The Role of Big Data Analytics in Decision-Making
Table of Contents
The amount of data created, stored, and used by organizations is staggering and growing at almost double the volume every two years. The most recent estimate of usable data reached 130 zettabytes in 2023.
These large volumes of data are known as “big data.” It incorporates structured and unstructured data of varying forms from multiple sources and differs from traditional data that often comes from one or two sources in a similar format.
Big data presents an opportunity since data can be used for more sophisticated insights and better decision-making than before. But with data engineers using 60% of their time to clean and sort that data to make it worthwhile, it also presents a challenge.
Applications across industries
Big data has value for any organization that’s willing to use it. Since data needs to be collected, protected, and used responsibly, it can be costly for industries not already well-versed in securely handling information. This is why finance and health have been leaders in some of the newer data technologies; they have paved the way for tools that even smaller industries can use.
Finance
Credit card companies flag transactions outside standard spending patterns to catch and reduce possibly fraudulent transactions. Financing companies use machine learning to determine a “just-right” monthly loan payment amount for each borrower and ensure they can keep paying on time.
Healthcare
Health test results create a personalized disease management plan that will work well with a patient’s lifestyle and goals. Historical cancer scan data helps inform future scans and is used to detect cancer earlier in new patients.
Marketing
eCommerce data from previous shopping seasons help businesses plan for peak sales periods and manage messaging (and inventory) accordingly. A buyer’s behavioral data and third-party data on weather can help create a customized travel planning experience that meets the customer’s budget and personal plans.
While there are relatively large business decisions, big data is often harnessed for the thousands of smaller, day-to-day decisions that customer agents make. Examples include using AI to monitor helpdesk chat conversations and provide clues on how to help a customer or even reduce the need for case escalation.
Decision-making process
Big data can support almost any stage of the decision-making process, although different tools may be used at various points. Here is an example of how data tools may be integrated throughout the journey.
- Data collection. Data gets sourced from various places, including existing onsite databases, historical sales data, e-commerce tools and plugins, social media channels, and customer conversations with support reps.
- Data integration. Since each source has its data type, with different formats (including structured and unstructured data), it must be unified or changed to a common data format to be used by different tools. With one body of data to access, more comprehensive insights can be made.
- Data analysis. This is the “brain” part of the decision-making process. Technology - usually in AI-powered software as a service (SaaS) tools - sorts through the data for useful trends and patterns.
- Decision-making. Human decision-makers discover trends from the larger overall data pool; they can then decide what’s best for their organization and which actions to take next. These decisions are more evidence-based than just going off hunches or acting based on the current situation since data is historical and can showcase years of trends from which to draw.
Predictive analysis
Predictive analysis is a data approach that reviews historical data for trends and takes it one step further; it uses AI and machine learning models to forecast future trends so that business decisions can be made in anticipation of what hasn’t even happened.
How can predictive analysis help organizations? It reduces the need for trial-and-error approaches that can waste resources and precious time figuring things out. Instead, it directs advertising spend, for example, on campaigns around what is most likely to happen. Instead of throwing money at multiple ad strategies to see which works best and then continuing to focus on the winners, predictive analysis empowers advertisers to forecast what's likely to resonate with customers.
Another example of predictive analysis is with inventory management. A company may use past sales numbers, market trends, and consumer insights to anticipate what buyers may want next, how many units they may buy, and how long the sales push will last. The company can then ensure they have plenty of the products in stock that are expected to sell better and reduce shelf space for those items that aren’t predicted to do well.
Big data technologies
Data has many big names, from the warehouses and data lakes that store the data to the tools that process it and make it usable. Here are a few big data solutions that may be familiar to you:
Hadoop: Apache’s Java-based, open-source framework supports data storage across remote locations and helps organizations process data between one computer and thousands. This distributed method helps spread the data processing across several devices to use resources efficiently.
