“Data Science is 99% preparation, 1% misinterpretation.” – @BigDataBorat
The most time consuming aspect of big data analytics is sanitizing data even before the smallest nugget of insight can be extracted out of it. It is also a routine chore most data scientists hate. In fact, a survey reveals that 76% of data scientists view data wrangling as the least enjoyable part of their job.
That effectively makes data wrangling the least sexy aspect of the sexiest job of the 21st century. The survey revealed that data preparation can take up 80% of the average data scientist’s work load. Which means they can invest only 20% of their $100 k and upwards salaried time for actual work. You’d think ‘that does not make any sense’ and yet that’s the reality for those who get their hands dirty so you can get your hands on actionable insights.
If you are wondering why companies are throwing so much money for such little output, you’ve only scratched the surface. Those who invest in big data projects eventually realize that all their spent time and money hasn’t made them agile to thrive in a highly competitive space. Marquee big data solutions can act like good repositories of information, but talk about real-time analytics and they fall flat.
It’s the data scientist’s job to collect data from every critical information silo. He is the guy who ensures that all dates are in the same dd/mm/yyyy or yyyy/mm/dd format. He’ll fix currency values to US dollars. He’ll deal with random blank spaces and incorrect values too before he can give you any insights on that one question you asked him. Now imagine him sifting through millions of rows of data every single time. No fun, but it needs to be done.
Enterprises today shouldn’t have to worry about sanitizing data, not when a new breed of tools are available to help with this. These tools automate common data wrangling issues so both data scientists and business users can give their best. Enterprises won’t have to settle for just 20% output from their data scientists and business users won’t have to wait days and weeks for insights.
DataScout is one such tool that takes the pain out of data wrangling. It is a self-service BI tool that’s built by a team of data scientists who have rich experience in extracting insights from marquee enterprise systems. Our scientists know the common pitfalls; they know the tricks of the trade and they’ve built a tool that does the dirty work so IT can get on with its job.
DataScout is a business analyst on tap for the average business user who just wants to get stuff done. Data wrangling is a means to an end and we’ve kept it that way. Business users will be pleasantly surprised to discover how easy it is to get powerful insights. If you can operate a mouse, you are ready to go.
DataScout’s self-service BI capabilities are built on a big data analytics platform that is an amalgamation of clever algorithms and machine learning capabilities. Data wrangling is done automatically, you just select the sources and timelines you want and DataScout does the rest.
Here’s a quick look at how DataScout supports your data-driven efforts:
- Discovery: Sophisticated statistical processes scaled via cloud computing sift through your data and allow key insights to reveal themselves in hours, not days or weeks.
- Transformation: Shared services organizations produced mountains of data, often referred to as exhaust. By converting this exhaust into insights, DataScout can effectively change the perception of the shared services organization as a ‘cost-center’ to a group that adds substantial value.
- Charting: Unlimited charting options like line chart, pie chart, bar chart etc. are available so you can take charge of your data the way you want and when you want.
- Export to Dashboard: While you are using DataScout to take complete control of insights you need; you can export insights you find are worthy of more attention on dashboards. This facilitates faster team based decision making.