In today’s all-pervasive digital data world, we are flooded with data, but starving for information. Data are everywhere, but what enterprises require are actionable insights, which can only be obtained by analyzing such data. Data are the most valuable asset of any enterprise—in fact, they are said to be the new oil. But this oil by itself is useless without a proper engine that can use the oil to drive the enterprise to its destination. Data analytics is one such engine that can derive value from data with given objectives. It provides valuable information that can be used to infer insights for practical use in decision-making.
Data Analytics and Digital Trust Professionals
Digital trust professionals perform various roles in the areas of governance, assurance, risk management, compliance, consulting, and technology management and deployment. Such roles primarily involve the application of domain knowledge and expertise to the digital data of the enterprise as needed to ensure that enterprise objectives are achieved. Data analytics is a critical skill for professionals who are tasked with accessing and analyzing digital data. It empowers them to use their domain expertise to create new possibilities for adding value.
There are primarily two types of professionals in the field of data analytics:
- The data scientist/engineer focuses on applying various statistical techniques to data. They are involved in developing intelligent applications that help users draw inferences from data and big data. Data scientists work with unstructured data (e.g., data from Twitter or Facebook) and structured data.
- The data analyst focuses on drawing insights from data from a business perspective. They are a business domain expert who uses simple, easily available features of Microsoft Excel, application software, querying tools, utilities, or data analytics to access, analyze, and interrogate data. Data analysts primarily work with structured data (e.g, data in a tabular format).
Benefits of Possessing Data Analytics Skills
Data analytics involves processes and activities designed to obtain and evaluate data to extract useful information. The results of data analytics may be used to identify areas of key risk, fraud, errors, noncompliance or misuse; improve business efficiencies; verify process effectiveness; and influence business decisions. Analytics is gaining increasing prominence because it is transforming the way business is conducted by providing powerful new insights to inform business decisions. While enterprises have always analyzed data to help support business decisions, the volume, velocity, variety, and veracity of data being analyzed have changed alongside the computing power of new, advanced analytics technologies. But what is also different is the importance of the human element of analytics, which requires professionals to apply thought to technology. Analytics is at the heart of all business decisions. It empowers the modern professional by blending human intelligence and domain expertise with artificial intelligence (AI) and digital data.
Several key benefits of learning data analytics are:
- Learning new digital methodologies of analyzing digital data and being a thought leader.
- Learning to apply new perspectives to new digital platforms and business models.
- Learning to obtain practical actionable insights and inferences from digital data.
- Upgrading skill sets to move to new orbits in one’s professional career.
- Adding tech skills to one’s knowledge and skills repository to accomplish more in less time.
- Learning to provide better assurance to clients, with greater self-assurance.
- Learning new skills to add value in different roles across domains such as governance, assurance, compliance, consulting, risk management, and IT management and deployment.
Types of Data Analytics
Data analytics is defined as, “The science of examining raw data for the purpose of drawing conclusions about that information.” There are four major types of data analytics:
- Descriptive analytics uncover what happened in the past by analyzing data using data analytics, business intelligence and data mining techniques.
- Diagnostic analytics explain why something happened in the past by analyzing data via root cause analysis.
- Predictive analytics propose what will happen in future through forecasting.
- Prescriptive analytics reveal how to make something happen by using optimization and simulation of various scenarios.
The four types of data analysis may be used in tandem to create a full picture of the stories data tell and to help enterprises make informed decisions. Depending on the problem to be solved and the goals to be achieved, one may also opt to use only two or three of the analytics types.
Practical Applications of Data Analytics
Data analytics can answer questions related to hindsight, insight and foresight:
- Hindsight—What happened? How many times? Over what time period? Where?
- Insight—Where is the problem? What actions are needed? Why is this happening?
- Foresight—What if these trends continue? What will happen next?
Data analytics is used for several different purposes. Some use cases of data analytics from various perspectives include:
- Auditors—May seek answers to audit questions such as:
- Is management reporting accurate sales?
- Are receivables/payables complete?
- Are compliance standards being met?
- Enterprise management—May seek answers to questions such as:
- How can we better manage the enterprise?
- Have our information systems captured all sales, purchases, payables, receivables, expenses, and compliance efforts correctly and completely?
- What are the root causes of issues with cash flow and increasing debts or expenses?
When used by management, data analytics is also referred to as business intelligence.
- Fraud investigators—May seek to identify possible fraud by asking questions such as:
- Where is the evidence of fraud?
- What is the audit trail?
- What is the modus operandi (MO)?
- Who is the culprit?
Seven Steps to Performing Data Analytics
The key to becoming a successful data analyst is to develop a data-driven mindset. This helps one identify and understand a problem; identify and analyze the underlying data using the relevant tools and techniques; and recommend data-driven solutions.
There are seven steps to performing data analytics (figure 1):
- Set scope, objectives and deliverables—Clearly define the business challenge or objective intended to be addressed through data analytics. Articulate the purpose and desired outcomes, which should guide data analysis efforts.
- Understand the technology platform and identify data required—The primary purpose of data analytics is to identify and obtain the relevant data from various deployed information systems. The data may be available in different formats depending on the technology platform.
- Document relevant processes, compliance standards and controls—Data are generated by information systems performing business processes. The compliance standards and controls applicable to business processes should be embedded in the information systems. Hence, it is important to understand these and document them for reference as required.
- Design controls and control objectives to analyze—As per the scope and objectives, relevant data and their controls should be identified and analyzed. Each area of business has objectives that require a set of control objectives to be defined. A control objective is an objective, direction or standard that acts as guidance for enterprise interactions and operations (e.g., “ensure compliance with applicable laws and regulations”). Tests should be designed with control objectives at the macro level and specific controls should fall under each of them.
- Obtain and curate relevant data—Data must be obtained in a specific, predefined format. These data may need to be curated to organize them into a standardized structure.
- Use relevant functions and perform tests—Microsoft Excel, Microsoft Power BI, Structured Query Language (SQL) programs and other specialized data analytics software programs offer functions for performing data analytics. The specific functions of the chosen software and technique must be documented and used as required to perform tests for data analysis.
- Analyze findings and report recommendations—The results of data analytics must be analyzed. Based on these results, relevant findings should be used to obtain meaningful and actionable insights. The report documenting these insights should include valuable conclusions and actionable recommendations that can drive business improvements or remedy noncompliance or lack of controls as per the required deliverables.
Conclusion
Data analytics is a must-have capability for every professional whose primary role is to access and analyze digital data. This skill set is widely expected to become a significant part of the professional toolkit of the future. Acquiring the skill set of data analytics optimizes time, knowledge, skills, competency and intelligence. Any digital data in any format can be potential candidates for data analytics.
Every professional is known for the tools and skill sets they possess. They cannot use yesterday's tools and expect to be relevant tomorrow. In the emerging digital tsunami of data, professionals face the key challenge of data deluge. The most effective way to meet this challenge is to use data analytics as required to derive value for enterprises and move up the value chain.
The skill set of inferring insights from digital data will be the key differentiator between professionals who remain relevant and those who fail to keep pace with evolving workplace needs.. Every type of professional can complement their core competencies by developing data analytics skills relevant to their role. This enables them to take advantage of insights offered by digital data and advance their enterprise and career goals. Honing data analytics skills empowers professionals to be better prepared for the digital future and to future-proof their careers.
ABDUL RAFEQ, CISA, CA, FCA
Is the managing director of Wincer Infotech Limited. He specializes in IT governance and analytics.