The Pentaho BI Suite is the most popular BI suite in the world and Pentaho data integration catered to all state-of-the-art solutions offerings to customers worldwide.
The power of Pentaho Data Integration or PDI for accessing data, blending and governance has been documented and demonstrated a lot of times. Nonetheless, probably less well known is how the PDI as a platform, with all its data power is ideally suited for orchestrating and automating the three stages of the CRISP-DM life-cycle for a data science practitioner, such as generic data preparation/feature engineering, model deployment and predictive modeling.
Building the future of Business Analytics
Pentaho and the Pentaho software is building the future of business analytics. The open source heritage of Pentaho drives continued innovation in an integrated, modern, embeddable platform that’s built for accessing all sources of data. With support for all leading Hadoop distributions, high performance analytical databases and NoSQL databases, Pentaho offers the widest support for big data analytics and an orchestration and integration of big data as well as traditional sources.
Integrated Data is Accesible Data
The relevance of data integration is obvious to anyone who spent time fetching information from numerous systems for a basic report. When a business grows, the demands on information systems grow as well. New revenue streams, additional locations and changing priorities will impact the form that a data take. Moreover, a lot of businesses rely on legacy systems to provide historical data from sources that may no longer exist. Data integration frees employees to focus on forecasting and analysis, tasks which need human touch. Also, it greatly reduces the chances for errors to be introduced in the data translation process.
A business analytics software depends on accurate integration of data to create visualizations, dashboards and reports, which reflect consistent, accurate information. Failure to clean up data will result in questions on useless apples-to-oranges comparisons. The diversity and volume challenges made by big data make effective integration much more important.
It’s important to realize that data siloes could result in inefficiencies in operations that could affect the growth of a business and its agility. From inaccurate reporting to money and resources wasted, siloed data could lead to poor decision-making, affecting profitability and efficiency.
Pentaho’s Four Pillars of Artificial Intelligence
Today, in its guise as a Hitachi Group Company, Pentaho continues its job to be a data analytics business. Focusing on an area that could be labeled as ‘information orchestration’, the company aims to help organizations to better navigate and direct their machine learning.
With machine learning living at the heart of a new understanding of AI as it does, there’s a true need for IT departments to train, tune, test and to deploy predictive models that they are using to build what is called ‘automation intelligence’ and make artificial intelligence for business happen. What Pentaho is doing is focused primarily on collaboration. The company insists that most organizations trying to use machine learning automation and AI usually struggle to put predictive models to work since data professionals operate often in silos and the workflow, from preparing data to updating models, ends up making bottlenecks.
Four Lobes of a Machine Brain
Pentaho’s Data Integration and analytics platform aims on ending the ‘gridlock’ associated with machine learning through enabling a smoother team collaboration. The company states that its reach in the field spans four pillars that in effect, nearly come to represent the four lobes of a machine brain.
1. Data and feature engineering. Tools to help data engineers and scientists prepare and blend traditional sources of data, such as EAM, ERP and big data sources, like social media and sensors. Also, Pentaho addressed the ‘notoriously different’ task of the so-called feature engineering through automating data onboarding, data validation and data transformation.
2. Model deployment and operationalization. A thoroughly trained, tuned as well as tested machine learning model still has to be deployed. Pentaho aims in letting data professionals embed models that are developed by data scientist in a data workflow directly. This way, they could actually begin to use AI for business and utilize ‘embeddable API’s to be able to put the power across the whole base of apps in an organization.
3. Model training, tuning and testing. Often, data scientists apply trial and error method so trike the correct balance of performance, complexity and accuracy in models. With machine learning packages such as Weka, Spark MLlib, Pentaho states that it enables data scientists to train, to tune, build and test models much faster.
4. Regularly update models. Less than a third of companies use an automated process for updating their models. Pentaho is working to help in re-training existing models with new sets of data or make feature updates with custom execution steps.
The BI niche is showing a brand new life, which was not apparent when first launched. What once was an extremely archaic, complicated and confusing discipline has witnessed an almost-complete transformation as a result of big data and next generation artificial intelligence. Data analytics are so ingrained to the modern field of BI, which the two terms are almost interchangeable. In reality, business intelligence and the analysts in the profession, rely on advanced data analytics for supporting their decision-making process.
This year, the lines between artificial intelligence and business intelligence would blur even more. Thanks to the present capabilities of machine-learning systems, which are capable of determining habits, patterns and trends, human decision-makers may soon be a thing of the past.
Artificial Intelligence is truly happening
The technologies are happening. It is real artificial intelligence and machine learning for business. It real use of the word ‘automation’ signifies automatic execution of business processes, which do not need a real person to be present with the feeble human level of making decisions. The question of how to train computer brains at the intersection of business and technology could well provide some early indicators as to how far the advancements would go in the future.
Dhrumit Shukla has been working as Business Development Manager in a software development company named TatvaSoft since 5 years. He is profoundly skilled and well experienced in providing software development services on various technologies ranging from Microsoft .NET to JAVA, Salesforce, BizTalk, SharePoint, PHP, Open Source, iOS, Android, Pentaho data integration and the list goes on.
Dhrumit has the true potential to manage client from a wide range of Industries like BFSI, Supply Chain, Healthcare, Retail, Hospitality etc. Another aspect that makes him a trusted technical advisor and IT solution partner in the eyes of his clients is his effective communication skills. He keeps track on status of each Project during SDLC and provides extended support to clients to make sure Project deadline is matched and delivered within the budget.