IV. The management accountant getting useful hold of Big Data.
‘Big Data’ is not simply a buzzword or hype that will fall into oblivion in the following years. Instead, Big Data already has, and will, gain further deep implications for the business world.
Nonetheless, as it still forms a relatively young technological trend, there is not a precise definition of what Big Data is. To put it simply, Big Data can be seen as the mass volumes of data being externally and internally generated through the boundless interaction of devices and applications with their specific users, while measuring all kinds of imaginable and unimaginable data, and using the internet as an intermediary. Ever more businesses across different sectors access the world of Big Data, whether to improve the efficiency of their business operations or to pursue more strategic purposes.
So, as an example, Big Data can directly flow into an intensified predictive approach of planning and analyzing (also read Part 1, “Raising awareness of predictive analysis by management accounting”). While change processes, and the management of change itself, quite often prove to be complex and complicated matters, more available data might assist in handling these issues (read Part 2, “The management accountant taking on the role of a change agent” as well). Furthermore, companies can strengthen their competitive position by gaining Big Data that yields, for instance, insights into consumer behavior, and identifies customers’ needs (also see Part 3, “Management accounting focusing on the company’s customers”).
As the examples above show, Big Data impacts the management accounting discipline significantly. Part 4 of our four-part article series about the evolution of management accountants’ role, therefore, sheds light on the implications of Big Data, and explains how management accountants can adapt their role and master the Big Data challenge successfully.
To start with: Where and how is Big Data generated?
Big Data is generated in a multitude of external and internal sources, coming from literally everywhere. It does not comprise only operational output from financial transactions, smartphone and mobile apps usage, social media screening (of Facebook, Twitter, etc.), review data (e.g., Amazon and TripAdvisor reviews), point-of-sale terminals, or common sensor technology. “Connected/smart devices” (also called “internet of things”, or IoT, devices) are also becoming an increasingly hot topic. That means physical objects, buildings, or vehicles are getting equipped with software, electronics, sensor systems, and wireless network connectivity, allowing them to collect and share data.
Just to give some keywords, Big Data accrues from the following items: cameras, satellites, thermostats, sensors, houses, automobiles, search engines, televisions, smartphones, vending machines, printers, GPS devices, points-of-sale scanners, credit card transactions, online videos, garage doors, plants and work machines, medical monitoring devices, or other wearable technology like smartwatches.
As practical examples of Big Data generation based on sensor technology, just think about modern cars and their equipment with several dozens of sensors that measure and transmit data ranging from fuel efficiency, interior temperature to tire pressure and cruise control. In the future, houses will be equipped with numberless sensor devices that, for instance, would turn on the air-conditioning system based on the homeowners’ anticipated homecoming. Or, an empty refrigerator will automatically result in the purchase of preferred vegetables at an online grocery store.
Possessing and processing that Big Data, as well as aggregating it and drawing correlations can mean building a competitive advantage against competitors—for instance, by knowing the (prospective) customers and their individual habits/preferences much better, segmenting consumers, predicting demand, optimizing prices, and eventually, offering them appropriate, targeted products and services on a “need” basis. In the manufacturing sector, advanced analytics based on Big Data can help identify potential faults in equipment and product anomalies, optimize process efficiency and product quality, as well as to install a preventative maintenance schedule, inventory parts management, and production planning, for example.
To put it straight, the beginning of that development has already happened. As more connected technology finds its way into our everyday lives and impacts our behavioral patterns—each of it generating vast amounts of digital breadcrumbs—Big Data will grow exponentially. To be successful in the future, every organization will need to avoid ignoring the data or losing track of the Big Data challenge.
Management accountants’ role with Big Data—the status quo
Is there a direct threat for management accountants to fall behind these developments and changes initiated by Big Data? For the moment, we have to state that there is still plenty of room for improvement in embracing that Big Data wave. Thereby, one of management accountants’ main problems is the nature of the data itself, which is mostly unstructured—or in other words, does not directly fit into tables, Excel spreadsheets, and eventually, financial reports.
Usually, management accountants are trained and inclined to work with structured data. Mainly unstructured Big Data, however, is in need of additional analysis due to its sheer volume, great variety, velocity of data generation, and erratic quality compared with traditional and structured financial data (or rather accounting transactions). Unless these analyses are performed, Big Data should not be used to make key operational, pricing, performance, strategic, and other decisions.
