What is Big Data?
Big Data refers to large amounts of structured, semi-structured, and unstructured data collected by companies. It can be analyzed for valuable information and used in advanced analytical applications, predictive modeling, and sometimes machine learning projects.
Systems that process and store Big Data are commonly found in enterprise data management architectures and have the following characteristics:
- They handle large amounts of data from multiple sources.
- They can quickly generate, collect, and process data.
- They support a wide range of data types.
Is it important for your business?
Using Big Data can help companies improve operations, create personalized marketing campaigns, provide better customer service, and take other actions that can lead to higher sales and profits. This gives companies that use it effectively a competitive advantage over those that do not, as they can make faster, more informed business decisions.
Many companies still rely on Excel spreadsheets to gain very limited insight into their business goals and operations. It is time-consuming to (re)collect all the data this way, for such poor results. On the other hand, modern platforms collect data instantly! Also doing it over long periods of time and combine it with other data sources like internal databases, SaaS tools or external data and APIs.
For instance, most companies today collect large amounts of unstructured data from their interactions with customers. This data is collected form social media messages, customer support interactions, weather information and web browsing history on their site. The sheer volume of data points makes it impossible for an individual to collect, collate, and analyze them manually. That’s why advanced analytics and machine learning algorithms have become essential tools for companies to derive value and insight from their unstructured data and predict future outcomes.
Advantages of Big Data
Advanced analytics
Advanced analytics is closely related to machine learning and artificial intelligence. These concepts use computer programs that analyze massive amounts of data to identify patterns and learn from them. As mentioned earlier, machine learning helps analyze unstructured data, combine it with structured data, and provide better insight over longer periods of time, enabling better strategic decisions and business success.
Data analytics benefits everyone:
- Customers can make better decisions about orders, leading to more sales and fewer returns, and view the company as a partner in their success.
- Employees can make decisions based on data and increase productivity.
- Managers and company leaders can make informed decisions and implement new strategies.
Predictive models
Predictive models rely on high-quality datasets to accurately forecast future outcomes. A larger dataset often leads to better results. Businesses across all sectors and sizes use predictive analytics to understand customer behavior and business processes using accumulated data. Examples of using predictive analytics include generating better lead qualification with predictive lead scoring and improving delivery time estimates by incorporating it into the supply chain.
Incorporating customer and process data into predictive models enables companies to make data-driven decisions and better predict future behavior, rather than relying on intuition.
Automation through Big Data
Companies of all sizes struggle to introduce Big Data and AI-based automation into their operations, while small businesses worry that these cutting-edge technologies are too complex or costly for them. In reality, AI and ML technologies help manage the complexity of an increasingly global and digital world, even in the most challenging of times.
The adoption and use of advanced technologies is critical for companies of all sizes to gain business resilience and shape the way they operate.
Businesses are increasingly relying on solutions to automate repetitive and mundane tasks. But this is just the beginning. Organizations that are more agile want to use predictive analytics to make more accurate budget forecasts and strategic decisions and give employees the tools they need to outperform in what is expected of them with ease.
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