Seamless Data Integrator

Data Integration has been the key priority among many companies during transformation or scaling of their business activity and because of that integration or integrating Data between systems was a must without which there wasn’t a legacy being continued. Moving Data from one system to the other or migration of their legacy systems to the new system was always a challenge when it comes to communicating between two different platforms or different tech brands. At times when data was transported from one system to the other, Data was sometimes received in excel files or text files and because of which, Data could be manipulated, mismanaged or even lost when moving from one system to the other.

This is where a role of a Data Integrator comes into play where information or Data need not be kept in flat files whereas the Data Integrator can help to establish a seamless connection between your legacy and the new system to help you with Data Integration or Data Transformation jobs. Pentaho is one of the best leads in Data Transformation or Data Integration where those activities are automated with less human intervention. It comes with built in adapters to help you establish connection between multiple systems whether it may be Oracle, Microsoft, Postgres, Mongo…etc. It also doesn’t matter if Data might be present on-premise or cloud, no matter where your Data sits, Pentaho will be a seamless Data Integration to help you transform your Data at a speed never imagined.

Every Data Transformation or Data Integration job must have a workflow maintained to keep a track of the data movement happening during this process. So that at any moment in time when there is a gap or bad data, it must be able to identify those gaps and rectify. This is also where Pentaho will help you to keep track of the Data Integration activity using workflows to help you stop and be informed to improve your Data Integration job by cleaning off bad data.

Data Analytics Golden Rules

It may not be rocket science to interpret Data with your analytics, as everyone have their own way of understanding or reading Data and it all depends on how well have you understood the information provided. There can be many conclusions to a piece of Data, as there are many tools available today to read these information for you and provide you insights about what Data means. But the key idea behind understanding your Data is not just to interpret the information rather provide meaningful information which makes sense. This clarity to information and understanding the depth of productivity or impacts must be the strategy towards understanding your Data.

The 3 golden rules to keep in mind while understanding Data or developing Data Analysis must be based on these principles :

Make it Simple above everything and let your data not make too much noise rather provide meaningful clarity.

Don’t crowd your insights with lots of graphical representations and make it look like a crowded Data market place rather create a narrative story.

Always understand what are you trying to communicate with Data or what type of Data and finally to whom.

Following the above 3 Golden rules, must make your Data more impactful and meaningful and guide you or drive your business towards better decision making.

Data Cleansing

The real data that needs to be presented needs to be managed and formatted to the desired shape and size to make meaningful information for data that goes to be presented. Data cleansing is one of major task before you prepare your data sheets for financial analysis or summary reports about your project or data audit. Data cleansing has changed to be a major transformational job for data analysis. During this transformation you basically decide the category or type of data whether it may be a text or number. Some of the major benefits of Data Cleansing.

  • It removes major errors and inconsistencies that are inevitable when multiple sources of data are being pulled into one dataset.
  • Using tools to clean up data will make everyone on your team more efficient as you’ll be able to quickly get what you need from the data available to you.
  • Fewer errors means happier customers and fewer frustrated employees.
  • It allows you to map different data functions, and better understand what your data is intended to do, and learn where it is coming from.