Article

Data

Unlock­ing the full val­ue of data

June 11, 2021

Over the last few decades, sev­er­al acronyms have sprung up nam­ing essen­tial sys­tems for stor­ing and pro­cess­ing busi­ness data, such as CRM (cus­tomer rela­tion­ship man­age­ment), ERP (enter­prise resource plan­ning) and MRP (mate­r­i­al require­ments planning). 

Com­pa­nies invest a lot of time and mon­ey in these tools, as exec­u­tives know that col­lect­ing data to report on what’s hap­pen­ing in the busi­ness is vital. But invari­ably more can be done with the data to help man­age the business. 

Con­sid­er­ing the huge expense and effort that goes into col­lat­ing data, it’s sur­pris­ing how many organ­i­sa­tions fail to reap the full ben­e­fits. A lot of busi­ness­es have become data junkies, addict­ed to col­lect­ing and stor­ing infor­ma­tion but not putting it to good use. 

The pur­pose of col­lect­ing and analysing data should be to inform decision-making and to guide the busi­ness so it can meet the needs of its cus­tomers bet­ter and become more profitable. 

Busi­ness intel­li­gence vs busi­ness analytics 

So, let’s break it down. What is busi­ness intel­li­gence (BI) and busi­ness ana­lyt­ics (BA)? How do they dif­fer? And why do we need them? 

Busi­ness intel­li­gence (BI) is an umbrel­la term used to encom­pass the process­es, meth­ods, mea­sure­ments and sys­tems busi­ness­es use to analyse raw data. These might include report­ing, auto­mat­ed mon­i­tor­ing and alerts, dash­boards, score­cards and ad hoc queries. Using busi­ness intel­li­gence is a good way to check how well a busi­ness is doing and bench­mark it against past performance. 

Today, arti­fi­cial Intel­li­gence (AI) is pow­er­ing up BI’s con­tri­bu­tion to busi­ness­es, expand­ing its func­tion­al­i­ty by sim­pli­fy­ing data pro­cess­ing and devel­op­ing crit­i­cal insights with pre­dic­tive ana­lyt­ics, machine learn­ing and nat­ur­al lan­guage processing. 

Many of us go for rou­tine med­ical check-ups just to check whether we’re in good health and to pick up any issues that need to be addressed. Mon­i­tor­ing your BI is sim­i­lar, in that it allows you to see how healthy your process­es, meth­ods and sys­tems are and to check whether things are improv­ing or deteriorating. 

Busi­ness ana­lyt­ics takes things a step fur­ther. Put sim­ply, we can use BI to man­age our day-to-day busi­ness. We can then take that same infor­ma­tion and use BA to change the business. 

It’s through BA that we can lever­age the val­ue of the data we have col­lect­ed. So, BI is the first step for com­pa­nies to take when they need the abil­i­ty to make data-driven deci­sions, and BA is the analy­sis of the answers pro­vid­ed by BI to dri­ve the busi­ness forward. 

Min­ing val­ue from data 

BA includes sta­tis­ti­cal and quan­ti­ta­tive analy­sis. It also encom­pass­es data min­ing, which we can use to find out what has hap­pened (descrip­tive analy­sis) and why it hap­pened (diag­nos­tic analy­sis), as well as tech­niques to dis­cov­er whether it’s like­ly to hap­pen again (pre­dic­tive ana­lyt­ics) and opti­mi­sa­tion and sim­u­la­tion to help with decid­ing on the best course of action (pre­scrip­tive analysis). 

So, busi­ness ana­lyt­ics enables lead­ers to make deci­sions based on deep learn­ing rather than instinct. 

As an exam­ple, dur­ing week­ly sales meet­ings, you might review your sales KPIs and use a dash­board to see where you stand against tar­gets, then dis­cuss what hap­pened, when and how. This is busi­ness intel­li­gence. If you fin­ish your review there, you will need to cross your fin­gers that you will reach the tar­get next week. 

On the oth­er hand, busi­ness ana­lyt­ics applies sta­tis­ti­cal mod­el­ling to extrap­o­late from past results what future results can be expect­ed. Using a BA tool will help you pre­dict, if you go on as you are, whether you will reach your month­ly tar­get or not. Clear­ly, this is a use­ful fea­ture to sup­port busi­ness planning. 

The insights gained from BA enable com­pa­nies to auto­mate and opti­mise their busi­ness process­es in four dis­tinct categories: 

• Oper­a­tions analytics 

• Finan­cial analytics 

• Cus­tomer analytics 

• Employ­ee analytics 

Don’t col­lect data for data’s sake 

Busi­ness intel­li­gence and busi­ness ana­lyt­ics should, in the­o­ry, make every­thing vis­i­ble, trans­par­ent and easy to man­age. In real­i­ty, while hav­ing all this deep insight is great, actu­al­ly apply­ing it to mak­ing deci­sions on a day-to-day basis is not always easy. 

Despite acknowl­edg­ing the impor­tance of BI and BA, and invest­ing good mon­ey in them, many busi­ness­es fail to unlock the poten­tial gains avail­able. This may be due to sev­er­al factors: 

Inad­e­quate sys­tems or struc­ture to cap­ture data 

Not only does the organ­i­sa­tion need the right tools to cap­ture data but they need to ensure that the organ­i­sa­tion struc­ture is opti­mised to lever­age ana­lyt­ics. For exam­ple, does data flow freely across the organ­i­sa­tion? Or is it siloed and locked up in paper form, mak­ing it dif­fi­cult to access? 

No strat­e­gy dri­ving the col­lec­tion and organ­i­sa­tion of data 

Some prob­lems with data include unre­li­able or bad inter­pre­ta­tions of data, wrong or too many KPIs. To pre­vent this, there needs to be a strate­gic plan for col­lect­ing, organ­is­ing the right data and using it in a way that will add val­ue to the organisation. 

No buy-in 

Lead­er­ship needs to define and com­mu­ni­cate the vision of what it means to be a data-driven organ­i­sa­tion and how ana­lyt­ics will con­tribute to a business’s health. Only then can there be buy-in from employees. 

Miss­ing skillsets and capabilities 

It may be appro­pri­ate to set up a ded­i­cat­ed team equipped with the right tools and skillsets to enable the organ­i­sa­tion to inter­pret and devel­op data-driven insights. 

Becom­ing a data-driven organisation 

Data is your best friend to sur­vive tomorrow. 

Effec­tive use of busi­ness ana­lyt­ics will help you quick­ly iden­ti­fy key oppor­tu­ni­ties to boost effi­cien­cy, dri­ve prof­itabil­i­ty, improve cus­tomer sat­is­fac­tion and moti­vate employees. 

How­ev­er, for an organ­i­sa­tion to trans­form into a data-driven one, it needs to be equipped with the right skillsets and capa­bil­i­ties, strat­e­gy, sys­tems and struc­ture. Only then can they reap full val­ue from BA and keep pace with a fast-moving, ever-evolving marketplace. 

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