NLP (Natural Language Processing) and other AI functionalities have advanced considerably. That said, businesses still face a series of technical challenges that make the latest models difficult to use in real time, as they’re often limited by the need for advanced, albeit costly, computing requirements and GPUs. Solving this problem comes down to properly managing big data, but that’s easier said than done. Preparing data for analysis is a recurring challenge for data scientists, with ever-increasing data volumes making AI work more complex.
Organisations can do several things to meet this challenge. First off, it’s essential to have well sourced, consistent and labeled data. Then organisations need to foster data literacy by hiring specialists with the know-how to deliver actionable insights that they can disseminate across the entire business. They’ll also need the right data warehouse extensions to manage information flows, allowing users to apply AI/ML close to source systems, without extensive migration or building entire data pipelines.
Integrating additional industry standard tools and ML ecosystems will also be necessary to help users get value from the data quickly, efficiently, and cost-effectively. Lastly, well integrated no/low code ML solutions will help in streamlining AI-related work. All of this can be made possible with a powerful, high-performance in-memory database that combines all these functionalities.
Let’s discuss these steps in more detail:
Getting the right talent
It’s vital that organisations foster a data-driven culture across the entire business. A Chief Data Officer (CDO) is best placed to spearhead this, who can advocate for data literacy from the top down. Neglecting recruitment of a data leader can lead to skill shortages and business impacts that become difficult to ignore, with Exasol research suggesting only 32% of decision makers feel they’re able to get the insights they need from their data.
A CDO is also crucial to articulate a solid data strategy, which is needed in order to realise the importance of AI and the value it can bring to a business. Research from KPMG suggests organisations that have one are twice as likely to have a well-articulated data strategy. Forming the right strategy will provide a roadmap for addressing data-related challenges for successful digital transformation, allowing for the smooth integration of AI into operations.
Such investment of time and resources not only helps in automating and saving time but also in reaching a robust and resilient state for each business division in an organisation to leverage AI. For example, business units can optimise customer service, supply chains, user experience, etc., developing chatbots, predictive analytics, and user behaviour analytics respectively.
ML tools for efficient model development
Effective model training can be streamlined by interfacing industry-standard tools and ML ecosystems with powerful databases to get value out of big data quickly, efficiently, and cost-effectively.
For example, our platform interfaces with AWS Sagemaker so users get easy access to machine learning capabilities to make reliable predictions addressing a range of business scenarios. Users can load their data into an environment optimised for fully integrated ML development, granting access to next-level analytics performance that unifies AI/ML with BI.
Developers can create models in an intuitive interface, including classification models for categorising data, regression models for assessing relationships among dependent and changing business variables and forecasting to uncover trends within data to improve operations at a faster pace.
Common ML use cases include forecasts on stock levels to anticipate supply requirements from vendors, fluctuations in demand by product, identifying at-risk customers to reduce churn rates, predictions to detect fraud, and maintenance predictions to plan the upkeep of equipment.
No code and low code solutions for ML accessibility
Basic model training is becoming less of a technical undertaking with the greater availability of “no code” and “low code” solutions. Organisations will need to adopt these to democratise ML development, especially with the ongoing STEM skills shortage.
One of our partners, TurinTech, a leading provider of AI optimisation, tackles the inefficiency of manual machine learning development and coding processes so that organisations can build and deploy accurate AI models for a variety of use cases.
evoML, TurinTech’s ML platform, speeds up the entire end-to-end data science process from months to weeks, and from weeks to days, by optimising model efficiency for quicker inference speed and lower memory usage and energy consumption to drive sustainability. The platform provides visual explanations and cues to streamline AI work for non-technical staff, allowing them to run projects independently and freeing up specialist ML engineers for other tasks.
Streamlining AI
AI is not a state, it’s a journey. Technical capabilities need to scale with data requirements and culture so businesses can derive business value without being overwhelmed. The journey commences by acknowledging the potential of AI/ML, and it ends by realising this potential with market leading, in-memory, high-performance data solutions that integrate with the best AI tools.
