The Main Challenges Business Face In Data Science

Data Science

March 7th, 2021   |   Updated on June 25th, 2022

Using data for running a business together with enhancing user engagement and improving customer experience has gradually become a necessary discipline for most business operations and development strategies.

Almost all businesses recognize the significance of data-driven business decisions but deriving value out of the data is quite another challenge.

Most companies face the problem of aggregating data from several resources, many times in silos, accommodating a range of data elements, and integrating all the data in a unified format to support simple access with a quick extraction.

As many companies are trying to become driven by metrics, several businesses are not aware of how to use data, and what are the proper questions they need to ask? In 2016 a large event called Structure Data 2016 took place where data analysts and data scientists discussed various aspects of the data-driven revolution, how it has become more significant for most industries, and the various challenges faced by the businesses these days.

1. What To Do With It?

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It is a fact that all functional departments of a business are not aware of what they need to do with the data.

In several businesses, the sales figures can define the metrics clearly for measuring the success of the company in terms of sales objectives. However, it is tough to fathom customer attrition even when you have access to the user engagement data for multiple channels throughout the customer life cycle.

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2. Getting Quick Answers From Data Analysts

There are several challenges faced by businesses while getting the services of data analysts. Several businesses struggle with trying to derive value out of the data.

There is a gap between the technology assets a company has and the underlying data the organization has collected.

There is a constant need to get quick answers to some important fundamental questions. For instance, it is tough to get answers for straightforward queries such as “What is going on in the company?” or “How do I get the bigger picture of what is happening across all the businesses?”, or “What does a business do in this situation?”. Three main areas divide the challenges,

Technology obstacles: These challenges involve, is it possible for the data scientists to build an environment where there is unified and accessible data in real-time? Can you produce the data as required? Is it possible to derive insights from the data for all the involved business units?

Organization structure: The organizational structure can pose obstacles to accessing data. Who can access which files? How often can a person access the files and when? What processes are required to get the data with no or minimum data segregation?

Culture issues: In most companies, the various units of the business have their analysts and data scientists, so a single business unit may not get the answers it needs for high-level or strategic queries.

3. More Accessible Data Analytics Tools

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When you become data-savvy it also means that the different non-technical departments in a company and various functional subject experts require user-friendly tools for accessing necessary metrics and data.

The tools used for data analytics have to become more accessible, intelligent, and simple to use. Keep in mind that all these challenges also present different opportunities and the data-driven market has several players offering different software solutions.

The over-promising made by certain software companies is also a problem. Some open-source software framework that can be used for storing data and for running applications does not necessarily mean that you get all the answers you ask for.

The dashboards offered by the software will display information but the human factor remains the key for achieving the necessary insights and making the proper decisions.

There are tools that provide massive storage solutions making it possible to answer questions such as how to send large videos. The businesses will need qualified and trained data scientists to ask the right queries to achieve the sought-after insights that can aid the decision-makers.


The use of technology is not only going to make things such as decision making, making work-streams, business processing easier but machine learning also brings the promise of reinvention of new ways of solving the challenges.