Data analytics is the process of storing, organizing, and analyzing raw data to answer questions or gain important insights. Data analytics is integral to business because it allows leadership to create evidence-based strategy, understand customers to better target marketing initiatives, and increase overall productivity. Companies that take advantage of data analytics reap a competitive advantage because they are able to make faster changes that increase revenue, lower costs, and spur innovation.
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In today&#;s digital world, the ability to make data-driven decisions and create strategy informed by analysis is central to successful leadership in any industry. The Certificate in Data Analytics at Penn LPS Online was created to help you enhance your data literacy and increase your professional opportunities. This Ivy League certificate is not designed to train you to become a data scientist but rather to provide a strong foundation in data analysis techniques that may be utilized in a variety of career paths. Possibilities include business analyst, policy analyst, market researcher, digital marketer, and quality assurance professional.
Read on to explore five key benefits of making data analytics a priority in business.
When businesses have access to the digital footprints of their customers they can learn invaluable knowledge about their preferences, their needs, and their browsing and purchasing behavior. Analyzing data collected from targeted markets can also help companies more swiftly identify trends and patterns and then customize products or services to meet these needs. The more an organization knows about who its customers are and what they want, the better it will be able to grow the customers&#; loyalty, ensure they are happy, and boost sales. If leaders don&#;t take notice, they run the risk of losing their consumer base to a competitor who does.
Whether you&#;re seeking an entry-level or leadership role, it&#;s increasingly apparent that to be successful in today&#;s job market, it is critical that you are able to analyze data and communicate the findings in a way that is easily understood. DATA : Introduction to Data Analytics at Penn LPS Online introduces you to important concepts in data analytics across a wide range of applications using the programming language R. You&#;ll complete this course with a clear understanding of how to use quantitative data to identify problems in real-time, make decisions, and create solutions.
Data analytics also gives companies the power to make faster, better-informed business decisions&#;and avoid spending money on ineffective strategies, inefficient operations, misguided marketing campaigns, or unproven concepts for new products and services. By using a data-driven decision-making model, leaders also set up their organizations to be more proactive in identifying opportunities because they can be guided by the validity of data rather than simple intuition or industry experience. However, it is also important that decision-makers understand that although data may show a certain pattern or suggest an outcome, a flaw in the analysis or collection process could potentially render it inaccurate or misleading.
Once you&#;ve completed the introductory course in data analytics, the next logical step is to enroll in DATA : Intermediate Data Analytics. In this course, you will learn two fundamental skills: survey and experimental research. You&#;ll be trained in every step of the survey research process, including how to design good survey questionnaires, draw samples, weigh data, and evaluate the responses. By the end of this flexible online class, you&#;ll understand how to develop and analyze a randomized experiment and build upon your skills in R programming.
Businesses can also use data to inform their strategies and drive targeted marketing campaigns to help ensure promotions engage the right audiences. By analyzing customer trends, monitoring online shopping, and evaluating point-of-sale transactional data, marketers can create customized advertising to reach new or evolving consumer segments and increase the efficiency of overall marketing efforts. And by taking advantage of these insights on consumer behavior and desires in customer-oriented marketing, businesses can meet and exceed expectations, boost brand loyalty, and encourage growth.
If you are interested in developing targeted marketing or advertising campaigns, it&#;s critical you understand the process by which quantitative social science and data science research is conducted. And that&#;s where DATA : Introduction to Statistical Methods at Penn LPS Online comes in. This course comprises three complementary tracks. In the first, you&#;ll learn the basic tools necessary to perform social science research including descriptive statistics, sampling, probability, and statistical theory. In the second, you&#;ll discover how to implement these basic tools using R. And in the third, you&#;ll study the fundamentals of research design, including independent and dependent variables, producing testable hypotheses, and issues in causality.
Another major benefit to data analytics is the ability to use insights to increase operational efficiencies. By collecting large amounts of customer data and feedback, businesses can deduce meaningful patterns to optimize their products and services. Data analytics can also help organizations identify opportunities to streamline operations, reduce costs, or maximize profits. Companies can use insights from data analytics to quickly determine which operations lead to the best results&#;and which areas are underperforming. This allows decision-makers to adjust their strategies accordingly and proactively anticipate problems, manage risks, and make improvements.
