Home Data Engineering Data News Solving Business Problems with Data Science

Solving Business Problems with Data Science

Audio version of the article

To the outsider, data science can appear to be modern-day alchemy. It may look like a combination of broad mathematical and statistical knowledge, the ability to hack and expertise in some specific field the data scientist has chosen to pursue. Finding that data scientist, however, who has a high proficiency across a broad spectrum of industries and technologies is an ideal that might not be possible to attain. Thankfully, with the help of data science, the results are available without depending on one super-scientist.

The deeper and more relevant truth is data is not a magical realm operating outside of normal business practices and disciplines. Rather, data and the insights it provides are powerful tools used to identify, assess and resolve business problems in real-time. In this way, data science can be applied to business problems to improve practices while reducing inefficiencies and redundancies – strengthening customer satisfaction.

The Dynamics Of Data Science And Business Problems

It’s easy to lose sight of the forest amidst so many towering trees. Many times, the real problem isn’t the problem you’re looking at. It’s what you cannot see. This analogy can easily apply to any business, big or small, that has so many priorities it is unable to see the most pressing. That is where data science can step in.

The answers to your toughest problems are right in front of you. The best source for the data needed to solve your business problems is your business. The challenge is in the actual size of the data. Human eyes cannot see patterns in datasets this massive. It takes a computer, often more than one, and analytics to harvest meaningful insight. Data analytics and business intelligence can use KPIs to identify priorities based on the relevant data for that problem.

Here are three examples of how your data, analytics and scientists can make beautiful music by employing data to identify problems and opportunities – while orchestrating a perfect, almost artistic, solution.

1. Innovative Upgrades And Improvements

Understanding what drives and motivates your purchasing public is a secret businesses have longed to know ever since commerce began. It is often driven by gut feelings or broad and rough analysis of glaringly obvious data. Now, it is possible to refine data analysis to the point where your data scientists can understand what will trigger action from prospective buyers sometimes better than those buyers know themselves.

Innovating your existing product or service through upgrades and improvements is one way to use data at your disposal to boost revenue-deepening customer relations. Customers love their familiar devices, but they may love them more when they are given a new look, feel, or function which makes them better and more relevant.

Data science solutions can show developers opportunities where increased interest and sales are simply hidden within the product or service itself. Discovering this is a direct result of a focused effort to use analytics to understand customer motivations.

2. Developing New Products And Services

There are times when the need arises for an entirely new product or service, often one which is interrelated with your existing business goals and operations. A prime example of this new development may be found in the company Netflix. It began its service as a convenient and affordable alternative to renting movies. As demands and technology evolved, their service gradually evolved too. First, by offering streaming services as a secondary viewing option. Quickly, customers recognized its convenience and streaming service grew to become the most popular viewing model.

Smartly, Netflix also anticipated the demand by gamers, adding games to their entertainment rolls. Finally, they wisely saw the opportunity of squeezing the last pennies of profit by selling their previously viewed games and movies to loyal customers. This example perfectly demonstrates how a company can follow data each step of the way to piggyback off initial successes for continued growth and success.

3. Data-Value Identification

It is one thing to have data, it’s another to see the potential it offers. Earlier, I mentioned that some view data science as magical and mystical. While this is not exactly accurate, it must be admitted that there are some magical aspects of data science. Data can unlock new value in familiar situations and opportunities by providing new potential and direction.

Freeform analysis is an arena where data science can flex its muscles. The ability to analyze and assess without having a specific goal, search or preformed conclusion in mind can lead to unexpected and sometimes illuminating places.

Patterns that are far too nuanced for the human mind to anticipate can easily be captured by algorithms. Think of it as a broad scanning of your massive data landscape which can often highlight or reveal previously unseen or unexplored terrain and offer something entirely new. This treasure trove of valuable data and information can potentially expand your customer base and increase individual sales volume because you are now serving a different set of clientele. 

One of the best outcomes of using data science to solve business problems is that you often end up inspiring and motivating data scientists on your team. They can feel like more than simply analysts of information. Rather, they’re part of a team imagining elegant solutions that add value to customers, communities and business culture. By using the data your business already collects, data science has the potential to help solve various problems in new ways. It’s another tool in your toolbox to build up your business by tackling obstacles head-on.

This article has been published from the source link without modifications to the text. Only the headline has been changed.

Source link

- Advertisment -

Most Popular

Introductory Guide on XCFramework and Swift Package

In WWDC 2019, Apple announced a brand new feature for Xcode 11; the capability to create a new kind of binary frameworks with a special format...

Understanding Self Service Data Management

https://dts.podtrac.com/redirect.mp3/www.dataengineeringpodcast.com/podlove/file/704/s/webplayer/c/episode/Episode-159-Isima.mp3 Summary The core mission of data engineers is to provide the business with a way to ask and answer questions of their data. This often...

Understanding Machine Learning Data Preparation Techniques

Predictive modeling machine learning projects, such as classification and regression, always involve some form of data preparation. The specific data preparation required for a dataset...

Java and Python in Top List of Self taught Languages

Here's a report for the times: Specops Software sifted data from Ahrefs.com using its Google and YouTube search analytics tool to surface a list of the programming languages people most...

Crypto bulls predict the future for Bitcoin

Bitcoin is back. The cryptocurrency last week passed the $18,000 level for the first time since its all-time peak in December 2017. As...

Tracking Machine Learning experiments with Allegro AI

https://cdn.changelog.com/uploads/practicalai/97/practical-ai-97.mp3 DevOps for deep learning is well… different. You need to track both data and code, and you need to run multiple different versions of...
- Advertisment -