Data Agents Boost­ing data-dri­ven deci­sion making

One of the key suc­cess fac­tors of a mod­ern orga­ni­za­tion is the abil­i­ty to make data-dri­ven and fast deci­sions through­out the organization.

Tra­di­tion­al data ana­lyt­ics solutions 

Dash­boards and reports are a crit­i­cal start­ing point for any orga­ni­za­tion in their data jour­ney. How­ev­er, they have cer­tain lim­i­ta­tions in terms of flex­i­bil­i­ty, dis­tri­b­u­tion and time­li­ness.

• Flex­i­bil­i­ty: Reports have a fixed for­mat opti­mized for answer­ing a set of pre­de­fined ques­tions. They can become con­fus­ing if over­loaded with infor­ma­tion and details. As a result, reports are always a trade-off between pro­vid­ing details and pre­sent­ing the big pic­ture. In prac­tice, they can­not be designed to answer “every­thing.”
• Dis­tri­b­u­tion: Reports are built into spe­cif­ic apps such as Pow­er BI, and users are required to nav­i­gate in these apps specif­i­cal­ly. Often users have sev­er­al reports they use and must find the cor­rect one to answer the ques­tion at hand. There is always fric­tion when nav­i­gat­ing e.g. from the typ­i­cal MS Office envi­ron­ment to exter­nal app.
• Time­li­ness: Due to tech­ni­cal lim­i­ta­tions in report­ing solu­tions, they typ­i­cal­ly con­tain data from the pre­vi­ous day back­wards and do not reflect the cur­rent sit­u­a­tion exact­ly. For very oper­a­tional needs, this is not suf­fi­cient, and users need to rely on get­ting data direct­ly from ERP or oth­er IT sys­tems to under­stand what is hap­pen­ing right now.

Data Agents unlock­ing new val­ue in data

To boost data-dri­ven deci­sion-mak­ing, espe­cial­ly in more oper­a­tional, fast-paced roles, orga­ni­za­tions can start to har­ness the ben­e­fits of AI-dri­ven Data Agents.

A Data Agent is a Large Lan­guage Mod­el-pow­ered intel­li­gent appli­ca­tion equipped with a set of crit­i­cal data ana­lyt­ics tools. In prac­tice, users chat with the Data Agent in Teams using nat­ur­al lan­guage and can ask detailed ques­tions about their dai­ly oper­a­tions. Think of a Data Agent as a super­charged, per­son­al data ana­lyst always at your dis­pos­al.

Exam­ple:

Under the hood, the Data Agent trans­forms the user’s ques­tion into data­base queries, exe­cutes them, and final­ly trans­lates the numer­i­cal results back into nat­ur­al lan­guage. By com­bin­ing the unprece­dent­ed flex­i­bil­i­ty of Large Lan­guage Mod­els, tra­di­tion­al code exe­cu­tion, and the wealth of data in the organization’s data plat­form, we can achieve amaz­ing results.

Every oper­a­tor, man­ag­er, and leader in the orga­ni­za­tion can get answers to their ques­tions relat­ed to the organization’s oper­a­tions in sec­onds instead of hours or days. Data Agents democ­ra­tize data uti­liza­tion in the orga­ni­za­tion and pro­vide a mas­sive boost towards holis­tic, data-dri­ven deci­sion-mak­ing at all levels.

Data Agents com­ple­ment tra­di­tion­al dash­boards and reports by offer­ing much-need­ed flex­i­bil­i­ty, time­li­ness, and ease of use. When a prompt answer to a spe­cif­ic ques­tion is need­ed, a Data Agent is always ready to help.

Imple­ment­ing Data Agents into pro­duc­tion-lev­el use cas­es is not sci­ence fic­tion. They are ful­ly with­in the grasp of every orga­ni­za­tion, and use cas­es are plen­ti­ful — from Pur­chas­ing to Cus­tomer Ser­vice to Sales and beyond. The ques­tion is not whether your orga­ni­za­tion will start uti­liz­ing Data Agents, but when. The most sig­nif­i­cant com­pet­i­tive advan­tage can be achieved by adopt­ing them now.

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The blog author is Antti Luo­to, an expe­ri­enced Data Archi­tect well versed in tech­nolo­gies such as Data­bricks, Synapse, Data Fac­to­ry, Delta Lake, rela­tion­al data­bas­es, Pow­er BI, Azure func­tions and Azure Ope­nAI Service.