Alternative data: clean dataset and why it’s impotent
Today, more than 80% of the data is unstructured. Data is being produced as we speak – from every conversation we make in social media to every content generated from news sources. In order to produce any meaningful actionable insight from data, it is important to know how to work with it in its unstructured form.
Data and data scientist work in the financial industry
Investors are looking at everything from satellite images of store parking lots, to trending topics on social media, to key events, to product release dates and many more as leading indicators of market movement. For these investors, the approach to doing “great research” has morphed from just reading all available analyst reports to taking a methodical approach to combining potential sources of insight to reach the objective, statistically based conclusions and make smarter decisions. It’s imperative for investment managers to constantly be on the lookout for data streams that can lead to smarter investments.
Structured and unstructured, traditional and alternative data are the bread and butter of data scientists who are professionals with the capabilities to gather large amounts of data to analyze and synthesize the information into actionable signals. It is highly complicated work that produces a mindful output, and with an average yearly salary of $100-150K, every min of their work is count. The reality is that most data scientists spend most of their time cleaning data. According to several studies, Data scientists spend on average 60% of their time on cleaning and organizing data.
David Blackwell, head of client analytics, at UBS Wealth Management, estimated in a study that as much as 70% of analysts’ time can be spent on managing raw data, cleaning it and preparing it for analysis. “This means that only a fraction of analyst work-hours is left to extract insights and guide strategic decisions,” he added. In other words, data scientist who works with different traditional and alternative datasets to find the alpha gener¬ation potential, spend a massive portion of their time on challenges like data connectivity, data cleaning, varying quality.
What is involved in data cleaning?
Data cleaning, also called data cleansing, is the process of ensuring that your data is correct, consistent and useable by identifying any errors or corruptions in the data, correcting or deleting them, or manually processing them as needed to prevent the error from happening again.
Incorrect or inconsistent data can create a number of quality issues that lead to the drawing of false conclusions. Therefore, data cleaning can be an important element in some data analysis situations.
Data cleaning comes in all shapes and sizes and there is no one template to handle all situations, but key factors must be in place:
Accuracy – The degree to which the data is close to the true values.
Completeness – The degree to which all required data is known.
Consistency – The degree to which the data is consistent, within the same data set or across multiple data sets.
Uniformity – The degree to which the data is specified using the same unit of measure.
What can be done to reduce data scientists’ time on cleaning datasets?
1. Outsource the cleaning stage to contractors
2. To use NLP in the cleaning process to save time.
3. When buying external datasets, make sure that the data is structured, clean, and linked to tickers.
To conclude, while hedgefunds and asset managers recognize the alpha generation potential of traditional and alternative datasets, they face many challenges like data connectivity, data cleaning and varying quality which reduces their new dataset’s beck testing capacity.
Using big data and NLP technologies to capture alpha by collecting, structuring, and revealing events from news articles, press releases, and financial social media.
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