Democratizing Data & AI

Unlocking Value for SMB's with Lean Investments

Today, the integration of data analytics and artificial intelligence (AI) is no longer exclusive to large enterprises with vast resources. Small to medium-sized businesses (SMBs) now have a unique opportunity to leverage these solutions through strategic, cost-effective investments.

The democratization of data and AI tools in recent years has also led to the development of more user-friendly tools tailored for SMBs, which offer intuitive interfaces, automated functionalities, and scalability options, making it easier to adopt and benefit from advanced technologies without requiring extensive technical expertise or significant upfront costs.

One challenge, however, remains: It can be difficult for SMBs without an IT department to know which solutions vendor to choose from, and which features are a must for your requirements.

This article addresses these key challenges in investing in data analytics & AI solutions, and explains how you can democratize data access and unlock value with smart, lean investments.

Data and artificial intelligence: An overview

The potential for data analytics and AI to drive business growth and innovation is immense.

For SMBs, leveraging these technologies can mean the difference between staying competitive and falling behind. Data and AI tools help your team make smarter decisions, optimize operations, and deliver personalized customer experiences, fueling growth and innovation.

Despite these benefits, many SMBs still believe that successful data and AI initiatives require massive investments. This is a myth, as the market has evolved to make such powerful tools and solutions accessible and affordable for businesses of all sizes in several key ways:

●????? Cloud-based services: The availability of cloud computing platforms and the Software as a Service (SaaS) delivery model allows SMBs to access powerful AI and data analytics tools without the need for significant upfront investments in hardware and infrastructure. Pay-as-you-go (PAYG) models provide flexibility, enabling your business to scale its usage of these advanced tools based on your current needs and budget.

●????? Subscription-based models: Many AI and data analytics providers offer subscription-based services that allow you to pay for what you use rather than invest in expensive software licenses. This also reduces the financial barrier to entry and provides ongoing access to the latest features and updates.

●????? User-friendly tools and platforms: Advances in technology have led to the development of intuitive, user-friendly AI and data analytics tools that do not require extensive technical expertise to operate. This democratization of technology capability means your SMB can implement and benefit from advanced solutions without needing to hire specialized staff. Such data and AI solutions currently are marketed under a variety of terms, including AI analytics, augmented analytics, and automated analytics.

Ultimately, the previous obstacles that once limited access to both data and AI-powered business solutions and the ability to leverage our business data for decision-making are now reduced, making it important for your SMB to begin assessing such tools to keep competitive.

The value proposition of data & AI for SMBs

Data and AI offer immense potential for SMBs to achieve both quick wins and long-term benefits. Here are a few specific areas where these technologies can make a significant impact:

●????? Marketing and sales optimization: You can use data analytics to segment customers and tailor marketing campaigns, resulting in immediate improvements in engagement and conversion rates. Over time, AI-driven predictive analytics capability can forecast sales trends and optimize pricing strategies, enhancing your customer lifetime value.

●????? Operational efficiency: By automating routine tasks like invoicing, inventory management, and customer support with AI tools, you can quickly reduce operational costs and improve productivity. In the long term, machine learning algorithms can refine supply chain logistics, minimizing waste and boosting overall efficiency. Automation also allows for constant monitoring of your data to detect any statistically interesting trends or changes and alert your team to take action, opening up more actionable insights.

●????? Customer experience enhancement: In the long run, having the ability to analyze customer feedback and behavioral data in detailed dashboards and reports allows your business to continually personalize and enhance the customer experience, fostering loyalty and repeat business - instead of continuing to rely on gut feeling, or outdated data reporting methods such as Excel spreadsheets.

Modern data and AI solutions encompass a set of tools and technologies designed to help your business collect, analyze, and act on data efficiently, and some vendors offer more advanced capabilities than others. Typically, the best tools heavily leverage AI and machine learning (ML) algorithms to uncover insights, automate processes, and make predictions, with key features including user-friendly interfaces, real-time data processing, and scalability. In recent years, data & AI vendors have begun to offer generative AI capability as well.

Outside of the value proposition values listed above, at its core, these tools can help greatly transform your SMB’s raw data into actionable insights, as their integration of automation, AI and ML significantly streamlines the traditionally manual data analysis process of yesteryear.

Related reading: The Complete Guide to Generative AI in Business

Real-world case studies of democratized data & AI unlocking ROI

Several companies have recorded achieving significant ROI through the use of data & AI solutions that have democratized data access and insights for more people in the business.

