Excite­ment about arti­fi­cial intel­li­gence (AI) and its appli­ca­tions is pal­pa­ble, with com­pa­nies eager to dive in.

While busi­ness lead­ers may be ready to explore AI, their data may not.

To uti­lize AI effi­cient­ly, the infor­ma­tion — data— fed to the soft­ware must be ful­ly matured. That upfront prepa­ra­tion is a long-term invest­ment, as AI per­forms strongest with a detailed and com­plet­ed roadmap. How­ev­er, the long-term results can trans­form busi­ness operations.

The chal­lenge we face is that many busi­ness lead­ers need help track­ing down their data or deter­min­ing how much orga­ni­za­tion it needs.

In this arti­cle, we’ll explore the prac­ti­cal steps for improv­ing data matu­ri­ty, com­mon mis­con­cep­tions about AI readi­ness, the role cloud infra­struc­ture plays, and how Michi­gan­Labs helps orga­ni­za­tions over­come these challenges.

Data matu­ri­ty and prac­ti­cal steps to improve it #

1. Track down your data
   • Data frag­men­ta­tion is always going to be a chal­lenge, regard­less of how large or small a com­pa­ny is. It’s impor­tant to know where data is stored to ensure quick access and effi­cien­cy, so take the time to track it down.
2. Ensure data ownership
   • Do you have access to your data? Pass­ing data own­er­ship to a third par­ty may be a quick and easy solu­tion, but it will not ben­e­fit you in the long run.
3. Build safe­guards
   • Data is sen­si­tive infor­ma­tion and there must be safe­guards in place to keep it from get­ting lost, erased or frag­ment­ed. Are you think­ing about cyber­se­cu­ri­ty threats? Do you know who has access? How do you man­age pass­words? These must be con­sid­ered and tend­ed to regularly.
4. Cre­ate stan­dard practices
   • When data comes into your plat­form, you need to know where it is stored, how it is being used, where it orig­i­nat­ed, and how to trans­fer access safe­ly among users. Cre­at­ing a stan­dard of pro­ce­dures that sup­ports your data matu­ri­ty goals and con­sid­ers your data’s secu­ri­ty will keep you orga­nized and on track.
5. Know which data is valuable
   • Data matu­ri­ty is not always about the qual­i­ty or quan­ti­ty of a sin­gle data source, it is also about the breadth of data you have. Do you have the data to accom­plish what you want? For exam­ple, let’s say a com­pa­ny wants to reduce ener­gy costs and only has data on the total elec­tric­i­ty usage of its build­ing. While this helps, it doesn’t pro­vide a com­plete pic­ture because vari­ables like spe­cif­ic equip­ment elec­tric­i­ty use, peak usage times, and envi­ron­men­tal fac­tors also impact ener­gy consumption.

Take these steps to improve your data matu­ri­ty and you’ll see a more effi­cient company. 

Is your data AI-ready? #

Even with­out AI, it’s impor­tant to under­stand what’s going on with your prod­uct. Are you tak­ing care of it and mak­ing sure it’s high-qual­i­ty data? Invest­ing in data matu­ri­ty ensures both hap­pen. When a com­pa­ny is not mon­i­tor­ing and improv­ing on its data, get­ting that data to a mature place is a huge leap and takes a lot of time.
A few areas that pro­long the process include: 

• Review­ing and intro­duc­ing his­tor­i­cal data: What data is impor­tant to keep? What is no longer relevant?
• Deter­min­ing how often (dai­ly, week­ly, month­ly) data comes in: The high­er the fre­quen­cy, the big­ger the invest­ment need­ed for data maturity. 
• Know­ing how much data is collected:<u/> Hav­ing a clear idea of how much you’re work­ing with will help us know where to start. 
• Strate­giz­ing what to do with the data: How do you plan on using this information?

All these steps will ensure your data can max­i­mize AI’s ben­e­fits.

While we’re talk­ing about AI’s benefits… 

…a big mis­con­cep­tion about AI is that it’s a sil­ver bul­let that can solve any­thing. The real­i­ty about AI is it’s only as good as the data it is fed. 

Under­stand­ing how much data you have and how to use it is the best way to use AI to its full capacity.

Cloud infrastructure’s role #

At Michi­gan­Labs, we’re often asked about cloud technology’s role in sup­port­ing data infrastructure. 

Cloud tech­nol­o­gy is a secure net­work of servers that host data. Man­aged off­site by experts, these servers take data-host­ing off busi­ness­es’ hands. 

Uti­liz­ing cloud ser­vices to host your data is one of the best things you can do to improve data maturity. 

In many cas­es, we are pro-cloud. Here’s why:

• Ease: You’re not per­son­al­ly man­ag­ing the infrastructure. 
• Data man­age­ment: Servers, back­ups, and SLA is all tak­en care of.
• Scal­able: Cloud infra­struc­ture is easy to adjust based on growth.
• AI inte­gra­tion: AI train­ing loops can be sup­port­ed by cloud technology.
• Cost: Cloud infra­struc­ture is cheap­er than in-house solutions.
• Per­ma­nence: Pur­chas­ing servers and hir­ing staff for in-house man­age­ment is a big­ger and longer-term invest­ment than hir­ing a cloud company.
• Over­all less risk-averse: Cloud man­age­ment could be the dif­fer­ence between can­cel­ing a con­tract and track­ing down an employ­ee who left. 

A uni­fied data approach is how Michi­gan­Labs pro­vides solu­tions to clients and cloud tech­nol­o­gy is one of the ways we get there. With data always expand­ing and evolv­ing, trust­ing it with a cloud net­work is the best way to orga­nize it so it has room to grow. 

How Michi­gan­Labs over­comes these chal­lenges #

Data matu­ri­ty is key to solv­ing a prob­lem, how­ev­er, most busi­ness lead­ers don’t know how to get there. Lead­ers come to us with requests and learn that data matu­ri­ty is often part of how we solve their requests.

When busi­ness lead­ers see how data matu­ri­ty improves day-to-day oper­a­tions, it becomes a prac­tice they want to con­tin­ue. Teams often start data-focused projects because they see how invest­ing in data matu­ri­ty sup­ports their mis­sion, goals, and bot­tom line.