Referring to the quality of Big Data, for example, one has to be aware of potential fraud (fake online reviews, etc.) or different interpretation of words’ meaning (e.g., software programs cannot determine sarcasm used in a review). Assessing Big Data’s usefulness and desired quality can be a tough challenge. Altogether, that might make it necessary for management accountants to learn new skills and new analytical tools.
Which data competencies do businesses need to benefit from Big Data?
Each company trying to cope with Big Data needs the ability or skills to analyze the data input, as well as the data analyzing talents who understand the drawn insights and can create an additional value out of it. Oversimplified, we can especially distinguish between data scientists and “data champions”.
As Big Data usually comes along with great volume and complexity, the analysis of these datasets requires data scientists who have the technical ability and are skilled in an advanced level of analytics. That is, for example, data mining, predictive analyses, forms of data visualization, the building of patterns and creation of relationships, as well as the derivation of algorithms. Besides, data scientists also need to ensure a proper level of data integrity. This means, among other things, that the systems and processes should collect the relevant data correctly, and store (sensitive) data properly.
According to predictions, businesses will face a shortage of advanced data skills related to data scientists in the future. However, that shortfall might be eased by an increased usability and advanced options of future analytics software, for example, data visualization tools, which might help more non-data scientists conduct data analysis on their own.
While data scientists are trained in handling Big Data, they are usually not skilled to transform their findings into a direct value for their organization, as they often lack practical understanding of its business operations. Data champions, on the contrary, are the individuals capable of doing this. They are qualified to “translate” or interpret the analytical insights, and finally—by dint of their business acumen—to convey them as commercial insights, which the organization can understand, implement, and benefit from (enabling decisions based on evidence).
In using their wide range of competencies and creating commercial value, data champions intensively collaborate with the management, data scientists, as well as finance and IT professionals. In the end, they show that Big Data can become an important asset to enhance the long-term competitive position and develop new strategies.
Like the data scientist’s profession, in the data champion’s field of activity, too, a shortage of adequately skilled managers, consultants and analysts—who understand the data and make decisions based on it—is expected. Shouldn’t these job market forecasts mean excellent job prospects for management accountants?
Which key roles can management accountants play in the Big Data era?
To point it out at the beginning, management accountants will not thrive from the boom in Big Data without at least a modest level of adaption to the new working requirements. But then—as they already have many of the competencies needed to realize the potential of Big Data—their job prospects can be rated as quite promising.
Usually, management accountants are numerate and skilled in basic (mostly descriptive) analyses because of their traditional work experience. However, to meet the challenges of analyzing Big Data streams—or to become a data scientist—they normally need to invest in further education to gain an even greater skill set in the techniques of statistics and advanced data analytics. Not every management accountant will embrace such a transition and be inclined to work in such a technical, quantitative, and highly specialized field, which is often quite different from the traditional management accounting sphere.
Data champions, on the contrary, need many skills that are already in the individual skill set of a management accountant. These skills are also inherent in the role model of being “business partners” for the management—a role which management accountants themselves, at least in parts, have already adapted to in the past.
Very often, management accountants have a good understanding of the operational business and its information systems. Based on appropriate business acumen, they can diagnose and communicate the business’s needs, and therefore, are competent to determine which data is needed to properly support decision-making and manage performance.
Furthermore, they are already sensitized to the general importance of data quality, accuracy, structure, and relevance. Besides, according to their day-to-day routine, management accountants are used to collaborating with the management and professionals of other departments and divisions. Not the least, they are qualified to deliver meaningful insights and draw the right conclusions from the available datasets—which have been provided by the in-house data scientists—on their own, eventually challenging defective assumptions as well.
Therefore, management accountants seem to be best suited to become data champions of Big Data within their organization, thanks to their already existing potential and well-developed core competencies. They can take over an important role in ensuring Big Data achievements and business prospects in today’s time, as well as in the future data-driven era, while supporting the exploitation of the value of data. In addition, that could also position them ahead of other disciplines in favor of a continuing career development and success.
Read the previous parts of that article series about the evolution of the management accountant’s role:
I. Raising awareness of predictive analysis by management accounting
II. The management accountant taking on the role of a change agent
III. Management accounting focusing on the company’s customers
About this blog
Must-read blog posts about management accounting and financial control—classical topics, as well as modern subjects, latest trends, and current challenges in the management accounting discipline. Aimed to inform, inspire, and entertain management accountants and anyone with a deeper interest in management accounting.