Data innovation involves tapping into alternative data gathered from new or non-traditional data sources and combining and enhancing existing data sources with that new data to gain additional insights. With advancements in technology, the leveraging of non-traditional data sources has increased exponentially. While the traditional sources such as ERP and CRM systems assemble structured databases for internal business functions like financial, manufacturing, distribution, and customer relationships, the non-traditional sources include farther-reaching digital platforms such as social media, websites, and the Internet of things (IoT). Combining data from traditional and non-traditional data sources can provide more comprehensive, timely, reliable, and granular information that may foster a significant competitive advantage for an organization. Further, harnessing this big data through leveraging new-age technology like AI, machine learning, and deep learning often reveals cost and efficiency opportunities through newly acquired insights that may have previously been hidden or overlooked.
Data is the new fuel enabling people to better understand the complex world around us and use that understanding to make better decisions. Data innovation promises to elevate insights into this data to new heights. It is no secret that the world is generating an enormous amount of data and that there has been a significant increase in that data generation over the last decade resulting in vast data lakes. For example, Statista predicts that by 2025, the total volume of global data generated, captured, copied, and consumed would be ~180 zettabytes, compared to ~64 zettabytes in 2020.
Intelligence buried within these vast bodies of structured and unstructured data collections comprising traditional and non-traditional sources can be harnessed to provide new and profound insights with the help of modern AI, machine learning, and deep learning technology to process, dissect, analyze, and correlate this information. This data innovation enables companies to remain fully informed through synthesized broad-reaching operational and customer data, elevating confidence and trust in corporate planning processes and decision making.
Why are non-traditional sources and AI gaining momentum?
Relying solely on traditional data sources has significant challenges when it comes to:
– Availability of new data as key data points from traditional sources may either be outdated or simply not available.
– Accessibility as traditional sources may not provide rapid access to the data.
– Data from traditional sources may be either insufficient or incomplete.
Apart from the availability, accessibility, and timeliness of the data, traditional sources may also lack disaggregated data necessary for planning and decision-making. Thus, these cumulative challenges present a tremendous opportunity to leverage new or non-traditional data sources alongside existing traditional sources to gain new, more meaningful, and powerful insights into operations and customers.
Data innovation for industry
The digital revolution has led to the increasing use of technology like mobile and the AI-integrated internet of things (IoT). These technologies provide abundant opportunities to improve the way services are delivered, including harnessing valuable data and insights into products, services, and customer behaviors. Such data innovations are rapidly changing businesses and industries and the data sources available to them.
1. Better healthcare
A healthcare industry revolution is underway to produce better health outcomes and improve patient care by making informed decisions from the insights gained from previously hidden data. Technology and rapidly expanding data sources (information stored in the cloud from connected devices and wearable devices like smartwatches, electronic blood glucose monitors, and pacemakers) facilitate the construction of analytics sharing pertinent information with healthcare providers and patients alike. Natural language processing (NLP) leverages medical information in new innovative ways by parsing through diagnoses, procedures, findings, labs, and drug data quickly. Electronic health records (EHRs) contain sensitive patient information that remains protected and securely analyzed through AI. Blockchain securely shares decentralized healthcare information, eliminating challenges associated with differences in data standards, rules, and privileges.
2. Strong supply chains and logistics
Supply chain and logistics center around a product’s long journey from raw materials to being transformed into finished goods awaiting distribution. Companies across industries maintain accurate and automated inventory and delivery management systems leveraging AI, IoT devices, GPS, and weather tracking, optimizing communication with suppliers worldwide, automating processes, and reducing human errors.
3. Convenient travel
Data analysis technologies enhance the customer experience within the travel industry by mitigating the negative travel experiences of travelers. When applied to the customers’ travel data, data analytics identifies patterns and empowers companies to foresee demand preferences facilitating a hassle-free, safe travel experience.
4. Smart farming
Data analysis and IoT prove to be the backbone of smart and precision farming by enhancing crop production capacities. Smart devices help farmers manage crops, land, and animals. For example, the monitoring of crop data, including height, soil nutrients, moisture levels, etc., can be obtained from soil sensors, drones, and livestock monitors. With the help of such intelligent data, farmers can more accurately predict their crop yields and make necessary adjustments to their crops.
The analytics pipelines of fintech companies focused on the credit space depend mainly on new or non-traditional data sources. They predict customer default probability utilizing sourced, cleansed, model data identifying default rates using AI-powered tools. The Fintech industry gathers information from multiple sources, including social networks, and creates a score representing a possible default estimate giving the industry clear insight into distributing credit with little to no risk.
Data innovation is significantly changing the way we gain insights into the digital age. As the generation of data is drastically increasing year over year, new or non-traditional data sources are available like never before to enhance already existing data or as a fresh new way to gain powerful insights from vast sources that have never before been harnessed.
If you would like to learn more about how you might tap into the full potential of your data, send us your query to firstname.lastname@example.org. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with . develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.