Today, businesses are in a rat race to derive relevant intuition and make best use of their data. Several notable organizations are skimming with cutting edge data science terms and resolving intricate problems (some being more successful than others).
However, the crux lies in determining the present stage of data science your organization has embraced, followed by ascertainment of the desired level of data science.
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Take a look at the 3 stages of an actually mature data science organization.
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‘Dashboards’ is the source of data science. It helps in answering the crucial questions, like “How much” and “what happened”, just by taking a look at the sheets of historical data. In fact, if it’s well implemented by the company, it may even answer the most-debatable question “why it happened”. Now, due to this specialty, various organizations refer to this phase – Business Intelligence.
As a spoiler, the dashboard stage of any organization can be a bit expensive, both in terms of money and time taken. It basically calls for heavy investments in:
- Data Warehouse – or similar kind of storage options to store data in a single location for simple yet efficient reporting.
- ETL (Extract Transform Load) Tools – for plying, integrating and shifting data to data warehouse systems.
- Reporting Tools – ideal for showcasing results and helping users with data exploration.
Check out the most common questions that are answered through conventional dashboards:
- How many customers visited your store?
- How many people live in a particular region?
- What was the sales figure on the New Year’s Eve?
From above, you can observe a massive stretch of values that is obtained from this stage alone. Unfortunately, this is where most organizations stop functioning.
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It’s here that the real science of data science kick-starts. Machine Learning is all about determining quantities, which are impossible to directly observe. It works by using the data from the preliminary phase, along with applying statistical or other relatable methods to imbibe additional acumen.
Machine learning tackles the following questions:
- When a credit card is swiped, what is the probability rate of the charge being fraudulent?
- If there’s a forecast of hurricane, what stuffs (pop tarts?) people are more likely to purchase?
The correlation between an event and its outcome is prominent – machine learning phase, coupled with data mining and statistical modeling helps us take a peep into the future.
Acclaimed to be the third and final stage, Actions tries to exploit the outcomes of Machine Learning and responds the way it should – judiciously.
The actions corresponding to the above-mentioned events are as follows:
- Either deactivate the credit card, or reject the fraudulent charge.
- When a hurricane happens, put up Pop tarts in the front of your shop.
So, from all this, it’s clear machine learning stage gives way to clear actions, which is of course good for any establishment.
To ring the bells of success in Data Science, on needs to master these three stages of Data Science. Each stage is related to the other – so if you have invested in the first stage, then why not extend it to second and third stages, considerably?
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