In the Next Wave Of Innovation, Big Data Is Your Competitive Advantage Data is becoming a valuable commodity, like oil in the 20th century. Here's how companies should be using it.
By Artur Kiulian Edited by Dan Bova
Opinions expressed by Entrepreneur contributors are their own.
Demand for data has been surging over the past few years. Companies are rushing to adopt in-house data warehouses and business analytics software, and are reaching for public and private databases in search of data to kick-start their artificial intelligence/machine learning (AI/ML) strategies. Due to the growing demand, good data is becoming a valuable commodity, like oil in the 20th century, and companies are beginning to compete for the most lucrative reserves. In order to understand why data is important for your business, you must first understand the five reasons if gives you a competitive advantage.
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Data is a source of insights.
Until very recently, companies did not realize that they were sitting on a goldmine of data and did not know what to do with it. With the revolutionary advances in data mining and AI/ML, companies can now make use of data generated by consumers and users. A really good example is how Moz used artificial intelligence to predict customer churn. It has designed a deep learning neural network that analyzes user actions and is able to predict the behavior of users. Since actions customers are about to perform within the system are caused by a vast variety of factors from the past, it makes it possible to mine some valuable business insights and decrease churn of existing customers, which has an enormous effect on overall company growth. Data analysis and data visualization facilitate data-driven decision-making that finds hidden opportunities in consumer data, like when Target figured out that some of its customers were pregnant before their families knew it.
Related: You Can Collect Customer Data and Deliver a Better Experience Without Violating Privacy
You can leverage offline data.
Until very recently, online consumer activities such as search queries, clicks or purchases were the main sources of data for large enterprises. However, as it turns out, data is abundant in our physical environments and offline experiences as well. Large tech companies like Amazon are already introducing corporate surveillance strategies in grocery stores across the world. New sensors and actuators installed in shops can collect data about consumer preferences and behaviors. Drones, AI personal assistants and IoT are other examples of tools that can turn every single moment of human lives into valuable data.
This data will become a driver of price setting algorithms that will react to changes in consumer demand. Uber has already begun using this model in its price mechanism. Other companies will soon follow suit, integrating business intelligence solutions into smart malls and city infrastructure. Those companies that stand on the bleeding edge of this innovation will have the best opportunity to extract value from consumer behavior.
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Data helps you manage your online reputation.
When your consumers post thousands of comments and reviews about your products and services on social networks and forums, it might become hard to manage your online reputation. Luckily, however, there is an upside! Consumer feedback can be efficiently turned into data that can be analyzed using state-of-the-art AI/ML models and tools.
One of the most promising directions is the sentiment analysis that uses NLP (Natural Language Processing) techniques to understand dynamics of users' emotions and feedback. To benefit from these techniques, you can create a Facebook application that will retrieve and analyze public posts about your company using sentiment analysis approach. With sentiment analysis, one can also identify positive and negative reviews of your products on ecommerce platforms such as Amazon.
Also, knowing the sentiments related to your competitors can help companies assess their own performance and find ways to improve it. One of the greatest benefits of sentiment analysis for managing online reputation is automation, since it can be hard (and expensive!) to process tons of user feedback manually. Turning feedback into data to be piped into your business intelligence software is one of the most efficient solutions that will set you apart from the competition.
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Data is fuel for AI research.
From chatbots and intelligent narrative generators to business analytics tools, AI/ML is becoming a real competitive advantage for businesses that promotes automation, cost reduction and intelligent decision-making. However, to kick-start their AI/ML strategies and train their ML models, businesses need high-quality data.
Companies such as Facebook or Google have solved this problem naturally by leveraging the user-in-the-loop model where users generate data for them via posts, comments or search queries. Other companies gain access to data by reaching out to public and commercial databases, crowdsourcing data collection and classification services, or collaborating with data-driven businesses, to name just a few.
Whatever approach best fits your business model, you need to introduce effective data acquisition strategies to leverage the power of AI/ML. Stable access to quality data will help you build efficient AI/ML solutions that improve the productivity of your employees and create a better experience for clients and business counterparts.
Related: 5 Misconceptions Small-Business Owners Have About Big Data
Data is an instrument of personalization.
In the new era dominated by social media, customer personalization becomes one of the main sources of competitive advantages for companies offering their products and services online. Until very recently, companies lacked instruments to personalize content, an application's features and services for their consumers.
Data, consumer analytics tools and state-of-the-art AI/ML software for recommendation engines are the main game changers that make an efficient personalization possible in your business. Data on user preferences, interests, real-time and past behaviors can be now easily collected, stored and analyzed using business analytics tools and AI/ML algorithms. For example, insights from this data allow marketers to deliver relevant content to website visitors, video game designers to adjust the game difficulty and features to players, or recommendation engines to suggest music, videos or products that the consumers might like. Personalization powered by the data thus becomes a great tool for retaining consumers and offering them products, services and features that they are really looking for.
There is an abundance of data flowing through businesses nowadays and all this data is impossible to analyze by human workers. Artificial intelligence and machine learning are paving the way to transform these piles of data into a true competitive advantage that any data-driven business can tap into to run their company more efficiently, make smarter decisions and boost profits.
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