Four Ways Analysts Can Increase Value Across Your Data Strategy

It sounded like it was going to be a good meeting. The data science team briefed company leadership on what the data said about a proposed cost-cutting initiative, excitedly revealing findings that took them a few weeks to pull together due to the dozens of similar mission-critical requests they received.

But by the meeting’s end, the execs around the conference table realized the team, through no fault of their own, has left behind as many questions as answers: Switching up the supply chain is risky, can we be sure about the tradeoff? Will cutting sales headcount hurt us more than help us? Was that a foreign language? What did all that actually mean?

Similar scenarios play out all the time around data-driven decision-making because data scientists don’t have the bandwidth to quickly go back into the data and answer new questions, nor do they always have the ability to translate technical findings into business strategy. This leaves the job of determining what to do next to the business stakeholders.

However, a 2023 CIO.com survey of data science and analytics leaders revealed that a majority found it difficult to determine the value of the data they are collecting, which means it will be even more challenging for business leaders to interpret how to implement it.

So, how can business and data analysis teams get on the same page and each pressure test the other’s thinking? One part of the solution: Upskill business analysts with advanced analytics tools powered by AI.

How AI-Guided Tools Help Analysts Improve Business Strategy

AI-guided analytic platforms give business analysts the ability to add more value to the enterprise data strategy and provide answers in the context of the corporate strategy.

Here are four ways this technology enables this:

(jijomathaidesigners/Shutterstock)

1. Take On More Complex Data Analysis

It used to be that business analysts needed advanced analytic skill to explore the complex, interconnected data sets required to answer big business questions, but AI-guided analytics can do a lot of the heavy lifting, automatically finding insights for the analyst and suggesting, in plain language, what to do to explore the data more in-depth. This leads to business leaders getting the important answers they need in a shorter time frame.

2. Surface New Opportunities to Improve the Business

In today’s world datasets have become increasingly intricate and multidimensional, with numerous recorded attributes (dimensions) for each sample. This complexity in data structure presents both challenges and opportunities in data exploration, as analysts must navigate and analyze relationships across multiple dimensions, allowing for a more comprehensive understanding of patterns, trends, and interdependencies within the dataset.

Due to its challenging nature, multidimensional data often goes unused because analysts don’t have the tools to explore it and data science teams don’t have the bandwidth to use code to find the information and apply a visualization that would adequately communicate what it all means.

With AI, both the insights and visuals can be automatically generated, enabling the business analyst to spend more time deducing the strategic value of a new opportunity and working in collaboration with stakeholders to test the idea, analyze the results, and iterate as needed.

3. Validate and Prioritize AI Use Cases

The 2023 CIO.com survey also found that more than half of AI projects fail to produce actionable results. Ironically, it’s by putting AI in the hands of analysts that they can do the necessary strategic analysis on the potential value of big data initiatives upstream.

(Sergey Nivens/Shutterstock)

For example, a business analyst can explore customer journey signals across credit scoring models, risk management systems, and regulatory requirements to identify the best AI solutions for a new marketing program. The right tools help them do this kind of in-depth investigative work so the business can invest in the right AI use cases.

Going a step further, a data analyst can use AI to help identify risks associated with potential AI use cases. By taking steps to prove the proposed concepts, those analysts can demonstrate the feasibility and anticipate the impact of those AI use cases. With powerful AI-guided data exploration, teams can do more with their data and deliver massive results.

4. Act as Data Translator to the Business

Not many business leaders can accurately decipher data science speak. But business analysts are the perfect data translators, acting as the liaison between the data science team and the business. While AI-guided analytics helps the analyst do deep data exploration, it’s their knowledge of the business goals that not only keeps them on the relevant analysis path but also enables them to connect insights back to their impact on the strategy and help leaders act on the data with more confidence.

Invest in Your Analysts

Software alone is not a strategy, which is why it’s important to empower your people with the right tools they need to do their jobs.

As much as you’re investing in better leveraging resources to find hidden competitive advantages in your data, you’re also investing in your analysts, helping them develop and grow their skills. Not only are analysts more likely to stay at organizations that empower them this way, but they are also helping companies adapt to the future when data will truly drive all parts of operations.

About the author: Ciro Donalek is the Chief Technology Officer and Co-founder of Virtualitics. Ciro previously was a computational scientist at Caltech, where he successfully applied machine learning techniques to many different scientific fields and co-authored more than 100 scientific and technical publications (e.g., Nature, Neural Networks, IEEE Big Data, Bioinformatics). Ciro has also pioneered some of the uses of Mixed Reality for immersive data visualization, exploration and machine learning. He has five patents in the fields of AI and 3D Data Visualization. During his 20 years career as a data scientist, Ciro has been awarded different research fellowships, served as reviewer for numerous major scientific journals, and has given many invited talks on Machine Learning, Virtual Reality and Data Visualization. Ciro holds a PhD in Computational Sciences / Artificial Intelligence from the University Federico II of Naples, Italy and a MS in Computer Science from the University of Salerno, Italy.