However, the size of data collected across the globe is increasing each day. According to an infographic shared by the World Economic Forum, the digital universe is expected to reach 44 zettabytes by 2020. The sheer magnitude of data available to businesses today, makes it challenging to convert it into knowledge, conclusions, or actions.
No wonder, most firms are using Big Data AI to drive business results. The convergence of AI and Big Data is the most-sought after tech trend today that is helping businesses compile data and respond to it. Data fuels AI, allowing machine learning applications to learn independently and improve their pattern recognition capabilities. On the other hand, AI helps businesses wrangle data and leverage it in the best way possible.
Big data AI is changing the manner in which enterprises drive value from their data and improving their analytics capabilities as follows:
1. Data Is Becoming ‘More Relevant Data’
The term ‘Big Data‘ refers to the massive amount of unstructured, semi-structured, and structured data accumulated through sensors, social media activity, emails, satellite imagery, and other sources. If analyzed thoroughly, this data can offer valuable industry insights to businesses. However, the job of sifting this data and parsing and analyzing it is a lot for the human brain. Hence, businesses are turning to Big Data AI to clean this data and gain actionable insights from it.
Big data AI relies on AI algorithms to process data instantly and get valuable insights from it, thereby reducing the time required to benefit from Big Data. Further, AI ensures that the insights derived are most applicable to the user’s needs and preferences. Big data AI sifts the good from the bad and the relevant from the irrelevant, thereby transforming the data in hand into usable contextually-relevant information. For instance, Big Data AI can play a critical role in content curation by helping content marketers create personalized email content, identify relevant topics, and improve their content marketing campaigns. The Associated Press is already using Big Data AI to enhance their data analysis skills, identify patterns, and use the insights to create content that’s adapted to the reader’s personality, tone, location, and time of day.
Not just relevant data, Big Data AI can help firms offer customer-responsive products that fulfill their needs. Customer behavior changes each day. Hence, it is tough to determine what kind of products or services a customer is expecting. Rather than relying on customers to share their product preferences, businesses can use Big Data AI to study buying habits and predict what they want.
2. Marketers Are Gaining Access to Location Intelligence
Big data AI is enabling marketers to experience the disruptive impact of mobile location data. Location intelligence can help them derive relationships between the various data sets and use the insights to make key business decisions and drive customer conversions. For instance, location intelligence can help retailers study location-based buy behavior and store visits, allowing them to make informed decisions pertaining to the store layout, in-store product placement, and staffing.
Understanding the physical location of an individual has always helped businesses improve customer experience. Location intelligence allows them to determine how customers behave in the offline world and create tailored communications and advertising for their customers.
3. Improves Risk Assessment Capabilities
Big data AI will be a game-changer for financial services and firms that operate in the risk assessment realm. Take the insurance domain, for instance. Often, the insurance firm representative has to determine whether or not offering insurance to a particular client is a good idea. Big data AI can help them make the most suitable risk assessment decision by evaluating client data and financial health within a short time.
Big data AI firms like Cytora and Scienaptic use the AI data engine that collates information from various sources and smartly assigns a risk grade to the client seeking insurance. This enables the insurance firm to make an informed risk assessment decision.
4. Triggering the Growth of the Gig Economy
Terms like ‘freelancing, ‘ ‘solopreneurship, ‘ ‘self-employed, ‘ and ‘work-from-home’ among others are becoming quite popular in this modern digital age. Technology is a huge contributor to this trend, allowing individuals to monetize their skills and expertise as a side hustle or even a full-time business. Big data AI holds a huge promise for firms promoting the work-from-home culture or anyone who’s looking for an alternative work arrangement. For instance, in order to reduce their operating costs, most firms prefer hiring remote programmers, front-end developers, and web designers. However, finding the most suitable candidate for this job profile can be quite challenging as the digital world is full of competitors and everyone wants to hire the best talent.
Smart recruitment has made the hiring process easier for HR professionals. With Big Data AI-powered applications and machine learning algorithms firms can optimize their recruiting process and reduce the time and effort needed to screen through the applications of the candidates. Within a few seconds, the HR professional can gauge the job requirements and find an optimal match for the position. Further, the Big Data AI-driven recruitment process is less discriminatory in comparison to the traditional method.
Conclusion: Big data AI is not merely a buzz word accompanying Big Data or AI. Its vast applications are making it a must-have tool for businesses who desire to handle their data in a smart and efficient manner. Businesses in several domains, namely eCommerce, cybersecurity, insurance, finance, digital marketing, and health are already benefiting from Big Data AI.
Now that businesses are looking at digging deeper into data to boost their efficiency and gain a competitive edge, the role of Big Data AI is becoming more important than ever. Big data and AI are disruptive technologies that are here to stay. However, you can expect businesses to increasingly couple these two to make sense of data and boost their decision-making abilities.
