As B2B businesses seek to overcome data challenges, make data-driven decisions, and stay competitive in the ever-evolving business landscape, learn more about data analytics and Analytics as a Service (AaaS).

In brief:

• Data has become a valuable currency for B2B businesses
• B2B businesses face challenges in harnessing the full potential of data analytics
• Analytics as a Service (AaaS) seeks to address these challenges
• AaaS applications have real-world use cases in various industries

Table of contents:

In the modern business landscape, data has become the new oil, powering businesses and economies worldwide. Data has been transformed into a valuable currency in the digital transformation age, empowering businesses to make more informed decisions, optimize operations and create new growth opportunities.

However, amid this data-rich environment, B2B businesses encounter their fair share of challenges when looking to harness the full potential of data analytics. With concerns relating to data security, scalability and expertise, businesses are turning to AaaS.

This innovative solution is providing businesses with the opportunities to grow through data-driven insights and strategies. This is particularly key at a time when 43% of CMOs at our recent CMO Digital Summit voted that joining up the data was their biggest challenge when it comes to technology.

The power of data analytics in B2B

By now it’s no secret that B2B data analytics can be a game-changer, providing valuable insights into customer preferences, market trends, competitor behavior and more. Armed with this information businesses can tailor their offerings, optimize processes and stay ahead of the competition.

Various applications of the data, from analyzing trends or patterns in your data can help optimize sales and marketing strategies to improve Key Performance Indicators (KPIs) such as campaign performance and/or overall conversions.

As Baseline puts it, “Data has become the lifeblood of industries across the board”. Leading companies have achieved impressive growth and efficiency gains by leveraging data analytics – Netflix has been at the forefront of the streaming service race with a staggering retention figure of 93%, using data to discover customer behavior and buying patterns. E-commerce platforms, social media companies and even HR benefit from diving deeper into their data.

“Surface data and have the analytics to guide that strategy… shift your [Employee Value Proposition] EVP and think differently about how you are engaging with talent.”
Jill Stover, Regional Vice President, Enterprise Employer Partnerships – Handshake, speaking at our recent HR Digital Summit

Challenges in implementing data analytics

The full potential of harnessing all those data-driven insights can often be impacted by various obstacles B2B businesses face. As data becomes more valuable, the more crucial it becomes to protect it from cyber threats. Businesses in the B2B space across industries handle vast amounts of proprietary and customer data, and as we know, any breaches can be high profile and catastrophic causing reputational damage if not legal and/or financial financial ramifications. Hindering confidence in exploring collaboration with external analytics providers.

Scalability is another common issue. The datasets B2B businesses deal with are often complex and vast, requiring significant computational power and storage capabilities. As the volume increases, traditional IT infrastructures may struggle to keep up, causing bottlenecks and slow processing. Scaling up can be daunting with cost factors and other resource and expertise in managing these systems.

Expertise is required not only for scaling, but it is also challenging for businesses to create a professional in-house staff capable of managing complicated data analytics duties successfully. As competition for talent in the data analytics area intensifies, attracting and maintaining experienced experts can be difficult, particularly for smaller organizations that may not be able to provide the same wage packages and perks as larger corporations.

The data analyst job market is predicted to grow by 23% from 2021 to 2031 according to the U.S. Bureau of Labor Statistics.

Building an in-house analytics infrastructure is a significant undertaking which requires substantial investments. Smaller B2B businesses may struggle to allocate the necessary funds to establish and maintain a robust analytics infrastructure. The costs of acquiring hardware, software licenses, and ongoing maintenance can be prohibitively expensive, leading to a competitive disadvantage compared to larger organizations with more significant resources.

Given these challenges, B2B companies are increasingly relying on AaaS to address them. AaaS suppliers offer scalable, cloud-based solutions that handle data security problems by utilizing cutting-edge security and compliance methods. Businesses can gain access to advanced analytics tools and knowledge by partnering with AaaS providers without making large upfront investments or keeping an in-house team. This method democratizes data analytics, allowing organizations of all sizes to harness the potential of data-driven decision-making and achieve a market advantage.

Analytics as a Service (AaaS)

As a cloud-based model that allows businesses to use advanced analytics capabilities without significant capital investment or technical expertise, AaaS provides a scalable and flexible solution to manage and analyze vast amounts of data, enabling companies to make data-driven decisions quickly and efficiently.

By outsourcing analytics to a service provider, companies can focus on core business activities while gaining access to the latest analytics tools and technologies. AaaS offers cost benefits, as companies only pay for the services they use, eliminating the need for upfront investment in hardware and software.

