Data mining tools uncover patterns inside large sets of structured data. The CRISP-DM methodology is used by all tools. It encompasses six phases: business understanding, data understanding, data preparation, modelling, evaluation and deployment. The patterns generated in this way are used to predict future outcomes.
When run through BI and data analysis tools, they can be used to increase revenues, reduce risks and expenses, and so on. When it comes to the said tools, automated data warehousing and interactive dashboards have proven to be best practice. When choosing optimum tools, speed and relevance need to be taken into account.
We recommend Ajilius data warehouse automation tool and Yellowfin BI and analytics platform. Not only are they fully customisable and fast, but they are also cost-effective and allow for custom coding.
What to expect of data mining software?
As already mentioned, data mining software uses the CRISP-DM methodology. The first two steps, business and data understanding, are preparatory and depend on the questions you need answered. You need to make certain that the data to be mined is centralized and accurate.
The mining tool takes it from there. It helps in data preparation, modelling, evaluation and deployment. During the data modelling phase, algorithms are applied to the data in order to define patterns. Patterns generated in this way can be used as a model and applied to new data.
There are two types of mining algorithms: supervised and unsupervised. Supervised learning algorithms require a target (predefined sets of outputs), while unsupervised don’t.
Minerra offers data mining tools that simplify this phase. In addition, we fully customise them to meet your requirements. One such tool is Ajilius, a web-based BI and analysis platform that automates data warehousing.
The next phase, data evaluation, will determine whether your model is good, bad or somewhere in between. Some examples of data evaluation processes in data mining tools include testing for false positives and cross-validation.
The final phase, deployment, is where you start using the results.
Data warehousing and data mining
Data warehousing and data mining are of immense importance for organisational performance. Data are normally pulled from various sources, such as spreadsheets, ERPs, CRMs, HRs and cloud sources. Data warehousing is the process that extracts data from all sources, loads them into a centralized location and displays them as structured data. Structured data are optimised for analytics, as they are displayed in an integrated view.
While data mining tools provide models and patters, data warehouses store fact tables and summarized business events, both of which contain descriptive attributes. These allow for in-depth, timely comparison that, in turn, enables you to make data-driven decisions at a glance. Data warehouses ease data mining, cluster and segmentation analyses, and overall managing of organizational performance.
Data warehouses cannot be altered, as they analyse historical data. They focus on data changes over time. The difference between a standard database and a data warehouse is in that the latter aggregates structured data. A database updates real-time data and keeps only the most recent information.
Data warehousing involves multiple steps. Data extraction being the initial phase, it is followed by cleaning, warehouse format conversion, sorting, consolidation and storing. More data are being added to the warehouse over time during updates.
Data warehousing and mining being both costly and lengthy, the need to automate them has arisen. The manual ELT/ETL methods involve up to 75% of manual coding, which, with volumes of data now being used, takes so much time that updates don’t even get the chance to be applied.
Best practice business analytics tools are fully automated, turning years of analyses into months and months into days. Such tools are driven by metadata, which calls for minimum manual coding. Automated BI and analysis tools compare data warehouses to the contemporary design patterns.
Ajilius and Yellowfin
Ajilius and Yellowfin are industry-leading data warehouse automation and BI and analytics tools, respectively.
Ajilius is a data warehouse automation tool that delivers on multiple DW platforms (SQL Server, Azure SQL, SQL Server Parallel Data Warehouse, PostgreSQL/EnterpriseDB, AWS Redshift, Exasol, Snowflake Elastic Data Warehouse and MariaDB Column Store). Its three-click migration feature is one of the reasons it is among the most convenient products on the market, cost-efficiency being second.
Ajilius is a fully customisable warehousing tool that integrates with most popular BI and analysis tools, Yellowfin included. Being web-based, Ajilius can be accessed from desktop, notebook, tablet and mobile phone. It runs on Windows, Linux and OSX operating systems. What sets it apart from other similar data warehousing tools is that it supports JDBC drivers, which are synonymous with swift connection and retrieval.
Yellowfin loads Ajilius’ data into a centralised location and presents them in an integrated view. Yellowfin’s interactive dashboards and reports are shareable and report-ready. The content is always presented by relevance and in an easily understandable narrative.
What makes Yellowfin superior to other similar tools is the fact that it explains business results in addition to simply monitoring them. The Assisted Insights feature provides automated help and allows you to ask only the right questions. Instead of just listing answers, Yellowfin digs the exact information you need. The Insight Wizard feature enables you to provide the context to the question and set the depth of analysis.
Data mining tools guarantee long-term success
Data mining tools are a step towards the future, but in order to fully utilise their benefits, professional training is recommended. That is exactly what we offer at Minerra – personalised BI consultancy and Ajilius and Yellowfin training. We also offer decision-focused software versions and a number of services that will teach you how to lead your business towards ultimate success.