How Big Data Is Revolutionizing Business Strategy and Operations

Published on: June 24, 2024

It’s no secret that data collection has become an integral part of our everyday lives. No matter where we go—be it on the web or in person—we leave a trail of data every step of the day.

In the world of big data, businesses are now beginning to understand how this information can be leveraged to optimize business strategy, operations and more. With a better understanding of big data and its role in strategic planning, as well as the available big data tools and solutions, organizations can streamline their operations and boost their bottom lines.

Understanding Big Data

Big data refers to the enormous amounts of data that is collected in both structured and unstructured ways. The sheer size and amount of this data makes it impossible to process and analyze using “traditional” methods. Instead, more advanced big data solutions and tools are required to handle the volume, variety and velocity of big data.

The Role of Big Data in Strategic Planning

When big data technologies are effectively incorporated into an organization’s strategic planning, managers and other higher-ups can make data-driven decisions with a greater sense of confidence. In fact, there are numerous ways in which big data and business intelligence can go hand in hand.

Improving Business Intelligence

For starters, being able to collect and properly analyze big data allows businesses and organizations to improve business intelligence by seeing real-time trends as they emerge. This thereby helps big data insights shape business strategy and business intelligence as changes occur.

Pricing Products and Services Strategically

Another way in which big data can assist businesses with strategic planning is through strategic pricing. With the insights gained from using data analysis techniques, it is possible to optimize pricing on products and services in a way that maximizes profits. This type of strategizing can be especially effective when big data solutions look closely at metrics such as competitor pricing, market demand trends and customer buying habits or customer data analysis,

Developing Better Products

In addition to being used to make strategic pricing decisions, big data can play a key role in product development. Through the analysis of things like industry trends and customer behavior, businesses can determine exactly what consumers are looking for in a particular product or service. They can also narrow down pain points that may inhibit customers from purchasing, making changes to alleviate them and put out better products as a result.

Enhancing Operational Efficiency With Big Data

Another integral way in which big data and big data analysis techniques and tools are being employed in the business world is to improve operational efficiency and overall productivity. More specifically, this is done by optimizing quality control, improving logistics and maximizing inventory management strategies within operations of all sizes and scopes.

Maximizing Quality Control

Quality control issues can be a pressing problem for businesses, especially those that manufacture a substantial volume of their own products. While having humans manually check for quality as products roll off the lines can be effective, there is always the risk of human error. This is where utilizing big data for quality control can make a big difference, especially when pattern recognition features are used to pinpoint quality control issues before they occur on a large scale.

Optimizing Logistics

Businesses with complicated logistics operations can also benefit from the use of big data analysis tools and technologies to gain valuable insights into shipping routes, shipping patterns, shipping cost trends and more. In doing so, it is possible to make better-informed decisions to optimize this segment of the business and potentially cut down on shipping costs.

Improving Inventory Management

Inventory management can be a challenge for many businesses. Keep too much inventory, and it’s taking up valuable space; fail to maintain enough inventory, and your business could face catastrophic supply issues. Enter big data, which can help predict changing trends in demand so businesses can consistently ensure the proper supply. This is just one of various ways in which we can see the benefits of big data in supply chain management and supply chain analytics.

Other Potential Impacts

Big data can also be used to make much-needed improvements in the areas of productivity and customer service. Consider, for example, an organization’s ability to analyze data from customer service interactions. From there, it may be possible to see trends and other areas of improvement to focus on for customer service teams. The end result? Better training for customer service professionals and improved customer service for all who come into contact with the brand.

Big Data Tools and Technologies

These days, many tools and technologies are available to make processing, analyzing and gaining insights from big data faster and more accurate than ever. Namely, machine learning algorithms and predictive maintenance/analytics have both been effective helping businesses make data-driven decisions while optimizing overall efficiency.

Machine Learning Algorithms

Machine learning algorithms can be leveraged to build predictive models and extract patterns from data as these algorithms are exposed to more data over time. Typically, these algorithms are automated and can be changed to reflect the needs of the organization.

Predictive Maintenance and Analytics

Likewise, predictive analytics tools involve a combination of data, algorithms and machine learning techniques to help businesses anticipate and react to market changes and trends long before they actually occur. Generally, predictive maintenance and analytics techniques are based on historical data but can be considerably useful in preparing for the possibility of certain events.

Challenges and Considerations

As much as big data has been beneficial to businesses across the globe, the reality is that the techniques and tools used to find meaning in big data are still relatively new. As such, there remain some potential big data challenges that organizations will need to overcome.

Information Overload

With so much data being collected, there is always the inherent risk of what’s known as “information overload.” The sheer amount of information collected can present its own unique challenges in terms of storage, retrieval, analysis, and the like. This, in turn, can make it more challenging to actually use the data to gain the necessary insights.

Security Issues

Another major challenge regarding the use of big data is the potential for security issues to arise. These days, cyberattacks are unfortunately more common than ever before. And with more potentially sensitive information being collected and stored, there comes the inherent risk of data being compromised. With this in mind, organizations preparing to use big data analytics to inform their decision-making must also have a robust security plan in place to keep data safe and out of the wrong hands.

Choosing the Right Infrastructure

For many organizations, the biggest hurdle preventing the shift to using big data is implementing it or integrating it into an existing IT setup. The costs and amount of time associated with doing this can be extremely high. Plus, the amount of training needed for employees to understand how to properly use new systems can create additional obstacles.

Future Trends in Big Data

So, what does the future of big data look like? Only time will tell, but there are some anticipated big data trends that may emerge as the years progress.

Currently, the biggest emerging trend is that of artificial intelligence and big data. Through the use of big data and AI, it may be possible for businesses to gain even more accurate and insightful information from data collected. Big data and AI are already closely intertwined—and some experts might even argue that you can’t have one without the other. However, it is anticipated that the two will become even more closely related in the coming years as AI is relied upon more heavily to process, analyze and gain insights from large amounts of data.

Likewise, while the accuracy and efficiency of AI continues to improve, more advanced algorithms and statistical models can be applied to find patterns and other useful insights in data sets.

Ready to Advance Your Business Education?

Businesses are collecting a great deal of data that can be used for everything from predictive maintenance to customer behavior analytics, data compliance and much more. Of course, many businesses need to bring in outside help to handle their data integration and analysis needs.

If you’re interested in learning more about business analytics and the data analysis techniques needed to tackle large amounts of data, the business analytics certificate program at Park University may be ideal for you. Get in touch with our team today to learn more about any of our four-plus-one degree programs or get started with your online application.

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