Why Business Intelligence -projects fails?

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The­re are seve­ral rea­sons why a BI-pro­ject might turn sour. Here are a few tips on why that might be -and how to make your pro­ject a success­ful one.

THE TECH GETS THE BETTER OF YOU
Your IT -depart­ment and busi­ness mana­ge­ment most like­ly have a dif­fe­rent point of view to what BI looks like, and how your new BI pro­ject should sha­pe up. If the dia­lo­gue between tech -orien­ted con­sul­tants, and prag­ma­tic busi­ness mana­ge­ment is ina­dequa­te, the end result might not ser­ve busi­ness pur­po­ses or end users as well as inten­ded.

Problem sol­ved: Don’t lea­ve it up to your IT -depart­ment alo­ne to plan and purc­ha­se BI. It is not strict­ly just an IT -pro­ject. Get your mana­ge­ment invol­ved, make sure your IT and busi­ness mana­ge­ment unders­tand each other’s needs. Plan an ove­rall BI -process with clear goals and then choo­se the tech to suit it.

THE PROJECT DOESN’T HAVE A CLEAR GOAL OR FRAMEWORK TO START WITH
If eve­ry­one wants eve­ryt­hing imme­dia­te­ly, it most like­ly means no-one is going to get a thing. Trying to sol­ve eve­ry problem in the very begin­ning will overflow the size of your pro­ject befo­re you even get star­ted. Whilst your pro­ject is stretc­hing on eve­ry cor­ner, you will be spen­ding time in mea­ningless mee­tings with your tar­ge­ted sche­du­le, bud­get, and most of all goals, tur­ning to dust. Wit­hout a clear game plan, pha­ses of progres­sion, and tar­gets, you’ll end up with a bund­le of use­less data models to answer none of your ques­tions.

Problem sol­ved: Imple­ment your BI -pro­ject in clear pha­ses with indi­vi­dual goals to suit the ove­rall tar­gets. Con­si­der for example, imple­men­ting each busi­ness area or orga­niza­tio­nal func­tion indi­vi­dual­ly -finance, ope­ra­ti­ve, top mana­ge­ment, HR etc.

YOU CAN’T PREDICT THE FUTURE – OBJECTIVES ARE NARROWED DOWN TOO MUCH
A BI -pro­ject is always agi­le. A sequence of ite­ra­tions. An ongoing, revol­ving process.
Be care­ful whi­le defi­ning your needs and the answers you want from your busi­ness intel­li­gence -solu­tion. Nar­rowing down and restric­ting your cur­rent needs, might make your life dif­ficult in the futu­re. The answers your brand new solu­tion is pro­vi­ding might rai­se brand new ques­tions and pro­ve your cur­rent solu­tion ina­dequa­te as it is. You’ll face an unfor­tu­na­te situa­tion, whe­re your finis­hed solu­tion hasn’t been desig­ned to pro­vi­de answers to the­se new­ly found enqui­ries and exten­ding your solu­tion means basical­ly retur­ning back to point A.
This kind of a pro­ject com­pa­res to a wave hit­ting a wide sho­re­li­ne (eve­ryt­hing for eve­ry­one) but even­tual­ly sli­ding back to the star­ting point.

Problem sol­ved: When plan­ning and execu­ting the pro­ject gat­her as much data as you can, even though it might not seem rele­vant at the time. Try to see a few steps furt­her. May­be you should start trac­king his­to­ry or ver­sio­ning data models alrea­dy from the begin­ning. Who knows when you’ll want to see the past and futu­re trends or snaps­hots of data? The execu­tion should start with adding new ele­ments ins­tead of always star­ting from the begin­ning.

QUESTIONS, NEEDS, ANSWERS – SOLUTION ISN’T AT ALL DATA DRIVEN

The solu­tion doesn’t sca­le itself well enough accor­ding to the chan­ging needs. A solu­tion that ser­ves you today might beco­me obso­le­te tomor­row. Not an ideal outco­me, when dea­ling with resource gree­dy pro­jects. You should be able to pro­duce new con­tent and modi­fy the exis­ting enqui­ries wit­hout out­si­de help. Your solu­tion should ser­ve your busi­ness needs wit­hout eve­ry user having a degree in infor­ma­tion tech­no­lo­gy. You should be empowe­red to answer your busi­ness rela­ted ques­tions, not your IT depart­ment or some out­si­de con­sul­tant.

IMPLEMENTING THE NEW PROCESS IS DIFFICULT

Imple­men­ting the solu­tion throug­hout the orga­niza­tion has been incomple­te and the new solu­tion is left on the shelf.

Even if Arti­ficial Intel­li­gence and mac­hi­ne lear­ning are hot topics, they are not going to subt­ract the human from the equa­tion. The solu­tion should invol­ve your who­le team and sup­port discus­sion and com­mu­nica­tion. You can ditch unneces­sa­ry emails and inc­rea­se awa­re­ness as people sha­re disco­ve­ries ins­tead of bar­rica­ding behind indi­vi­dual job desc­rip­tions and num­bers.

The orga­niza­tion should crea­te a mana­ge­ment cul­tu­re whe­re ana­ly­tics isn’t just reports and lists, but ins­tead discus­sion, deci­sion making and fol­low-through based on cor­rect infor­ma­tion.
Aut­hor: Simo Ves­te­ri­nen, CEO of Vision­bay Solu­tions Oy

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