Evaluate a Good Data Mining Tool
This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "Why You Need Data Discovery Software." The speaker is Abhishek Gupta, Product Manager at InetSoft.
Any good data mining tool has to have some text analytic capabilities built into it. They basically extract additional signals that can be used for more traditional structure analysis. The second point I want to make is about looking at new paths of inquiry. Beyond being able to get additional signals from unstructured text, it provides an opportunity to look at data along different dimensions.
There are dimensions that weren’t necessarily intended by the data architect or the organization setting up the infrastructure for end users. And we find that keyword search is really one of the best ways to effectively slice new dimensions through your data and uncover new linkages and new correlations. So for example you may be looking at the impact of media or news on sales performance.
You are looking at social media, being able to look at trends around key topics within tweets and within news. It can allow for new trends to be surfaced and new correlations with sales performance to be seen.
Dashboards are the kind of technology that really helps enable that sort of inquiry into a structured universe of analytics.
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Understanding the Need for a Good Data Mining Tool
We definitely see a huge trend toward people wanting and needing deep big data analysis. There is just more data available to organizations. They are better at capturing it and cataloging it, and the key is to be able to analyze massive amounts of data. How do you make it available and useful to your users to business managers to make decisions?
It’s one thing to say hey I’ve got this amazing petabyte database, but who’s making sense of it. And I think that’s really important. We have a particular customer, probably the fastest growing company ever in the online gaming world, where they’ve done amazing things with a quick stream of data people playing their games. They are analyzing comment, doing some text analysis.
They are using that data to try to understand not just the area of retention and acquisition of customers but literally for game design and coming up with new revenue models. They are using it to improve the experience of playing the game and make sure people stay on board with the games. I mean they’ve brought analytics through their big data all across their organization in deep ways.
Now they’re looking at it in real time accessing these big data databases. They’re actually pulling down extracts into a data grid cache, and their local product managers are going against data asking questions. They can be naturally inquisitive and do deep analysis of big data as questions rise. This is an important part of what we’re seeing where people can actually make sense of things that would have taken six months before to accomplish. Now people are accomplishing this real time. It’s a pretty important trend we see, and we hear a lot about it.
If you think about the flexibility of a tool of BI, you want to use it for investigative purposes, right. You can speak to a relational database engine, true data warehouses, and back end unstructured content. The ability to do those kinds of things and have them part of the discovery natural tool is extremely important whether that data is coming from Web feeds or other sources as well.
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Integrating Diverse Data Sources
In some cases you might have integration with third party data source and maybe sometimes commercial information. So certainly you can drive a whole host of information based upon analyzing what's coming through the pipe. If you also consider how easy is it and what other facilities are available for merging that information with survey based information from commercial providers, behavior analytics, demographic information and append special attributions, it will go a long way to adding that next layer of value to the things that you derive, the things that you discover during this process.
It certainly gives a lot of context. It’s one thing to say a 100 customers in this zip code behave one way, but if there are a thousand households or 10,000 householder in the area, there is a big difference in terms of your potential and your upside performance.
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How Is StyleBI Better Than Power BI as a Data Mining Tool?
As a data mining tool, StyleBI is considered better than Power BI primarily due to its user-friendliness and focus on self-service for non-technical users. While both tools offer strong business intelligence and data visualization features, StyleBI is designed to simplify complex data mining processes, making them more accessible to business users without extensive technical or coding knowledge.
Core Differences in Data Mining Capabilities
Ease of Use
StyleBI's core strength lies in its intuitive, drag-and-drop interface that allows non-technical users to perform data mining tasks like querying datasets, creating reports, and visualizing insights without writing code. This reduces reliance on data science or IT departments, allowing business professionals to quickly and independently analyze data to inform their decisions.
Power BI, on the other hand, requires a steeper learning curve for advanced data mining tasks. While it's user-friendly for creating standard dashboards and reports, more complex data analysis often requires a good understanding of its native formula language, DAX, and advanced data modeling.
Advanced Analytics and Self-Service
StyleBI provides a higher degree of self-service at the data level. Users can easily perform "data mashups" to combine and analyze data from disparate sources on their own. This agility allows for quick and experimental analysis without the need for formal IT intervention. StyleBI is also praised for its predictive analytics capabilities and ability to handle large volumes of data through its data caching technology.
Power BI's self-service capabilities are excellent for visualization and reporting, but its data preparation and advanced analytics often rely on the more technical Power Query and its integration with other Microsoft services like Azure Machine Learning. While it offers predictive analytics features, they are often less accessible to the average business user than those in StyleBI.
Other Considerations
Data Preparation
StyleBI automates many data preparation tasks, such as cleaning and structuring raw data, which streamlines the data mining process. Power BI's built-in Power Query Editor is powerful, but it may require more manual effort for complex data transformations.
Performance with Big Data
StyleBI's data caching technology is designed to manage and analyze large volumes of data efficiently. While Power BI can handle big data, its performance can sometimes be affected by the size of the datasets, especially if the data model isn't optimized using best practices like a star schema.
Target Audience
Power BI is a robust tool for a wide range of users, from data analysts and engineers to business stakeholders, and it fits seamlessly into the Microsoft ecosystem. StyleBI seems to be particularly well-suited for organizations that want to empower their business users to perform data mining and analysis without relying on a dedicated data science team.
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