{"id":2344,"date":"2021-05-02T19:39:16","date_gmt":"2021-05-02T19:39:16","guid":{"rendered":"https:\/\/na-ramenima-divova.com\/?p=2344"},"modified":"2021-05-08T22:23:37","modified_gmt":"2021-05-08T22:23:37","slug":"data-driven-pristup","status":"publish","type":"post","link":"https:\/\/equestris.hr\/eng\/portfolio-istrazivanja-i-uvida\/data-driven-pristup\/","title":{"rendered":"Data-Driven approach"},"content":{"rendered":"
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Data-Driven approach<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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What is a data-driven approach?<\/strong><\/p>

Intuitive solutions conducted with years of industry experience and expertise instil confidence in your clients, as well as the products and services you put out on the market. Being versatile, well\u2011read, highly educated and experienced in your area of business is very important. But if you are driven only by intuitive solutions and educated guesses, being a walking encyclopaedia will not necessarily get you the best results.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Educated guesses and intuition offer the best results, if they are corroborated and based on verifiable data. The basis of a data-driven approach is real, measurable data<\/strong>. Companies which function this way, all base their strategy, planning and decision-making on thorough data analyses and interpretation so they can maximize their effect.<\/p>

A data-driven<\/em> <\/strong> approach allows companies to inspect and organize the insight obtained through data collection to best optimize each part of their business. From user and customer relations to developing new communication and business strategies, establishing protocols, and even developing new products, services and key messages to communicate to the public. Using data to catalyse company actions lets them contextualize and define, not only internal and external communication, but also every other business process. It creates a framework that places the customer at the centre of their business.<\/p>

Dr. sc.Carl Anderson<\/strong>, one of the world\u2019s leading data scientists, states that a company\u2019s data-driven approach consists of five prerequisites:<\/p>

1.) Data Collection<\/strong><\/p>

Data is the basis of this approach. This does not include all data \u2013 just the data which an organization deems relevant to its business. It needs to be timely, accurate, clean, and unbiased. Perhaps most importantly, it must be trustworthy. However, merely collecting and accumulating data does not make your company data-driven. Your employees, especially your data scientists, are crucial in this respect. Their focus, thoroughness and creativity in data research, organization and presentation of data insights all provide you with a new, valid and empirically proven perspective.<\/p>

\u201cGarbage in, garbage out.\u201d<\/em><\/span><\/p><\/blockquote>

\u2013George Fuechsel, IBM <\/em><\/p>

2.) Data Access<\/strong><\/p>

Data must be accessible and queryable. Three characteristics of data need to be combined if you wish to be data-driven: joinability, shareability, queryability. Joinable data allows for the data you use in providing and performing services to be joined with all data from other sources, regardless of you or your clients\u2019 needs. Put simply, your data needs to be processed in a universally compatible way; or at least in a way that it can be easily transformed to be applied on other platforms, or combined with the data from another department. Sharable data requires your data to be universal and adjustable. Then if you need to combine the data on your clients, it can be shared among your departments, as well as be read and interpreted by any individual team member. Queryable data allows for the data to be \u2018dissected\u2019 in a way; the data needs to be organized in a clear, precise way, and needs to be processable. This includes the ability to filter, group, reallocate and redistribute data, transforming from raw data to more specific, more compact datasets and allowing more accurate insights.<\/p>

Djeljivost podrazumijeva da podatci koje koristite moraju biti izra\u0111eni univerzalno i prilagodljivo, kako bi, ako do\u0111e do potrebe za kombiniranjem podataka Va\u0161ih klijenata, mogu dijeliti me\u0111u odjelima, ali i da ih svaki \u010dlan svakog tima mo\u017ee interpretirati. Mogu\u0107nost pretra\u017eivanja podrazumijeva svojstvo podataka da se mogu \u201esecirati\u201c; podatci moraju biti organizirani na jasan i precizan na\u010din te moraju biti obradivi. To uklju\u010duje mogu\u0107nosti filtriranja, grupiranje, prenamjene i preraspodjele kako bi se od velikih, \u201esirovih\u201c baza podataka do\u0161lo do specifi\u010dnih, manjih setova podataka koji daju precizne uvide.<\/p>

3.) Reporting<\/strong><\/p>

Data reporting must be engaging and contextualized; the data needs to be presented within a larger context relevant to the data itself. A 4.1% increase for a given variable does not say much, but when presented like this for instance: \u201cProfit growth from selling product A is 4.1% for the last 15 days.\u201d \u2013 it is much more engaging. Of course, it needs to be absolutely clear why this particular information occurred: this is the very core of a data-driven approach.<\/p>

4.) Alerting<\/strong><\/p>

Alerts are essentially reports about what is happening right now. They typically provide very specific data with well-designed metrics. But like reports, they lack crucial context. There is no causal explanation. Causal contextualization and explanations are the key to solving challenges \u2013 the data needs to be thoroughly examined and analysed repeatedly to get to the heart of a challenge and deal with it adequately.<\/p>

5.) Combining Reports and Alerts for Analysis<\/strong><\/p>

Combining reports and alerts leads you to analysis \u2013 the complete overview of the data on a given topic. Reporting gives you the basis and overview of what happened in the past. However, the data-driven approach does not stop here \u2013 data-driven analyses include thoroughly processing and understanding the data at hand: why each data aspect looks a certain way, why it is like that, what the data points to and how exactly predictive models could be based on your data; also, finding out if experimental business decisions can be made and how to test them in real time. What is a data-driven approach?<\/em> pristup ne staje na tome \u2013 What is a data-driven approach?<\/em> pristup analizi uklju\u010duje dubinsku obradu i razumijevanje podataka \u2013 za\u0161to je svaki aspekt podatka upravo takvi, koji su uzroci tome, na \u0161to to\u010dno ukazuju te kako se mogu izraditi prediktivni modeli, mogu li se donijeti eksperimentalne poslovne odluke te kako iste testirati u realnom vremenu.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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