Spectrum

<p>In a world where data reigns supreme, organizations are increasingly recognizing the immense potential of harnessing data to drive decision-making and innovation. Yet, a recent survey by NewVantage Partners reveals a stark contrast between the aspiration to be data-driven and the reality on the ground. While 93.9% of surveyed executives expect to increase their data investments in 2023, only 23.9% of organizations consider themselves truly data-driven. The question arises: where is all this investment going, and why do so many companies struggle to embrace a data-driven future?</p> 
<p>The answer, it seems, lies in the realm of people. Cultural issues, cited by 79% of these executives, emerge as the primary roadblock to realizing the vision of a data-driven future. This article delves into the importance of prioritizing people and the role of Python and accessible data tools in achieving a truly data-driven organization.</p> 
<h2 class="wp-block-heading" id="empowering-people-with-data">Empowering people with data</h2> 
<p>Gartner analyst Svetlana Sicular offered two fundamental insights into the world of data that continue to hold true today. First, organizations already have employees who possess an intimate knowledge of their own data, often more than data scientists do. Second, learning complex data technologies like Hadoop can be less daunting than mastering a company’s intricate business processes. To make the most of data, companies must empower their existing workforce to ask intelligent questions of their data.</p> 
<h2 class="wp-block-heading" id="accessible-data-tools">Accessible data tools</h2> 
<p>One key strategy to achieving this empowerment is to lower the barrier to programming literacy. While specialized data tools can be daunting, the most valuable “tool” in any employee’s arsenal is their understanding of the company’s business. Employees who are experts in their field can pose more meaningful questions to company data.</p> 
<p>In this context, it’s crucial to focus on making data tools accessible to a broader range of employees. Microsoft Excel, a widely used tool, should be promoted as a key component of data analytics. Recent initiatives to use Excel for data transformation have gained traction. With a much larger user base proficient in Excel than in more specialized tools like TensorFlow, enabling employees to do more with a tool they already know represents a significant win.</p> 
<h2 class="wp-block-heading" id="python-the-universal-language-of-ai">Python: The universal language of AI</h2> 
<p>Python, a versatile programming language, has emerged as a powerful enabler of AI productivity. While other languages like R and specialized tools hold value, Python stands out as the driving force behind AI’s widespread adoption. Python’s accessibility and versatility make it the ideal choice for an enterprise looking to democratize data-driven decision-making.</p> 
<h2 class="wp-block-heading" id="sql-the-unsung-hero">SQL: The unsung hero</h2> 
<p>SQL, often overshadowed by its flashier counterparts, proves to be indispensable in the data landscape. A recent IEEE Spectrum analysis identified Python and SQL as the two most popular programming languages. Python leads the pack, and for employers seeking to hire data professionals, SQL is the most sought-after skill. By emphasizing Python and SQL, organizations tap into skills that many employees already possess, eliminating the need for steep learning curves associated with new data technologies.</p> 
<p>Generative AI, or GenAI, presents another avenue for empowering employees to work with data effectively. While tools like ChatGPT aim to automate tasks, it is essential to remember that the technology is only as good as the people using it. Integrating GenAI tools into workflows requires careful consideration to ensure that AI-driven responses align with technical accuracy and user expectations.</p> 
<h2 class="wp-block-heading" id="prioritizing-people-over-technology">Prioritizing people over technology</h2> 
<p>The NewVantage report highlights a recurring trend: the principal challenges to becoming a data-driven organization are primarily human-related, such as culture, people, processes, and organizational issues, rather than technological barriers. Despite recognizing these human challenges, organizations often invest heavily in non-human issues like data modernization, AI, and various data architectures.</p> 
<p>In the quest to become truly data-driven, it is crucial for organizations to recognize that the most valuable asset is not the data itself but the people who interpret and leverage that data. These employees already work within your organization, armed with a deep understanding of your business. To unlock the full potential of data, the key is to empower them with accessible data tools, such as Python and SQL, and to foster a culture that embraces data-driven decision-making. By prioritizing people over technology, companies can bridge the gap between aspiration and reality, paving the way for a more data-driven future.</p>

Unlocking the Power of Data to Prioritize People and Python in AI

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