Data Analytics Advantages and Challenges

4 min readMar 26, 2022



Any digital marketing effort requires the ability to track, analyze, and optimize. Data analytics has an advantage and lets you fast-track and change your marketing efforts quickly. When you know how to make the appropriate decisions and modifications, you’re one step closer to success.

However, data analytics has its own set of issues and constraints. For example, you can easily fixate on the numbers rather than checking the trends in your campaign. Remember that macro metrics should be your priority before the microelements in data analytics.

Advantages of Data Analytics

When you use data analytics properly, you can reap several benefits, such as:

Data Analytics Improves Customer Service

Data analytics can improve the response standard and follow-up you can offer clients. Make sure to use it responsibly.

Big data, machine learning, and artificial intelligence should power technical support. You may personalize how you provide customer services for more engagement with brands. Doing so also leads to more satisfying experiences for the customers.

Data Analytics Targets Potential Customers

You can display the relevant online advertisements on sites relevant to the users’ preferences. Analytics in Facebook, Instagram, Google, and others have made it all easier.

You can display the ads according to the purchase behavior and historical data through analytics. It can be cookie-based, server-based, or universal analytics. All of this is feasible thanks to machine learning algorithms.

Looks for Errors

It lets you look for errors through a so-called data cleansing. It helps you boost the data quality, benefiting you and your customers. This feature is most helpful in insurance, finance, and bank companies.

But it also saves memory because it removes duplicate info from data sets. This process also reduces the overall cost in the long run.

Data Analytics Increase Productivity

Productivity levels for every worker can enhance, thanks to the faster speed of analytics. When personal productivity increases, the whole organization quickly has access to all the necessary insights. They know about their own operations that let management see which areas require betterment.

Reduces Cost

Because of the increased efficiency that has resulted from data analytics, your company will save a large amount of money. It expands efficiency and streamlines operations and makes individuals more productive.

All of these contribute to the reduced overall cost and increased profitability. If you are a big business that wants to reduce costs, use big data in prescriptive and predictive analytics. Familiarize yourself with machine learning and artificial intelligence.

Improves Your Products and Services

Data analytics has the power to help you enhance your products and services, which is one of its most appealing features. For example, you can detect and correct errors while preventing tasks that offer no value.

Data analytics lets you understand how customers interact and engage with tools so you can improve their experience. Data cleansing is also more straightforward, enhances data quality,, and continuously benefits your clients.

Challenges of Data Analytics

However, data analytics may also have limitations. Some of these challenges are more likely to occur if you misuse analytics in your organization.

Inability to Select the Right Tools

If you’re new to data analytics, it can be challenging to select the right tools. Analytics requires relevant knowledge of different devices. You should also know the accuracy of the data you’re analyzing and applying. It can increase the time and money you spend at first, but it will be worth it. Engaging a competent web analyzer is the best course of action.

Inadequate Patience and Commitment

The return on investment of data analytics is not instant. Implementing implementation will not be a problem once you have a professional to work with. However, the cost and duration of waiting are not that good.

It might take time to set the procedures in place if there is no existing available. And the accuracy and model are not perfect at first, although they improve over time. Some people lose interest right away because there are no results.

Make sure you have a stable mechanism and feedback loop to help you understand what’s working and not. Without them, senior management might think that analytics is malfunctioning.

No Unity Within Departments

Another challenge with it is a lack of coordination across corporate units. Ideally, there’s a team of people in charge of data analytics but working with the others for optimization. However, the insights made by the core team may not be helpful to the other groups. Or they are not applying it. Others simply do not want to cooperate. Communication matters if you want your organization to drive the appropriate actions and behaviors.

Security Risks

Data analytics includes inputting the enterprise’s information in a vast data lake. These data have sensitive records that need a specialized type of protection level. Many cybercriminals target this data type because they are high-value.

Data Quality

An excellent data-driven decision and operational strategy depend on the quality of data you are tracking and analyzing. It also depends on the resulting analysis of the underlying data set. There’s a massive danger that these insights might be worthless and not applicable to your business. If you continue with your insights and assumptions, your organization can be harmful.

Cost Issues

It takes a huge capital to start and maintain your infrastructure for analytics. For instance, analytics hardware and distributed storage alone can be costly to buy and manage. However, there are issues with the governance and deployment that you should solve even there.

Lack of Skills

As mentioned, you need to hire a professional to perform data analytics on your digital campaign. Data scientists and other professionals should develop, implement, and manage it to achieve your desired results.

However, this field is severely lacking in talent. So even if there’s talent, their salaries are too high for some businesses. Startups and small organizations may not have enough funds for it.

© Image credits to Anni Roenkae

Posted in data analytics