Benefits Of Becoming A Data-First Enterprise

Data is everywhere but only data is not sufficient to reap the benefits that come with it. It needs to be organized to enable the organizations to make more informed business decisions. In this article, we will learn what are the various benefits of being a data-first enterprise and using the data in developing a business intelligence solution.

By Vidhi Chugh, KDnuggets AI Strategy Content Specialist on July 22, 2022 in Data Science

Introduction

Data is a new oil but is not a differentiator these days. A lot of organizations are sitting on humongous amounts of data and need to know how to organize, access, and manage the data. Hence, speed i.e. agility to access the data and working with the quality data have become the key enablers to keep an organization ahead of the competition.

Benefits Of Becoming A Data-First Enterprise

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We will discuss the benefits of tools centered around data-driven insights. Further, we will learn how such tools can derive more meaningful insights when powered and fused with Business intelligence (BI) and Artificial Intelligence (AI) solutions.

But before we go deep into our main agenda, let's first understand what is business intelligence, and what is the difference between traditional and modern intelligence. Further, the article also briefly touches upon how BI is different from AI before elaborating on various benefits of being a data-first organization.

What is Business Intelligence?

It is the intelligence derived by managing and analyzing raw data and being able to generate actionable insights to help businesses take critical decisions. Put simply, it is a collection of data analytics tools to help leaders make informed business decisions.

But what kind of business decisions are driven by data? Well, almost all the decisions if your organization adopts data as a culture. Data-driven organizations do not rely on intuition or the number of years of experience. In the fast pace shift to digitalization, it is imperative to understand the customers’ changing dynamics by directly learning their preferences derived from the data.

Traditional and Modern Intelligence

There has been a shift towards the adoption of modern intelligence solutions. It gives the business users a more hands-free approach to visualizing the data and answering their questions.

Traditional BI solutions are more top-down i.e. the static reports answer certain questions for the business. In the case of follow-up questions, the process is repeated and goes through the multiple displeasing iterations of seeking the response from the analysts before being passed back to the business leaders. This slow and iterating reporting cycle blocks and weakens the business’ position to take timely decisions. As we highlighted the importance of speed at the outset of this article, such queued requests of follow-up queries lead to stale data and a competitive disadvantage for the business.

Hence, data democratization is the go-to solution that enables faster access to data, new data discovery and exploration, and effective information-sharing. Modern intelligence allows businesses to use analytics and respond to changing expectations, customize dashboards and create reports quickly. While IT teams continue to manage and secure the data access, seamless collaboration between business users and IT teams leads to a win-win situation for driving critical business decisions, faster.

Difference Between BI and AI

BI consists of a set of processes, tools, and technologies that transform raw data into meaningful information and enable effective decision-making.

Organizations, these days, have Data Analytics and Data Science teams driving data-related initiatives, hence it is important to understand the benefits of BI and AI solutions.

Broadly, the data analysis is divided into 4 categories:

Descriptive: it focuses on answering specific questions about what happened
Diagnostic: it answers the ‘why’ part i.e. why did a particular event happen?
Predictive: As businesses gear towards learning from historical data, predictive analytics help them to answer what is likely to happen in the future?
Prescriptive: Given the predictions of possible future events, prescriptive analytics enables the organizations with the best course of action or their response to that event.

While BI mostly gets to work on structured data and generate reports and analysis, the AI solution also takes unstructured data as an input and transforms it into the machine-understandable data format.

BI is mostly backward-looking i.e. it helps in answering high-level questions about what has happened and why? AI can extend beyond the past and is forward-looking i.e. it answers what is likely to happen in the future and what should a business do next?

The Benefits of Data Continue to Increase

Needless to say, data is crucial for an organization’s success and offers a multitude of benefits. Let us learn a few use cases where good quality data can set the right direction for your business:

Data is not just restricted to the analysis, but also includes the knowledge of which data resources will yield maximum value. When the leaders are tasked with identifying the projects with maximum ROI or high impact and value, data-driven insights help them prioritize projects leading to efficient utilization of time and resources.
Data also gives a holistic view of the different verticals, and regions in an organization. The insights can be generated at an aggregated level or can be narrowed down to zoom in to a specific division of interest. It helps businesses identify their strengths and weaknesses and eventually design their roadmap accordingly.
Quality data relieves the employees of spare time to make discoveries from the underlying data patterns. Such innovation ultimately opens new avenues for driving business value.
Data helps an organization answer questions like how to attract and onboard new customers, why a customer should choose their product, and how to improve it further to retain the customer base. Notably, the key to a successful business is to build a base of happy customers aka repeat customers.
Essentially, an organization can reap the benefits of data only if it's promoted as part of its culture i.e. every employee (including top management and developers alike) is contributing to making data-driven decisions. Good quality data leads to high confidence decisions and effective business outcomes. To summarize, smart use of data sets up a business for success.

Developing Data-Driven Insights

A lot of organizations are working towards developing an insights tool, the benefits of which are even quoted by Forbes.

“According to Forbes, companies that use the insights from big data experience an average increase in revenue of around 44%.”

Let’s understand what are the other organic reasons to invest in building such a tool:

It helps businesses understand what customers need, faster and quicker.
It assists in the business growth model by identifying how to attract and onboard new customers while building a happy customer base
It enables the establishment of the business value proposition by focusing on specific customer groups concerning what services and products are they most interested in
It reveals customers buying patterns, how they perceive the utility of a particular product and are likely to generate a significant Roi for business
To summarize, such a tool makes businesses understand the entire customer lifecycle by knowing them at a deeper level and designing appropriate campaigns to leverage such insights and serve their needs better

In this article, we learned the difference between traditional and modern intelligence, and how BI is different from AI. Next, we learned various dimensions where organizations can reap the benefits of good quality data. In the end, we also talked about what is the need to build a data-driven insights tool and how it acts as an enabler for business growth.

References

Vidhi Chugh is an award-winning AI/ML innovation leader and an AI Ethicist. She works at the intersection of data science, product, and research to deliver business value and insights. She is an advocate for data-centric science and a leading expert in data governance with a vision to build trustworthy AI solutions.