With AI being implemented as an essential aspect of data lifecycles in businesses, it’s no wonder that people are asking whether AI will replace data analysts.
While there may be some worry around the subject, it’s important to remember that we are a long, long way away from a situation in which that could happen.
Analysts have data that they use and are in the business of constantly generating insights to improve company performance. They create key insights like revenue forecasts, efficiency-driving tactics, and sales predictions.
While the need for automation that has a direct line to business impact is a huge win for analysts, what they have that machines cannot is a deep awareness of the business. They understand their specific role in understanding what the challenges are and subsequently act on data-driven decision intelligence to solve these challenges.
Machine learning can be for anyone with swaths of data who want to solve pressing business challenges that result in a real, measurable impact. And without the subject matter experts that have a deep understanding of your business, you won’t get what you need from AI. Here are three reasons why AI will not replace data analysts.
The shortest pathway to implementing AI successfully is to start with a clear and tangible business problem. Who knows your business problems better than the people working in your organization every day?
Businesses require subject matter experts to get the most out of AI. In many cases, businesses will employ data scientists and give them what they need to create the kind of exciting solutions that make investors think that something is really happening here.
However, those teams will often not be able to meet the mark, especially when time is an issue. They just don’t have the knowledge on the subject at hand that people already working with data at a business already possess.
This article in Techwire states that “anyone (especially business leaders) developing AI models and using data to create insights should take note of the fact that solving most problems require subject matter expertise and hence, should involve experts who can provide the context required to make data more meaningful.”
The article uses an example from the restaurant business. Loyalty programs can track customer profiles and log their tastes and preferences based on time of day, location and menu. That data is handed to a team of data scientists who build an AI model. From that model they recommend that a restaurant needs to do more to reach targets for white wine sales at noon because they’re selling white-sauce pasta and chicken dishes.
If that restaurant is in a business district or near a religious institution, patrons would most likely be avoiding alcohol during working hours. The person who really understands the issue is already managing the restaurant, and without the manager’s input, the model makes much ado about nothing.
The seasoned employees at your business have already seen what happens to your company in a variety of conditions. They may have seen economic downturns and hypergrowth during their tenure. That level of experience is immeasurable and irreplaceable.
Your teams already know who to go to for the information they need, and they’re acutely aware of your long-term vision.
The best part of platforms that allow anyone to automate machine learning and data science tasks is that they let your subject matter experts keep a razor-sharp focus on your business goals with augmented decision making. Success in machine learning starts with a broad, well-informed picture of your business and it’s your experts that will help you get the most out of any AI platform.
In every business, data will be highly dependent on departments. In addition, there are always specific people who hold the keys to that data. In order to get the most out of an AI for business platform, businesses can make a shift from “data readiness” to “data ecosystems.”
What’s the difference? Data readiness refers to a model in which data is restricted and divided into different departments. Shifting to a data ecosystem model means that team members get a more holistic view of the business.
Deloitte defines a data ecosystem as, “a network of actors that directly or indirectly consume, produce, or provide data and other related resources. What you get is an interconnected network in which your employees can store, release, and share information.
When you grant more access to data, you’re making it easier for everyone to understand that data. Now they can use it to uncover previously missed opportunities and help to accelerate the decision-making process.
Building company-wide data literacy doesn’t happen overnight. Educating teams on how to better use the data they own and care for helps to create a deeper understanding of the true impact that data can have on reaching your goals.
If you can equalize access to machine learning tools, you’re getting one step closer to bringing your entire team even closer to the data they use. Even more than that, you’ll augment their data literacy in the process.
With this process in place, you’ll create a system that can’t be matched by AI, but it can be leveraged to make AI give your business even greater insights into your business goals.
Your subject matter experts who are already working with your data have been working towards making sure that results are communicated clearly and succinctly. A machine learning platform won’t necessarily make them better at sharing that data, but where they’ll be able to outshine their previous results is in their ability to say something more with their data.
For business analysts, especially, the heart of the job is in communication. What you don’t want to have, to borrow the line from the movie, is a failure to communicate. It is not just the results that need to be communicated, but the problem, as well. This prevents anyone from making assumptions about what can be done with data or what conclusion one might be able to reach with that data.
By identifying and controlling this critical aspect of a data workflow, subject matter experts insert the expertise in a way that allows you to derive meaningful information from AI, and not the other way around.
It doesn’t matter whether it’s a marketing strategist speaking to a CMO about optimizing promotional offers, or an operations manager delivering critical insights on organizational efficiency. The business context, relationship and historical knowledge that these subject matters hold can’t be underestimated.
Machine learning is nothing without data, and data is useless without a solid foundation and meaningful story behind it. Your people hold those keys to your success with AI and are an essential aspect of that transformational journey.
AI will not replace those people. Instead, having those people on your team will help you to see real, positive results from the AI workflows that can process information and give recommendations faster, but will still require a human touch along the way.