Spark: Another Apache distributed system, Spark is a unified analytics engine that handles intense data processes. It’s often used with Hadoop and is the more flexible option offering in-memory processing.
Databricks: Another unified, open analytics platform, Databricks is a popular choice for building AI-powered data solutions. It works with your cloud storage and relies on the cloud provider's security to manage and deploy the tools you connect to it.
Other big data technologies include the full suite of Amazon Web Services (AWS) products, Microsoft’s Azure solutions, and those managed by Google Cloud.
Challenges and ethical considerations
As much as big data analytics tools create opportunities for organizations, they bring new concerns.
Data volume
Big data is called “big” for a reason, and as more and more data sets get created, collected, and stored, the sheer amount of this data can be difficult to manage. There is usually a financial and time cost associated with storing and processing it, which only increases with the data volume.
Data velocity
Data has the most value when fresh, but it’s changing by the second. To harness the most from big data, organizations need tools to capture data in its most recent form, from social media posts to e-commerce shopping behaviors.
Data variety
Data exists in two forms: structured data (organized data that is defined and searchable, such as names or email addresses) and unstructured data (undefined data with no set format that is hard to search.) Most data is unstructured, requiring more processing power to make it searchable and useful. Data tools need to be able to work with both types and bring them together for the best insights.
Data veracity
It only takes one flawed data source to skew the accuracy of an entire data pool. This makes it important to vet data sources, regularly audit data, and have a plan for dealing with data “noise.”
Data value
In the best scenario, data will be clean, accurate, timely, and easy to work with. Even then, the sheer volume can make it hard to know how to extract the best insights from it. Value, therefore, can be very dependent on the use case.
These are the major challenges. Additional concerns around big data include balancing data privacy and security with personalization techniques and lacking skilled workers who can handle data tasks meaningfully.
Ethical concerns include things like:
- Determining who owns the data and the products of the data
- Transparency around how data is used, if data has been collected from individuals without their consent
- Accountability for data policies and to the governments who regulate it (i.e., UK’s GDPR)
- Instances of bias and AI hallucinations that may create incorrect or harmful outcomes
As big data, particularly the AI tools that process it, evolve, the list of ethical considerations is expected to grow.
Real-world examples
Using big data to drive business decisions isn’t just a theory; it’s happening right now to help change the growth trajectory for companies in many industries. Here are just a few success stories:
Deepnote, a worldwide data science tool, recently unified its data to create customer insights and inform future marketing campaigns. Not only was it able to standardize the data from all sources, it created a single, comprehensive profile of each customer using that data. It could even see where each new customer was coming from. It used advanced insights to see which customers were more likely to switch from the free to paid plans and used personalized messages to help improve product adoption.
Payzen uses AI and machine learning to determine the right amount for patients to pay each month and helps ensure that hospitals and clinics get paid on time and can continue serving patients in their community. Using data-based insights instead of traditional signals (like a credit score), each patient gets an individualized payment plan that’s more likely to work with their budget.
These are current use cases, but the future holds even more promise. Big data has been proposed to make important, even life-saving decisions where humans can’t do all the work alone. Using AI to detect failure points in roads and bridges, for example, can be done with AI to help people decide the best way to fix structures before they become a danger.
Benefits and advantages
Big data offers opportunities to make better decisions over time. Benefits of using data for business insights include:
- Being able to more quickly adapt to volatile market conditions
- Having a better understanding of the customer, their behaviors, and habits
- Getting a more accurate look at sales, inventory, or other metrics over time, with the ability to create very granular reports and drill down to specific pieces of information
- Forecasting opportunities that weren’t possible with traditional data methods
- The ability to gather data from hundreds (or even thousands of sources) and stitch it together for a unified view
Summary
We are in the age of data. Those who can put it to use may get new insights that far exceed what was possible with traditional data models and make the best decisions for our rapidly changing times. However, each new data technology comes with additional risks. Companies that balance innovation with security and good data stewardship are in the best competitive position.
