Key Strategies for Profitable Business Analytics

Key Strategies for Profitable Business Analytics

Key Strategies for Profitable Business Analytics

Business analytics serve only one purpose – that of helping people make better decisions. These decisions might occur at the level of transactions, tactical operations, and strategy. Business intelligence for example is largely concerned with analysis of prior performance and support for diagnostics. It is mainly used to support tactical decision making by managers at various levels in the business, and is usually not useful for transaction based decisions or strategic decisions. These latter generally tend to consider macro factors such as economic conditions, competitor activity, market trends and so on.

The business analytics space is becoming quite crowded, with machine learning, prescriptive analytics and artificial intelligence adding to the analytic mix. Machine learning concerns itself mainly with applying algorithms to historical data, in an attempt to detect patterns of behavior that might be useful in future activities. In loan approval for example, we interrogate historical data looking for characteristics that might indicate a loan applicant will have no problem repaying a loan. Many loan approvals are now processed automatically with very little human involvement. Clearly there is considerable scope here for adding intelligence to operational applications dealing with customers, suppliers, employees and trading partners. As such the intelligence needs to be embedded into these applications so they are available at the point of work. This is also true of business intelligence, and particularly the embedding of various visual artifacts (charts, graphs, dials etc) into the applications that are used day after day in a production setting.

Read Also:
The Case for the Data Management Maturity Model

The current fascination with all things visual is understandable, but a business will not realize the efficiencies that business intelligence and machine learning can deliver, until analysis is embedded into production applications. Such analysis can speed up processing of transactional activity, and ultimately will completely automate a good deal of it.

Prescriptive analytics is fundamentally different from BI and machine learning in that it establishes how processes should execute to make best use of resources. BI and machine learning are concerned with what has happened or will happen. Prescriptive analytics is concerned with how to best use resources given various forecasts and plans.



Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
The Case for the Data Management Maturity Model

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
Data is your customer

Chief Data Officer Summit San Francisco

7
Jun
2017
Chief Data Officer Summit San Francisco

$200 off with code DATA200

Read Also:
Why business intelligence finally loves maps
Read Also:
Why MS Excel is a Poor Choice for Data Projects

Customer Analytics Innovation Summit Chicago

7
Jun
2017
Customer Analytics Innovation Summit Chicago

$200 off with code DATA200

Read Also:
The IT Pro view: Data protection and GDPR

HR & Workforce Analytics Innovation Summit 2017 London

12
Jun
2017
HR & Workforce Analytics Innovation Summit 2017 London

$200 off with code DATA200

Read Also:
Artificial intuition will supersede artificial intelligence, experts say

Leave a Reply

Your email address will not be published. Required fields are marked *