The term “knowledge worker” was first coined by Peter Drucker in his book, The Landmarks of Tomorrow (1959). Drucker defined knowledge workers as high-level workers who analyze data and apply theoretical/analytical knowledge to develop insights, trends, products, and services. He noted that knowledge workers would be the most valuable assets of a 21st-century organization because of their high level of productivity and creativity. “Organizations that rely solely on the IT department or analytics teams to fulfill queries around analytics are likely to be dissatisfied with the results”, says Alan Jacobson, Chief Data and Analytics Officer (CDAO) at data science and analytics firm Alteryx. Data is no longer a byproduct of transactional systems. Applications and technology need to be designed around an understanding of what data is needed to make better-informed, data-driven business decisions. The principle of silos or least privilege security model does not enable data-driven decision-making. Enterprises should make sure knowledge workers have access to the up-to-date and timely data they will need to run analytics, identify trends, and make informed business decisions. Organizations should make sure that knowledge workers have access to the best data tools.
Knowledge Worker Tools
There are several tools that a knowledge worker uses to develop insights, trends, products, and services. They might include data dictionaries, SQL query tools, python packages, data visualization tools, data manipulation tools like PySpark and analytics, and perhaps machine learning tools. The challenge for knowledge workers is that they often need to combine the use of many of these tools to perform their work. The tools often are not integrated, don’t run in the same environment, and are not on the same computers. Knowledge workers are often forced to check data dictionaries, run SQL tools and export the data to a file, log into a new computer, and transfer the file to that computer so that they can run Python packages and then export the Python results to a file again. Knowledge workers then recheck data dictionaries before feeding the file to machine learning tools and then exporting the result again to a file so that the file can be used as input to a data visualization tool. If you think this sounds disjointed, anti-productive, and an impediment to using data to make better business decisions then you would be correct.
BigQuery Studio
BigQuery Studio provides a single, unified interface for all data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. It also allows knowledge workers to use simple SQL to access Vertex AI foundational models directly inside BigQuery for text processing tasks, such as sentiment analysis, entity extraction, and many more without having to deal with specialized models.
Enabling Knowledge workers
BigQuery Studio makes knowledge workers more productive and decreases the time it takes them to develop insights, trends, products, and services. Bigquery decreases the cost of specialty computing engines for SQL, Python, and Machine learning. BigQuery simplifies working with data sets and visualizing results. The bottom line is that BigQuery Studio makes knowledge work more productive and helps them generate more revenue, save more cost, and increase customer satisfaction.
Perficient’s Cloud Data Expertise
The world’s leading brands choose to partner with us because we are large enough to scale major cloud projects, yet nimble enough to provide focused expertise in specific areas of your business. Our cloud, data, and analytics team can assist with your entire data and analytics lifecycle, from data strategy to implementation. We will help you make sense of your data and show you how to use it to solve complex business problems. We will assess your current data and analytics issues and develop a strategy to guide you to your long-term goals.
Download the guide, Becoming a Data-Driven Organization with Google Cloud Platform, to learn more about Dr. Chuck’s GCP data strategy
