The Critical Role BAs Can Play in Companies Using Advanced Analytics


Big data, artificial intelligence, Internet of Things -- we keep hearing in the media about the technological advances that are helping organizations of all sizes innovate, make better decisions, improve the quality of products and services, eliminate costs, and more.

Until a few years ago, technological limitations made it hard for most companies to generate insights from most of the data it produces. Businesses had to rely on traditional data warehouses not always suited to managing the high volumes of structured and unstructured data available to support better decision-making and optimization of customer-facing products, services, and features. However, recent advances in technology are not only creating more powerful capabilities to manage and analyze data, but also making them much accessible to everyone with the help of open source initiatives and self-service tools.

As a result, in the current business environment using data to its full potential is much more about identifying and following through with high-value opportunities than it is about deploying technology. And that’s where business analysts in general and IT BAs in particular can find a big opening to elevate their roles within their organizations.

Let’s take a look at three of the many opportunities that the advanced analytics technologies developed over the past decade are creating for business analysts:

1) Building the business case for high-value analytics efforts

Skilled business analysts are some of the best positioned people in an organization to help decision-makers choose where to direct resource-intensive analytics efforts. Imagine a company where recent sales data is stored in a different system than historical sales, preventing the sales organization from boosting sales through personal product recommendations or evaluating the lifetime value of a customer. Making it possible to combine data from these different data sources costs money. A business analyst could help the company create a competitive edge by figuring out the initiative's expected ROI (return on investment) and identifying the best ways to remove the existing barriers to insight extraction.

2) Accurately defining and framing the problem to be solved

In addition to being able to produce solid business cases for data analytics projects, skilled BAs are capable of preventing costly mistakes commonly seen in analytics initiatives, like this one described by Carl Carande et al. in an article for HBR:

“Consider the case of a large global life sciences company that spent a significant sum of money building an advanced analytics platform without first determining what it was supposed to do. Executives allowed their technology team to acquire a lot of products, but no one understood what the advanced tools were supposed to accomplish or how to use them. Fortunately, executives recognized the problem before it was too late, conducting an enterprise-wide needs assessment and rebuilding the platform in a way that inspired confidence in its ability to drive efficiency and support business transformation."

Had a team of experienced business analysts been assigned early on to this initiative, the risk of the company needing to go through a costly rework effort to “rebuild the platform” would have been minimized. Understanding the problem to be solved would be one of the first tasks a seasoned BA would perform in the project. This would have helped prevent the deployment of any analytical IT tools not fully aligned with the goal of optimizing business outcomes.

3) Translating insight into action

In You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role, Nicolaus Henke, Jordan Levine, and Paul McInerney make the case for another important role in Data Science teams--the “translator”:

“(...) translators play a critical role in bridging the technical expertise of data engineers and data scientists with the operational expertise of marketing, supply chain, manufacturing, risk, and other frontline managers. In their role, translators help ensure that the deep insights generated through sophisticated analytics translate into impact at scale in an organization. By 2026, the McKinsey Global Institute estimates that demand for translators in the United States alone may reach two to four million.”

It isn’t hard to notice the similarities between the translator role described by the authors and the typical role BAs play as the vital link between business decision-makers and the information technology team. For example, when a manager needs data to support a specific, bounded decision, a business analyst can play a critical “translator” role both at the beginning of the analytics process, helping the manager frame the decision or business problem for the data analytics team, and at the end, helping present the results of the analysis in a way that becomes actionable for the decision-maker.

Everyone with minimum experience in quantitative analysis knows that effective quantitative decisions are not about the math or statistics; they are about the relationships. Business analysts tend to be people who communicate well and have a passion for solving business problem. From that vantage point, they can play a key role “telling the story with data” in order to enable the business to make better decisions as a result of complex analytics findings.

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We live in a time when business in many industries offer similar products and use comparable technologies. One of the last points of differentiation are processes, and the evidence is clear, in sector after sector: companies that figure out how to combine business domain expertise with advanced analytics to improve their internal and customer-facing processes are winning the market.

Without question, many barriers exist to leveraging advanced analytics to improve a company’s operational and strategic performance. From new and unproven technologies to cultural challenges and regulatory and privacy concerns, there are myriad barriers for investments in data science to pay off. On the flip side, the same challenges create unprecedented opportunities for data-literate business analysts to deliver superior value to their organizations. From finding opportunities in modeling and optimization, to identifying new ways to use data to increase the value extracted from internal processes or transforming analytics findings into a compelling story that cause stakeholders to take action, these BAs can help change the way organizations think, work, and bring value for their customers.

Have you been involved in advanced analytics efforts in your organization? If so, how did your experience in business analysis helped ensure the effort produced insights that would help advance business goals? Do share your experience in the comments section below.

Author: Adriana Beal, Data Scientist, Carnegie Technologies

Since 2004, Adriana Beal has been helping Fortune 100 companies, innovation companies, and startups in the U.S. gain business insight from their data, make better decisions, and build better software that solves the right problem. In 2016 she decided to follow her passion for quantitative analysis and specialized in Big Data and Data Analytics. In her current role as Data Scientist with Carnegie Technologies, Adriana works with data from IoT sensors and mobile devices in support of the company’s mission to empower businesses and people to connect with greater reach, ease, and trust. She has two IT strategy books published in her native country, Brazil, and work internationally published by IEEE and IGI Global. Her educational background includes a B.Sc. in electronic engineering and MBA in strategic management of information systems received from two of the most prestigious graduate schools in Brazil.

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BAKiwi posted on Thursday, April 4, 2019 9:54 PM
Thanks for posting this article. I definitely agree with the role of the BA being integral in understanding the problem at hand. However, I disagree with the statement about effective quantitative decision making not relying on good math or statistics. Just as it is important to frame the problem correctly, it is equally important to use the correct statistical or mathematical tool. Failure in performing correct math will expose one to wrong results and ineffective decision making, regardless of who you know.
Adriana Beal posted on Tuesday, April 9, 2019 8:11 PM
Hi COsborn,

I think there was a misunderstanding as to what I meant by "Everyone with minimum experience in quantitative analysis knows that effective quantitative decisions are not about the math or statistics; they are about the relationships."

I wasn't trying to say that good math or statistics is not important -- far from it!

My point was simply that rigorous quantitative analysis is the "price of admission" for a good evidence-based decision. Essential but not sufficient, since without the ability to translate the findings into something decision-makers will pay attention to, no action is going to be taken to materialize the business benefit. Does that make sense?
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