What is data scientist and How it relates to auditing?
Defining Data Scientist
Data scientist is a skilled professional adept at extracting valuable insights from complex sets of data. Armed with a combination of statistical prowess, programming acumen, and domain-specific knowledge, these individuals navigate the vast sea of information to uncover patterns, trends, and hidden correlations.
The job profile of the Data Scientist is still young but is often searched for on the job market. They are required in many industries, such as:
• Banking and insurance
• Business and organizational consultancies, market researching
• Social Media, Telecommunications, online trading and network management
• Bio, pharmaceutical, chemical and medical industries
In 2012, Tom Davenport, a Professor at the Harvard Business School, described the competence profile as follows: "a hybrid of data hacker, analyst, communicator, and trusted adviser. The combination is extremely powerful – and rare."
In times of "big data", Data Scientists are experts in demand, who are paid above average and enjoy great freedom in companies as "gold diggers". Using methods of mathematics, computer science and statistics, they gain facts and knowledge from large amounts of data, the “gold of the 21st century”, and discover new business areas. In addition, they are something like interpreters. They formulate the data records into legible results and display the essential information in a comprehensible language.
Skill Set Required
Data Scientists are required to bring diverse skill sets to the table. Proficiency in programming languages such as Python and R, coupled with a deep understanding of machine learning algorithms, forms the bedrock of their expertise. Data Scientists are trained in statistics, graph theory and other mathematical fields and are proficient in methods such as data mining, process mining, machine learning and natural language processing (NLP). Added to this is knowledge of practical computer science. Knowledge of operating systems, databases, networks and data integration tools, as well as the most important programming languages and analytics tools, are mandatory.
Furthermore, knowledge about the Hadoop ecosystem, social networks and other systems from the internet and big data environment is a compulsory requirement for professional practice. The competency profile is that of an all-round talent and accordingly (currently) difficult to find. Also, a keen analytical mind, effective communication skills, and the ability to translate data-driven findings into actionable strategies distinguish an exceptional data scientist.
The Data Scientist and the financial function within the company
The question of whether a controller can assume the tasks of a Data Scientist must be clearly denied in the context of the described competence profile. The current opinion in the industry is that it is illusory to believe that controllers could also assume the tasks of a Data Scientist. However, controllers should know the job profile of a Data Scientist as well as the possibilities and limitations of Big Data. The cooperation between the tasks of a controller and a Data Scientist is an important source for the future economic success of companies.
Data Science and Auditing
Advancement in digitization also places new challenges on internal auditing in the selection of audit methodology. The intersection of data science and auditing is not a mere coincidence; it is a strategic alignment born out of necessity in the digital age. Traditional auditing practices, once confined to financial scrutiny, now extend their reach into the realms of audit data analytics. This evolution is driven by the surge in data availability and the imperative to ensure the integrity and accuracy of information.
In the landscape of auditing, the advent of audit data analytics marks a paradigm shift. Internal auditors now leverage sophisticated data analytics tools to enhance the efficiency of audit procedures. This, in turn, translates into improved audit quality and a proactive approach to risk management.
Data Science offers the possibility to consider the analytics of data masses as a test step within an audit and, in this way, to create an additional benefit. This means, however, that the internal audit department must also acquire expertise in data science in addition to the already acquired competencies, such as finance, business management and compliance. Since an individual auditor can hardly have all the competencies mentioned above, these should at least be available within the team. If necessary, remember to include an external Data Scientist.
Along the lines of internal auditing, external auditing is placed before conditions that were changed by digitization: the flood of data, the appropriate audit methods, and the concern of finding young recruits within the auditors underline the need for efficiency gains. The surge in job advertisements for data scientists in audit centres, as well as the first attempts to use artificial intelligence in this area, underscores this.
Audit analytics can provide several benefits, namely improving risk management, providing greater assurance, and overall helping auditors gain more confidence in their audit findings.
The Data Scientist's Role in Auditing
Enhancing Internal Control
One of the pivotal roles that data scientists play in the audit process is in fortifying internal control mechanisms. By leveraging advanced analytics, they identify anomalies that may elude traditional audit methods. This not only ensures the accuracy of the audit data but also provides a proactive stance in risk mitigation.
Data Analytics for Internal Audit: A Strategic Imperative
In the pursuit of enhancing audit quality, the use of data analytics for internal audit is not a choice but a strategic imperative. Data scientists collaborate with internal audit teams to format and automate the analysis of vast datasets. This not only expedites the audit process but also allows for more in-depth scrutiny of the subject matter of an audit.
Generate Audit Analytics: Empowering Decision-Making
Data scientists play a pivotal role in generating audit analytics that empowers decision-makers within an organization. Through the use of data analytics tools, they sift through massive datasets to generate actionable insights. This proactive approach ensures that audit findings are not just retrospective but serve as catalysts for informed decision-making.
Data Security and Privacy: Paramount Concerns
Analyzing Data Securely
As the role of data analytics in auditing expands, so does the significance of data security. Data scientists not only analyze data but do so with an unwavering commitment to data security. This ensures that sensitive information remains safeguarded, adhering to stringent privacy standards.
Data Privacy in the Age of Analytics
The intersection of data privacy and analytics is a critical consideration. Data scientists navigate this terrain with finesse, ensuring that the use of data analytics aligns with regulatory frameworks and ethical standards. This commitment is not just a compliance measure but a testament to data scientists' responsibility in their role.
The Future: Data Science and Auditing Hand in Hand
As we gaze into the future, the symbiotic relationship between data science and auditing is poised to deepen. Audit firms recognize the transformative impact of data analytics as part of their services. The integration of data analytics and audit analytics is not just a trend; it's a strategic imperative to remain competitive in the digital age.
The role of a data scientist extends far beyond crunching numbers. It is about deciphering the narrative hidden within data and providing organizations with the tools to navigate the complexities of the digital landscape. The synergy between data science and auditing is not just a collaboration; it's a strategic alliance driving organizations toward a future where actionable insights reign supreme.
This feature blog was written by Prof. Dr. Nick Gehrke (Zapliance)