Data Analytics for Effective Business Intellegence
In the past we mainly relied on structured data, the type that we can put into tables and neatly organize, such as sales transactions by customer, region, etc. Less structured data, such as text files, photographs, video content etc. was largely ignored. Today, we have the ability to use and analyses a large variety of data including written text, spoken words, even the tone in our voice, as well as biometric data, photographs and video content which we analyze through these processes.
Very technical layer of DataIntel™’s workspace, data-intensive, part of data science and closely related to data mining. Machine learning is about designing algorithms (like data mining), but emphasis is on prototyping algorithms for production mode, and designing automated systems (bidding algorithms, ad targeting algorithms) that automatically update themselves, constantly train/retrain/update training sets/cross-validate, and refine or discover new rules (fraud detection) on a daily basis. Python is now a popular language for ML development. Core algorithms include clustering and supervised classification, rule systems, and scoring techniques. A sub-domain, close to Artificial Intelligence is deep learning
Traditional analytics tools are not well suited to capturing the full value of big data.The volume of data is too large for comprehensive analysis, and the range of potential correlations and relationships between disparate data sources — from back end customer databases to live web based clickstreams —are too great for any analyst to test all hypotheses and derive all the value buried in the data.
Basic analytical methods used in business intelligence and enterprise reporting tools reduce to reporting sums, counts, simple averages and running SQL queries. Online analytical processing is merely a systematized extension of these basic analytics that still rely on a human to direct activities specify what should be calculated.Machine learning is ideal for exploiting the opportunities hidden in big data.
Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.
DataIntel™’s expertise in this working layer is about designing algorithms to extract insights from rather large and potentially unstructured data (text mining), sometimes called nugget discovery, for instance unearthing a massive Botnets after looking at 50 million rows of data. DataIntel™™’s techniques include pattern recognition, feature selection, clustering, monitored classification and encompasses a few statistical techniques (though without the p-values or confidence intervals attached to most statistical methods being used). Instead, emphasis is on robust, data-driven, scalable techniques, without much interest in discovering causes or interpretability. Data mining thus have some intersection with statistics, and it is a subset of data science. Data mining is applied computer engineering, rather than a mathematical science. Data miners use open source and software such as Rapid Miner.
Data mining is not a one-time event. It’s a process – an ongoing evolution of discovery and interpretation. It’s a process that uncovers new and meaningful patterns in your collected data, patterns you can use to address challenging business questions that require prediction and inference. And it’s a process that demands a unique set of skills and resources. Data mining also plays a pivotal role in maximizing the value of your Data Intel solutions. Analyzing huge volumes of historical data can deliver the knowledge you need from models built for prediction,
estimation, and other inferences involving uncertainty. The result? More informed strategic business decisions and more effective interactions with individual customers. The Power of Data Mining Data mining experts guide tools that analyze huge amounts of data stored in your data warehouse using pattern recognition technologies and statistical and mathematical modeling algorithms. Data mining’s biggest benefit? The process reveals hidden patterns that can’t be
detected using traditional query and OLAP tools.
Data Intel`s suite of Data Mining Services is a blend of technology, tools and expertise unavailable from any other source. But what truly sets the Teradata solution apart is our unique approach to mentoring and our optimal use of data warehouse technology. Rather than simply providing generic off-the-shelf services or support programs and then walking away, we’ll work with you at every phase. We’ll help ensure that you have the skills and resources needed to launch, maintain, and build a successful data mining practice. By using our Data Mining Services, We will mine data and how to integrate the process into your existing business procedures and technologies. That’s because we bring data mining into your environment, using our solution and your existing practices. And, because we can leverage Data Intel Warehouse Miner, which pushes data mining functions directly into your data warehouse, you’ll see results quicker. It’s a one-stop solution that leverages the technology you have today – and extends the scope of the business questions you can explore tomorrow.
Data Intel Data Mining Services bring much more than a carefully crafted set of suggestions about how to proceed through data mining projects. We’ll show you how to identify and refresh models that may have decayed over time and how to build and extend others to answer new business questions. And we can also help you become a self-sufficient data mining organization –
one ready to face the challenges ahead without the need for the all too common never ending consulting engagement.
DataIntel™’s core strength area . Predictive modeling projects occur in all industries across all disciplines. Predictive modeling applications aim at predicting future based on past data, usually but not always based on statistical modeling. Predictions often come with confidence intervals. Roots of predictive modeling are in statistical science.
Data modeling is the formalization and documentation of existing processes and events that occur during application software design and development. Data modeling techniques and tools capture and translate complex system designs into easily understood representations of the data flows and processes, creating a blueprint for construction and/or re-engineering.
A data model can be thought of as a diagram or flowchart that illustrates the relationships between data. Although capturing all the possible relationships in a data model can be very time-intensive, it’s an important step and shouldn’t be rushed. Well-documented models allow stake-holders to identify errors and make changes before any programming code has been written.
Data modelers often use multiple models to view the same data and ensure that all processes, entities, relationships and data flows have been identified. There are several different approaches to data modeling, including:
Conceptual Data Modeling – identifies the highest-level relationships between different entities.
Enterprise Data Modeling – similar to conceptual data modeling, but addresses the unique requirements of a specific business.
Logical Data Modeling – illustrates the specific entities, attributes and relationships involved in a business function. Serves as the basis for the creation of the physical data model.
Physical Data Modeling – represents an application and database-specific implementation of a logical data model.
Data visualization is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. With interactive visualization, you can take the concept a step further by using technology to drill down into charts and graphs for more detail, interactively changing what data you see and how it’s processed.
With big data there’s potential for great opportunity, but many retail banks are challenged when it comes to finding value in their big data investment. For example, how can they use big data to improve customer relationships? How – and to what extent – should they invest in big data?
Because of the way the human brain processes information, using charts or graphs to visualize large amounts of complex data is easier than poring over spreadsheets or reports. Data visualization is a quick, easy way to convey concepts in a universal manner – and you can experiment with different scenarios by making slight adjustments. Data Visualization Also helps to