Invest in right talent and technology, for data and AI will drive business decisions

With so much market hype around Artificial Intelligence (AI), Big Data, Machine Learning (ML) and other technologies, most companies are finding it difficult to ingrain data as a key driver of business decisions.

Aashish Kalra Jun 06th 2018

The time has come to make technology decisions around data and its potential applications in accentuating technologies like Artificial Intelligence (AI), Automation, IoT and Smart systems. With so much market hype around AI, Big Data, Machine Learning (ML) and other technologies, most companies are finding it difficult to ingrain data as a key driver of business decisions.

As repeatedly pointed out, data is the next natural resource just like air, water and oil. It is no longer a by-product of business activities, but a catalyst to support decision making in business activities.

Statisticians were working with business data long before AI became a commercial reality. However the results were delayed, tasks were repetitive and process was prone to human error. As technology took leaps and the world is witnessing the largest technological revolution, AI-powered Big Data is set to become a functional option to extract insights out of structured and unstructured data. The relationship between AI and data is that AI powers efficient and accurate data science.

Increasingly, AI-powered data science tools are fostering data-driven companies to have a firmer grip on operational performance, effective go-to-market strategy and predicting the future course of action in industry. Eventually it is possible because businesses have data from decades or centuries to extract, process and derive insights from.

How data is shaping new business models?

The rise of data economy and data monetization has helped businesses to use historical data to contribute to the bottom-line. The barriers to data monetization using AI has been overcome and the implementation has become possible because of three reasons. First, availability of high speed computing and infinite storage resources; second, connectivity and bandwidth; third, access to volumes of unstructured and structured data.

Data and analytics powered by machine learning (ML) support disruptive business models like utilizing orthogonal data, real-time demand and supply platforms, radical personalization, combining and storing large amounts of data for cross-functional usage, data-driven prediction, and AI-supported decision making free of human biases and cognition.

Data proliferation leads to increase in volume, variety, velocity and veracity. Previously, due to limitation in computing power and high-speed processing, businesses were working on a standardized set of data easy to process and comprehend. Businesses now have access to infinite computing and storage capacity with massive data ingestion capabilities. They can break through the organizational and technological barriers to look for new types of data sets that can bring more value to the process or decision making.

Similarly, with the advent of fog computing and advancements like real-time interaction, geo-positioning systems, and geo-tagging, digital applications are transforming transportation, logistics and commuting businesses. B2C businesses are using granular details about the target audience to provide them with hyper personalized products and services. This breaks the traditional models of industries like retail, healthcare, media and education.

Large sets of data with applied AI techniques support new discovery of drugs and medicines for health care industry. With all these types of emerging business models, data powered by AI applications help in improving decision making free of human perceptions.

Data is crucial

Big data powered by AI and ML are transforming the way data is being utilized by businesses. Previously, the talks revolved around data storage, management, warehousing and retrieving whereas with infinite storage and advanced technologies, data is now being strategized to be used lately.

For Instance, Energy is one such sector that is seeing early signs of this AI and data transformation. Available structured and unstructured data and disruptive technologies have paved way for Energy Forecasting, Energy Efficiency and Energy Accessibility. Industry data is used by AI developers to train machines to forecast energy consumption, and manage energy supply and demand.
Here is another example from the healthcare industry. The National Cancer Institute did a project to learn about the role of genetics in cancer. With the capabilities of AI to process the research data available, scientists searched through a 4.5-million-cell matrix in 28 seconds along with cross-referenced genes from 60 million patients. That’s the power of data, enhanced by AI technologies.

The way to go forward

Going forward companies will have to revamp their Big Data environment and start employing data-lakes and other technologies to leverage AI. This saves the need to have a predefined schema, enabling an agile and flexible architecture and making it easier for businesses to get a panoramic view of cross-functional data sets. Companies are on their run to manage data; but they have to look for data strategy to build AI-first futuristic businesses. AI-enabled re-engineering for business data sets will be crucial and must be geared up to increase revenues, improve worker productivity and save costs.

Companies have to invest in the right talent and technology. At the same time, companies need to look beyond traditional ways of employee assessments, and start building a multi-disciplinary workforce that can work in collaboration with AI to reap the benefits AI is promising to the human race.

Aashish Kalra is Chairman, Cambridge Technology Enterprises.

Disclaimer: This article is published as part of the IDG Contributor Network. The views expressed in this article are solely those of the contributing authors and not of IDG Media and its editor(s).