The 3 Pitfalls of your AI strategy

Define data AI machine learning analytics strategy

Organizations are now investing heavily in multiple data analytics, machine learning, and artificial intelligence solutions.  However, large investments do not guarantee success. To maximize Return on Investment (RoI), a strategic approach is imperative. Here are a few reasons why defining your analytics strategy can be challenging, and how to avoid these pitfalls.

Getting lost in the endless possibilities

The good news is that there is a plethora of the kinds of insights you can derive from your data and the automation you can achieve through AI. However, it can also keep moving the goal post and get overwhelming pretty quickly. When you are just getting started on this journey, prepare to face challenges.

The first step is to identify if you even need to build complex machine learning models. Can you just get by with a heuristic based approach instead? A heuristic based option may be viable if you don’t have enough historical data yet, if you are still figuring out if and how to build machine learning models, or if your heuristics can be easily tracked and remain mostly unchanged. Next, you will need to identify what is actually feasible given the data you have and what your data platform supports. Following this, you will have to decide where to start. This depends on what your goals are, and what your technical expertise is. Finally, you will have to focus on what will bring the most benefit, or what will be the most effective solution.

You are not focused on the long-term gains

A myopic vision may result in building a data platform that cannot be upgraded easily. You could start with a rule-based models that does not need a complex platform. As you build advanced machine learning models, you will need a scalable and performant platform with minimal downtime for your customers. Your machine learning platform may have to support ingest of streaming or batch data. If you implement compute intensive models for your artificial intelligence or deep learning use cases, you may also need to invest in hardware infrastructure that supports that. Your platform will need to mature as your data sources grow and your models evolve.

You want to ship something out the door as quickly as possible. As you do that, you should also have a sound long term vision for your product portfolio. Migrating platforms is more arduous than investing in planning for growth in usage and capacity of your platform.  Building robust machine learning models requires long term vision. Your platform and your models will evolve based on this vision.

You did not anticipate the challenges with hiring the right talent

Executing on your AI strategy can be challenging if you don’t hire the right talent. You will need to establish a cross functional team. You will need platform engineers to build your scalable cloud or on-premise platform. Experienced data engineers will help clean, extract, transform, load, and carry out data governance tasks. QA automation engineers are required to build frameworks that can identify failures and problems in your data. Data scientists build, train, and validate intelligent machine learning models. Your full stack engineers will develop the visualizations to showcase your data insights.

If your budget does not allow hiring an army of people right away, you can outsource some of the work and partner with a software consulting company to jumpstart the execution. With outsourcing, the resources can be fungible, which is particularly advantageous when various skillsets are needed during different stages of development.

About the author: ExaWise