Data analysis moves the company forward
As the business environment has become increasingly dynamic and volatile, administrative or support functions in companies are obliged to manage large amounts of data to substantiate management decisions. The need for advanced data analysis requires the mobilization of additional financial resources.
Considered a cost center, will the administrative functions receive this much-needed financial resource? In order to be able to obtain funding for the introduction of advanced analytics, the managers of these teams must identify the factors that increase the operational capacity of the companies.
The importance of advanced data analysis
The increasing importance of advanced data analysis was best highlighted during the pandemic when administrative functions were severely tested by the high rate of requests and the transition to remote work.
Managers of supply departments needed advanced analytics with predictive and prescriptive purpose to guide the decision.
Challenged by the reality of remote or hybrid work, office managers had to analyze the occupancy rate and the remote work rate in order to make the right decisions. At the same time HR managers have turned to advanced data analysis for candidate planning, selection and attraction.
These trends have a transformative effect on companies, and only those with maturity in implementing analytical models and technology can implement advanced data analysis.
What’s next? Labor market studies show that HR professionals expect the demand for advanced data analytics specialists to increase by 73% over the next year.
Identifying the right solution is not simple
In order for each company to know what is suitable for its specific situation, an assessment must be made. Depending on the evaluation result, one of the two ways of implementing the advanced data analysis will be chosen:
- Adoption of tested solutions, or
- Building a custom model
For companies just starting out in advanced data analytics, adopting proven solutions is more appropriate. That way I can adapt examples that have been successfully implemented elsewhere.
The following example will help us understand the differences more easily:
- For the HR department to understand the factors that matter for retention, or the establishment of key factors for workplace performance can be achieved with a standard application of advanced data analysis.
- For companies with an average level of maturity regarding advanced data analysis, the targeted objectives can be the identification of potential candidates and the motivation of employees.
- For companies with a higher level of maturity regarding advanced data analysis, the areas of interest go as far as predictive models of hiring and employee evaluation, predicting the absenteeism rate or succession planning.
While any initiative to adopt advanced data analytics solutions is fraught with the temptation to follow the beaten path for many companies, those that prioritize the means with the highest impact and lowest upfront cost, that leverage existing capabilities early on and exercise their analytical muscle, are able to realize value faster.
Alina Făniță este CEO și Partener al PKF Finconta. A lucrat cu companii multinaționale sau firme antreprenoriale din domenii diverse de activitate, pentru a le oferi servicii de audit financiar, due diligence, restructurări de grupuri, audit intern și alte servicii conexe activității de control intern. Este membră a celor mai prestigioase asociații profesionale din domeniu: ACCA (Association of Chartered Certified Accountants), CECCAR (Corpul Experților Contabili și Contabililior Autorizați din România), CAFR (Camera Auditorilor Financiari) și IIA (Institute of Internal Auditors). A absolvit EMBA Asebuss la Kennesaw State University, a fost trainer pentru cursuri IFRS și este invitată ca expert la numeroase conferințe de business. alina.fanita@pkffinconta.ro