For example, a major UK insurer implemented Thorogood’s Reporting and Analytics Hub to overcome information silos and enhance their collaborative efforts. This solution allowed their employees to better share analyses across the organization, breaking down barriers between departments and democratizing access to valuable data. It also acted as a centralized platform to facilitate easier access to data, improved reporting, and a place to leverage insights from various reports, significantly enhancing operational efficiency and decision-making for all.

In the financial sector, Charles River Development helped investment firms and asset owners by enhancing interoperability between legacy systems through an analytics solution that facilitated seamless data communication, improving workflows and collaboration across the organization. The democratized data access led to better customer satisfaction, retention, and revenue growth by enabling employees to access and utilize data more effectively across various departments and systems.

For a data & AI-specific example, Dialog Axiata transformed its analytics capabilities by launching the "Analytics at the Edge" program to decentralize its data analytics capabilities, empowering employees across 20 different business units to analyze and act on data independently. The transformation included training for employees with little to no previous IT knowledge and the implementation of a cloud-based platform with AI features, resulting in significant revenue gains and cost savings - contributing to $12 million US in revenue in 2020.

Challenges in adopting data & AI solutions

Adopting data and AI solutions presents several challenges that your business must first recognize and then carefully navigate to ensure successful implementation and utilization.

●????? Data hygiene and clean data: Maintaining clean, organized data is essential for effective analytics and AI implementation. Poor data quality can lead to inaccurate reporting, compromised model predictions, and ineffective AI applications. Common issues include duplicate entries, missing values, and inconsistent formats, which can significantly hinder AI performance. Establishing robust data governance frameworks, conducting regular data audits, and using automated data cleaning tools are critical to maintaining high data quality standards.

●????? Computational limitations: Training AI models often requires substantial computational resources, which can be costly. To address these limitations cost-effectively, your business can assess cloud computing platforms like Microsoft Azure , which offers scalable computing power and flexible pricing models, to reduce your need for expensive on-premises infrastructure. Deploying edge computing to process data closer to the source reduces latency and saves costs associated with cloud computing, and leveraging pre-trained models can significantly reduce the computational resources required for training new AI models. These latter two options, however, may require the help of an IT partner to guide your SMB’s implementation.

●????? Ensuring practical application of data insights: For AI and data analytics to drive business outcomes, insights must be actionable and aligned with your business objectives. Adopting a shiny new solution looks good on paper, but if your workforce does not understand its value, they may not practically apply it to your work, leading to wasted costs and opportunities. This extends to modern data & AI solutions, which, while more streamlined and geared towards non-technical business people than ever, still require encouragement for your leadership teams to ensure people use such tools consistently. Close collaboration between your in-house data experts or business leaders is necessary to help translate these tools and their delivered insights into practical business strategies, but ultimately, we recommend SMBs find a managed service provider (MSP) specialized in data & AI to lead your deployment and training.

Learn more: The Ultimate Guide to Managed Services (2024)

Start small with data & AI

For SMBs, it’s clear that data & AI solutions present significant value, but require some planning and technical expertise should you lack the internal resources to spearhead the initiative. But while you investigate MSP or external IT partners for assistance, you can start with high-impact, low-complexity applications of data and AI to demonstrate value to your leaders quickly.

Some examples could include:

●????? Customer segmentation: Data analytics can segment your customers based on behavior, preferences, and demographics and can help tailor marketing efforts and improve customer engagement. This requires relatively simple data analysis but can significantly enhance marketing efficiency and customer satisfaction in the long and short term.

●????? Inventory management: Implementing AI-driven inventory management solutions can optimize stock levels, reduce waste, and improve supply chain efficiency. AI models can predict demand based on historical data and seasonal trends, providing actionable insights without extensive complexity.

●????? Chatbots for customer service: Deploying AI-powered chatbots can automate responses to common customer inquiries, freeing up your human resources for more complex tasks. This application is straightforward to implement and can improve customer service efficiency and response times.

Democratizing data & AI: Next steps

Increasing access to data insights and AI-powered tools that inform decision-making and streamline workflows is evidently a significantly beneficial solution you can bring to the business. However, to align these amazing solutions with your business requirements, budget and long-term strategy requires a guiding hand and proven expertise in the field.

Speak to the team at SparkNav today and learn how we can help kickstart your data & AI initiatives with customized strategies, support, and services tailored to your needs.