Tag: Data Analysis and Visualization
Over 95% of businesses struggle to manage unstructured data in their day-to-day operations. Inability to decipher data prevents them from navigating the market successfully, making business forecasts, and customizing their offerings to match the changing market trends. This proves why data analytics is crucial in enterprise strategy planning. By 2030, the global big data and analytics market value is expected to touch $684.12 billion. As more companies embrace data analytics to enhance customer experience, optimize existing business processes, and lower costs, it’s important to take note of the data and analytics trends that will hold the reins in 2024 and beyond.
Here’re ten trends to behold:
1. Scalable and Responsible AI
Research and Markets report that AI makes analytics 48% more effective for industry applications. Traditionally, artificial intelligence (AI) techniques were applied to analyze historical data. However, unpredicted events such as the COVID-19 pandemic increase the demand for real-time data analysis. Adaptive machine learning promotes scalable, responsible, and intelligent AI that offers insightful business analytics even with smaller datasets. Scalable AI will enhance learning algorithms, reduce time-to-value, and make business systems and data more interpretable. AI integration will increase the precision of data analysis in 2024.
Read more: 6 Ways Artificial Intelligence is Driving Decision Making
2. Hybrid, Multi-cloud, and Edge Computing
According to McKinsey, 70% of companies will adopt hybrid or multi-cloud technologies and processes by 2022. Hailed as the hallmarks of distributed IT infrastructures, multi-cloud management and edge computing enable companies to extend their computing capacity to the edge of their networks. This allows businesses to reach more data-hungry devices as the data is analyzed locally, close to the data source. Edge and multi-cloud reduce latency and improve decision-making with advanced, on-demand analytics. Today, every business generates volumes of unstructured data. Relying on traditional batch-based reporting to analyze big data cannot help anymore. 2024 will see the rise of distributed cloud models powered by hybrid, multi-cloud, and edge environments.
Read more: Future-proof Your Business with 5G, Edge Computing, and Cloud
3. Data Fabric Architecture
Data fabric architecture supports businesses to seamlessly navigate the complex digital business landscape that generates a lot of unstructured data every minute. It allows organizations to adopt a modular approach, known as composability, through which organizations can integrate new capabilities or features as low-code, reusable, individual components. Unlike the traditional monolithic architecture, composability allows businesses to integrate new features and changes to their enterprise applications without redoing their tech stacks. According to Gartner, data fabric reduces the deployment time by 30% and maintenance time by 70%. The ability to reuse technologies and capabilities from numerous data hubs, data lakes and data warehouses is expected to go a long way in tailoring analytics experiences.
4. Data Democratization and Self-service Analytics
The rise of low-code/ no-code digital platforms is accelerating the shift to self-service analytics. These platforms empower non-technical business users to access data, garner insights and make faster decisions. Today, self-service analytics is improving business response, enterprise agility, speed-to-market, and decision-making. InfinCE, a low-code workplace orchestration platform enables seamless team collaboration by extending your ability to integrate multiple business apps. Its data-powered business dashboard software supports marketers and non-technical users to analyze data, glean insights, track KPIs, and make strategic decisions. As data becomes the key to unlocking business value, 2024 will see the democratization of data extending beyond the realms of technical analysts and data scientists to ensure better inclusivity.
5. XOps
The merger of development (Dev) and IT operations (Ops) has given rise to the “Ops trend.” The list of acronyms with the suffix Ops is expanding pretty fast. XOps aims to bring all these terms (DevOps, DataOps, MLOps, ModelOps, etc.) under one umbrella to advance automation and AI adoption, and minimize the duplication of technologies and processes. XOps enables data and analytics deployments to function effectively in tandem with other software fields. In 2024, more data analytics experts will start using XOps to operationalize and automate their processes in conjunction with the software development cycle. This eliminates data management and insights generation challenges from the very beginning of software development. XOps will augment the power of enterprise technology stacks to deliver high-quality on-demand analytics.
Read more: DevOps: Building a New Culture of Software Development and Delivery
6. Graph Analytics
Gartner estimates that by 2025, 80% of data and analytics innovations will be crafted using graph technologies. Graph analytics employs deep learning algorithms to correlate multiple data points (entities such as people, events, things, locations, etc.) scattered across various data assets by exploring their relationships. This offers businesses a holistic understanding of the market, customer segments, consumer preferences and behavior, logistics, and risks. Graph analytics improves contextual understanding which enables businesses to identify problems and address them faster. SAP HANA is a leading graph database that comes with built-in processing engines to perform context-based data search. It allows users to access the correct data quickly. In 2024, graph technology will be used widely in search engine optimization, fraud and identity detection, supply chain logistics, social network analysis, and so on.
Read more: SAP HANA Helps Unlock Massive Health Data
7. Small and Wide Data
Until 2020, historical data replicating past conditions was enough to train AI and ML models. Disruptions caused by the COVID-19 outbreak have made such past data obsolete. It means that data analytics professionals should find new ways to use the available data more effectively. “Small data” and “wide data” techniques reduce the volume of data required for training AI models and help extract more value from diverse and unstructured data sources. By 2025, 70% of organizations will switch from big to small and wide data, improving contextual analytics and making AI systems less data-hungry.
8. Decision Intelligence
Decision Intelligence (DI) is a data analytics discipline that analyzes the sequence of cause and effect to create decision models. These decision models visually represent how actions lead to outcomes by observing, investigating, modeling, contextualizing, and executing data. DI helps make faster and more accurate decisions that result in better outcomes. Gartner forecasts that in the next two years, one-third of large corporations will leverage DI to augment their decision-making skills.
9. Generative AI
Generative AI is an artificial intelligence technique that uses existing text, images, and audio files to generate new content. This technique proves to be highly useful in producing new and authentic data that mimics the original in data-scarce situations. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are the two key technologies that support Generative AI. By 2025, generative AI will account for 10% of all data produced, up from less than 1% today, states Gartner. In 2024, Generative AI is expected to augment targeted marketing, drug development, and software code creation.
10. Natural Language Processing
If you’re using Google Assistant or Amazon Alexa, you’ve already experienced NLP in action. NLP supports data analytics in multiple ways by leveraging techniques such as speech recognition, machine translation, chatbots, text classification, sentiment analysis, and so on. It offers business leaders, marketers, salespeople, and researchers with the precise insights needed to make better decisions. Reports show that the rising demand for advanced text analytics is driving NLP adoption in sectors like healthcare, social media analytics, and consumer and market intelligence. 2024 will witness the rise of no-code and low-code NLP platforms that will make AI and ML more ubiquitous.
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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.
Read more: 5 ways in which big data can add value to your custom software development
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.
Read more: The Future of Artificial Intelligence – A Game Changer for Industries
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.
Read more: 9 Examples of Artificial Intelligence Transforming Business Today
Conclusion
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.