Technewsky: Business Intelligence

Google Ads

javascript:void(0)

Thursday, 20 September 2018

How to Combine Data Using Business Intelligence and Machine Learning

03:55:00 0
How to Combine Data Using Business Intelligence and Machine Learning

As artificial intelligence (AI) and machine learning (ML) start to go out of academia into the business world, there’s been a number of goal on how they can achieve business intelligence (BI). There are a lot of unique in systems that use natural language find to help management more rapidly investigate corporate information, working analysis, and specify business action plan. A previous column discussing “self-service” business intelligence (BI) mainly highlighted two technologies where ML can help BI. While the user interface, the user experience (UX), matters, its visibility is only the tip of the iceberg. The data being provided to the UX is even more crucial.



While that is useful, being able to reputation the information being displayed is even more critical. AI and machine learning can help address that issue.

It Really Does Debut With Data

While authority still exist, the day of the mainframe providing all data and detail is long gone. While the 1990s saw venture at data warehouses, information is a fluid platforms that exists in too many places to ever make the warehouse the “single version of truth” that some hoped. Today’s data lake is just the working data store on steroids. It will help but it will no more be a single repository than have the exits efforts at the same thing.


Data survives in so many systems and the boost of IoT and cloud computing means data tracks enlarge far away from the standard of on-site computing. Working to analyze all the data and determine what is detail is a glowingly difficult issue.



Therefore, the business has three key issue with the latest explosion in data:

Without addressing those issues, the business is at risk through poor decision making based on inaccurate data and from increasingly strong data compliance regulations.

Don’t Re-invent the Wheel
Provided the issue, a solution is needed. Thankfully, there is no require to debut from scratch. Rather, there are techniques in other areas of software that can be held and accepted to the issue. ML ideas and other tools can be taken from other areas of IT to help both compliance and business decision making.

Machine learning is creating inroads in network and application security. Best condition deep learning systems are investigating transactions to look for irregularity and identify attacks and other security risks. At same time, asset management systems are being pushed by both the explosion of mobile handsets and the growth of SaaS applications to better understand what physical and intellectual property assets are gathered to the joint networks and infrastructure.

Those systems can be used to query network nodes finding for data sources in sequence to help develop an improved corporate metadata structure. Transactions on the community can be cross platform for new information and for specific usage.

Helping Self-Service through Data Management

Of critical useful, the ML system can help boost manage to data alongside accessing assent. It’s not enough in BI to search special case and identify threat. If analytics are honestly to become self-service, quicker access to information is necessary.

In today’s structure, compliance guidelines and analyst power set an employee’s manage to databases and specific sector. That especially limitations self-service through the easy fact that we can’t imagine all requires ahead of time.



As NLP gets an easy way for personnel to query business information, to understand business processes, and to discover new innovations between business data, there will automatically available concepts based in instinct and insight. An employee will ask a FAQ about data or relationships she hasn’t existing included, request data not yet manage, or otherwise fill-up to extend past the hard-set information boundaries.

In the classic process, that means the analysis available to an unexpected stop, emails must be sent to IT, discussions must happen and then systems must be adjusted to allow new access rules.

An ML system can individually speed that process, using guidelines and experience to rapidly search latest data, see if previous data fits within adjustment rules and allow immediate access, or flag the request for quick review by a compliance officer.


This problem is more complicate than what is moving now with modifies in the UX, but the issue is just as critical. It doesn’t issue how simply a manager can ask a question if there isn’t a rapid move to understand where the detail to answer the question occupied and to decide if the questioner has the authority to know the answer.

Machine learning gives a unique to far better update enterprise detail in today dispense world. While the industry move at ways to ask better questions, it requires to be finding at how to distribute and manage the information that provides answers.