Putting Data to Use
You might remember it from the very beginning of the Data Journey: Insight, value, decision support. Efficiency, automation, new ways of working. These are some of the promises that come with a data-driven approach.
Since then, we have talked a lot about the lifecycle of data, and how data undergoes a journey from situation, to collection, storage, processing, and finally, value. We’ve observed how data is constantly being generated everywhere and how we can go about capturing, storing, and preparing it for use.
All this effort and knowledge—from coding and preparation to data processing agreements and structuring of big datasets—are not only the very tools we need to mine for gold. These are like the paved roads into Klondike. Now that we have spotted the golden veins and filtered out the nickel, let’s take a closer look at our catch!
Insight
What was data literacy, again?
Do you remember the term data literacy from chapter 1? The term sums up the ability to read, understand, analyse, and think critically about data and how it’s used. You don’t need to be an expert in data analysis or visualisation to be data literate. It’s about having a basic understanding that allows you to engage in discussions about data and its implications, ensuring you make well-informed decisions regarding its use.
The last “levels” of data literacy involve using data for visualisation, interpretation, management, storytelling, and ultimately, evidence-based decision making. In this chapter, we’re going to tie all these threads together.
Chapter 5
What you will learn
I dette kapittelet skal vi lære om de siste stegene i livssyklusen, som rapportering, analyse og visualisering av data – og hvordan vi endelig kan ta dataene i bruk for å oppnå mer og bedre innsikt og hente ut verdi.
Fordi dataanalyse kan gjøres på en rekke ulike måter, vil også verktøyene variere ut i fra hva du ønsker å oppnå – for ikke å snakke om graden av kompleksitet i problemene du skal løse. I mange tilfeller vil et regneark gjøre susen. I den andre enden av skalaen kan man bygge egne programmer og maskinlæringsmodeller for spesifikke behov.
Ved siden av å lære om statistikk, analyse og data mining, vil vi her også se på andre måter vi tar data i bruk og skaper verdier.
Her er det mye spennende å dykke ned i!