Max­imis­ing the val­ue of finance func­tions through big data

 

Organ­i­sa­tions world­wide, includ­ing finance depart­ments, are using data to boost the effi­cien­cy of exist­ing process­es, dri­ve oper­a­tional costs down, achieve strate­gic excel­lence, and mon­i­tor and improve per­for­mance. And accord­ing to a study by the Busi­ness Appli­ca­tion Research Cen­tre, they are gain­ing ben­e­fits from big data such as bet­ter strate­gic deci­sions, improved con­trol of oper­a­tional process­es, an increase in rev­enues and reduc­tion in costs. 

Digi­tis­ing trans­ac­tion­al process­es have pro­duc­tiv­i­ty ben­e­fits asso­ci­at­ed with them and can also help cre­ate new datasets for ana­lyt­ics, which in turn enables finance func­tions to deliv­er new rapid insights. This can ele­vate the function’s rel­e­vance and val­ue to the organ­i­sa­tion. How­ev­er, the right ground­work needs to be put in place for the trans­for­ma­tion to take root. 

The pow­er of data 

Data ana­lyt­ics is a broad term that encom­pass­es a pletho­ra of diverse tech­niques and process­es which draws insights from finan­cial and non-financial data to improve oper­a­tional deci­sions that are crit­i­cal to an organisation’s success. 

By digi­tis­ing fit-for-purpose process­es with cutting-edge tech­nolo­gies, finance func­tions can pro­vide organ­i­sa­tions with eas­i­ly acces­si­ble and trans­par­ent data and reporting. 

This in turn will give organ­i­sa­tions the capa­bil­i­ty for data-driven deci­sion mak­ing, which will enable a more forward-thinking and agile finance function. 

For exam­ple, when finance organ­i­sa­tions auto­mate their process­es, they can free siloed data and allow for increased data shar­ing between all depart­ments with­in the organisation. 

With data ana­lyt­ics, KPIs can be iden­ti­fied, mea­sured, improved, fil­tered and arranged accord­ing to what’s most rel­e­vant at any need­ed time. 

Process digi­ti­sa­tion can also increase work­force pro­duc­tiv­i­ty, which in turn improves cost effi­cien­cy. Trans­ac­tion­al process­es, for one, stand to gain the most. 

The chal­lenge of data 

One of the biggest data chal­lenges faced by organ­i­sa­tions is turn­ing the del­uge of raw data into action­able insights. CEOs and CFOs are look­ing for quick, easy, and auto­mat­ed ways to obtain them. 

To tru­ly extract val­ue from their data, organ­i­sa­tions must address the fol­low­ing challenges: 

• Reg­u­la­to­ry com­pli­ance  
  Finance func­tions must ful­fil strin­gent reg­u­la­to­ry requirements. 

• Data secu­ri­ty  
  Data gov­er­nance mea­sures can help mit­i­gate risks com­mon in finance such as hack­ing, unau­tho­rised access, cor­rup­tion, and theft. Top-tier big data man­age­ment tools can help ensure that data is secure, pro­tect­ed and monitored. 

• Data qual­i­ty  
  Organ­i­sa­tions want to do more than just store data. Extract­ing val­ue from unstruc­tured data sets requires col­lat­ing and com­pil­ing var­i­ous sources of information. 

• Data silos  
  The inabil­i­ty to con­nect data across depart­men­tal and organ­i­sa­tion­al silos is a major busi­ness intel­li­gence chal­lenge, lead­ing to com­pli­cat­ed ana­lyt­ics that stands in the way of big data initiatives. 

• Data usage 

Amer­i­can astronomer Clif­ford Stoll once said, “Data is not infor­ma­tion, infor­ma­tion is not knowl­edge, knowl­edge is not under­stand­ing, under­stand­ing is not wisdom.” 

One of the biggest short­com­ings of dig­i­tal ini­tia­tives is the fail­ure to use the tech­nol­o­gy. Despite all the hype about arti­fi­cial intel­li­gence, human intel­li­gence is still required to inter­pret data, iden­ti­fy gaps in per­for­mance, estab­lish root caus­es and actions, and to see that the actions are exe­cut­ed suc­cess­ful­ly. This requires much more than installing an app to fix. 

Regard­less of the chal­lenge, organ­i­sa­tions must thor­ough­ly define their over­all data roadmap — one that lays the ground­work for how data is han­dled and its role in help­ing the finance func­tion address its issues and shortcomings. 

The roadmap should include: 

• Sys­tems or struc­tures to cap­ture data 
• A com­mu­ni­ca­tion plan to obtain buy-in from employ­ees so that they can under­stand their role in a data-driven organisation 
• A plan to fill miss­ing skillsets and capa­bil­i­ties to inter­pret and devel­op data-driven insights 
• A strate­gic plan to col­lect, organ­ise and use the right data in a way that will add val­ue to the organisation 

As dif­fer­ent organ­i­sa­tions have dif­fer­ent ideas of what they want from their finance func­tion, the roadmap will be unique for every organ­i­sa­tion. Some organ­i­sa­tions may want the finance func­tion to be more proac­tive and focused on analy­sis or plan­ning. Oth­ers may require their finance func­tions to be more trans­ac­tion­al, or they may find a need to reduce trans­ac­tion­al and report­ing activ­i­ties and shift it towards higher-value activ­i­ties such as strat­e­gy or decision-making sup­port. This may call for process reengineering. 

By tak­ing a strate­gic approach towards data, the finance func­tion is well on its way to becom­ing a proac­tive part­ner that deliv­ers val­ue to the organisation. 

How­ev­er, while data and ana­lyt­ics is a great enabler, finance trans­for­ma­tion also involves trans­form­ing sys­tems, process­es, the organ­i­sa­tion struc­ture and the cul­ture of the finance func­tion itself to be successful.

Dis­cov­er the frame­work to imple­ment suc­cess­ful finance trans­for­ma­tion in our white paper, Win­ning Finance Trans­for­ma­tion: Har­ness­ing the Full Val­ue of the Finance Func­tion.