intro-to-data-analytics

Introduction to Data Analytics

The amount of data that organizations and governments continue to gather is growing exponentially year over year. And a recent report by Microsoft explains, an average smart city will generate 250 petabytes of data in a single day by 2020. In this intro to data analytics, you’ll get a taste of what it really is, and why it has become the buzz word of the decade (or the century).

There is a need to justify the vast amount of data is being collected, and to truly be able to make sense out of it. What use is so much data of, when we we wouldn’t know what to do with it?

Data, if segmented and utilized in the right way, can enable organizations to derive meaningful insights. Build actionable plans. Improve efficiencies in business and lifestyles. Improve overall health. And the uses can be numerous.

And this is where data analytics comes it. Analytics help gain organizations useful insights from data that they may have access to.

Let’s dive into a short intro to data analytics.

Intro to Data Analytics

Data analytics uses data by applying analytical techniques to gain useful insights from it.

The analytical techniques had been there for a long time and however, it became popular only recently because of the availability of so data due to advanced computing and data storage technology..

What use do you get with Data Analytics?

Now that we’ve covered the tip of the ice-berg and looked at what the term data analytics means, let’s see what can businesses, organizations, governments, statisticians and authorities can gain from it.

Improved Decision-Making process

By analyzing the data generated from the organization, a business can make its decision-making process better by eliminating the guesswork.

Google has the People Analytic Department that makes HR decisions based on insights from only the data.

Understand the Customers

The reason why Data Analytics is the most sought after is, it better helps understand your customer. Which in turn, means more profitability for businesses.

Data Analytics applied to customer data infers useful insights about them. With the insights about the customer, a business can enhance its productivity by tailoring specific services to a specific group of customers.

Unethically abusing customer data is against the law, and Facebook’s Cambridge Analytica scandal is a good example of such activity.

Better Streamlining of Operations

Data Analytics helps to better understand how various operations in business works and Business operations are streamlined to minimize the cost using the insights from Data Analytics.

This post from Michigan State University explains how Data Analytics helps the business.

How does Data Analytics work?

intro-to-data-analytics
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In order to utilize data analytics tools properly, a the data analyst needs needs to source vast amounts of data to draw parallels and mine useful insights.

So collecting the data is of utmost importance, and without it, analytics cannot be utilized. Data and analytics are important components of the process.

Data extraction is also a big subject that doesn’t necessarily come under Data Analytics, but is a key component to source data to be analyzed.

With the collected data, different data analytics techniques are applied to forecast different outcomes.

Descriptive Analytics – Here, data is collected from multiple sources to give insight into the past.

For eg, the Business report provides a review of an organization’s operations, sales, financials, customers, and stakeholders to better improve the organization.

Diagnostic Analytics – It takes a deeper look at data to understand why something has happened?

For eg: With this type of data analytics set up, an organization can conclude why their sales have increased or decreased.

Predictive Analytics – It analyzes the data to predict future outcomes. The accuracy of the outcome depends upon the data used. Predictive Analytics derives probability for different outcomes and using this, an organization can make better decisions.

For eg: Netflix uses predictive analytics to suggest movies to its viewers based on the movies they had watched.

Prescriptive Analytics – This is based on Predictive Analytics and it prescribes actions to take to eliminate future problems or grab potential gains.

Healthcare industry uses prescriptive analytics to better prescribe treatments or protocols as mentioned in this post.

After analyzing the data. The result is visualized to get useful insights. And data visualization is another detailed subject on it’s own.

Tools for Data Analytics.

Data analytics is a complex process that uses statistical and mathematical combinations to make sense of raw and unprocessed data.

There are numerous tools available for the companies to successfully start implementing data analytics in their business processes themselves. Alternatively, they can also hire data consultants and digital transformation agencies to help them in their journey.

Here are a few tools and languages that can be used in data analytics:

  • Excel – The most common tool used by organizations to understand their data.
  • Python – An open-source programming language that has inbuilt libraries to perform the mathematic operations to analyze the data.
  • Apache Spark – A large scale processing engine that executes the Data analytic process on a huge volume of data.

Apart from the above, there are various open-source and proprietary tools to help businesses analyze data. Organizations have new roles that have been created in the past decade, dedicated to this kind of work.

These roles are often carried out by titles called Data Analysts, or Data Scientists.

Data is considered the new oil, and it can’t be more true because major tech companies like Google & Amazon have a high market cap than the once highly valued oil companies.

With cloud storage being ever more economical, and ever-growing data collection and utilization by entities to make better decisions, data analytics will play an important role in shaping the present and the future.

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