Data and AI: How It Has Transformed Over The Years And Trends To Watch Out For!
Undoubtedly, data is what we see almost everywhere, and it is enormous. And it doesn’t stop there, it is growing continuously at a level beyond imagination! Let’s have a look at how it has changed over the years.
A look into how Data and AI transformed in years!
In the 1950s, when there were fewer technological developments, companies would collect the data(offline) and analyze it manually. This was also backed by limited data sources that made it time-consuming in obtaining the results.
The mid-2000s paved the way for changing the world for the better and it was during this time the term “big data” was coined. Almost every business that had something to do with digital infrastructure started looking for ways to use the large data and come up with meaningful insights.
This era also saw the invention of tools like Data mining, OLAP, etc., taking technological advancements to the next level. In general, the internet gained immense popularity not only for organizations but also for households. During this time, technology became more advanced and provided automated options for managing data, and data analysts could analyze data, trends, etc., and provide better recommendations.
Google, Amazon, Paypal, and others also made a mark causing the volume of data to reach newer heights. However, all this posed a storage and processing problem.
The late 2000s to early 2010s saw a surge in Facebook, Twitter, Smartphones, and connected devices. The companies used improved search algorithms, recommendations, and suggestions driven by the analytics rooted in the data to attract their customers. Enterprises also realized that would have to deal with unstructured data and so they got familiar with databases such as NoSQL. New Technologies were introduced for faster data processing and machine learning models were used for advanced analytics.
Now, businesses are a step ahead and using automated tools using cloud and big data technologies. With cloud platforms, it is now easier to enable massive streaming and complex analytics.
Having seen how data has evolved over the years, let’s have a look at how Artificial Intelligence has transformed in the last generation.
In 1950, a British mathematician and WWII code-breaker- Alan Turing was one of the first people to come up with the idea of machines that could think. To date, the Turing Test is used as a benchmark to determine a machine’s ability to think like a human. While this notion was ridiculed at the time, the term artificial intelligence gained popularity in the mid-1950s, after Turing’s death.
Later, Marvin Minsky, an American cognitive scientist picked up the AI torch and co-founded the Massachusetts Institute of Technology’s AI laboratory in 1959. He was one of the leading thinkers in the AI field through the 1960s and 1970s. It was the rise of personal computers in the 1980s that sparked interest in machines that think.
That said, it took several decades for people to recognize the true power of AI. Today, Investors and physicists like Elon Musk and Stephen Hawking are continuing the conversation about the potential for AI technology in combination with big data could have and how it could change human history.
AI technology’s promising feature is its ability to continually learn from the data it collects. The more the data it collects and analyses through specially designed algorithms, the better the machine becomes at making predictions.
Impact on business
AI and big data have an impact on businesses like never before. Whether it is workflow management tools, trend predictions, or even advertising, AI has changed the way we do business. Recently, a Japanese venture capital firm became the first company ever to nominate an AI board member for its ability to predict market trends faster than humans.
On the other hand, data has been the primary driver for AI advancements. Machine learning technologies can collect and organize a large amount of data to make predictions and insights that otherwise cannot be achieved with manual processing. This not only increases organizational efficiency but reduces the chances of any critical mistake. AI can detect spam filtering or payment fraud and alert you in real-time about malicious activities.
AI machines can be trained to handle incoming customer support calls thereby reducing costs. Additionally, you can use these machines to optimize the sales funnel by scanning the database and searching the Web for prospects that have similar buying patterns as your current customers.
5 trends in data and artificial intelligence that can help data leaders.
1. Customer experience will be the key
Supply chain and operating costs will mean nothing if you are unable to hold on to your customers. Today, businesses have to be more connected with their customers to be on top of the game. From in-person and digital sales to call centers, companies will have to collect data to have a holistic view of the customer. Businesses must consider other forms of interaction such as using voice analytics to understand how customers interact with call centers or chatbots.
2. Leveraging External data
External data can provide early warning signs about what’s going on. To make external data work, companies must start with a business problem and then think about the possible data that could be used to solve it. That said, companies might need to modernize data flows to leverage external data.
While many businesses have started leveraging external data, some companies haven’t leveraged it yet as they are either too focused on internal data or finding it difficult to transfer data.
A prime example of brands that used external data is Hershey’s Chocolates. It leveraged external data to predict an increase in the number of people using chocolate bars for Backyard S’mores and a decline in sales for smaller candy bars for trick-or-treating.
3. CDOs leading the way towards a data-driven culture
Introducing any new technology without training your employees to adapt and figure out new skills and processes will not be effective. According to Cindi Howson, chief data strategy officer at analytics platform provider ThoughtSpot, Chief Data Officers (CDOs) need to take the lead and empower their employees and the organization to gain time and efficiency with data. Also, CDOs will have to make sure to upskill employees to take full advantage of new technology.
4. Multi-Modal learning
With advances in technology, AI can support multiple modalities such as text, vision, speech, and IoT sensor data. All this is helping developers find innovative ways to combine modalities to improve common tasks such as document understanding.
For example, the data collected and processed by healthcare systems can include visual lab results, genetic sequencing reports, clinical trial forms, and other scanned documents. This presentation, if done right, can help doctors identify what they are looking at. AI algorithms that leverage multi-modal techniques (machine vision and optical character recognition) could augment the presentation of results and help improve medical diagnosis.
5. AI-enabled employee experience
Business leaders are starting to address concerns about the ability of AI to dehumanize jobs. This is driving interest in using AI to improve the employee experience.
AI could be useful in departments such as sales and customer care teams that are struggling to hire people. Along with robotic process automation, AI could help automate mundane tasks to free up the sales team for having a better conversation with customers. Additionally, it could be used to enhance employee training.
Leveraging data and Artificial intelligence has grown due to the pandemic and businesses are digitally connected than before the lockdown.
At Fingent, we equip business leaders with insights, advice, and tools to achieve their business goals and build a future-proof organization. To learn more about how we fuel decision-makers to build successful organizations of tomorrow, contact us.