From helping organizations make better decisions with their data to improving workforce productivity, AI and Machine Learning have proven their value in the business world. It makes sense, then, that companies of all sizes are trying to figure out how best to integrate these tools into their organization.
Just like every new technology, it is imperative to build data strategies that account for the effects of AI. Where and how should you deploy AI and Machine learning tools? How does AI change your current data strategy?
The goal of this article is to address the above questions and provide guidance on the why and the how an organization should go about building a data strategy that incorporates AI and Machine learning.
What is data strategy?
An organization’s data strategy is a devised set of processes, actions, and goals that specify how to gather, create, analyze, and utilize data to drive business outcomes. If your organization is incorporating AI into their toolset, understanding how AI affects your business in the short- and long-term is essential. You need to understand how AI will fit into your existing data structures and how it will help realize your goals. Like every new tool, there are benefits and risks. Your organization needs to know the difference.
Why do you need a data strategy?
Businesses need a strong data strategy to align their company goals with technical applications. This will help you retain a competitive edge in the rapidly changing economy. A strong data strategy addresses core business needs and provides a clear roadmap with measurable milestones. This ensures that you are getting value out of your data.
However, a data strategy is only so good as the thought and insight that goes into it. How will our organization utilize big data? How will we secure that data? And now, how does AI affect our data?
AI and Machine Learning Value
It’s no secret that AI and Machine Learning tools are making waves across almost every industry. While there were initially fears that AI would be used to replace the ‘people’ element in the workforce, that has so far not been the case in most businesses. Instead, the value has come from automating repetitive tasks and increasing organizational efficiency.
Increased ROI: Investing in AI and machine learning systems has a high ROI.
Improved Analytics: Utilizes advanced statistical algorithms to make better predictions.
Automation of repetitive tasks: Automation of repetitive tasks allows more focus on tasks with a higher ROI and reduces human errors.
Freeing Human Capital: AI tools can work 24/7, allowing employees to focus on other things.
Digesting Big Data: Analyze millions or billions of rows of data.
Machine learning offers the ability to learn from data and use that to improve upon its processes. These tools learn by studying patterns in data and apply that learning to predict things in the future. This becomes immensely useful when processing large amounts of data, meaning AI and Machine Learning have enabled the next level of analytics.
Where do Machine Learning and AI fit in data strategy?
Having the right data strategy can make or break your organization’s ability to scale with AI. Many times, organizations have come to us asking for help solving a problem with AI/ML, however it turns out that the root cause is much simpler – their data.
In order to get the most out of AI, you need good data.
There is no one size fits all approach to building a data strategy. It must be created based on your organization’s unique goals, timeframe, and budget. A data strategy needs to be flexible to be able to add new tools such as AI or when the overall business strategy changes as well.
Investing in AI and Machine learning without a detailed data strategy will result in an increase of data errors, miscommunications, and ultimately, wasted resources.
Developing a data strategy that integrates AI and Machine Learning
Building a strong data strategy will enable the effective utilization of AI and Machine learning. It will provide a timeline, architecture, and support to address challenges that may arise.
To develop an AI integrated data strategy several steps should be taken:
Determine the importance and relevance of AI in your organization in relation to the desired business outcomes. This requires consulting AI and Machine Learning experts to help determine which AI solutions are needed for your problem.
Identify the kinds of AI applications and other necessary software.
Discuss data governance, architectural, and organizational challenges.
Identify the most beneficial AI use-cases for your organization.
Identify the necessary data. Ask yourself: Is there enough data? Is the quality high enough to use for machine learning solutions?
Building a data governance strategy will help to improve data quality and reduce the chances of underlying bias in your data.
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Building your organization’s data strategy from scratch can be a daunting task, especially when needing to incorporate a quickly evolving technology such as AI. At RevGen Partners, we can help your organization build an effective data strategy that incorporates AI and Machine Learning to achieve your goals. To learn more about the services we offer, visit our Analytics and Insights page.
Derek Plemons is a Consultant in RevGen’s Analytics & Insights practice. He specializes in data science, machine learning, and big data.
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