Building a data-savvy team is a must-have for companies that need to stay agile and competitive.
Professional Services organizations are under ever-increasing pressure to create an adaptable, talented, and high-performing workforce. Because the core value proposition of professional services firms — providing knowledge-economy insights customized for every client — lies in the skills of their staff, these pressures strike at the core of their business models, and both threaten hard-fought competitive advantages and offer opportunities for enduring change. Consequently, investing in employee skills, training them in the latest technologies and techniques for business, is a central feature of most professional services firms, from accounting to strategic management consulting. Over the past three years, this has become particularly true of data science.
Data science has become a crucial part of businesses across industries, from optimizing internal processes to improving targeted marketing to predicting client trends and much more. Companies need to hire more data scientists, but building a data team without the right support network around them sets a limit to how impactful they can be. Any organization that is committed to becoming more data-driven needs to create future-ready teams across departments that can handle the new era of information.
Hiring additional data scientists is one solution, but as mentioned above, it’s a partial answer to a complex question. There is a wealth of talent that already exists in many organizations – their current workforce. Existing employees are not only familiar with the operations of the firm, but they have proven themselves as valuable members of their team. By upskilling existing employees with the necessary data science skills like data analytics, Python, R, artificial intelligence (AI), and deep learning, organizations are empowering their own people to innovate, communicate, and invest in their mission. The result is a win-win for both the organization and the employee.
We foresee three high-impact, short-term trends for the professional services industry in 2021:
Data Literacy becomes important for managers, not just analytical staff
Today, data is the new language of business, but only 25 percent of employees are fully prepared to use data effectively. Companies handle more data today thanks to increases in computing power, more individualized data tracking, and affordable cloud-based systems. Managers need to understand how to leverage that data by asking the appropriate questions, understanding key data science vocabulary and techniques, and building the right data strategies.
Data-driven decision-making improves business performance. For example, data literacy enables companies to track and understand customer behaviors, but only if the collected information is appropriately assessed and understood. Managers who feel comfortable working with data can gain insights that make business processes more efficient by identifying potential time and cost savings. Data-literate managers can leverage data to solve customer needs, make better hiring decisions, and improve existing services and operations.
Managers need to be able to interpret data from multiple sources and understand the implications and insights with confidence. Without understanding fundamental tools and techniques data, Managers and their executives will miss opportunities or even sign off on costly mistakes based on false assumptions.
Project Management enabled by AI starts to have an impact
By 2030, 80 percent of today’s project management (PM) work, including typical PM functions such as tracking, reporting, and data collection will be enabled by AI-enabled automation. No matter the industry, AI is automating workflow while staying on time, on budget, and on task. With AI-driven tools continuing to emerge, managers who can design and implement AI, Big Data, and Cloud Projects give their organization a competitive advantage.
AI can take over tasks like scheduling, sending reminders, and following up to save employees time. When less time is spent on busywork, managers can improve complex processes and focus on more extensive operational processes. However, to manage AI systems, your managers need to understand the overarching principles behind the technology and the underlying assumptions that the automations are built on.
In addition, AI Project Management training enables you to improve data acquisition and quality with data governance, reduce bias using technical and non-technical steps, and even increase your computing power using available technology. On a strategic level, a holistic understanding of AI allows you to build an expert team to address knowledge gaps and tackle deficits by fostering a forward-thinking environment.
Natural Language Processing completes its transition to mainstream
NLP has exploded into our lives over the past 5 years. Today, more than 1.4 billion people use chatbots, which enable companies to resolve problems quickly and effectively, as their primary means of interaction with companies and suppliers. Voice assistants are becoming ubiquitous across many industries (think Siri, Alexa, Google). Both of these tools leverage natural language processing (NLP) to take in language and generate a relevant response, usually within milliseconds. NLP is becoming a vital component for professional services, as it can help speed up processing of large volumes of text such as grant applications, reports, and transcripts.
Popular voice assistants such as Alexa and Siri use complex NLP algorithms to parse user input, convert input to text, analyze text to provide the relevant answer and convert the answer format back into audio output. But NLP is increasingly enabling higher-complexity and higher-value solutions: NLP is now used to process large amounts of patient electronic health records (EHR) in healthcare and pharmaceutical companies, help bankers and regulators quickly process large amounts of loosely constructed notes and relational materials, and provide detailed insights into consumer sentiment from social media streams.
Managers can apply their understanding of clustering, dimensionality reduction, and recommendation systems to oversee data resources and data projects. As text data sources become more prevalent, companies will need employees with NLP skills to perform text data analysis and build recommendation systems. In addition, these workers will need foundational techniques to quantify text data, including the document-term matrix, tokenization, and vectorization.
Data Science as the central differentiating skill
Professional Services organizations that aspire to superior client impact and enduring competitive advantage, are finding it mandatory to upskill their employees to keep up with data science technologies that drive business initiatives and processes. Supply and demand discontinuities such as those seen during the COVID pandemic, increasing competition for top-quality talent, and client demand for efficient and accurate insights, all show that companies need to keep up with accelerating technological advancements and provide staff the skills to be agile and weather change.
Data science is a driving force behind business innovation and growth. With rapid advancements in data science and technologies like AI and NLP, companies need to train their workforce in these crucial skills to stay nimble and competitive. Not only does data science training prepare your organization to identify new opportunities and revenue streams, but it also lays the groundwork for future data-driven initiatives to ensure that your firm will be well-positioned to be an industry leader.
Guiding a City’s Workforce Towards Data Maturity
Case Study
The City of Dallas envisioned a transformation to a future-ready workforce; however, it had some challenges. Between its aspiration and its realization existed a divide, a data gap. The City approached Data Society in the fall of 2019 to request a training academy to help guide the City’s workforce toward data maturity.
We’ll explore the outcome of the training academy including:
- Making lessons accessible for all learners
- Matching course materials to student interests
- Impactful capstone projects