AaaS offers several key benefits that make it an attractive option for B2B businesses:

• Cost-effectiveness: Eliminating the need for significant upfront costs, as companies only pay for the services they use on a subscription basis. This cost-effective model allows businesses of all sizes to access advanced analytics capabilities that were previously out of reach
• Scalability: AaaS companies offers scalable solutions that can adapt to changing business needs. As data volumes increase, AaaS can readily meet the additional demands without requiring extensive infrastructure modifications. This scalability means that firms can efficiently handle larger datasets and analytical processes
• Faster implementation: Businesses can get up and running quickly, as the service provider handles all the technical aspects, including data storage, processing, and analysis. This rapid implementation allows companies to start deriving valuable insights from their data without delay

Numerous companies have successfully embraced AaaS combined with AI to drive their business strategies as Inquirer.net explore:

• Amazon’s AI-powered recommendation engine has not only increased sales but also improved customer satisfaction, as users receive personalized product recommendations based on their browsing and purchase history
• Netflix’s significant investment in training machine learning (ML) algorithms has allowed them to tailor the streaming experience for their 406 million global subscribers, resulting in higher customer retention and user engagement
• General Electric (GE) uses AI-powered BI to improve maintenance processes and reduce downtime, resulting in enhanced operational efficiency and cost savings
• UPS leverages AI-powered BI to optimize delivery routes, saving time and fuel, which ultimately improves the overall logistics process

The combination of data analytics and AI technologies like ML (Machine Learning) and natural language processing increases the power of AaaS even further. Furthermore, AI systems have shown that they can evaluate massive amounts of data faster and more correctly than humans. By sifting through data, discovering patterns, detecting abnormalities, making predictions, and examining photos or video, it is possible to go beyond the limitations of typical BI (Business Intelligence) tools.

“There’s data analytics in AI with the exponential growth of data, we have to build these advanced techniques to be able to understand all these data signals, derive insights and feed that back into a marketing performance so that we can build effective customer experiences and personalization.”
Murari Gopalan, VP Search Marketing & Technology – Expedia speaking at our recent CMO Digital Summit 

Overcoming data challenges with AaaS

AaaS offers numerous benefits, ranging from its ability to handle large datasets and complex analytical tasks to its wide range of applications in optimizing operations and decision-making.

Robust data security and privacy measures:

Data security and privacy are major concerns for businesses as they handle sensitive information. However, AaaS providers are acutely aware of these challenges and invest heavily in implementing robust security measures and data governance protocols. By entrusting their data analytics to reputable AaaS providers, businesses can gain peace of mind knowing that their data is in safe hands.

Addressing the skills gap:

The shortage of skilled data professionals is a challenge faced by many organizations. However, AaaS providers are actively tackling this issue by investing in training and development programs to upskill their workforce. With AaaS, businesses gain access to a team of data experts who possess the required knowledge and expertise to analyze data effectively and provide valuable insights.

Real-world use cases:

AaaS applications have demonstrated their potential in real-world scenarios across various industries. For instance, conservation groups utilize GPS tagging and camera traps to collect real-time information on the movements of critically endangered animals, empowering them to make informed conservation decisions and aiding in preserving biodiversity. In the healthcare sector, wearable tech plays a vital role collecting vast amounts of data on patients’ day-to-day health, providing recommendations for preventive care, ultimately enhancing patient outcomes. With the increasing use of data analytics in healthcare, the conversation around data ethics becomes crucial. AaaS providers play a pivotal role in driving this conversation, ensuring that businesses adhere to ethical standards.

Future trends

Edge AI:

The rise of Internet of Things (IoT) devices has ushered in the era of Edge AI. This enhances privacy and security, as data is processed locally, reducing the need for data transfer to centralized servers. Tesla’s use of edge AI to power its Autopilot feature exemplifies how this technology ensures responsiveness and reliability in critical applications.

Federated learning:

This allows AI algorithms to learn from data across multiple devices without compromising privacy, by keeping data separate and only sharing insights with the central server. The Mayo Clinic’s application of AI federated learning to improve diagnostic accuracy for heart disease.

Ethical AI practices:

As AI becomes more pervasive, ethical and responsible practices are of paramount importance, as we touched on in our recent article on AI ethics. These practices will be central to building trust with customers and stakeholders.

Conclusion

The data analytics revolution offers tremendous potential for businesses across industries. By embracing AaaS, B2B businesses can overcome common data-related hurdles and leverage the latest analytics tools and technologies to drive growth and innovation. As the data landscape continues to evolve, the role of AaaS will only become more critical, shaping the future of business and technology.

“Without data, you’re just another person with an opinion.”
W. Edwards Deming