Algorithms and Data Science in Industries

This article tells us how data science is used for effective decision-making in industries and corporate.
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Decision making is an integral part of a company, an important factor which determines the growth of the company, a company has the power to control the outcomes of a process through effective decision making, one wrong decision can negatively impact the company.

How does an organization make decisions then?

The executive committee of course and their decision is largely determined by the data. The modern world largely depends on data to make effective decisions and help the company grow and get competitive advantages, data is the reason why Netflix successfully generates billions of customers, Amazon targets its potential customers, determines their spending habits, and recommends additional products.

The concept of big data has created potential opportunities and challenges in emerging markets, there was a time when companies used to completely rely on statisticians and researchers to explore the datasets, however, the modern world has found efficient data science tools to save time and produce more effective outputs for gaining a high-profit share.

The tools help to trigger operations on large and complex datasets and hence help the companies to take advantage of the data for decision making, consultancy, and policymaking.

Data Science is the intersection of programming skills, analytic skills, and good knowledge of the domain.

Algorithms play an important role in the field of data science for it helps the analysts to arrive at a potential solution in a structured manner, algorithms help to classify, categorize and correlate the data sets and minimize the error.

It helps to make accurate predictions and hence letting the company know about the current and future trends for effective strategic planning.

The major goal of companies is to satisfy the needs and demands of its customers in every possible manner, the businesses in today’s world use the algorithm business model to deliver customer services efficiently.

The internet age has made the availability of data abundant for the users, however not everyone knows how to leverage the data, that is where algorithms and data science play an important role, the companies first form an objective or a problem that needs to be solved for a gain of profits, they then collect data design algorithms to get the desired results.

Companies that provide transportation services such as Uber have algorithms designed to keep the driver and the passenger connected and make tracking of location easy for the drivers.

They track a customer’s journey and know their search habits and interest to know what exactly they need.

The field of data science is not just confined to marketing campaigns and customer satisfaction, but they are used in the sports industry to keep track of the players and how they play. The patterns help to decide how a player performs in a given situation, it helps to make accurate predictions.

The construction industry builds digital buildings that replicate the actual building and hence they need not rely on human visualization rather better predictions can be made using the replicate model and hence risks can be reduced.

Algorithms reduce the errors which may have occurred due to human cognition biases, they give a structural flow to the data and a rationale to make predictions.

Machine learning algorithms have changed the manufacturing process, the industries now no longer require constant human presence rather an efficient algorithm can guide all the machines.

The industry also utilizes optimization algorithms in their supply chain management to know the optimal retail price, the consumer demand, and minimum time required to deliver a product to the customer, also the algorithms help them to know the optimal route, thus saving time, money, and energy. Supply chain management is a combination of multiple links and nodes hence when an effective algorithm is applied on a node it creates a huge impact.

However, are algorithms completely reliable?

As the pandemic has arrived all industries can leverage advantage from algorithms, such conditions may lead to less precise predictions, even algorithms may sometimes lead to huge losses as well.

Algorithms are similar to a machine that has been instructed by the user to follow a certain number of steps and process the data given.

Therefore completely relying on the algorithm without making a proper analysis can lead to huge losses to a company.

Netflix for example ran a million-dollar competition to develop an algorithm that could identify which movies a given user would like, teams of data scientists joined forces and produced a winner. But it was one that applied to DVDs—and as Netflix’s viewers transitioned to streaming movies, their preferences shifted in ways that didn’t match the algorithm’s predictions.

An algorithm is a useful tool to make more accurate predictions however it also brings along risks that need to be tackled by the companies, therefore a proper analysis of the algorithm is required, a company needs to consider all the variables, a large vision is required to deal with the problem and critical 

Analysis of all the dynamics that could help the company design a perfect strategy with the least risk involved.

Algorithm bugs are not necessarily the reason for the failures of a company, rather it is poor implementation and analysis, algorithms require a good manager who can analyze the instruction set properly and can predict the consequences of the algorithm, a manager is responsible for analyzing what results in an algorithm leads to and what implications it can create in the changing world because the conditions cannot remain constant, hence algorithm must be flexible and must be implemented properly.

Algorithms are reliable and accurate but are not humane, hence good management of algorithms is required as per the changing scenarios of the business world.

Data is the new oil for the smooth running of the industries and we need to collect the right data and need to build appropriate algorithms for precise and accurate decision making.


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