Predictive modeling of data is one of the most sought-after skills in data science because it can help companies strategize future investments, nonprofits organize fundraising drives, or political candidates decide where to focus their canvassing efforts. DATA : Advanced Topics in Data Analytics at Penn LPS Online starts with a comprehensive discussion on basic regression analysis and progresses to more advanced topics in R, such as mapping, textual analysis, web scraping, and working with string variables. You will also learn about more advanced data visualization skills in the class, including how to create interactive data visualizations in an R tool called Shiny.
When it comes to innovation, data analytics allows businesses to understand their current target audience, anticipate and identify product or service gaps, and develop new offerings to meet these needs. Not only can companies use data to track customer feedback and product performance in real-time, they can also track what rivals are doing so they can remain more competitive. Insights from data analytics can also allow organizations to update their existing products or services to reflect changing consumer demands, tweak marketing techniques, and optimize customer services. The enhanced adaptability afforded by big data can mean the difference between thriving or failing as a business.
"The Certificate in Data Analytics taught me how to clean, organize, and analyze data in R with just a few lines of code, which is so much faster than the processes I had been using in Excel. The course content is really well done, and the instructors are excellent. The weekly synchronous sessions kept me on track and helped me master new material and reinforce concepts from previous weeks. These skills will save me a lot of time in my job, and now I feel equipped to keep learning about these topics independently."
- Susan Hassett, Enrollment Systems Analyst, College of Liberal and Professional Studies, University of Pennsylvania
The Certificate in Data Analytics at Penn LPS Online is a 4-course program designed to provide you with a point of entry to gain expertise in the field of data analytics. With flexible scheduling options and no required commute, you can develop your data literacy skills without sacrificing time dedicated to personal and professional responsibilities. The data analytics courses are taught by experienced practitioners, including members of the faculty from the Penn Program on Opinion Research and Election Studies. And the only prerequisites to succeeding in this credential are basic math skills, familiarity with using a computer, and an eagerness to expand your knowledge.
Whether you&#;re looking to immerse yourself in a personal area of interest or upgrade your skills to advance your career, the courses, certificates and undergraduate degree at Penn LPS Online are designed to fit your intellectual and professional goals. Applications and enrollment are open year-round. If you haven&#;t already, complete your enrollment and take the first step toward building your competency in data analytics today.
If was the year the world discovered generative AI (gen AI), is the year organizations truly began using&#;and deriving business value from&#;this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents&#; expectations for gen AI&#;s impact remain as high as they were last year, with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.
Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI&#;most notably, inaccuracy&#;as well as the emerging practices of top performers to mitigate those challenges and capture value.
Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents&#; organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. Looking by industry, the biggest increase in adoption can be found in professional services.
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Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in (Exhibit 2).
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Most respondents now report that their organizations&#;and they as individuals&#;are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development&#;two functions in which previous research determined that gen AI adoption could generate the most value &#;as well as in IT (Exhibit 3). The biggest increase from is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.
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Gen AI also is weaving its way into respondents&#; personal lives. Compared with , respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia&#;Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.
The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents&#;67 percent&#;expect their organizations to invest more in AI over the next three years.
Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations&#;in line with what we found last year&#;as well as meaningful revenue increases from AI use in marketing and sales.
As businesses begin to see the benefits of gen AI, they&#;re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.
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Exhibit 7
Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.
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Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.
The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions: takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs&#;though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents&#; business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.
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Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.
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Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it&#;s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations&#; EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations&#; EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions&#;though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.
To start, gen AI high performers are using gen AI in more business functions&#;an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they&#;re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They&#;re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.
What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.
Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions&#;that is, to &#;shift left.&#; They&#;re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.
In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.
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The online survey was in the field from February 22 to March 5, , and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent&#;s nation to global GDP.
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