Big Data 2022 Trends for Small Businesses
As devices and applications become more connected, they generate massive data. Companies like Netflix and Procter & Gamble analyze these large sets of data to anticipate customer behavior and produce new products.
The velocities and varieties of this data are forcing organizations to examine their approaches to managing it. They’re implementing new tools to ensure that data stays where it should, is secure in motion and at rest, and is appropriately tracked over its lifecycle.
1. AI/ML Solutions
There is an incredible volume of data collected globally that could not be managed without automated systems. These technologies analyze and sort data, identifying patterns and providing decision-making algorithms.
A popular use of these technologies is to understand the preferences and behaviors of your target audience, a practice that can help companies improve product development, customer service, marketing and sales. These tools can also be used to detect risks such as security breaches or credit card fraud.
AI can provide a better understanding of user experience and needs by collecting, analyzing and interpreting data to create more personalized interactions with customers. Natural Language Processing (NLP) allows users to talk to technology using complete phrases, allowing organizations to create customer support bots with voice or text conversations. ML can be combined with NLP to build intelligent assistants that can answer complex questions and adapt over time to new queries.
2. Data as a Service (DaaS)
Using a DaaS strategy, businesses can improve the agility of data workloads, reduce time-to-insight, and ensure security & integrity. It can also help them improve their data analytics capabilities and monetize their data.
The emergence of big data as a service is helping to democratize access to valuable insights that can drive competitive advantage. This trend will continue to grow in importance for the next decade and beyond.
With widespread security breaches eroding customer trust in enterprise data-sharing practices, organizations are now focused on data stewardship. New tools are emerging to make sure data stays where it needs to stay, is securely managed at rest and in motion, and is tracked through its entire lifecycle.
The demand for DaaS is growing among both large and small organizations that need to manage complex, structured data. For example, pharmaceutical companies use Tetrascience’s cloud-based data management solution to harmonize the information from their laboratories and systems, enabling them to conduct better drug development.
3. Predictive Maintenance
Using big data, companies can improve their maintenance procedures by understanding how their machinery is functioning. This way, they can prevent equipment failure and avoid costly repairs. However, processing such a huge amount of data can be challenging and require a lot of time.
It also requires specialized expertise to interpret the results and understand why an asset is performing as it is. In addition, the technology can cost quite a bit to implement. It is a good idea to consult experts before making any decisions.
This big data trend involves using predictive analytics and IoT sensors to predict equipment wear and tear. For instance, transportation and oil & gas industries utilize predictive maintenance to reduce the risk of safety hazards, such as a Deepwater Horizon disaster that resulted in 11 deaths and 5 M barrels of oil spilled. They also save money by predicting when maintenance needs to be done and reducing unscheduled downtime.
4. Customer Experience
Using analytics systems to track customer behavior can help your small business find ways to increase revenue. For instance, you can analyze the data to identify the customers that are most likely to buy from your company. You can also find out which of your employees are most productive and determine which ones need extra training. Boosting employee productivity helps to maximize your profits and improve customer experience.
Real-time data insights can be critical for companies, especially in the manufacturing industry. For example, if your equipment malfunctions or breaks down, big data can identify the problem in advance so that you can take action to prevent an accident.
Incorporating big data into your small business can give you a competitive edge over your competitors and boost your profits. However, storing and analyzing the massive amount of data can be difficult, but there are solutions. The emergence of DaaS and AI/ML platforms can make it easier for small businesses to collect, store, and analyze big data.