In today’s digital era, data is often referred to as the new oil. It fuels innovation, drives efficiency, and provides competitive advantages for businesses that are able to effectively leverage it. However, that is often easier said than done.
These days, companies usually don’t have a problem with collecting more data. Instead, the challenges lie in the storage, organization, and analysis of this data in order to gain insights that have tangible benefits for their business.
Ultimately, data without insights are nothing more than a jumble of numbers and characters – and insights without action are nothing more than just interesting anecdotes.
To maximize the benefit of data-driven initiatives, it is important to identify the common challenges associated with data analytics implementation so that you can develop strategies to overcome them.
How to Identify and Overcome the Hurdles
Now, let’s look at some common hurdles and strategies that you can use to overcome them.
Businesses that embrace data analytics often battle with the existence of data silos, which are isolated stores of information that are not connected to other systems. This makes it incredibly difficult to extract, evaluate and fully take advantage of the available resources at your disposal.
For example, the customer data stored in your enterprise resource planning (ERP) system may not be synchronized with customer data stored in your CRM, leading to an incomplete understanding of customer profiles.
To overcome this challenge, businesses should work on breaking down these silos by integrating different data sources and creating a centralized data repository.
A cloud data warehouse, for instance, can provide a single source of truth by bringing together all the disparate sources into one easy-to-access repository. This way, businesses can quickly and easily access the data they need to make informed decisions.
Low Data Quality
The data that companies collect can often be of low quality (such as missing, duplicate, or inaccurate data), which renders it almost useless for analytics. Even worse, this can result in misleading insights that can send a business off in the wrong direction – costing money, time, and resources in the process.
One way to address this issue is by investing in quality assurance (QA) processes that can ensure the accuracy and integrity of data before analysis.
Additionally, companies should look into leveraging automation tools to detect and eliminate errors during data collection, storage, and processing. Finally, businesses should also consider implementing data validation, data cleansing, and data standardization procedures.
Lack of Resources
If data is the new oil, then data analysts and engineers are the refinery workers. Without enough resources dedicated to managing and analyzing data, businesses will not be able to maximize their data-driven initiatives and will miss out on potential insights.
Furthermore, failing to implement the appropriate tech solutions will mean an over-reliance on manual data entry and analysis, which can lead to errors that can compromise the integrity of the data.
While there is no magic bullet to solve this challenge, businesses could look to budget more effectively by prioritizing their data analytics initiatives and focusing on the most critical projects. This will help make the most efficient use of their resources.
Low Data Literacy/Culture and Governance
Having a data-driven culture is essential for businesses to fully capitalize on their data. However, many organizations face an uphill battle when it comes to creating a data-literate workforce and establishing the right governance structures to ensure that data is used responsibly.
If your workforce is not equipped with the necessary skills to leverage data, it can be a huge obstacle for your organization and will impede progress.
Businesses should focus on developing a data-driven culture by investing in data literacy training programs and establishing robust governance structures. It’s also important to employ and retain people who are well-versed in data analytics and have the necessary skills to help guide an organization’s data strategy.
Finally, businesses must also consider the security implications of their data-driven initiatives. If sensitive and confidential information is not handled properly, it can lead to disastrous results – both for the company’s image and its bottom line.
This is especially true now that regulations such as the GDPR and CCPA are in effect, meaning businesses need to be extra vigilant when it comes to data security or else face hefty fines.
To effectively protect against cyber risks, businesses must implement a robust security strategy that addresses all potential vulnerabilities.
This includes investing in advanced security tools, implementing strict access controls to limit access to sensitive data, and regularly conducting security audits to identify and address any potential weaknesses.
While it may not be possible to completely eliminate cyber risks, businesses can greatly reduce their vulnerability to attacks by being proactive and taking the necessary steps to safeguard their data.
There are many challenges that can arise when it comes to managing and leveraging data effectively. So much so that data analytics implementation is quickly becoming one of the key differentiators of success in the modern business landscape.
By taking proactive steps to address these challenges, businesses can ensure their data initiatives are accurate, effective, and secure, and pave the way for a successful data-driven future.