One Article Review
| Source |  AlienVault Blog |
|---|---|
| Identifiant | 880805 |
| Date de publication | 2018-11-05 14:00:00 (vue: 2018-11-05 16:02:33) |
| Titre | Financial Data and Analysis Predictions for 2019 |
| Texte |
https://pixabay.com/en/analytics-google-data-visits-page-3680198/Paste
The use of big data and data from the internet of things (IoT) is changing business so rapidly it is hard to predict what is next, and financial analytics are certainly no exception. While the need for financial analysts continues to rise, the way analysts performs their day-to-day functions is evolving.
More data than ever before is put into the evaluation of company financials, market analysis, and investment predictions. A company’s decision to issue bonds, split stock, or even initiate stock buyback options is much more informed than ever before.
So where is data and financial analytics taking us in 2019? Here is a closer look:
Advanced Analytics and Data Science
https://www.gartner.com/ngw/globalassets/en/information-technology/documents/insights/100-data-and-analytics-predictions.pdf
Data and analytics are more pervasive than ever in nearly every enterprise. They are increasingly the key to nearly every process a business engages in. These statistics tell the story best:
Deep neural networks or deep learning is in 80 percent of data scientists’ toolboxes.
By 2020 more than 40 percent of data science tasks will be automated.
Nearly 50 percent of analytics queries are done via natural language queries (voice) or are auto-generated.
In large part, this is due to wider adoption of artificial intelligence options. What this means for business and the future of analytics is simply this: by the end of 2019, 10 percent of IT hires will be writing scripts for bot interactions.
In fact, according to the McKinsey Global Institute, despite the growth of both data and the use of artificial intelligence to analyze it, most companies are “only capturing a fraction of their potential value in terms of revenue and profit gains.”
Their weaknesses, ones that can be solved with proper data and analytics, are many. Here are a few:
Inefficient matching of supply and demand. Many companies are not taking advantage of analytics that can predict with amazing accuracy seasonal demand and annual lulls.
Prevalence of underutilized assets. Many businesses have assets that sit idle or employees and departments duplicating tasks, something easily determined by honest analytics.
Dependence on demographic data rather than more efficient behavioral data. Behavioral data says a lot more about both clients and employees, and is much easier to use.
Over the next year, more companies will become dependent on analytics, and those companies who do not adapt will be three times more likely to fail.
The Bloc |
| Envoyé | Oui |
| Condensat | “only this 2019 2019: 2020 3680198/paste ability about above according accuracy accurate adapt adoption advanced advantage all along always amazing analysis analysts analytics analyze analyzed analyzing annual approaches appropriate are around artificial assets auto automated based because become becoming before begin behavioral being best: better big blockchain bonds bot both boundaries breed bring bullish business businesses but buyback can can’t capturing certainly changing clients closer cnbc com/2018/09/04/credit com/en/analytics com/ngw/globalassets/en/information combines comes companies company company’s compare complex comprised computing conditions continual continues could counterparts coursework created credit data day decision decisions deep defining demand demographic departments depend dependence dependent despite determine determined diego different directly discussion does done due duplicating easier easily efficient employees end engages engine enterprise equation essentially evaluation even ever every evolving exception expansion expected fact factor fail fed fed’s feeds few: financial financials first forecasting fraction from functions future gains gartner generated getting given global goal going google growth hard has have help here hires honest how html https://pixabay https://www human idle impact increasingly inefficient informed initiate institute intelligence intensity interactions internet investment iot issue ist its itself job just key kind language large latest leads learn learning ledger like likelihood likely look look: lot lulls machines make making many march market matching mckinsey mean means mentioned method millions model models months more most much multiple natural nearly need needed networks neural new next not ones options out over page part pdf percent performs pervasive phds planning play points posed potential power predict predictable prediction predictions predictive prevalence process processing produced profit proper put queries raise rapidly rates rather real recent recently recession related releases relies requires revenue right rise role san saving says science scientists scientists’ scripts seasonal security seems sets setting several shared simply sit smallest solved some something sometimes specialized split stability statisticians statistics stock story suisse supply supporting system take takes taking target tasks technology/documents/insights/100 tell terms than these things this: those thousands three through time times toolboxes training transparent understanding underutilized until updating use uses value vary visits voice way weaknesses what where which who why wider will work writing wrong year |
| Tags | Guideline |
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