How a Financial Specialist Can Become a Business Analyst: Skills and Tools
“The best way to switch from the sphere of finance into business-analytics is not to learn from ground zero, but to integrate the past work experience into the new specialization” — Andrew Bush
I am often asked to contribute and advise on projects from an analyst’s perspective. In my view, the need for specialists to fulfill multiple roles within businesses demonstrates the rapidly evolving environment, not only regarding technology, but also in respect to the skill-set and expertise of employees. Managers and specialists use more and more digital tools in their work these days, therefore functions that would usually require the work of both a business analyst and data analyst are frequently merged into a single role. Surely, one would not want to fall behind, but instead, keep themselves updated and acquire new digital specializations according to the times. Before choosing a training course, one should explore which skills of a business analyst are presently relevant and how a financial specialist may develop them.
Process Approach
To think like a business analyst is to think in terms of processes. Ever since humanity discovered a process-driven approach to organization management, several ways to describe these processes have surfaced, such as IDEF*, UML, BPMN, and other notations with associated tools for process automation. In fact, the majority of modern applications for businesses are oriented toward supporting the working process;
- CRM for daily tasks with clients’ data and transactions;
- WMS for inventory control;
- BPMS-systems for setting discretionary processes, for example.
The above-mentioned results in several conclusions:
- The process approach can be applied to almost any task in business. This means that a business analyst possessing this skill will be highly sought in any field, from sales to after-sales support;
- One must deal with the digital model of a process. This is not only a sequential diagram drawn on a board. An important component is an insight on how this process goes, its timing, as well as its quantitative characteristics.
For those of you who would like to address the primary source of information on the process approach, I recommend studying ISO 9001 standard. Frankly speaking, it is somewhat difficult for comprehension, so it is worth getting hold of extended comments, or selecting a separate training course for this purpose.
Analysis procedure
The main benefit for a business that is employing an analyst is not merely their ability to «render» an ideal process in a correct notation, but rather their capability of improving existing processes. There are several processes in play within the company — they need to work with things at hand as well as upcoming challenges.
Consistent and constant improvement is a process itself; an analyst must be able to organize the following:
- Description of the current condition of the process, as well as its quantitative characteristics and metrics — IT tools, processes of extraction and integration of data (ETL), come to the rescue here;
- Data preparation and modelling — this will depend on which metrics should be considered, e.g., the number of processed queries per hour;
- Analysis — this is most often visual, i.e. using a chart, graph, or diagram. However, there is also a system to employ specialists, such as data scientists and for the development of software for one single analytical task.
- Changes in the process (which, according to an analyst, should improve the process). For example, several orders processed by a manager can be increased through selecting a client’s address from the Classifier of Addresses instead of manual searching and inputting;
- Development of monitoring reports. To ensure changes lead to positive outcomes, you must constantly monitor the key figures. This is where the need for administration automation arises.
As a beginner in business analysis, you would likely be involved in only a fraction of the stages. Without visualizing the bigger picture, a valuable contribution into process improvement on your part is impossible and the chances of realizing some improvement will be further away. This is why it is unlikely one can learn the procedure by reading books or watching videos alone — it takes practice and consistency.
Data Modeling
As a matter of fact, accountancy is a data model that has already been mastered by economists, financial experts and managers. However, it is not the only one out there.
De facto standard for business-analytics involves multidimensional analysis and multidimensional data models. You may have heard such terms as «Drill-down», «Measure», «Dimension». They have come to be everyday language but are initially a part of the OLAP system (online analytical processing).
There are many kinds of data models and each of them is specific for its task class:
- Cluster analysis can help find non-obvious groups of clients in your database, e.g., those who are likely to transfer to rival companies within a year;
- Regression analysis helps to predict future sales.
As an introduction to data modelling, I would recommend taking the course of discrete mathematics and the theory of sets. It may sound dull, but mastering basic operations on sets and their correlations is a foundational requirement to understand any complex data model.
Visualization
As correct as the data selection, calculation formulas, and the hypothesis may be — it can all be undone by a poor presentation of data for analyzing. Even after spending hours studying a summary chart, you might not be able to see any dependencies because of an unclear or inappropriate format.
Additionally, some graphs, e.g. those with a logarithmic scale and auto-scaling, can lead you to false conclusions if they are used incorrectly. Worst case scenario, if you are presenting the results of your analysis to launch a new improvement process with unclear representation, your superior would not be able to clearly read the information, and there would be little time to explain or clarify it.
Here is what I can recommend on the subject:
- Books by Gene Zelazny, for example, «Say It With Charts. Complete Toolkit», though you can choose any of the modern books on infographics and data visualization. Using the approaches described in them instantaneously puts you well above an average analyst who is unaware about the importance of comprehensible data presentation.
- For those of you who prefer higher difficulty levels and are ready to acquire an in-depth knowledge of the subject, I suggest books by Edward Tufte, such as «Envisioning Information». This one only has amateur translations, but the author is rightly considered as a “father” of infographics.
Tools for Data Processing
There should be no problems with tools for modelling and process description; these are simple, perceptible, and as easy as MS Excel. Let’s review some tools for data processing.
- SQL. The theory of sets and discrete mathematics should be reinforced by the practice of using SQL — structured query language. It is used to write short scripts for data extraction from databases and their transformation — bundling, intersections, seven, the averages, etc. The knowledge of SQL cannot be presently considered obligatory for a business analyst, but it’s more of an ace in the hole. The industry tends to lean toward self-service in terms of analytical activities, which is why I anticipate this skill becoming relatively basic.
- BI systems. A must-have skill for any business analyst is being adept at using software for visualization and data analysis. Start with Tableau; it can be downloaded to your PC and allows for a free trial within the test period. The most well-paid analysts use Qlik, and the most popular is MS Power BI.
You can learn more about this software in «Top 5 Best Business-Analytics Tools». Consult with your IT-department supervisor; some of these tools might have been already scheduled for implementation in your company.
Mastery of programming languages for data analysis (e.g., R, Python) and such statistics packages as IBM SPSS is not typical for a business analyst. The development of programs and models with specific tasks is a job for data scientists and data engineers.
Practice
The approach of «studying first, then looking for a job» is obsolete, which is why I would recommend constantly acquiring and exercising new knowledge, even if you are in the process of training on practical tasks. Ideally, find on-the-job training, particularly in another company.
Our team also has re-skillers — specialists who have learnt a new trade along the way in the process of working. From cooperating with them, I know that changes in your field of work is a good way to challenge your mind. These chances invigorate you, offer new opportunities, and raises your spirits.
However, a better approach is not resetting and learning from point zero, but instead integrating your past professional experiences into your new specialization. This condition generates a superpower in analysts that allows them not only to press buttons and retrieve diagrams, but also to understand the origins and meanings of key figures in the business.
