In recent years, Data Science and Advanced Analytics have been among the most talked about topics during the Strategy Sessions and Board Meetings of most of the Fortune 1000 companies. The pandemic and remote working have only accelerated these conversations. Consumers across the globe are getting more tech savvy and they are making educated expenditures from the comfort of their living rooms.

Global MNCs have understood the value of reaching and educating these remote professionals who surpassed their spending power more than ever before. It is no longer just the CXOs or Business Heads of B2B or B2C companies who are making business decisions. 

• • According to Pew Research Surveys, Gen Y’ers are increasingly getting involved in the decision-making process within their organizations. These under-40 power hitters focus on advanced technologies and their computing prowess to make decisions. They do not rely on word of mouth or old networking channels to decide on their technology service provider or business consultant.
    
    
  • Almost 56% of the Gen Y decision-makers and influencers surveyed said that they rely on AI/ML models and Omni-channel Auto-bots to arrive at their conclusions. This number was even higher (approximately 2/3) amongst Gen Z respondents.

Now every business leader of every Fortune 100 company who is selling some product or service across the globe is interested in knowing how to connect and influence these Gen Y and Gen Z professionals and consumers. 

They can start with knowing what the top trends in AI/ML are, it being one of the primary mediums to reach the Gen Y’s and Gen Z’s.

AI/ML Trend of The Town in 2024

We at Coforge believe that the following are the top 5 AI/ML trends for 2024. 

Some of these trends may have already been discussed in research forums and attempts might have been made to incorporate them but none of them have been successfully implemented so far. 

Most organizations today have successfully embarked on their data journey. Therefore, Analytics, Artificial Intelligence, and Machine Learning are commonly discussed topics within organizations, large and small. The effectiveness of making data-backed decisions using sophisticated AI/ML models is very well understood by business leaders. 

Over the past decade, organizations have heavily invested in technologies, systems, processes, and systems for Data Analytics. Despite all the interest and investments, the ROI (Return on Investment) on Analytics is poor across the majority of these organizations.

In a recent LinkedIn study, it was found that 42% of Data Scientists found that the data models that they had built were never used within their organization. 

Few other research firms found that most global organizations yield single-digit ROI (Return on Investment) on their data investments. There is a clear gap between expectations and realization in the Data Analytics space.

The important question is how to bridge the gap. To simplify the approach, we believe that the lack of the data ecosystem is the missing link.

Here is one of the trends that has been catching on promptly. 

Building a concentrated team that holds the different stakeholders together, the data culture team of data engineers, product designers, business analysts, and functional managers. Each of them needs to have a professional background in data analytics so that they act as a glue with all the data analytics stakeholders to design business-centric tech stacks insights-driven executive dashboards or user-friendly AI/ML models. 

The need to promote a data-first mindset within an organization that starts from the top has become imperative today. Data Culture must be initiated by the top leaders and their behavior should be modeled on data-centric decision-making. 

Having a centralized team will lead to promoting a strong data culture which will in turn help organizations to perform more efficiently using their in-house data, tools, processes & people. 

2. Small & Wide Data Analytics (or Small Language Models)

Small Data is all about collecting and analyzing data sets sourced within individual organizations or based on individual problem-solving examples. It answers very specific questions. 

While Wide Data is all about tying together disparate data sources across a wide range of sources to generate meaningful insights. A similar trend is being observed with SLMs (Small Language Models) selectively replacing the LLMs (Large Language Models). 

Designing LLMs tends to be time-consuming but on the other hand, running LLMs on the cloud is an expensive proposition. SLMs therefore provide you flexibility & cost savings at the same time. It's in its infancy stage and will evolve over the years.

Hyper-local food delivery is a classic example of effectively using small data analytics techniques. Consumer-facing e-commerce companies in the Fintech, Edutech, Entertech, and Agritech sectors have used small data to uncover their localized customer behavior patterns, likes, and dislikes. Retail companies can use small data to understand their customers’ psyche to design and promote localized sales, marketing, or distribution strategies.

