Learn how AI is revolutionizing analytics, from automated documentation to dynamic data visualization, increasing efficiency, reducing errors, and facilitating data-driven decisions in today’s data-centric world, says Giridhar Valmati, Senior Director – Analytics and Data Science, Merkle.
In today’s data-driven world, artificial intelligence (AI) is reshaping entire industries. AI accelerates data analysis at scale, improves accuracy, and delivers actionable insights quickly, generating tremendous value for organizations. By automating various analytics tasks and streamlining the analytics lifecycle, AI minimizes errors, frees up human resources for strategic efforts, and reduces operational costs. In this symbiotic relationship between AI and data, organizations find a powerful ally in scaling analytics and driving data-driven decision-making.
Understanding the analytics lifecycle
The analytics lifecycle consists of six phases. Let’s break each of them down into simple terms:
- Discovery phase: In this phase, we start by defining business objectives, gathering necessary information, choosing appropriate analysis methods, and clarifying the scope of work.
- Data understanding phase: Here we collect the initial data based on the requirements and data availability. We complete this phase by exploring the data and understanding its characteristics.
- Data preparation phase: We gather data from multiple sources and clean, blend and format it to make it usable for analysis.
- Exploratory analysis and modeling phase: In this phase, we develop our methodology, determine the important variables, build the model and evaluate its performance.
- Validation phase: This phase involves evaluating the results, reviewing the process, and determining next steps based on the results.
- Visualization and presentation phases: These phases primarily communicate results effectively. It’s about determining the best methods for presenting information based on analysis, understanding the audience, compiling a story, and making recommendations.
How AI Improves Analytics Across the Lifecycle
AI is a powerful tool for scaling analytics across the lifecycle. It can learn patterns, adapt to given parameters, and perform tasks that humans might not be able to accomplish effectively. Here are four key ways AI can improve and automate tasks at each phase of the analytics process:
Automated data documentation
Tasks that require the most time and manual effort are often disadvantaged. Data documentation is one of the classic examples. Using AI, we can create table documentation because it can identify data types, find possible relationships between data sets, and generate column descriptions. It can also generate natural language descriptions and code snippet summaries in various programming languages. This, in turn, helps developers understand and document their code more effectively. Having documentation in place can save us rework and avoid confusion.
AI systems can also read and understand the text of these documents to extract relevant information quickly and accurately. AI-powered document processing can help organizations stay compliant with industry regulations. With automated document review and analysis, organizations can quickly identify any areas of non-compliance or risk that need to be addressed before they become serious issues, such as identifying outdated contracts which must be updated for compliance reasons.
Automated code requests
In the analysis process, we often spend a lot of time understanding and developing the code and its purpose. AI can be deployed to configure and scale automated requests to find information based on specific needs.
A series of AI-powered data applications and a query explainer can read an SQL statement and instantly write a simple description of what the query does and how it is used. With Query Optimizer, AI can take a query and generate a list of suggested improvements. It can automatically identify areas where the query could be optimized, such as recommending indexing and join types and correcting SQL queries, among other tasks.
Writing a query may require significantly more expertise and time, but AI can be designed to understand complex questions, generate code, and solve problems quickly. AI can transform SQL queries using simple English sentences into SQL code. It also translates SQL syntax into a concise, logical sentence that the database can understand and suggests questions that a database table can answer.
See more : How AI-powered analytics can fill the information gap
Exploration and understanding of data
Exploratory data analysis is one of the important and time-consuming initial steps in creating a better understanding of a dataset and the underlying data. However, when we look at large data that includes multiple columns and rows, it becomes easier to understand exactly what we are looking at and becomes more complex when we are not sure what kind of information is hidden in the tables.
Most of the time, data is messy and is often created, processed and stored by various humans, processes and applications. As a result, a dataset may be missing individual fields, contain manual entry errors, or have duplicate data or different names to describe the same thing. Humans can often identify and correct these problems in their data, but the data used to train machine learning or deep learning algorithms must be automatically preprocessed.
AI can succinctly explain the contents of tables, identify patterns and trends, and note similarities and differences in the data. It can learn common human error patterns while detecting and resolving potential information flaws. It can also be used to automate and accelerate data preparation tasks, including data modeling and data mining.
AI can help improve data mining by providing intelligent suggestions, recommendations and insights based on data and objectives. It can also help generate natural language queries, summaries, and explanations about data, making interaction and interpretation easier.
Data Visualization and Storytelling
Data visualization and dashboard creation are essential to better understand data and communicate insights. This multi-step process can take days due to data preparation, analysis, storytelling, etc.
AI can have a significant impact by detecting patterns and trends in data that improve storytelling and analysis. It can automate and improve the process by detecting and correcting data quality issues, such as missing values and outliers. With AI-powered data visualization, businesses can turn their data into an asset, revealing insights that may have previously gone unnoticed. For example, it could reveal patterns in customer behavior, helping a business tailor its marketing strategy more effectively.
Additionally, real-time visualization allows businesses to respond quickly to changes, making operations more efficient and responsive. AI can also provide context and explanations, creating dynamic stories that change and evolve in real time as the data does. It can interpret data by generating natural language summaries, annotations and comments. This could potentially make data more accessible and valuable, opening up new possibilities for businesses and decision-makers.
The symbiotic relationship between AI and data
Although AI is a powerful tool for improving analytics, it is meaningless without data, and data management is impossible without AI. The two go hand in hand, forming a symbiotic relationship that is crucial to unlocking the full potential of data analytics.
In conclusion, AI is revolutionizing analytics, making it more efficient, accurate and accessible to organizations, helping them make better data-driven decisions. From automated documentation and streamlined inquiries to data mining and dynamic data visualization, AI is a powerful ally in data analysis. As industries continue to harness the power of AI, we can expect to see more innovative applications and new advancements in this area.
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