Big data is useless without analytical skills

Big data is useless without analytical skills

The advent of the big data era means that analyzing large amounts of messy and unstructured data will increasingly become part of everyone's job. Companies often ask managers and business analysts to use data to perform experiments, interpret data, and develop creative ideas to build data-based products and services. To get ahead in this world, many people need additional skills. Do your employees have the necessary skills to benefit from big data? In their article on the rise of data scientists, Tom Davenport and DJ Patil pointed out that in the era of big data The advent of the Internet means that analyzing large amounts of messy and unstructured data will increasingly become part of everyone's job. Companies often ask managers and business analysts to use data to perform experiments, interpret data, and develop creative ideas to build data-based products and services. To get ahead in this world, many people need additional skills. Some companies that must grapple with big data have identified a need for employees with relevant skills. A new survey report from Avanade shows that more than 60% of respondents said their employees need to develop new skills to turn big data into insights and business value. Anders Reinhardt, head of Global Business Intelligence at VELUX Group, an international manufacturer of skylights, solar panels and other roofing products based in Denmark, believes that “ The standard training method, where we only learn to explain to business users how to access data and reports, is no longer enough. Big data demands far more from users than before." Senior managers in many industries are developing plans to upgrade the skills of their employees. They told me that employees need to: Be willing and ready to experiment: Managers and business analysts must apply the principles of scientific experimentation to the businesses they are responsible for. They must know how to construct clever hypotheses. They also need to understand the principles of experimental testing and design, including population selection and sampling, to assess the validity of data analyses. As randomized testing and experiments become increasingly common in the financial services, retail, and pharmaceutical industries, people with a background in scientific experimental design are particularly valued. Google recruiters understand that experimentation and testing are integral to the company's culture and business processes, so they ask candidates "How many golf balls can fit into a school bus?" or "How many sewer covers are there in Manhattan?" Questions like that. The purpose of asking these questions is not to find the correct answers, but to test the candidates' skills in experimental design, logic and quantitative analysis. Good at mathematical reasoning: How many managers in your company today are truly “number-savvy” and good at interpreting and applying numerical data? This skill will become increasingly important. “Business users don’t have to be statisticians, but they need to understand how to use statistical methods appropriately,” explained VELUX’s Reinhardt. “We want business users to understand how to interpret data, measures, and the results of statistical models. "Some companies hire people out of necessity and have determined that they are good at mathematical reasoning. Capital One's recruiting efforts emphasize hiring people with strong analytical and number-crunching skills and assigning them to all levels of the business. Candidates, including senior senior executives, must pass a rigorous interview process that tests their mathematical reasoning, logic and problem-solving skills. Ability to see the big (data) picture: You might call this “data processing ability”, which refers to being good at finding, manipulating, managing and interpreting data. The so-called data includes not only numbers, but also text and pictures. Data processing capabilities must expand far beyond their usual IT functions and become an integral aspect of every business function and activity. Procter & Gamble CEO Bob McDonald believes that "data modeling, simulation and other digital tools are transforming the way we innovate." Such developments have changed the skills his employees need. In response to this challenge, P&G "creates benchmark digital skills scales tailored for each promotion level in the organization."

Verulux has made it a priority to develop data processing skills training courses for business users. Managers need to know what data is available and use data visualization techniques to process and interpret the data. “Perhaps most importantly, we need to help them imagine how new kinds of data can lead to new insights,” Reinhardt noted. Tomorrow's leaders will need to make sure their employees have these skills and build the culture, support and accountability to match them. In addition, when organizations no longer have only a few information technology experts and statistics Ph.D.s, but instead employ many employees analyzing messy, complex, and large amounts of unstructured data, they must feel comfortable taking on the leadership role. responsibility. Another challenge: the potential for employees to download and mix data, raising concerns about data security, reliability and accuracy. But research I’ve conducted has found that employees have become more responsible for the technology, data and applications they use at work. Employees must understand how to protect sensitive corporate data. Leaders need to learn to "trust, but verify" the analysis performed by employees, and work with employees to find the problem when the analysis fails. To ensure that big data can generate big value, companies should not only adopt new technologies, but also retrain skills to develop a data-focused mentality and analytical culture. The companies leading this revolution already have employees who are dedicated to experimentation and understand numbers and data. Are you ready to join their ranks?

The above is the relevant content shared by the editor about the uselessness of big data without analytical skills. For more information, you can follow Global Ivy to share more Dry information