• • Classic examples and the use of wide data analytics are Stock Market prices and valuation factors for a systematic investment strategy and the Marketing Campaign data across multiple channels.
    
    
  • Retail, CPG (Consumer Product Goods), QSR, Banks, Financial Services & Hospitality are the companies that will benefit from wide & small data analytics by understanding their consumers behavior patterns on a localized level.
    
    

Since late 2022 with the emergence of Chat GPT, Generative AI has caught the attention of every business leader. Gen AI use cases across business sectors are being designed in hordes. Their ease of use, applicability, and return on efforts have been qualified. 

Organizations have either developed their own Gen AI Platforms or have tested Gen AI Platforms provided by their Technology Partners with the latter being the more common occurrence. POCs (proof-of-concepts) for Gen AI models are being successfully run across global industries and geographies.

2024 is going to be all about putting these insights and learnings to test and operationalizing Gen AI within the organization. The key components for successfully operationalizing Gen AI are –

• • Cleansing, harmonizing and seamless alignment of data, systems, processes, tech stacks and people
    
    
  • Choosing the right Gen AI platform & Use Cases and ensuring buy-in from the business stakeholders
    
    
  • Educating and training the User ecosystem - internal users, vendors, customers, etc.
    
    
  • Future state planning and ROI (Return on Investment) justification

A data fabric is a data management architecture that can optimize access to distributed data and intelligently curate and orchestrate it for self-service delivery to data consumers. This technique provides a single environment for accessing and collecting all data, no matter where it is located and no matter how it is stored. 

Data Fabric delivers greater scalability that can adapt to increasing data volumes, data sources, and applications thereby making it easier to leverage the cloud by supporting on-premises, hybrid, and multi-cloud environments, with faster migration between these environments.

We believe that Data Fabric can complement the AI Frameworks for fraud detection, cloud security applications, real-time sales activities, and customer onboarding where the AI/ML model can highlight the topical map of anomalies and inflection points. Leading advisory firms like Gartner & Forrester have been highlighting Data Fabrics as the AI trend for the past 2 years. 

However, the primary reason it has not taken off is that organizational leadership lacks the knowledge and belief in data fabrics. If their leaders partner up with the right data advisors, their companies can transform the fundamental way in which businesses can learn from their past and evolve.

BFSI, Manufacturing and telecommunication companies can immensely benefit from the data fabrics if designed and implemented in a clearly defined and structured format.

Cybersecurity Analytics involves aggregating data to collect evidence, build timelines, and analyze capabilities to perform and design a proactive cybersecurity strategy that detects, analyzes, and mitigates cyber threats. 

With a normal security information and event management (SIEM) system, you have to depend on testing things as they exist in a singular moment within the network. Cybersecurity analytics applies to the network, including general trends that may not be evident in each snapshot. 

Cybersecurity analytics uses machine learning (ML) and behavioral analytics to monitor your network, spot changes in how resources or the traffic on the network are used and enables you to address threats immediately. 

Cybersecurity Analytics tools can provide benefits like prioritized alerts, automated threat intelligence, proactive incident detection, forensic incident investigation, and organizational network security health checks. 

In 2024, we expect the cybersecurity transition from protection to detection using AI/ML and analytics.

Conclusively

As we conclude our exploration of the top five trends in data analytics for 2024, a clear picture emerges: we stand at the forefront of a new era, one brimming with both challenges and opportunities. The shifting landscape, driven by the demands of Gen Y and Gen Z professionals and accelerated by technological advancements, necessitates a fundamental reevaluation of established business models. 

In conclusion, 2024 stands as a pivotal moment in the history of data analytics. It challenges us to overcome inertia, embrace innovation, and venture forward with boldness and agility. As we embark on this transformative journey, let us remember: the future belongs to those who leverage data insights, prioritize proactive strategies, and embrace the audacious possibilities that lie ahead. By chartering new horizons and redefining what's possible in the dynamic world of data analytics, we can collectively usher in a new era of growth and success.