Big data is no longer a buzzword in the business world.
According to the 2022 survey from NewVantage Partners, 97% of companies are investing in data initiatives. More importantly, 92% report reaping “measurable business benefits” from them, up from 48% in 2017.
However, only half of the organizations say they are competing on data and analytics, and just a quarter have succeeded in becoming a data-driven company.
Many have made a progress by building in-house capabilities or hiring big data consultants, but for many, there is still a long way to go to fully leverage the power of big data.
In this blog, we will explore big data benefits, what challenges hold organizations back on their big data journey, and tips on how to overcome them.
What big data is and why the deal
Traditionally, big data is defined by its four Vs: Volume, Velocity, Variety, and Veracity. Volume means it’s petabytes or even exabytes of data. Velocity means it’s generated at lightning speed, like every second. Variety means it’s a mix of structured and unstructured data from all sorts of sources like social media, emails, medical images, ecommerce purchases, and more. And Veracity means it’s uncertain and imprecise data that needs to be managed.
In 2020, every person generated 1.7 megabytes of data in just one second. But big data is not just about the sheer volume or speed, it’s also about valuable insights companies can gain from it
From personalized customer experiences to agile supply chains, data-driven innovations, predictive maintenance, and real-time alerts, big data offers companies tremendous benefits. (For instance, Netflix saves $1 billion per year on customer retention thanks to big data.)
Behind these benefits are advanced technologies and approaches used for:
- Storage: Distributed systems (Hadoop or Spark) and cloud-based storage platforms, such as AWS and Microsoft Azure
- Processing: NoSQL databases and stream processing
- Analysis and visualization: Tools based on ML and deep learning, and BI platforms
Why big data is a battle
When embarking on a big data journey, companies encounter various obstacles. Our list below highlights some of the common challenges and provides potential solutions for addressing them.
Poor data
With the constant influx of data from various sources, organizations face the challenge of consolidating this data for analytics. The data is sitting across multiple systems in different formats and with varying access permissions, with legacy systems only adding to the problem of data silos.
Additionally, this data may contain duplications, contradictions, and simply inaccurate information.
There is also a problem with the timeliness of data which becomes outdated too quickly. It is particularly serious in industries where real-time decision-making is crucial, such as monitoring the health of industrial equipment or detecting fraudulent activity.
To solve this challenge, organizations need to create a strong data management strategy and establish a robust data governance framework as part of it. This involves developing an enterprise data catalog, establishing strong access controls and data modification policies, defining data requirements based on specific use cases, and utilizing technology to automate data management processes.
Lack of company-level visibility
The overwhelming amount of data pouring in from different sources often leads to disconnected efforts to manage and analyze it. Without a clear point of accountability and coordination, different teams may be working on their own projects, leading to inefficient information sharing, missed steps, misinformed analyses, and unnecessary costs.
To overcome this challenge, it is crucial to introduce a chief data officer role responsible for strategic vision and establish a center of excellence (CoE) to steer big data and AI initiatives. This CoE should be tasked with identifying the business use cases of AI and analytics, infusing AI throughout workflows and processes, providing the necessary resources and expertise, and developing the skills the organization needs to become “data-powered.”
Skills gap
The skills shortage in the data science and analytics field is a daunting problem that is leaving companies struggling to implement and scale their brilliant data-driven projects.
Based on its analysis of job boards, QuantHub reported an estimated shortage of 250,000 data scientists in 2020. Hiring data science and analytics talent was a top priority for a third of CIOs participating in the 2021 State of the CIO survey.
But there is hope.
By looking for talent where it already exists companies can find the professionals they need. For instance, they can tap into the potential of current employees through reskilling, foster a strategic partnership with higher education, or turn to an AI consulting firm for help.
Another approach is to democratize data to allow the entire employee base to access the company’s data while enabling data scientists to focus on more strategic tasks. This involves leveraging self-service BI solutions that utilize ML and automation to extract meaning from data with minimal human involvement.
Misalignment with business objectives
Without a clear understanding of what business problem they want to solve with their next big data initiative, organizations risk wasting precious resources.
It’s at the very least frustrating to pour your heart and soul into a data-driven initiative to only find out that the insights you’ve uncovered are completely irrelevant to your business growth.
But how do you ensure that your data initiative is solving the right problem? The key is to start with your business strategy and work backward. This means taking the time to clearly define your business challenges and desired outcomes and then asking the right questions to determine what data is most relevant and how it can help you achieve your goals.
Data privacy and security
The abundance of information available to businesses today can be both a blessing and a curse. On one hand, companies have access to more insights and knowledge than ever before, but on the other hand, this wealth of data also opens the door to security and privacy breaches.
As a company dealing with big data, it is crucial to take proactive steps to protect your customers’ information.
One solution is to implement strict security protocols, such as encryption and multi-factor authentication. Another solution is to provide transparency and control to customers, allowing them to view and manage the information collected about them.
Companies should also stay up-to-date with the latest regulations and foster a culture of security and privacy within the organization. This involves educating and training employees on best practices for data protection.
Conclusion
Adopting big data can be a daunting and time-consuming task, with many obstacles along the way. These challenges are real. But with the right mindset and approach, you can overcome them and turn big data into a valuable asset for your business.
