo has a noteworthy tool for unsupervised data monitoring.
    • Microsoft introduced Fabric & its capabilities. Between OneLake, OpenAI Service & an integrated ecosystem, Microsoft is enabling data & AI innovation to help businesses get results faster while claiming to lower costs. The speed at which these technologies are evolving should prompt IT leaders to rethink how they choose to invest & innovate with technology. Questions in the room gave me a sense that several businesses are wondering if their data would be used beyond their own needs & boundaries.
  6. Purpose matters

    Finally, I want to close by sharing observations from two thought-provoking sessions:

    Teresa D’Andrea, Director General, Service & Data Modernization, Transport Canada – Transports Canada shared their approach for service design & modernization. It is grounded on the concept of designing for humanity where everything we build is to service some end user goal while rules, tools, and data are a means to fulfill them. I appreciated their focus on “consistent user experience” & giving the internal teams the time & space to evolve the backend to meet the user needs. How many leaders think like that?

    Dan Kershaw & Vanitha Lucas shared the partnership story between Viz for Social Good & the Furniture Bank. This is an excellent example where data & the power of storytelling is used to influence human behaviors – raise awareness & funds – to bring impact. What’s amazing to me is that the work here was done by volunteers – including Hemal Sheth, an Infoceptian – from 7 countries, each perhaps driven by the purpose to do good.

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80% of executives believe that automation can be applied to any business decision, according to a recent Gartner survey. Businesses are automating a wide range of business processes and operations, from simple and repetitive tasks to complex and mission-critical operations.

Companies have been working to become more data-driven for many years at this point, with mixed results – only 26.5% of companies indicate that their organization is data-driven. Automation tools directly impact brand success and are frequently adopted and integrated by businesses to stay competitive within their industry, and enable data-driven transformation. Data-driven automation enables businesses to improve operational efficiency, make better decisions, and deliver an enhanced customer experience.

Automation projects can be a slippery slope – if not executed properly, it can adversely impact data processes, usage, employee confidence and the customer experience. To realize the value of automation, data and analytics must champion data-driven automation as a strategic thread of business DNA, not a tactical one-off project.

As businesses look for opportunities to modernize their processes and optimize operations via data-driven automation tools, they must first develop a meaningful strategy. This requires a well-planned approach that includes clear objectives, appropriate technologies, and the right skills.

The first step in building a strategy for data-driven automation is to define clear objectives. These objectives should be aligned with the organization’s overall business strategy and should be specific and measurable. A scoring methodology can help businesses rate opportunities for automation according to business impact while sustaining an ongoing backlog for prioritization.

Organizations also need to have the necessary tools & skills in place to support their automation strategy. They must carefully consider which technologies are best suited for them – like robotic process automation (RPA), artificial intelligence (AI) or machine learning (ML). Having experts such as data scientists, engineers and specialists on board will guarantee faster results.

In short, data-driven automation is no longer a luxury but a necessity for today’s organizations who are looking to thrive in an ever-changing market.

Check out the full article for data-driven automation usecases and more steps for successful implementation.

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I had the privilege of speaking and actively participating in thought-provoking discussions in the recently concluded Data Engineering Summit 2023. In this article, I share key insights from my own talk, as well as my takeaways from the keynotes and engaging conversations I had with fellow data enthusiasts at the summit.

  1. Smart Data Engineering is flipping traditional approaches – Intelligent systems, techniques, and methodologies are being employed to improve Data Engineering processes and provide clients with added value. Organizations are dedicating resources to implementing cutting-edge AI technologies that can enhance various Data Engineering tasks, from initial ingestion to end consumption. The emergence of Generative AI is transforming the way data is analyzed and utilized in organizations. While it is currently revolutionizing the consumption side of the industry, the pace of developments indicate that it will soon have a significant impact on Data Analytics workloads. This shift towards Generative AI will pave the way for new approaches to Data Engineering projects in the upcoming quarters, resulting in increased efficiency and effectiveness.

  2. FinOps will be a game changer – As companies move their Data and Analytics workloads to cloud-based platforms, they are discovering the potential for costs to go out of control without careful management. Though various solutions exist, few provide a sufficient return on investment, leaving customers in search of fresh methods to manage expenses across cloud infrastructure. FinOps provides monitoring teams with tools they need for cloud cost screening and control while promoting a culture of cost optimization by increasing financial accountability throughout the organization. CFOs are especially pleased with this development and are keen on spreading this cost-conscious approach.

  3. Data Observability is not a buzzword anymore – Mature organizations are proactively utilizing observability to intelligently monitor their data pipelines. Unforeseen cloud charges can arise from occurrences such as repetitive invocation with Lambda or the execution of faulty SQL code, which can persist unnoticed for prolonged periods. The implementation of observability equips operations teams with the ability to better comprehend the pipeline’s behavior and performance, resulting in the effective management of costs associated with cloud computing and data infrastructure.

  4. Consumption-based D&A chargeback is the way to go – Shared services teams are encountering challenges when it comes to accurately charging their internal clients for their utilization of D&A services. The root of this problem is attributed to the lack of transparent cost allocation mechanisms for data consumption, which makes it difficult to determine the genuine value of a D&A service. The solution lies in implementing consumption-based cost chargeback, which not only addresses the current challenges but also prompts businesses to adopt more intelligent FinOps models.

In summary, the summit provided valuable insights into the latest trends, challenges, and opportunities in the field, highlighting the importance of collaboration, innovation, and upskilling. There are many exciting developments that promise to revolutionize the industry. As we move towards a data-driven world, it is clear that data engineers will play a crucial role in shaping our future, and it is essential that they stay informed, adaptable, and agile to keep up with the rapidly evolving landscape.

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