W. Edwards Deming, father of the Quality movement said: “Without data, you’re just another person with an opinion.” Data doesn’t just provide evidence for an underlying concern but also leads to a solution. The likelihood of resolving the problem statement increases with consistently measured and monitored data.
Technology has made it possible to gather data, in real-time, from the source, hence ensuring its authenticity. With IoT, the variety, volume, and velocity of data are no longer a challenge. Cloud technologies are successfully supporting mammoth amounts of data. Advanced, yet simplified data analytics setups are available to transport the rightful data into the cloud where it will be readily available for consumption in analytics, reports, and dashboards.
“What gets measured, gets managed,” Modern management theorist and consultant Peter Drucker once said. Every data science problem statement can be resolved by trusting Drucker’s theory. Data analytics will help you to know what your business is excelling in, what its pain points are, what the future pitfalls are, and the strategies to steer ahead.
According to Wikipedia, being data-driven means that progress in an activity is compelled by data, rather than by intuition or personal experience.
When British mathematician Clive Humby declared in 2006 that “data is the new oil,” he meant that data, like oil, isn’t useful in its raw state. It needs to be refined, processed, and turned into something useful; its value lies in its potential.
At a broad level, a data-centric and data-driven organization will implement the activities of Data Engineering, Business Intelligence, and Business Analytics and eventually AI.
Data Engineering
Data Engineering involves designing and maintaining eco-systems for processing, storing, and analyzing large amounts of data.
Suppose you have a requirement to collect your business data into a centralized data store where it will be readily available for consumption by various stakeholders. In that case, data engineering is the right strategy to adopt.
To get the complete picture of your business use case, you will have to ingest data from heterogeneous and multiple sources like APIs, spreadsheets, SQL and NoSQL databases, and flat files in on-premises siloed systems. Today, organizations employ systems to collect data at source, in all business functions. Identifying the data points that are critical to realizing the business use case is an imperative activity.
Data integration will follow to add the right contextual reference to the data and make it complete. At the same time, it will be critical to keep this data separate from your transactional data, in a central repository so that all kinds of analytics can run without performance hassles. Data pipelines will facilitate the movement of raw data from its source(s) into the desired central data store which can be a data warehouse or a data lake.
The business use case will help shape the data model and add a functional angle to the data. Define data models that will reflect the business requirement, making it easy to consume.
As per the business needs, data will be filtered, aggregated, and transformed either on the source or destination side. Data will now be available in an understandable format, self-service mode, ready to be consumed by business users.
Business Intelligence
Business Intelligence (BI) concentrates on current and past events recorded in the data. It helps you understand what has been happening in the past, along with the status through intuitive reports, dashboards, and charts.
BI services primarily focus on examining, cleaning, transforming, and interpreting data to discover meaningful insights.
Descriptive Analytics: BI primarily focuses on descriptive analytics, which involves analyzing historical data to gain insights into past and current business performance.
Data Visualization: BI tools are used to visualize data through dashboards, reports, and charts, making it easier for users to understand and interpret information.
Reporting: BI solutions enable organizations to generate standard and ad hoc reports to monitor key performance indicators (KPIs) and track business metrics.
Business Analytics
History drives the future. What has happened in the past should guide us to define what should or should happen in the future. While BI concentrates on current and past events recorded in the data, Business Analytics (BA) focuses mostly on what is more likely to happen in the future. Business intelligence from your historical data can be leveraged to envisage your future trends, drive business strategies, and take decisive actions.
BI is a crucial part of BA services, involving the examination and interpretation of data. This can be further enhanced by mining huge amounts of data to uncover the secrets in the form of patterns and knowledge.
Predictive Analytics: BA builds upon BI by incorporating predictive analytics capabilities, which involve using statistical models and machine learning algorithms to forecast future outcomes based on historical data.
Forecasting: BA solutions can predict trends, patterns, and behaviors to anticipate changes in customer demand, market dynamics, and business performance.
Advanced Analytics: BA leverages more sophisticated analytical techniques, such as regression analysis, time series forecasting, and decision trees, to uncover insights and make data-driven decisions.
Artificial Intelligence
AI services leverage advanced algorithms and machine learning techniques to enable computers to perform tasks that typically require human intelligence. The purpose of AI is to automate processes, identify patterns in data, and make autonomous decisions to solve complex problems and optimize business outcomes.
Machine Learning (ML): AI represents the next evolution in data analytics, encompassing advanced technologies such as machine learning, deep learning, and natural language processing.
Autonomous Decision-Making: AI systems can learn from data, identify patterns, and make autonomous decisions without explicit programming.
Prescriptive Analytics: AI goes beyond predicting future outcomes to recommend actions that optimize business processes and achieve specific objectives.
Automation: AI enables automation of repetitive tasks, cognitive processes, and decision-making, freeing up human resources for higher-value activities.
Data-Driven Insights and Innovation
Throughout this journey, organizations develop a data-driven culture where data is viewed as a strategic asset and decision-making is guided by insights derived from data analytics.
Continuous Improvement: The journey from BI to AI is not a one-time process but rather a continuous evolution, with organizations constantly refining their analytics capabilities and leveraging emerging technologies to stay ahead of the curve.
Innovation and Transformation: Ultimately, the journey from BI to AI enables organizations to innovate, transform, and drive sustainable growth by harnessing the full potential of their data to gain a competitive edge in the marketplace.
In summary, the journey of data from BI to AI represents a progression from descriptive to predictive and prescriptive analytics, leveraging increasingly advanced technologies and methodologies to derive insights, drive decision-making, and unlock new opportunities for business innovation and transformation.
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